interaction of manufacturing process and machine...

Interaction of manufacturing process and machine tool

C. Brecher (2)*, M. Esser, S. Witt

Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany

CIRP Annals - Manufacturing Technology 58 (2009) 588–607

A R T I C L E I N F O

Keywords:

Machine

Modelling

Process–machine interaction

A B S T R A C T

Analysing the machine tool and the machining process individually is necessary in order to tackle the

challenges that both have to offer. Nevertheless, to fully understand the manufacturing system, e.g.

vibrations, deflections or thermal deformations, the interactions between the manufacturing process and

the machine tool also have to be analysed. In cutting, grinding and forming there are important effects

that can only be explained through these interaction phenomena. This paper presents the current state of

research in process–machine interactions for a wide variety of manufacturing processes. It is based on the

findings of the CIRP research group ‘‘Process Machine Interaction (PMI)’’ and on the international

publications in this field. Cutting with defined and undefined cutting edges as well as sheet and bulk

metal forming are the key processes. The emphasis is on understanding, modelling and simulating all

modes of interaction. Additional needs of research in process–machine interaction are identified for

future projects.

� 2009 CIRP.

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1. Introduction and historical review

In an industrial context, production costs are often lowered byreducing manufacturing times. With this as the objective, machinetools are continually being improved with respect to their speed,acceleration and process force. Moreover, processes are alsocontinually undergoing optimisation. Higher cutting and formingspeeds, improved machine-tool concepts, wider contact andgreater degrees of forming should enable processes to be carriedout more economically. The problem is that when optimisedmanufacturing concepts are developed, the machines operateparticularly fast, but they either do not meet the requirements interms of part quality or else the machine components and toolshave short service lives.

1.1. Motivation

In many cases, the reason for such problems is not due to theincorrect planning of machines or processes, but rather due toadditional effects that can only be explained by the interaction ofmachine and process. Fig. 1 shows the results of such effects, usingvarious processes as examples.

In research, these production–technical processes and themechatronic structures involved, i.e. the machine tools have beendealt with separately up to now. However, in recent years it hasbeen considered increasingly necessary to treat processes andstructures in an integrated way, thereby overcoming the wide-spread independent treatment of such systems. Observationsgenerally originate in details and certain specific mechanisms ofthe interaction. Recently, Biermann et al. [28] documented for the

* Corresponding author.

E-mail address: [email protected] (C. Brecher).

0007-8506/$ – see front matter � 2009 CIRP.

doi:10.1016/j.cirp.2009.09.005

first time a series of different projects on process–machineinteractions during grinding. In 2004, Altintas and Weck [10]summarised a large number of the characteristics of theregenerative effects during grinding, turning and milling. Researchresults for the various forming procedures are very sparse. To date,there is no comprehensive compilation of the mechanisms ofinteraction for the different processes. For this reason, a vast rangeof different topics in the field of process–machine interaction havebeen gathered together in the CIRP Working Group ‘‘Process–Machine Interaction’’. For the purposes of this paper, the topicshave been complemented, structured, generally summed up andevaluated.

Fig. 2 shows the possible interactions between machine andprocess, using a milling machine as an example. The continuity ofinteraction, i.e. the continuous and mutual influence exerted byboth machine and process, results in the often unpredictableeffects of the interaction. In many cases, predictions can only bemade by means of complex simulations.

The challenges arising from this were recognised many yearsago, but were at first investigated only tentatively.

Fig. 1. Motivation for PMI research.

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Fig. 2. Interactions between process and machine tool.

C. Brecher et al. / CIRP Annals - Manufacturing Technology 58 (2009) 588–607 589

1.2. History of international PMI research

The first work on interaction in cutting machines was carriedout as early as the 1950s. Some researchers found that chatter inturning and milling operations does not result from negativedamping of the chip formation process. Instead, they outlined self-excited vibrations with a force–displacement interaction betweenthe machine tool and the cutting process [119,143,181]. Based onthis work, an effective circle of researchers formed within the CIRP-Ma group (today: STC M). From 1969 onwards, this group set itselfthe goal of ascertaining the dynamic cutting force coefficients thatdescribe the cutting process within this interaction [189].Ultimately, it was possible to predict the chatter phenomenonwithin certain limits [47,159]. Since then, modelling the range ofactions that includes processes and machines in a mutuallyinteractive system has become established as a possible means ofexplaining complex behaviour.

The broad application of this analysis concept resulted in a largenumber of papers in all areas of cutting with defined and undefinedcutting edges. For some years, forming operations have also beenviewed within the closed loop formed by process and machine.Since 2004, the (sometimes widely varying) research papers havebeen summarised as one of the Priority Programs of the GermanResearch Foundation (DFG) (Fig. 3).

At the international level, within the framework of the CIRPGeneral Assemblies and winter meetings, working sessions of thePMI (Process–Machine Interactions) Collaborative Working Groupwere held between 2003 and 2008. This group created a forum forthe presentation of very different papers, with the objective ofcomprehending and predicting such effects. The purpose of thiskeynote paper is not least to summarise the research resultspresented there.

2. Modelling of single phenomena

Long before the interaction between machine tool and processwas treated, great advances were made in understanding and

Fig. 3. History of PMl research.

describing the individual components of the overall system. Thissection is dedicated to achievements in the research of machinetools and processes as individual elements. Most of these havebeen summarised in keynote papers over the last few years.

2.1. Structural behaviour of machine tools

The task of machine tools and their components is togenerate the movements and forces necessary for executing aprocess. It is presupposed that the available forces are greatenough and the movements fast and precise enough to completethe process successfully. However, disturbances that take effectduring the process may negatively influence the behaviour ofthe machine. In general, such disturbances are forces, momentsor heat input.

The relationship between the thermal load of the machine andthe thermal drift of the cutting process is very complex. Due to theinