areviewonqualitycontrolin additivemanufacturing€¦ · and reliability, lead 48 per cent in total....

A review on quality control inadditive manufacturing

Hoejin Kim, Yirong Lin and Tzu-Liang Bill TsengDepartment of Mechanical Engineering, University of Texas at El Paso, El Paso, Texas, USA

AbstractPurpose – The usage of additive manufacturing (AM) technology in industries has reached up to 50 per cent as prototype or end-product. However,for AM products to be directly used as final products, AM product should be produced through advanced quality control process, which has acapability to be able to prove and reach their desire repeatability, reproducibility, reliability and preciseness. Therefore, there is a need to reviewquality-related research in terms of AM technology and guide AM industry in the future direction of AM development.Design/methodology/approach – This paper overviews research progress regarding the QC in AM technology. The focus of the study is onmanufacturing quality issues and needs that are to be developed and optimized, and further suggests ideas and directions toward the qualityimprovement for future AM technology. This paper is organized as follows. Section 2 starts by conducting a comprehensive review of the literaturestudies on progress of quality control, issues and challenges regarding quality improvement in seven different AM techniques. Next, Section 3provides classification of the research findings, and lastly, Section 4 discusses the challenges and future trends.Findings – This paper presents a review on quality control in seven different techniques in AM technology and provides detailed discussions in eachquality process stage. Most of the AM techniques have a trend using in-situ sensors and cameras to acquire process data for real-time monitoringand quality analysis. Procedures such as extrusion-based processes (EBP) have further advanced in data analytics and predictive algorithms-basedresearch regarding mechanical properties and optimal printing parameters. Moreover, compared to others, the material jetting progresses techniquehas advanced in a system integrated with closed-feedback loop, machine vision and image processing to minimize quality issues during printingprocess.Research limitations/implications – This paper is limited to reviewing of only seven techniques of AM technology, which includes photopolymervat processes, material jetting processes, binder jetting processes, extrusion-based processes, powder bed fusion processes, directed energydeposition processes and sheet lamination processes. This paper would impact on the improvement of quality control in AM industries such asindustrial, automotive, medical, aerospace and military production.Originality/value – Additive manufacturing technology, in terms of quality control has yet to be reviewed.

Keywords Additive manufacturing, Systematic review, Quality control, Data analytic and algorithm, In-situ correction,Real time closed-feedback loop

Paper type General review

1. Introduction

Additive manufacturing (AM) technology is a process of makinga three-dimensional (3D) object-based on layer-by-layer or drop-by-drop deposition of materials under a computer controlledsystem. It is composed of various types of printing methods suchas binder jetting, material extrusion, material jetting and sheetlamination. AM industries have grown rapidly since 2000, andhave shown almost six times the growth during the 2000s ascompared with the growth during the 1990s (Wong andHernandez, 2012). AM technology has a high potential to exploitideas and applications on various areas. In 2013, WohlersAssociates reported that AM system sales revenue of each sectorare industrial products (19 per cent), consumer products (18per cent), automotive (17 per cent), medical (14 per cent),aerospace (12 per cent), military (5 per cent). Automotive,

medical, aerospace and military, which require high precisionand reliability, lead 48 per cent in total. The global AM marketsales reached up to $2.2bn in 2012 and is forecasted for growth ofthe market size by $10.8bn in 2020 (Wong and Hernandez,2012). Therefore, final and functional part productions andrelevant researches are growing faster than the general market.Most of AM applications were purposed for prototyping andtooling; however, nowadays, industries invest 10 times more onend-part production than on prototyping (Cotteleer and Joyce,2014).Likewise, this new paradigm of AM technology is being

dramatically developed and brings high feasible applicationsinto real industry. However, before AMproduct is directly usedfor real-functional part fabrication, certified quality control(QC) is of vital importance to address specific requirements,especially quality and reliability. For high-quality products thatare used in aerospace and automobile industries, AM isrequired to certify high precision and mechanical properties tobe used and functioned as real part (Wong and Hernandez,2012). As a variety of materials are being used in the AM

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Rapid Prototyping Journal24/3 (2018) 645–669© Emerald Publishing Limited [ISSN 1355-2546][DOI 10.1108/RPJ-03-2017-0048]

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technology, such as ceramic, steel, alloy, composites and livingtissue, compared with traditional manufacturing, the currentAM technology around part quality and performance needs tobe constantly improved. Therefore, more the variety ofadvanced materials being used with AM technology, the morequality processes that will be necessary.For quality control improvement, there have been studies

that focus on quality-approached researches. For example,Kaveh et al. (2015) studied the effect of printing parameters onprecision and internal cavity of fabricated parts using fuseddeposition modeling 3D printer (Kaveh et al., 2015). Anotherresearch, Farzadi et al. (2015) investigated the effects of layerprinting delay on physical and mechanical properties (Farzadiet al., 2015). If these types of quality-related researches on AMtechnology keep happening and produce improved qualitycompared with the traditional manufacturing, AM productswill eventually substitute current technologies and expand toareas that require super-advanced technology in the nearfuture.This paper provides an overview of the research progress

regarding the QC in AM technology and the future direction ofAM development in the QC aspect, and especially discussesquality issues and the needs to be developed and optimized;further, it suggests ideas and directions toward qualityimprovement for future AM technology. This paper is organizedas follows. Section 2 starts by conducting a comprehensive reviewof the literature studies on progress of quality control, issues andchallenges regarding quality improvement in seven differenttechniques of AM. Next, Section 3 provides classification of theresearch findings, and lastly, Section 4 discusses challenges andfuture trends.

2. Overview of quality control in additivemanufacturing

2.1 Photopolymer vat processesPhotopolymer vat processes (PVP) are based on the selectivesolidification of a liquid photopolymer using light sources (e.g.ultraviolet; Vaezi et al., 2013). Stereolithography (SL or SLA)is the main technique that is widely used especially inbiomedical engineering field such as implant and tissueengineering (Gauvin et al., 2012; Cohen et al., 2009). Melchelset al. (2010) reviewed two different SL techniques: scanning SLand projection SL. The scanning SL builds a structure frombottomwith scanning laser, while the projection SL builds fromtop to bottom with digital light projection (Melchels et al.,2010). The latter technique not only provides better surfacefinish and dimensional accuracy but also saves materials andfabrication time (Vaezi et al., 2013).Some researchers (Huang and Jiang, 2003; Adolf et al., 1998)

have documented significant heat release owing to thermalreaction between laser source and resin solution during photo-polymerization, which causes residual stress developmentassociated with thermal shrinkage. Likewise, temperature profilehas an important role on quality. Corcione et al. (2006) foundthat temperature increase on vat surface is dependent on energydose during SL process. Lower scan speed leads to highertemperatures and reaction rates, which involve shrinkage andcurl distortion (Corcione et al., 2006). Lee et al. (2007) studiedthe effects of laser energy with respect to thickness of building

part and laser speed. Critical exposure (Ec) and penetration depth(Dp) are two primary parameters of resin determined bywindowpane analysis. In addition, molecular weight of polymers(i.e. viscosity) plays an important role to determine finalmechanical properties (Lee et al., 2007).Specific design tools used in the biomedical engineering for

patient such as computed-tomography (CT) and magneticresonance imaging (MRI) enable the SL technique to removeelaborate design process and provide accurate custom-designdata. In addition, these design tools allow evaluating thedimensional accuracy by comparing the scanned data withdesigned model. Cohen et al. (2009) used a commercial 3Dworkstation (EBW 4.0, Philips medical) which has capabilitiesof both MRI and CT, and can significantly enhance scanningtime from 2 hours to 5 minutes for mandibular reconstruction(Cohen et al., 2009).Cooke et al. (2003) incorporated the SL technique in

manufacturing biodegradable polymeric scaffold for use iningrowth repairs of human bone defects. In biodegradable resin(mixtures of Diethyl fumarate and poly pylene fumarate), it wasobserved that approximately 10 per cent of supports did notattach to the build table, which caused problems ofdimensional/geometric inaccuracy and rough surface finishbuilt parts. By customizing build tables and hole patterns thatcontrol the resin flow rate, the initial layer of cross-linkedbiodegradable resin mixtures can be securely adhered to thesurface of the build table so that it can prevent the part fromfloating freely in the resin. It was found that the viscosity anddensity of vat solution influence the lack of adherence as well asdesign of build table and convex-surface effect (Cooke et al.,2003). Many efforts to fabricate complex tissue scaffold hasbeen attempted to improve part quality with different types ofsolutions (Gauvin et al., 2012; Dhariwala et al., 2004). Theseattempts are increasing usage area and better quality control asvariety of materials are being used (e.g. epoxy or acrylate oradditional additives (PPF/DEF–HA, PDLLA/HA) as basedresins with photo-initiators, support material such mix ofpropylene/polyethylene glycols, glycerin and/or acrylate).Developing materials to be used in the SL technique must takeinto consideration variety, composition, strength and finishingprocedures to increase the fabrication versatility and quality(Gross et al., 2014; Bose et al., 2013).Kim et al. (2010) decreased contamination when fabricating

multi-materials using SL technique. When draining andcleaning the previous material before switching another vat, itwas difficult to removematerials entirely owing to high viscosityof the previous resin. By using low viscosity polymer resin andimproving a process-planning algorithm, contamination wasreduced (Kim et al., 2010). Han et al. (2010) developed anautomatic material switching method by cleaning out thecurrent solution in the vat using solvent purging after UV-exposure so that it was able to fabricate high-qualityheterogeneous 3D hybrid scaffolds (Han et al., 2010). For highquality fabrication process, Zhou et al. (2011) proposed toremove the previous material sticking on part surface using asoft brush for rough cleaning and then ultrasonic removal forfinal cleansing before introducing a different material (Zhouet al., 2011).Orientation in which a design is fabricated can influence the

SL performance. Lan et al. (1997) investigated that deposition

Quality control in additive manufacturing

Hoejin Kim, Yirong Lin and Tzu-Liang Bill Tseng

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orientation mostly influences on three significant parameters:surface quality, build time and support structures. On the basisof the parameter consideration, decision criteria and algorithmswere established and developed to identify the desirablefabrication orientations (Lan et al., 1997). Pham et al. (1999)developed feature-based system using an object-orientedprogramming language and solid modeling CAD environmentto obtain the best tradeoff by considering factors based on buildtime, cost and accuracy while having optimal part orientation(Pham et al., 1999).The SL technique has also been used in micro/nano scale

fabrication (Maruo, 2015). Ha and Yang (2014) studied thatsurface friction factor taken into consideration when fabricatingclosed structures does not affect open complex structure.Therefore, a micro-scaled open structure was erected securelyon the substrate from the mentioned process, as shown inFigure 1. This nanostructure fabrication is capable of havinghigh quality structures with higher efficiency than closedstructures. In this study, 10 � 10 � 10 mm open structureswere fabricated, CCD camera were used to monitor thenanoscale structure during fabrication (Ha and Yang, 2014).West et al. (2001) developed a new adaptive slicing algorithm

in the design process for improving build performance. Thisalgorithm assists SL operators in developing a process plan byquantifying tradeoffs among surface finish, geometry tolerancesand build time. By adjusting process variables within algorithm,it can predict geometric and dimensional properties, and cancompare the desired and actual values so that SL operators canapproach the specific design requirements (West et al., 2001).In summary, PVP has advanced in providing accurate data

acquisition during design applications that use CT and MRI,which makes this technique particularly useful in biomedicalapplications. In addition, these procedures allow partevaluation by comparing design and 3D printed model.Finding optimal build orientation to improve the quality andbuild time was mostly studied by incorporating a new type ofsolid modeling and slicing algorithm. This indicates that buildorientation mostly affects the quality. For printing process,

there are still many quality and process problems that occurowing to vat contamination, wasting materials, long processtime, material limitation and detachment of build table. Livingbiological cells can be incorporated using this technique so thathuman artificial organ or skin can be fabricated. In the nearfuture, more materials will be incorporated which will, in effect,demand more quality related studies on the variety of materialsneeded.

2.2Material jetting processesMaterial jetting processes (MJP) are based on the use of inkjetprinting technique to deposit droplets selectively through anozzle or orifice to build up a 3D structure. This droplet is aliquid that is jetted out of the nozzle and becomes solid afterdeposit through cooling or UV curing. There are two methodsfor creating appropriate material droplets: drop on demand(DoD) and continuous inkjet (CIJ). The latter uses fluids oflow viscosity and high drop velocity for fast printing speed; incontrast, the former uses smaller droplets and velocity for highdimensional accuracy; therefore, DoD has a higher potentialfor microfabrication (Vaezi et al., 2013). In the DoD system,two main kinds of pressure pulse methods in the manner ofejecting droplet from ink vat are divided: thermal andpiezoelectric actuators. Thermal DoD has a limitation on inkmaterial used, which is volatile, while the piezoelectric DoDhasno such limitations, but is required to have droplet generationrates in the range of 3-10 kHz to allow the meniscus to reachcapillary equilibrium (Sachs andVezzetti, 2005).Vaezi et al. (2013) reviewed the several significant

phenomena affecting the quality of the material jettingprocesses. The shape of deposited droplet influences criticallyon resolution, precision and accuracy (Vaezi et al., 2013).Beaman et al. (2004) demonstrated through fluid mechanicstheory that there is an important relationship between theReynolds number Re = r gvd/m , denoted as a ratio betweeninertial and viscous forces, and Weber number We = r1v

2d/s ,denoted as a ratio between kinetic and surface energy, where r g

Figure 1 Schematic diagram of nano-stereolithography apparatus (left) and SEM images of fabricated open structure (right)




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and r1 represent the densities of the process gas and liquiddrop, respectively. The variables v, d, m and s are dropletvelocity, droplet diameter, liquid dynamic viscosity and liquidsurface tension, respectively. It was found that Re and Weshould satisfy 1 < Re=

pWe < 10 to maintain the overall

printing quality of DoD system (Beaman et al., 2004). Thisauthor reported that droplets should not exceed more than 10mm in diameter owing to problems with air resistance. Ko et al.(2010) reported that fundamentally considerable andinterconnected conditions for high desired quality control onMJP are the following:� ink properties (viscosity and surface tension);� jetting parameters (signal width, voltage magnitude and

jetting frequency); and� environment (pressure, environment and substrate

temperature and humidity).

Research on metal nanoparticle inkjet printing investigatedthoroughly that proper substrate temperature and sufficientdrying of nanoparticle inks in each layer guarantee high quality(Ko et al., 2010).Jiang et al. (2010) studied on the accurate micro scale-

droplet creations. As shown in Figure 2, droplets merge witheach other to form bigger droplets owing to different dropletvelocities. These merged droplets degrade the droplet’sdiameter accuracy, which significantly affects surfaceroughness and total geometry. It was observed that uniformspherical droplets can be developed at optimal frequency of2.842 kHz and at low oxygen concentration environment (Jianget al., 2010). Kullmann et al. (2012) contributed on building3D microstructures of gold nanofluids with high aspect ratioand low-cost basis using DoD technique. It was discussed that

not only are accurate and optimal printing temperatures andvoltages essential factors in nanofluids’ control for micro scaledstructures, but also that thermal optimization such as substratetemperature and laser annealing are to be taken intoconsideration (Kullmann et al., 2012).Inkjet printing technique is used widely in biological tissue

engineering and electric circuit design due mainly to its multi-material capability (Nakamura and Henmi, 2008; Mei et al.,2005; Xu et al., 2006; Cui and Boland, 2009; Czy_zewski et al.,2009). However, Rengier et al. (2010) rated MJP technique onmedical applications as inferior in accuracy and strength incomparison with other techniques (SLA, SLS and FDM;Rengier et al., 2010). Ebert et al. (2009) found the process-related defects as a result of single clogged nozzles owing toeither dried up or blocked by agglomerates while printingdental prostheses made of zirconia-based ceramics (Ebert et al.,2009). To resolve this issue, sintering was adopted as postprocessing to enhance the strength and accuracy, andpreheating substrate was implemented to avoid internal stressand bending of layers during drying. In addition, aqueoussolution of 10 wt. per cent ethanol was found to be sufficient fornozzle cleaning to prevent clogging.Wüst et al. (2011) mentioned that protein and biological cells

are more likely to be denaturant depending on temperature;therefore, temperature of ink bubbles in thermal-based printingshould not exceed more than 41°C (Wüst et al., 2011). Owingto this reason, Wilson and Boland (2003) modified commercialinkjet printers by implementing temperature controller to avoiddenaturation of proteins and cells (Wilson and Boland, 2003).Henmi et al. (2007) developed a gelation technique to alignprinted cell on the substrate without drying and distributedrandomly so that living cells and biomaterials can be easilypatterned to create sophisticated 3D printed tissue (Henmiet al., 2007). Cui et al. (2010) noted the frequency ofpiezoelectric inkjet printers to be within a frequency range thatcauses cell damage and lysis after sonication at 15-25 kHz (Cuiand Boland, 2009). For a thermal inkjet printer, its heatingtemperature at the ejected nozzle increases up to 300°C for acouple of microseconds during the printing. Therefore, thereare significant concerns in printing processes that cause celldamage and death (Cui et al., 2010). In 2012, authorscomprehensively evaluated cell viability and damage onthermal inkjet cells and found 4-10°C above ambient for only2 ms avoids cell damage up to an average cell viability of 90 percent. Therefore, thermal inkjet printing technology is becomingmore biocompatible to the living system compared topiezoelectric printing (Cui et al., 2012).Derby (2010) and Yeong et al. (2006) discussed the

importance of fluid properties for quality and investigated themain factors of quality in theMJP technique to be:� generation of droplet;� position and interaction of droplet on a substrate; and� solidification mechanism.

In terms of interaction between droplets and substrates, partquality is significantly determined by properties such asgravitational forces, fluid density, surface energy, dropvelocity, droplet size, surface tension, capillarity forces,contact angle, drop spacing and print head traverse speed.In addition, during droplet transition from liquid to solid,

Figure 2 Water droplet stream using orifice diameter of 150 mmat a gas pressure of 8 kPa




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which may occur, volume change is important and can becontrolled by these considerations such as solventdistribution, fluid flow rate and evaporation rate. Proposednano/micro-scale patterning and structuring of substratecan overcome this resolution issue limited by drop size andcan achieve higher resolution more than drop size (Derby,2010; Yeong et al., 2006).Tourloukis et al. (2015) identified factors that affect quality

issues and printing performance and studied the potential ofcomputational intelligence algorithms particularly in neuralnetworks to assess and forecast quality of 3D inkjet-printedelectronic products. Results validated that one-step aheadpredicting model using nonlinear autoregressive neuralnetwork with external input brought up better overallprediction behavior compared with multi-step ahead one.However, more investigation is needed with larger data set tobe trained with respect to a wider range of values and trends forenhancing predictive performance (Tourloukis et al., 2015).Recently, Sitthi-Amorn et al. (2015) developed a material

jetting systems allowing for self-calibration of print heads, 3Dscanning and a closed-feedback loop to enable printcorrections. Real-time printing control technique of machinevision built in enables to correct overall platform design andprinting parameter in real-time through closed-feedback loop.The image processing algorithm computes depth differencebetween droplets and adjusts amount of jet droplet. Thistechnique opens up the real-time quality control duringprinting process. In addition, 3D scanning (i.e. opticalcoherence tomography, OCT and scanner) provides highresolution (less than 1 mm) to scan the depth map and overlaythe model to be printed over the 3D scanned auxiliarycomponent on the build platform. Furthermore, users caninteractively translate and rotate the model over the image tofacilitate the alignment (Sitthi-Amorn et al., 2015). (Figure 3)In summary, MJP have significantly advanced in micro-scale

fabrication.Most of the studies implemented for quality related

issues aim to control the droplet mechanism, whichsignificantly affects resolution and dimensional accuracy. Themain issue during printing process is nozzle clogging occurringowing to dry and agglomerates, which are detrimental tomedical application. Using thermal inkjet printing technique,sophisticated tissue structures such as biomaterials and livingcells can be easily fabricated without cell damage caused bypiezoelectric inkjet frequency and high temperature. Positiveearly stage research was made on data-driven approach usingneural networks to predict the quality. A great achievement ofMJP is a real-time closed-feedback loop system incorporatedwith machine vision. This can correct the design and printingparameters in real-time and eventually open up real-timeprocessing during printing process.

2.3 Binder jetting processesBinder jetting processes (BJP) have similar principals tomaterial inkjet process, but based on nozzles depositingdroplets of a water-based binder material over surface of apowder bed (Wong andHernandez, 2012; Stucker, 2012). Thepowder particles are held together by bonding with depositedbinder material and eventually shape 3D structures. Thisprocess is followed by lowering the powder bed through apiston and a fresh layer of powder spread over the previouslayer and deposited by binder again over the new layer. Thepossible materials used as powder includes ceramics, metals,shape-memory alloys and polymers (Vaezi et al., 2013). Gibsonet al. (2014) found that BJP technique provides better qualitycontrol ability for ceramic and metal fabrications to beproduced compared with MJP technique. However, a BJPprinted part tends to have lower accuracy and surface finishthan the part made by MJP. In addition, infiltration processesare typically needed to fabricate dense parts or to ensure goodmechanical properties (Gibson et al., 2014).Deposition ways of the binder are the same as the MJP

technique. The CIJ system of BJP technique is designed to

Figure 3 (A) Optical coherence tomography 3D scanner: the optical components are arranged in a Michelson configuration. The light source is acollimated, red LED. Polarizers are placed in the LED and camera paths to control the light density. The points in the sample that are at the same distanceas the reference mirror cause constructive interference patterns. The platform is moved along the Z axis while the interference patterns are detected bythe camera. The scanner features a circular scanning area with a diameter of 15 mm; (B) the machine vision feedback loop proceeds in the stages andimage processing pipeline for 3D scanner: We compute the 3� 3 standard deviation. Next, we choose the depth in which the standard deviation is thehighest. We also keep the maximum standard deviation as the confidence map. We perform the depth correction on the depth map from previous stage.Finally, we stitch the depth maps from all the location weighted by the confidence. We assume that the area where the confidence is below certainthreshold is a hole, and fill those holes at the end




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print in either uni or bidirectional mode. Bidirectional printingmode is faster than unidirectional one, but causes dynamic shifterrors. A part requiring higher quality surface finish should beprinted in unidirectional mode. Critical point on quality in thissystem is a catcher, which helps to achieve a selective printingstratagem in the presence of a continuous stream as shown inFigure 4(A). Figure 4(B) display how the catcher is operating.Deflected plates are guiding droplets to catchers whenunwanted droplet and drained for reuse. However, owing tosome disadvantages such as binder accumulation, pumpclogging and low cross-section of individual catchers, Sachsand Vezzetti (2005) proposed a single catcher system that isshared by all the nozzles, proportional deflection reducing thenozzle gap and 45 degree print head rotation improvingdynamic shift and enabling the droplets to land on exactpowder bed at certain time to improve surface finish anddimensional accuracy, as shown in Figure 4(C) (Sachs andVezzetti, 2005). By comparison, the DoD method providesbetter quality (e.g. resolution and minimum feature width) butis slower in regard to printing process (Cima et al., 2001).The BJP is capable of achieving accurate geometries in nano-

biomaterial fields (Liu et al., 2013). Factors that determine thefinal part dimensions are accumulative accuracy of depositedlayer thickness, accuracy of droplet placement, reproducibilityof droplet spread and of dimensional change during curingprocess. In addition, the dimensional accuracy is stronglyinfluenced by interaction performance between powders andbinders such as powder’s surface treatment, size, shape,packing density, distribution, binder’s viscosity, surfacetension, droplet size, velocity and temperature.Chumnanklang et al. (2007) studied the effects of binder

concentration in pre-coated particle on part strength. It wasfound that part strength can be increased by increasing thebinder concentration and pre-coated particle size. Thehydroxyapatite particle used was coated with maltodextrin andwater. Coated powders have easier-to-flow characteristic thanraw powders so that it could lead to high flow-ability anduniform distribution. These higher binder concentration andpre-coated particle enables better densification of intra-

particles during sintering process (Chumnanklang et al., 2007).For better powder distribution, Snelling et al. (2013) used asieve test to determine particle size distribution, which is ofcritical importance to mechanical properties and dimensionalaccuracy (Snelling et al., 2013).Lu and Reynolds (2008) analyzed printing variables mainly

influenced on integrity, dimensional accuracy and minimalfeature sizes that can be printed for less than 500 mm 3Dmeshstructures (TiNiHf shape memory alloy). This author classifiedthree groups in terms of the variables: 3D printer-, powder- andbinder-related variables. Two major 3D printing variables (i.e.printing layer thickness and binder saturation) are evaluated. Itwas found that breaking strength increases with the bindersaturation level up to 170 per cent at the same printing layerthickness, and 35 mm printing layer yields 3D mesh structurewith the highest integrity and the lowest dimensional deviation(Lu and Reynolds, 2008).In bone tissue engineering area, Farzadi et al. (2014)

investigated the effects of layer thickness and printingorientation (parallel to X, Y and Z) on dimensional accuracyand mechanical properties of printed porous scaffolds. It wasfound that concurrence between model’s orientation (i.e.longitudinal direction) and printing head movement results inbetter dimensional accuracy and mechanical properties, andlayer thickness as well as printing direction, which has asignificant effect on the compressive strength of scaffolds(Farzadi et al., 2014).Some of researchers have used mathematical analyses to

improve the quality for BJP technique. Asadi-Eydivand et al.(2016) found that the delay time of applying the new layerand printing orientation significantly influences thedimensional accuracy, compressive strength and porosity. Afull factorial design of experiment (DOE), signal to noise (S/N) ratio and analysis of variance (ANOVA) were used toinvestigate optimal process parameters (Asadi-Eydivand etal., 2016). Another research used statistical analysis(ANOVA) to determine significant factors affecting productquality. Parameters of binder saturation, layer thickness,building orientation were optimized through statistical

Figure 4 (A) Components of the continuous printing system; (B) flight path of a droplet during printing; (C) binary deflection and proportional one




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analysis (Uma Maheshwaraa et al., 2008). In addition, BJPtechnique has been studied for quality in metal casting area(Meisel et al., 2012; Snelling et al., 2013), foundryengineering (Budzik, 2007) and pharmaceutics (Yu et al.,2009).In summary, BJP have highly advanced in ceramic and metal

fabrications compared with other techniques because BJP canfabricate high melting point materials at low temperature usingbinder and post-treatment. The impressive achievement in BJPwas a study on DoD to develop a single catcher system on eachnozzle to control individual droplet and pre-coating particle toenhance part strength by solving drawback of BJP such as lowdensity and porosity. In addition, mathematical analyses wereconducted to find optimal printing parameters. The mainissues during printing process were the interaction performancebetween binder and powder, accumulative accuracy ofdeposited layer thickness, droplet placement, the delay time ofapplying a new layer and dimensional change during post-treatment. These issues significantly influence dimensionalaccuracy and challenges to investigate for better quality.

2.4 Extrusion-based processesExtrusion-based processes (EBP) deposit material in form of acontinuous flow layer-by-layer to build a 3D structure. Theseprocesses have diverse technique concepts but are classifiedinto two main groups: melting-based extrusion and non-melting-based extrusion (Vaezi et al., 2013). 3D-bioplotting isthe most representative of non-melting-based extrusiontechniques and fused deposition modeling in melting basedextrusion techniques. In here, fused deposition modeling(FDM)will be themain point of discussion.

2.4.1 Researches on general quality controlIn the FDM technique, there are two kinds of depositionmethodsmainly used: contour and raster. In contour method, extrusionpath is following outside contour and offset in until it fills the entiredomain. This technique’s, main disadvantage is slow printingoperation owing tomultiple paths creation. In a raster method, thematerial is deposited in zig–zag fashion. Raster method is a fasterprocess but less accurate and gives rise to voids in depositionprocess in comparison with the contour one. This can lead todeterioration in the in-plane properties of the structure. Kulkarniand Dutta (1999) proposed spiral geometric path, which caneliminate multiple paths and inaccuracy to provide faster processand reduce voids. In result, in-plane stiffness spiral method wasimproved up to 18,000 lb/in2 compared with 10,300 lb/in2 ofcontour paths (Kulkarni andDutta, 1999).Sun et al. (2008) evaluated the effects of processing

conditions in the bonding quality of FDM technique. It wasfound that the neck growth between adjacent filaments of thesame layer is expected to be larger in the bottom layers than inthe top layers because bottom layer remains longer period thanupper layers, indicating voids size in the lower region is clearlysmaller than in the upper region. It is the fact that convectiveheat from adjacent extruded molten layers during processallows neck growth between filaments, which occurs aboveglass transition temperature. This neck growth can be increasedby proposed lateral geometric path to have slow cooling rate.Therefore, bonding strength and voids were improved andminimized, respectively (Sun et al., 2008). (Figure 5)

Boschetto and Bottini (2016) used a design for manufacturing(DFM) method to overcome inconsistent geometricaldeviations that cause poor surface quality and depend on layerthickness, deposition angle, etc. Proposed DFM permits aredesign of the components during preprocessing to predict theobtainable dimensional deviations.Modifications carried out indesign stage can compensate for the deviations generated in thenext manufacturing stage, thus, providing improved partaccuracy, as shown in Figure 6 (Boschetto and Bottini, 2016).Reducing in build time and increasing part surface quality

contradict each other and there have been a number ofattempts to solve these issues. Pandey et al. (2003a, 2003b)proposed a method of real-time adaptive slicing procedures.This method predicts surface quality expressed by standard Ravalue using direct slicing and tessellated model (STL) so that inthe design stage, build time and surface quality can bemaximized based on the predicted Ra value (Pandey et al.,2003b). Huang et al. (2009) proposed a robust algorithm togenerate the support slice data that enable part’s self-supportability and reduce redundant support volume at maximumextent. This slice data based algorithm has the same efficiencyas the STL based algorithm, however, it is more stable androbust to generate support slicing process. The advantage ofthis algorithm provides necessary support for hanging not onlystructure surface but also vertexes and edges, which canguarantee dimensional accuracy (Huang et al., 2009).In the area of casting, Kaveh et al. (2015) used high impact

polystyrene (HIPS) materials to fabricate wax patterns forcasting technology that requires accuracy without nay internalcavity. They designed proper benchmarks regarding printingparameters and statistical equations that can calculatecalibration factors (precision and internal cavity) to preventerrors between designed and actual dimensions; eventually,these can be used to determine optimal printing parameters(Kaveh et al., 2015).

2.4.2 Researches on surface quality and post-processingFDM technique presents limitations in surface quality.Staircase effect influences surface quality of 3D printed partusing thick filament approximately 0.24 mm, which is the mostused thickness and 0.127 mm at the most. This is lowerresolution than other techniques such as SL (0.05 mm), SLS(0.02 mm), BJP (0.05 mm) and MJP (0.016 mm). Other

Figure 5 Microphotograph of the cross-sectional area of a 38 � 38 �30 layer part




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problems are related to thermal distortion and stress resultingfrom shrinkage (Boschetto and Bottini, 2015).Armillotta (2006) assessed textured surface quality printed

by FDM technique. Critical issues on surface quality aredependent on the rate of material deposition, shrinkage andresidual stresses during solidification. It was found thatsufficient resolution of surface without stair-stepping, as shownin Figure 7(A), can be obtained under the conditions providedwhich feature in-plane size � 1 mm on surfaces parallel to thebuild direction can be fabricated without excessive stair-stepping. Moreover, sinking effects made by contour offsetting,as shown in Figure 7(B), can be avoided when adjacent featuresare spaced out at least 1 mm away from one another(Armillotta, 2006). Campbell et al. (2002) implemented avisualization algorithm (AutoLisp language within AutoCAD14) that represents varying surface roughness of 3D designedmodel as color shading within a CAD image. Using thisalgorithm, the designer is given clear visual image of the overallsurface roughness of a model and any potential problem areas(Campbell et al., 2002).

Galantucci et al. (2009) proved significant improvements onsurface finish of FDM 3D printed acrylonitrile butadiene styrene(ABS) part using the chemical post-processing method.Dimethyl ketone (acetone) was used with 10 per cent of water aschemical because ABS has weak interaction with polar solventssuch as dimethyl ketone, ester and chloride solvents. Therefore,chemical reaction is controlled by the soaking and exposure timeto ABS parts (Galantucci et al., 2009). In biomedical micro-devices, McCullough and Yadavalli (2013) also used the samechemical as dimethyl ketone-based sealingmethod tomodify andseal the surfaces of ABS to render the surface of 3D printedmicro-structured features to be impervious to water and increasehydrophilicity and biocompatibility, as shown in Figure 8(McCullough and Yadavalli, 2013). With this chemical method,Galantucci et al. (2010) studied how this chemical reactionaffects mechanical properties as well as surface finish. It wasobserved that mechanical properties such as ductility, flexuraland compressive strengthwere improved as treated chemicals buttensile strength was slightly reduced because of a different actionof the solution on different surface pattern or different reaction of

Figure 6 (A) Modeled (left) and fabricated (right) air fan blade; (B) 3D maps of the thickness deviation measurements for original (left) and modified(right) blade

Figure 7 (A) Graphical simulation of the stair-stepping effect on textured surfaces and (B) resolution limits (right): (a) spacing of layer contours; (b),(c) feature sinking




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treated filaments (Galantucci et al., 2010). In addition, Percocoet al. (2012) studied compressive strength and surface roughnessof FDM 3D printed ABS part using chemicals, 90 per centdimethyl ketone solution and 10 per cent water. Results showedan increase in compressive strength and reducing roughness up to90 per cent when the immersion time is at 300 s, as comparedwith non-treated part (Percoco et al., 2012).Addanki Sambasiva Rao et al. (2012) used design of

experiment, ANOVA, to find significant factors affectingsurface finish by analyzing various parameters in chemicaltreatment process, such as different levels of solutionconcentration, exposure time, temperature and initialroughness. Two different chemicals, dimethyl ketone andmethyl ethyl ketone, were used. In the case of dimethyl ketone,solution concentration, temperature and initial roughness weresignificant factors that mostly affect surface roughness. ForMethyl ethyl ketone, solution concentration, temperature andexposure time were the most important factors to be surfacequality (Addanki Sambasiva Rao et al., 2012). Moreover,vapors treatment was introduced by Sratasys. Vapors oftetrahydrofuran created by applied heat interact with surface of3D part, which settles in the side of the closed vessel with a non-air tight lid (Stratasys, 2016). However, this chemicaltreatment can dramatically improve surface finish but has beenonly applied to ABS polymer and drawback limits using tochemical sensitive materials. In addition, it provides poorrepeatability, small variations in geometry of the part andunpredictable dimensional accuracy.Another method used was hot cutter machining, as shown in

Figure 9(A). Pandey et al. (2003a, 2003b) addressed a problemof surplus stock material, necessary tooling phase and pathplanning despite improved surface at low time (Pandey et al.,2003b). A method mostly used for surface finish is a barrelfinishing reproducing better surface finish using abrasive orlubricative polishing agents with 3D printed parts put inrotating closed chamber creating friction against each other, asshown in Figures 9(B) and 9(C). This method provides highrepeatability and surface finish. Boschetto and Bottini (2015)studied the quality control for predicting surface roughnessafter barrel finishing by using theoretical models developed

through statistical analysis validated in comparison withexperimental results (Boschetto andBottini, 2015).Many researchers have found that build orientation affects

surface quality on FDM parts. Vijay et al. (2011) investigatedthat surface roughness is proportional to the layer thickness for20° and 45° build orientation; however, roughness is an inverseproportion to layer thickness when 70° is used for buildingorientation (Vijay et al., 2011). Sreeram (1995) developed anauto-compute method to decide optimal orientation directionbased on variable slicing thickness for a polyhedral part(Sreeram PN, 1995). Frank and Fadel (1995) proposed a user-friendly decision-making tool that recommends the optimalorientation direction before printing (Frank and Fadel, 1995).Chakraborty et al. (2008) developed a new method for

curved layer 3D structure. This curved layer FDM (CLFDM)method has an advantage in fabricating thin and curved partwith improved surface finish owing to reduction of stair–stepeffect as well as the reduction of layers. The method alsoincreased mechanical strength, reduced material waste andbuild time, as shown in Figure 10(A). In regular FDM, tableand head movement do not deviate from vertical (z-axis) whenx- and y-axes are being controlled, as shown in Figure 10(B).However, the proposed method uses x- and y-axes withcontouring control and z-axis with linear interpolation control.Therefore, the table and head movement can have a 3D linearinterpolator for the deposition of curved layers. In addition,they worked on determining adjacent filament paths for properreproduction of part shape (Chakraborty et al., 2008).Ahn et al. (2009) proposed a theoretical model to express

surface roughness with respect to surface angle u . Asthe surface angle is closer proportionally to fabricationdirection, surface roughness improves quality. The validity ofthis proposed model was further proved by comparingempirical data with computed values (Ahn et al., 2009).

2.4.3 Data analytic and algorithm researchesMany researchers used statistical analyses to optimize bothprocesses and product design for quality control. Anitha et al.(2001) performed experiments to assess the effects of theparameters on quality characteristics using the Taguchi

Figure 8 (A) Permeability of micro-channels fabricated using FDM before and after treatment; (B and C) FDM ABS feature fidelity before and aftertreatment; (B) channel and (C) ridge shape fidelity; (D), percentage change in ridge radius of curvature (E) and channel wall incline angle with varyingacetone solution concentration, treatment length constant at 4h




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method. Analysis of the Taguchi results usingmethods of signalto noise (S/N) ratio, ANOVA, correlation and regressionanalyses indicated that without pooling only one layer thicknessis effective to 49.37 at 95 per cent level of significance. But onpooling, the layer thickness is mostly effective to 51.57 at 99 percent level of significance. This result was further strengthenedby afore mentioned analyses which indicate a strongrelationships with surface roughness. Moreover, based on theS/N analysis, the layer thickness was mostly effective at 0.3556mm, road width at 0.537 mm and deposition speed at 200 mm/s(Anitha et al., 2001). Similarly, Lee et al. (2005) used theTaguchi method and orthogonal array, main effect, S/N andANOVA analyses to investigate the optimal printing parametersneeded to achievemaximum flexibility of ABSmaterial. Throughthis statistical analysis, it was concluded that layer thickness,raster angle and air gap has significant impact on the elasticperformance of ABS printed structure (Lee et al., 2005). Pandeyet al. (2003a, 2003b) studied the improvement of surface finishby staircase machining using the FDM technique. Theyproposed hot cutter machining method to improve the surface of

a staircase structure on 3D printed parts and used ANOVA andfractional factorial design of experiment to analyze the effects ofthree important machining parameters (i.e. cutting speed anddirection) and for developing statistical model, respectively(Pandey et al., 2003a). Sood et al. (2009) adopted the Grey–Taguchi method to obtain optimum level of printing parametersby finding the significant factors and their interactions and tominimize the percentage change in length, width and thicknessinto a single objective known as the Grey relation grade. ThisGrey–Taguchi method provided good results on overallimprovement in part dimension. In addition, artificial neuralnetworks (ANN) was used to predict overall dimensionalaccuracy and provide errors between predicted data andobserved value varying between 0 and 3.5 per cent. Smaller errorpercentages mean better suitability of present model (Sood et al.,2009).Sood et al. (2010) conducted central composite design for

design of experiment and ANOVA for validity to investigate theimportant impact of printing parameters related to mechanicalproperties such as tensile, flexural and impact strength. It was

Figure 9 (A) Different directions of machining using hot cutter; (B) scheme of barrel finishing; (C) 3D map of surface before machining and after 960min barrel finishing working time

Figure 10 (A) Model development of different complex geometries using CLFDM – (a) a hemispherical surface with a straight trim on one side(b) trimmed surface of a part in the interior, discontinuous filament deposition method (c) trimmed surface of a part in the interior, continuous filamentdeposition method (d) surface made of two patches, continuous filament method; (B) (a) sectional view of a part produced by FDM. The overlappedsection of two adjacent layers in FDM is shown by shaded lines. (b) Adjacent filaments of CLFDM




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concluded that the increase in layer thickness, which meansdecreased number of layers required, leads to minimizingdistortion effects within the structure so that total mechanicalstrength will be increasing (Sood et al., 2010). Sood et al.(2012) studied that compressive strength of 3D structureexhibits high dependence on five important printingparameters (i.e. layer thickness, part orientation, raster angle/width and air gap). This research not only provides an insightinto the complex dependency of compressive stress on processparameters but also created a statistically validated predictiveequation to find optimum parameters for maximumcompressive stress using a quantum-behaved particle swarmoptimization (QPSO). However, to solve involved largenumber of conflicting factors and complex phenomena for partbuilding to predict output characteristics accurately. ANNwithback propagation algorithm was adapted to predict the finalstructure’s compressive stress as shown in Figure 11. In result,this optimized statistically predictive equations that gave amaximum compressive stress of 17.4751 MPa at optimumvalues of layer thickness, orientation, raster angle, raster widthand air gap as 0.254 mm, 0.036°, 59.44°, 0.422 mm and0.00026 mm, respectively (Sood et al., 2012). Li et al. (2010)also used particle swarm optimization algorithm to obtainappropriate fabrication orientation by optimizing support area,fabrication time and surface roughness so that it was proved toenhance processing efficiency and reduce the costs significantly(Li et al., 2010). Rao and Rai (2016) used the teaching–learning-based optimization algorithm (TLBO) and non-dominated sorting TLBO (NSTLBO) to solve optimizationproblems on FDM. When it comes to objective function valuewith a higher convergence rate, it was proved that TLBO showsbetter performance as compared to QPSO and genericalgorithm (GA) in terms of solving single-objectiveoptimization aspect and that NSTLBO demonstrated betterperformance as compared with non-dominated sorting geneticalgorithm (NSGA-II) in terms of solving multi-objectiveoptimization aspect (Rao and Rai, 2016).Ahn et al. (2002) used design of experiments to study the

relationship among the printing parameters of ABS material

such as raster orientation, air gap, bead width, color and modeltemperatures. It was found that the air gap and rasterorientation significantly influence the tensile strength of printedpart a lot more than bead width, model temperature and color(Ahn et al., 2002). Thrimurthulu et al. (2004) used real codedgenetic algorithm to obtain optimum parameters of partdeposition orientation, which significantly affects the surfaceroughness and build time. Also, this optimized parameterconsiders minimization of support structures estimated bygenetic algorithm. This genetic algorithm helped predictingsurface quality and build time (Thrimurthulu et al., 2004).Patel et al. (2012) emphasized that the Taguchi method is a

versatile tool for process and product design optimizations andto solving current quality control issues on 3D printingtechnology (Patel et al., 2012), based on the references (Soodet al., 2009, 2010; Shaji and Radhakrishnan, 2003; Çaydas� andHasçalik, 2008; Gopal andChakradhar, 2012).

2.4.4 Researches on compositesMostafa et al. (2009) investigated the behavior of melt flow ofABS–iron based composite materials deposited by FDMtechnique. 2D and 3D numerical analysis (i.e. CFD, FEA andANSYS FLOTRAN/CFX software program) were used toanalyze melt flow parameters: mainly temperature, velocity andpressure drop at extrusion. These analyses help preventclogging and allow smooth flow out of the extrusion andenhance quality of composite based 3D structure by predictingthe flow behavior (Mostafa et al., 2009). Bellini and Güçeri(2003) developed a novel FDM technique mounted a mini-extruder on deposition of FDMmachine for agile fabrication. Itstreamlined filament preparation for continuous process offabrication ECG9/PZT ceramic composites. This high-precision-based extruder was designed for a wide range ofmaterials as well as ceramic based composites. The authorstudied flow behaviors influencing quality in terms of granulessize, nozzle temperature and nozzle size during extrusion anddeposition. Two thermocouples were used at both the entranceand exit of the extruder qualifier to determine the influence oftemperature in upper and lower parts of the qualifier (BelliniandGüçeri, 2003).In summary, EBP has been studied in various research

approaches as they are the most popular processescommercialized in public. The general quality issues currentlyidentified in FDM technique are the surface quality arose fromstaircase effect, limited layer thickness, thermal distortionshrinkage causing low resolution and dimensional inaccuracy.Many kinds of post-processing and build orientation methodshave been investigated to alleviate the surface and mechanicalproperty issues. This EBP has advanced especially in dataanalytics and algorithms, which signify that other fabricationtechniques can possibly adopt to predict their optimal designand printing parameters. Research in these fields can be thenext step to understand the effects of various parameters on thequality of fabricated parts.

2.5 Powder bed fusion processesMost of the processes in powder bed fusion (PBF) system use ahigh source of thermal energy to melt powder-based materials(i.e. polymer, metal and ceramic) into desired structures andshapes (Stucker, 2012). In the PBF processes, three different

Figure 11 The ANN architecture




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techniques have been developed: selective laser sintering (SLS)technique, which uses partial melting to fuse powders together,selective laser melting (SLM), which uses full melting onpowders transferring into homogenous parts, as well as fusingand electron beam melting (EBM), which uses an electronbeam to design to fuse only metal powders in vacuumenvironment. For medical applications, SLS technique iswidely used for human skeleton or organ reconstruction owingto its high accuracy and repeatability when compared to FDM,LOM and inkjet printing techniques (Silva et al., 2008; Rengieret al., 2010).Edwards et al. (2013) studied mechanical property

improvement of Ti-6Al-4V alloy part printed by EBM. Byelevating certain bed temperature, residual stresses wereminimized; therefore, post heat treatment could be eliminated.In addition, fatigue performance is highly dependent on roughsurface and porosity, which means that the technique highlyrequires post-process (e.g. peening). However, this approach isnot agile and smart enough to enhance part quality. Ideally, theprinting parameters need to be quickly and dynamically tunedup by optimized input variables. Accordingly, researchattempts have been put into practice to monitor process tocontrol process input variables (Edwards et al., 2013). Melvinet al. (1994) used a video microscopy system to observesintering and flow behavior in real-time, which can evaluatedifferent sintering characteristics of the materials (Melvin et al.,1994). Benda (1994) developed an infrared light thermalsensor to control laser power for better uniform sinteringperformance (Benda, 1994). Zeng et al. (2012) reviewedliteratures on the thermal modeling method in SLS and SLMtechniques. It was summarized that uniform temperaturedistribution of fields during printing processes leads to betterquality; there is a need to provide information frommonitoringtemperature of the melt pool to be able to control processparameters for part quality. As a temperature monitoringsystem, pyrometers and thermocouples were used formonitoring its temperature (Zeng et al., 2012).Pyrometer is defined as the noncontact measurement of body

temperature based on emitted thermal radiation. There are twotypes of pyrometer used depending on the application:photodiodes and digital cameras [e.g. charged-coupled device(CCD), complementary metal oxide semiconductor (CMOS)].Both detects radiation and light, respectively, and convert theminto electric signal. Bi et al. (2007) developed a novel lasercladding head with photodiodes and digital camera (CCD) toenable real time monitoring to monitor the status of machineparts, thus, improve part quality (Bi et al., 2007). Kleszczynski etal. (2012) used a high resolution CCD camera for surface errordetection through image processing, as shown in Figure 12(Kleszczynski et al., 2012). Lott et al. (2011) analyzed images ofmelt pool size to adjust laser output power by using a CMOScamera with an additional illumination source required for highscanning velocities, resolution, as well as photodiodes (Lott etal., 2011). Kolossov et al. (2004) developed a thermal modelthat the temperature evolution and sintering formation can besimulated by a 3D FEA to predict thermal properties (i.e.thermal conductivity and specific heat; Kolossov et al., 2004).Thermocouples, in contrast with pyrometers, are defined as

contact measurement of temperature which can reduce thefreedom of process. Therefore, pyrometer has been used

dominantly more than thermocouples for temperaturemonitoring in PBF processes. Shishkovsky et al. (2008) usedthermocouples to monitor the build of six differentintermetallic powders and measure the temperature on thepowder bed (Shishkovsky et al., 2008). In addition, Van Belleet al. (2013) used thermocouple attached to the bottom of thebase plate with a strain gauge to record residual stresses (VanBelle et al., 2013), and Taylor and Childs (2001) also usedthermocouples positioned under the bed’s surface to monitorenergy absorption and powder effective conductivity for betterunderstanding of heat transfer in metal powder during laserprocessing of the powder bed (Taylor and Childs, 2001). Lowand Ake (2004) invented a thermocouple control system toimprove uniform distribution of temperature on powder bedduring part build. The thermocouple was attached inside of thepowder bed along with IR sensor and communicates withtemperature transmitter through circuitry in real-time (Lowand Ake, 2004).Besides, research was focused on the IR thermography-

based monitoring and control system incorporated in theEBM system. Price et al. (2012) demonstrated the feasibilityof using a near IR thermal camera for temperaturemeasurement in hatch melting, preheating and contourmelting events during the EBS process (Price et al., 2012).Yadroitsev et al. (2014) used a galvanometric scannersystem for temperature distribution monitoring of melt poolof Ti6Al4V alloy in the SLM system (Yadroitsev et al.,2014). Dinwiddie et al. (2013) developed continuous datacapturing method using the IR camera to demonstratefeasible work to detect porosities inside materials andunderstand thermal phenomena such as it happens whenbeam and powder interact with each other (Dinwiddie et al.,2013). Kruth et al. (2007) developed temperature feedbackcontrol system by positioning photodiodes and CMOSdigital camera on laser beam to stabilize melt pooltemperature distribution in SLM system (Kruth et al.,2007). In 2013, Mireles developed automatic feedbackcontrol system using IR camera for defect detection, andnotification tool to stop printing process when porosity levelreaches to certain level. In addition, achieved data can helpprocess parameters to be optimized and stabilizetemperature automatically through image processing andauto-decision-making (Mireles et al., 2013). In 2015,Mireles developed in-situ defect monitoring methods usingIR thermography inside an EBMmachine. This captured IRimages provided a good indication of measured defectsgeometry presence, which can be used to re-melt defect (i.e.porosity) for the purpose of in-situ correction, as shown inFigure 13 (Mireles et al., 2015). This method saved totalmanufacturing time and decreased altering microstructurecompared with HIPing (hot isostatic pressing) process, andeventually increased the mechanical properties throughoutthe fabricated part.In summary, the PBF processes, as compared with other

processes, have mostly advanced in temperature monitoringsystem using sensors. Currently, the main quality issuesidentified was the heterogeneous distribution of powder bedtemperature and laser output power. Non-contact method,which is more adaptable owing to the freedom of process, wasused to solve these quality issues. In addition, thermal




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modeling techniques such as 3D FEA were implemented topredict thermal properties. Post processes (e.g. heat treatment,peening, etc.) are necessary after the 3D printing process toalleviate residual stress, rough surface and porosity. Researchefforts were made to develop feedback control system fortemperature distribution, defect detection and in-situcorrection.

2.6 Directed energy deposition processesDirected energy deposition (DED) processes use a laser beamto fuse materials (e.g. metal powder and wire feedstock)delivered from a material deposition head and substrate wherematerials are deposited (Tapia and Elwany, 2014; Vaezi et al.,2013). PBF process feeds a powder layer using a blade or rolleronto the powder bed and selectively melts sections to build 3D

structures, while in DED metal powders are fed from thedeposition head and melt onto a substrate by energy sourcecoming out from middle of the head. There are different typesof techniques such as laser engineering net shape (LENS), lasercladding (LC) and direct metal deposition (DMD). Majorconsiderable processing parameters in DED are melt pooltemperature, material delivery rate and nozzle tip-substratedistance.The processmonitoring and control focused on aforementioned

major printing parameters have been of increasing interests toimprove quality and reproducibility. Bi et al. (2013) developedclosed-loop controller that sends monitored temperature feedbackand adjust laser power density in real-time to improve dimensionalaccuracy by using the IR pyrometer that measures melt pooltemperature. This closed-loop controller can eliminate the heataccumulation and achieve oxidation-free clad surface with feedingargon gas shielding to avoid disturbance from IR temperaturesignals, as shown in Figure 14 (Bi et al., 2013). Fathi et al. (2008)used similar closed-loop laser cladding process that can reducestair–step effects aswell as production time (Fathi et al., 2008).In addition, Bi et al. (2006) investigated printing parameters

relationships by looking at microstructure, hardness anddimensional accuracy. It was proved that variations of meltpool temperature and size influencing cooling rate andsolidification conditions result in heterogeneous dimensionalaccuracy and microstructure (Bi et al., 2006). In a similarmanner, Hu and Kovacevic (2003) implemented a system tomonitor and control the powder delivery rate through a real-time sensing and control using IR high-speed camerapositioned with laser beam. Captured images of melt pool bythis camera were also used for analysis to adjust laser power foruniform temperature distribution. The reflected data from thesensor helped improve the 3D printed part quality (Hu andKovacevic, 2003). Similarly, Hofmeister et al. (1999) used aCCD camera mounted with laser beams to capture melt poolimages and analyze the melt pool cooling rate. Someresearchers studied process monitoring with DOE method(Hofmeister et al., 1999). Ermurat et al. (2013) and Lee (2008)used a high-speed camera and CCD camera to monitor nozzleand powder delivery rate. With its extracted data, DOE is used

Figure 12 Setup of the CCD camera system in front of machine window (left) and setup of CMOS camera system with illumination source andphotodiode for high scanning velocities and resolution (right)

Figure 13 Correction of un-melted powder through layer re-meltshown by the build file with the re-melt area shown by the red arrow(top), showing the defect present in the IR image by the black arrow(middle) and showing the defect not shown on the left column owing tothe re-melt (bottom)




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to analyze the behavior of the nozzle dimension and powdersize along with changes of standoff distance and gas flow(Ermurat et al., 2013; Lee, 2008).Some researchers adopted analytical models to analyze the

thermal behavior of the process. Ye et al. (2006) andWang et al.(2009) reported that DED fabrication requires an in-depthunderstanding of the entire thermal behavior of the process. Inthese papers, the finite element method (FEM) was used toobtain the thermal distribution and numerical simulation,known as Thermaviz. Two-wavelength imaging pyrometer wasused to provide temperature data such as temperaturemapping, cooling and heating rate, modeling and simulation,etc. The numerical simulation and FEM results provide theuser further predicting models and parameters (Ye et al., 2006;Wang et al., 2009). Alimardani et al. (2007) also used a 3Dnumerical method for predicting transient geometrical andthermal characteristics of multilayer laser solid freeformfabrication. Thermal and geometrical properties can benumerically obtained and predicted even with variation ofparameters such as the powder feed rate, elapsed time, meltpool temperature, substrate roughness, etc. so that the qualitycan be more precisely optimized (Alimardani et al., 2007).(Figure 15).For the studies on material delivery rate, Hu et al. (2011)

developed amonitoring and control system using IR diodes andlow-power laser. This system is designed for sensing thepowder flow rate out of nozzle to ensure consistent powder feed

rate (Hu et al., 2011). In addition, Smurov et al. (2012) usedhigh-speed cameras for calculating the powder speed and fluxby positioning the cameras next to the nozzle head (Smurovet al., 2012).Some of the researchers conducted studies on themonitoring

layer height through the use of CCD cameras and neuralnetwork algorithm. Iravani-Tabrizipour andToyserkani (2007)used three CCD cameras-based optical detectors positioned at120 degree of each other around the nozzle head used in LCtechnique. A novel algorithm composed of an image-basedtracking protocol and a recurrent neural network weredeveloped to measure the clad height in real-time. Thesefeatures tracking and trained network showed a promisingresults in detecting the clad height in various trajectory anglesin real-time, which measured the clad height with 10 Hz and60.15 mm average precision independent from clad path withabout 12 per cent maximum error (Iravani-Tabrizipour andToyserkani, 2007). In a different approach, Toyserkani andKhajepour (2006) used a CCD camera-based optical detectorin close-loop control to be able to effectively measure not onlyclad height but also angle of liquid/solid interface in real-time.Using the recorded different images of each layer, it estimatesits roughness and product quality (Toyserkani and Khajepour,2006).In summary, DED processes have dealt with advanced

researches such as process monitoring, real-time closed loopfeedback system, thermal behavior and data analytic algorithm.

Figure 14 Front view of the deposited sample, the laser power and IR-temperature signals after (left) and before (right) stabilizing IR–temperaturesignals

Figure 15 Finite element mesh and geometry to simulate the LENS process for ten layer wall (left) and molten pool size distribution for each layer atP = 600W and V = 2.5 mm/s (right)




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The main quality issues identified were related toheterogeneous melt pool temperature, laser power density andmaterial delivery rate. These heterogeneities can be solved by aclosed-loop controller with data acquisition equipment such asCCD and IR high speed cameras, which can sense printingprocess data in real time and adjust them to optimal values.Researches such as FEM simulation and data analyticalgorithmwere also executed to predict the quality.

2.7 Sheet lamination processesSheet lamination processes (SLP) are one of the additivemanufacturing techniques to build a 3D structure bysequentially laminating, bonding and cutting 2D cross-sectionsheets of such polymer, ceramic coated paper or compositepaper by either a laser or a knife, and layers bonded by glue oradhesive coating. This technique is also called laminated objectmanufacturing (LOM). The bonding process is accomplishedby heat or pressure applied from a heated cylinder rolling alongthe sheet. While there is another technique called ultrasonicconsolidation (UC), which has the capability of ultrasonicwelding of metal sheets, it will be not covered in this paper asthis technique is a hybrid fabrication method combining theadditive manufacturing with subtractive process (Vaezi et al.,2013; Kechagias, 2007a).Although LOMprocess provides faster processes with sufficient

quality characteristics on large physical prototypes than othertechniques (Kruth, 1991), this technique hasmany difficulties anddrawbacks inmanufacturing process, which are poor surface finishbefore post-processing, difficult disengagement and only goodtensile strength properties in laminates direction compared withother techniques (Chryssolouris et al., 2003; Upcraft and Fletcher,2003).The LOM process is separated into two methods when it

comes to the way of laminating a sheet of material: cut-then-bond and bond-then-cut. The cut-then-bond method, whichcuts first and then re-position on workpiece to bond, is effectivefor de-cubing, however, causes a loss in positioning precisionand accumulated error. The bond-then-cut, which bonds asheet on workpiece and then cuts it by laser, is not able toperform complicated parts (e.g. hollow and vase) owing todifficulty in de-cubing inner waste material so that it consumeshigh laser power for de-cubing and crosshatching. In addition,it is time-consuming and labor-dependent.Liao et al. (2003) proposed an online de-cubing process in

the bond-then-cut method and with a developed bridge-supporting algorithm. Shielding paper laminated on a sheet wastorn for laser cutting a sheet material and then used to removewaste materials online by using adhesion force betweenshielding paper and wastematerial, as shown in Figure 16 (Liaoet al., 2003). For easy de-cubing process, an algorithm forbuilding bridges from 2D drawing was developed to supportand position islands part while de-cubing process (Chiu et al.,2003). This proposed process enabled the fabricating processto perform various geometric shapes with significantly reducedprocessing time up to 20-50 per cent. Chiu and Liao (2003)proposed a novel laser path planning strategy to improve de-cubing process problem. Burn-out rule proposed in this studywas applied in laser path planning to reduce damaging part andbonding strength between the part and waste, eventually saving68 per cent of de-cubing time. Overlap zone between fabricated

part and waste is determined for the burn-out rule withcalculated optimal laser energy levels, velocity and burn-outdistance to predict better de-cubing process. It is suggested thatoptimal laser paths can be obtained by ordinary laser powerwith the burn-out rule and low cutting velocity (Chiu and Liao,2003).Themost important quality characteristic of LOMpart is the

vertical surface roughness (Ra). Kechagias (2007a, 2007b)worked on experimental investigation of the surface roughnesson LOM part. Improving vertical surface quality leads toreducing post-processing time, easier disengagement, easier de-cubing, process optimization and less finishing (Kechagias,2007a). Using statistical models, Chryssolouris et al. (1999)found that vertical surface roughness (Ra) is mainly influencedby process parameters such as heater temperature, layerthickness and laser speed when measured on ZX plane,whereas pressure and heater speed have more effects on surfaceroughness when on ZY plane according to ANOVA analysis(Chryssolouris et al., 1999).Another critical issue is a delamination between bonded

layers owing to weak bonding or process malfunctioning (Vaeziet al., 2013; Kechagias and Iakovakis, 2009). Some researchersproposed mathematical models to enhance the bonding oflaminates. Pak (1994) developed a mathematical equationcontaining a relation between temperature at arbitrary depth ofthe part (Z-axis) and critical parameters in terms of bonding oflamination such as temperature (Th), speed (�), and pressure(d ) of heater roller. Reeves and Cobb (1995) presented ananalytical model to predict optimal heater temperature neededfor good laminate bonding. Sonmez and Hahn (1998) analyzedheat transfer and stress using FEA simulation and found thefollowing:� surface temperature of heater should not be higher than

glass transition temperature of the used paper;� surface stresses of laminate increase with the decrease in

layer thickness or roller diameter; and� higher rolling speed is more likely to cause delamination or

malfunctioning on sheet bonding (Sonmez and Hahn,1998).

Flach et al. (1998) developed a thermal analytical model withvarious process parameters for fabrication of functional ceramicand composite parts and founded the following:� chamber air temperature affects temperature of part being

built;� roller speed significantly affects surface layer in direct

contact; and� delay time of the process affects the temperature of the

part being built (Flach et al., 1998).

Some researchers studied on predicting surface roughness.Reeves and Cobb (1995) developed a general model forpredicting roughness on sloped surface. The surface angle (u )and the layer thickness (Lt) are critical parameters in predictingsloped surface roughness. Paul (2001) reported that theorientation angle and sheet thickness are statistically significanteffects on surface roughness and proposed a mechanistic modelfor sloped surfaces considering the two critical parameters(Reeves and Cobb, 1995). Chryssolouris et al. (1999)developed a semi-empirical model in consideration withparameters such as changes of layer thickness, heater speed,




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heater temperature and platform retraction, based on matrixexperiments with different surface angles and cross of laminatesdirection (Chryssolouris et al., 1999). Ahn et al. (2012)proposed theoretical expressions to quantify average surfaceroughness according to surface angle variation. In case ofupward or downward facing surface (0° < u < 90°, 90° < u <

180°), theoretical model was expressed by R að Þu ¼ 1l A,

where R(a) denoted the surface roughness, l the base length ofunit profile, which is distance between edge point of sheets andA the total surface area; therefore, R(a) is affected by the baselength. On the other hand, in case of vertical surface, roughnesslayer thickness (t) affected R(a) expressed by R að Þ90 ¼ 1

t A.These expressions were verified by comparing with empiricaldata (Ahn et al., 2012).Kechagias (2007a, 2007b) used a design of experiment to

investigate dimensional accuracy of the LOM part. It wasfound that parameters such as heater, speed, temperature andnominal layer thickness mainly affect dimensional accuracy inX and Y directions (Kechagias, 2007b). In 2008, he proposed asimple model to predict and optimize LOM processperformance using a feed-forward back propagation neuralnetwork (FFBP–NN). It was concluded that this model is aquick prediction of each performance measurements and canbe used to select optimal process parameters values (Kechagiasand Iakovakis, 2009). Vaezi et al. (2013) reported that qualityof the LOM also requires well-prepared material sheets toreduce variations in layer thickness and materials content.Depth of cuts varied with variations of layer thicknesses andmaterial content so that these variations lead tomore damage toa bonded layer below. In addition, the LOM part can be post-processed to reaction bonding process during a pyrolysis cycleto enhance surface quality and laminates bonding (Vaezi et al.,2013). Chryssolouris et al. (2003) investigated the influence ofdifferent process parameters of LOM on the tensile strength ofLOM part. It was found that the most significant processparameter affecting the tensile strength of part was layerthickness. Therefore, it can be concluded that layer thickness canprovide a prediction on tensile strength of part (Chryssolouriset al., 2003).

In summary, there have been issues on de-cubing process,vertical surface roughness and delamination. Development ofonline de-cubing process and bridge-supporting algorithm hassolved several issues such as saving energy, post-process timeand complicated structure arose by de-cubing. Research inmathematical models and neural network were implemented toenhance bonding of laminates and predict optimal processparameters. Vertical surface roughness and delamination arethe future challenges to be solved to improve quality. Seventechniques in AM technology was summarized and comparedin quality point of view, as shown inTable I.

3. Classification of research findings

Researches of QC in AM are being rapidly investigated recentlyowing to high feasibility, reliability and growth of mechanicaland material aspects applied to various areas. The firstconsideration on AM technology when it comes to QC is thatseveral steps are classified to achieve design requirement, asdescribed by Hollister et al. (2015) who studied a splint used inmedical area. This paper designed five steps of the designcontrol process, as shown in Figure 17. The first step is a designinput considering all parameters on designing and modeling apart to meet mechanical and specific requirements such as laserpower, speed and particle size. Design output as the secondstep comprises tests performed to measure the design inputbased on the standard operating procedures by determining thecompressive, bending and opening stiffness of the splint byusing the method of destructive evaluation. The first two stepsare to test sample parts to generate the design variables foroptimized final part being considered in design implementationas third step. Design verification as fourth step is to ensure thefinal manufactured part meets design input via geometric andmechanical evaluation (e.g. calipers, Micro-CT, etc.). The lastdesign validation is to test pre-clinical model and humanclinical implantations of final part.This outline suggested as a good framework for providing

steps of QC in medical-related research. However, there aredrawbacks when figuring out the design variables. It is time-consuming and wastes materials in making samples for

Figure 16 (A) The mechanism of the proposed online de-cubing process; (B) the original sliced 2D contour drawing; (C) the altered 2D drawingdeveloped by bridge-supporting algorithm




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TableISummaryandcomparison

ofsevenAM

techniques

inquality

pointofview

Catego

ryIm

portan

tqua

litypa

rameters

Adv

antage

sDisad

vantag

esAdv

ancedskills

Challeng

es

Photop

olym

ervat

processes(PVP

)Resinviscosity,densityandtemperature

Build

orientation

Laserscanspeed

Exposureenergy

Penetrationdepth

Excellent

dimensionalaccuracy

Goodsurfa

cefinish

Biocom

patibleprocess

Microscalefabrication

(�10

mm)

OnlyUVcurablepolymerresin

Materialcontaminationandwastes

Mechanicalpropertylim

itedby

resin-

basedpolymer

Thermalshrinkage

ProjectionSL

technique

Accuratecustom

-designandevaluation

processesthrough

MRIandCT

Dataanalyticandalgorithm

Biom

aterialfabrication

Nano/microscalefabrication

Developm

ento

fvarietyvatsolution

Expensivescanning

equipm

entsuchas

MRIandCT

Resolution

Materialjetting

processes(M

JP)

Dropletshape,velocity,diameter,viscosity

andsurfa

cetension

Jettingfre

quency,signalw

idth

and

voltage

magnitude

Substratetemperatureandevaporation

rate

Humidity

Excellent

resolution

Fastprocess

Excellent

dimensionalaccuracy

Multiplematerialprintingcapability

Microscalefabrication

(�100mm)

Nozzleclogging

Celldamageincertaintemperature

orfre

quency

Nanoparticlesa

gglomerates

causes

clogging

Lowviscosity

DoDtechnique

Micro-scalefabrication

Biom

aterialfabrication

Real-timeclose-feedback

loop

forprinting

correctionthroughimageprocessing

ondropletand

depthmap

Self-calibrationofprinthead

Qualityprediction

Exposureenergy

Nozzleclogging

Morefabricationfre

edom

onbiom

aterials

Bind

erjetting

processes(BJP)

Powdersurface

treatm

ent,size,shape,

packingdensity

anddistribution

Binderviscosity,surface

tension,droplet

size,velocity

andtemperature

Printinglayerthickness,orientation,

bindersaturationanddelaytim

e

Excellent

quality

controlabilityforceram

icandmetalfabricationcomparedtoMJP

technique

DoD

Pooreraccuracy

andsurfa

cefinish

comparedwith

MJP

technique

Infiltrationprocessneeded

forp

ost-

treatm

ent

DoDtechnique

Ceramicandmetalfabrication

Interactionperfo

rmance

betweenbinder

andpowder

Accumulativeaccuracy

ofdepositedlayer

thickness

Dropletp

lacement

Delaytim

eofapplying

newlayer

Dimensionalchange

duringpost-process

Extrusion-ba

sed

processes(EBP

)Layerthickness,buildorientation,raster

width

andangleandextrusion

temperature

Post-processing(exposuretim

eand

chem

icaltemperature)

Fastprocess

Goodmaterialproperties

Fairresolution

Staircaseeffect

Thermaldistortion

Thermalshrinkage

Lowdimensionalaccuracy

Delamination

FDMtechnique


Post-processing

Optimalbuild

orientation

Resolution

Dimensionalaccuracy

Shapechange

Clogging

issues

oncompositesmaterials

Powde

rbed

fusion

processes(PBF

)Powderb

edtemperature

Laser/b

eamoutput

powder

Powdersize

Atmosphere

Widerangeofmaterials

Excellent

dimensionalaccuracy

Excellent

repeatability

Goodmaterialproperty

Fairresolutionlim

itedby

particlesize

Residualstress

Porosity

SLStechnique

Temperaturemonitoringsystem

Thermalmodelingandimageprocessing

Temperaturefeedback

controlsystem

Real-timedefectdetectionandin-situ

correction

Resolution

Lasero

utputfeedbackcontrolsystemin

realtim

eAtmospherecontrol

Directeden

ergy

depo

sition

processes(DED

)

Meltpooltem

perature

Materialdeliveryrate

Distance

betweennozzletip

andsubstrate

Laserpow

erdensity

Widerangeofmaterials

Goodmaterialproperty

Fairresolution

Thermalstress

Closed-loop

controllersystemforreal-time

feedback

oftemperature,laseroutput,

clad

heightanddeliveryrate

Resolution

Surfa

cepost-process

Atmospherecontrol

Sheetlam

ination

processes(SLP)

Heatertemperature

Layerthickness

Laserspeed/pow

erRo

lling

speed/pressure

Cham

berairtemperature

Delaytim

eOrientation

Fastprocess

Sufficientq

ualityon

largeprototype

Goodtensile

strengthinlaminated

direction

Poorresolution

De-cubingor

crosshatchingprocess

Delamination

Shrinkage

Poortensile

strength

inZdirection

Waste

LOMtechnique

Burn-outrule


Onlinede-cubingprocess

Resolution

Consistent

sheetthickness

Repositioning

precisionandaccumulation

error(cut-then-bondprocess)

Verticalsurfa

ceroughness

Materialdam

ageby

lasercut

Tensile

strength

inZdirection

Delamination




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mechanical test and generating optimal design variables. If thedesign variables are adjustable during the design and printingprocess, the entire process of QC can be more agile andreliable. Figure 18 shows proposed classification of four mainprocesses to take account of QC during AM cycle and describesdetail works recently studied and also included in Section 2.On the basis of the four steps, modeling, printing, post-

treatment, and part evaluation during the entire 3D printingprocess, the following four categories are described briefly andissues are discussed.

3.1 Design processThis is so-called pre-QC considering from raw material’squality to model design. Material selection is importantbecause it may be contaminated or may have air porosities,which affects directly to the part property. Good design abilityby setting up optimized values of design parameters (e.g. layerthickness, infill pattern, printing speed and delay, mesh, etc.) isdirectly correlated to part’s good quality. Recently, Hollisteret al. (2015) studied on designing splint structure meetingquantitative clinical requirement by finding optimized design

variables and printing parameters generated from mechanicaltests. This approach finally brings the optimized splint partindividually customized for patients who needs perfect clinicalrecovery (Hollister et al., 2015). However, it requires a longtime-consuming procedure to fabricate and iterations ofmechanical testing depending on patients to generate optimaldesign variables. Recently, one research (Sitthi-Amorn et al.,2015) investigated on developing real-time printing controltechnique to correct design and printing factors in real-timethrough closed-feedback loop. This technique analyzes eachprinted layer in real-time through the machine vision,computing depth difference between the layers, and sendsfeedback to central computer to control amount of jet dropletinjection. This technique opens up the real-time design processduring printing process, but is limited to MJP. Someresearchers have studied optimizing design parameters bydeveloping analytical modeling and data-mining based onquality prediction algorithms as mentioned in Section 2.However, this research is limited to BJP, FDM and SLPtechniques. There are needs to launch this research in otherprinting techniques.

Figure 17 Schematic outline of the splint design control process by reference

Figure 18 The proposed classification of the four main processes during AM cycle




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3.2 Printing processThis is the so-called in process-QC considering from printingsetup to process finish of part. Agarwala et al. (1996) reportedthat internal and surface defects of fabricated wax pattern couldbe eliminated by the optimization of FDM parameters(Agarwala et al., 1996). Likewise, optimized set-up of printingparameters (e.g. extruder temperature, calibration, pattern,feed and flow rate, etc.) is critical for the quality. In recently,researchers have studied the optimization of printingparameters through real-time monitoring and printing controlas mentioned in Section 2. To date, more research has beenconducted in terms of the printing process compared to otherprocesses. Farzadi et al. (2015) studied the effects of the layerprinting delay on 3D printed part’s physical and mechanicalproperties for better selections of printing parameters and thebest printing conditions (Farzadi et al., 2015). In addition,Kaveh et al. (2015) designed proper benchmarks (e.g. extrudedtemperature, etc.) and statistical equations calculatingcalibration factors (precision and internal cavity) used todetermine or to optimize the value of printing parameters(Kaveh et al., 2015). Similarly, Hollister et al. (2015) createdstandard test coupons with consistent geometry and knownmechanical properties included in every build providing astandard with which to compare builds at different performingtimes because printing parameter should be changed accordingto custom devices for different patients (Hollister et al., 2015).These studies aim to optimize printing process for quality alongwith different printing parameters in different 3D printingtechniques such as PBF, EBP, BJP and MJP techniques. Asmentioned above, a research by Sitthi-Amorn et al. (2015) andMireles et al. (2015) studied real-time optimization of printingparameters by closed-feedback loop control in MJP (Sitthi-Amorn et al., 2015) and in-situ defects correction in EBMtechnique (Mireles et al., 2015). These techniques might not belimited to other 3D printing techniques when it comes to itsapplication. However, PVP and BJP techniques could not beapplied owing to the inaccessibility of the machine visionsystem and IR thermography. Therefore, there is a need foralternatives for real-time monitoring, closed-feedback loopprinting control, and in-situ defect correction processes to beapplied in these techniques.

3.3 Post processThis is so-called post-QC considering finished part’s qualityafter printing process and before part evaluation. Some of thetechniques such as PBF heavily rely on this process forimproving better surface and mechanical property of printedpart through sintering and peening processes. Edwards et al.(2013) attempted to remove post process on EBMprinted alloyparts by using the elevating bad temperature to minimizeresidual stresses (Edwards et al., 2013). In the EBP and SLPtechniques, post processing was used in chemical, thermal andhot cutter processes for surface quality improvement. However,this research is limited to certain materials such as ABSpolymer. Therefore, there is a need to launch research on othermaterials. In the MJP technique, sintering was adopted as postprocess for 3D printed Zr-based ceramic dental prostheses.

3.4 Evaluation processThis process is to evaluate part’s reliability. This includes allactivities measuring external and internal characteristics ofprinted parts whether it meets specific design and mechanicalrequirements. The external characteristics are measured bysuch a calipers, machine vision, roughness tester, etc., while theinternal characteristics are measured by destructive or non-destructive evaluations. Some studies have been attempted onoptimizing evaluation process. Muller and De Jean (2015)developed a novel stereo microscope adapter, called SweptVue,for cost-effective microfabrication quality control, as shown inFigure 19. Using this optical technique, surface elevation andaccuracy were examined over multiple regions on plateaus andhemispherical surfaces, and building errors such as decreasedheights were discovered when approaching the edges ofplateaus, inaccurate height steps and poor tolerances onchannel width (Muller and De Jean, 2015). In addition toexternal evaluation, Hollister et al. (2015) used a Micro-CTmethod, which non-destructively builds an image of internalstructures allowing quantitative and qualitative measures(Hollister et al., 2015). These two approaches used traditionalevaluation methods (e.g. calipers and SweptVue) and high techevaluation methods (e.g. Micro-CT) for the evaluation ofgeometry accuracy and internal structure characteristics.However, the latter evaluation method requires high cost andbig volumetric facility.

4. Challenges and future trends

As a consequence of what has been discussed above, it isevident that vast researches and variety approaches have beenstudied to improve the quality of AM processes. However, ithas been seen that there are still many issues that occur fromresearch reviews. To achieve agile and streamlined AMprocesses, from design to part evaluation and enhanced qualityAM product, novel AM processes for QC could have thepotential to address these issues:� Prediction of optimal printing parameter: Iterations works by

external and internal evaluation to find optimal printingparameters; these evaluations are tedious and not agile. Ifit can predict printing parameter based on data analyticanalyses (e.g. data mining) at design process, final part canbe expected with better dimensional accuracy and surfacequality.

� Prediction of mechanical property: As mentioned above,iterative mechanical testing to meet the requirementseventually will waste materials. If a designing tool(software) before printing process has simulationcapability or mechanical prediction module, better designprocess can be performed by users based on the predicteddata.

� Robust real-time monitoring and process control in buildprocess: In MJP, PBF and DED, real-time monitoring andclose-feedback loop control and defect correction duringbuild process has been recently developed. Thesetechniques are advanced technology that integratesmachine vision, data collection, image processing andclosed-feedback loop (Sitthi-Amorn et al., 2015). In PVP,real-time monitoring systems have been used to monitortemperature, layer height, etc. for data collection but




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image processing (computation) and closed-feedback loopcontrol. If these can be achieved in other processes, theycan provide better quality and agile AM process.

� Feedback interaction between design or printing process andpart evaluation: Closed-feedback loop recently developedfor correcting design and printing factors in real-time is agood example of intercommunication between design andprinting processes. There is another research thatcompares CT scanned images with initial design model tocalculate dimensional discrepancy. If there is a module orsoftware that enables feedback of part evaluation (e.g.inner defect, dimensional inaccuracy and surface crack) tobe considered when redesigning to remove thesediscrepancies, high quality of product can be expected inshorter time. For example, on an unresolvable part defectin spite of several printing efforts, design software canadjust design or printing parameter (e.g. printing speed,extrusion rate, etc.) using the feedback data generatedfrom part evaluation; therefore, defects can be preventedbefore printing process.

� Agile part evaluation: Part evaluation is also time-consuminglike the printing process and waste lots of materials owing toiterative destructive evaluation. Non-destructive evaluation(NDE; offline inspection) is needed to enable validation ofprocess performance. However, it is expensive and requiresskills to operate the machine. If machine vision or NDEtechnique can be imbedded into the 3D printer for externaland internal quality evaluation as online inspection, partevaluation process can be agile and finished in 3D printerswithout separated evaluation process. For example, 3Dscanners that are commercially available are imbedded into3D printers can be used for surface quality evaluationthrough image processing right after printing process.

� High speed fabrication and scale: Future technology willrequire more rapid and bigger scale customized productionskills. However, in the current technology, the faster printingprocess is, the worse its quality will be. Fabrication speed issignificantly correlated to part quality in all techniques anddependent on part price. Increasing this speed with anincrease in quality will be the challenge in the future of AM.

� Cyber quality control: AM technology can be incorporatedwith remote connection for quality monitoring andcontrol. If all the processed data attained from the sensor

and printing process are displayable and controllableunder remote control, massive and customized AMproductions would be feasible in less time and cheapercost of production in the near future.

5. Conclusion

AM technology has reached a crossroad right before being usedas end-part production. Many techniques have been developedup to now to respond to the demand of high-quality 3D printedparts but more studies are still required to improve theirsystems and quality. This paper presented a review on qualitycontrol in seven different techniques in the AM technology,and provided detail discussions in each quality process stages.Most of the AM techniques follow the trend of using in-situsensors and cameras to acquire processing data for real-timemonitoring and quality analysis. This effort opens up the in-situcorrection by the closed-feedback loop and image processing.However, owing to the closed geometry of the 3D printingsystem, some techniques such as BJP are not eligible to adoptthis technique. More sophisticated process monitoring andagile feedback control are required to enable end-product to bebetter quality and faster process. Processes such as EBP aremore advanced regarding data analytics and predictivealgorithms-based research regardingmechanical properties andoptimal printing parameters. This data analytics research iscritical for optimization and prediction of design and printingprocess. This research effort would lead to artificial controlsystem that enables to produce perfect quality of AM product.MJP technique has advanced with systems that are integratedwith closed-feedback loop, machine vision and imageprocessing tominimize quality issues during printing process. Itis expected that future challenges would use these predictivealgorithms based research and integrated system in the nearfuture for proved QC-based agile and accurate printingprocess.

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Figure 19 (A) A pair of hard-baked photoresist pads used for a microfluidic device, seen in monochrome when imaged through a traditionalmicroscope (left). The SweptVue adapter measures the relative depth of the features in the image and colorizes them in software using a jet color map,with feature heights increasing blue to red (right); (B) depth maps can be exported to MATLAB software for further image processing and 3D plotting;(C) the SweptVue stereo microscope adapter, shownmounted to an Olympus SZX7 stereo microscope




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Corresponding authorHoejin Kim can be contacted at: [email protected]

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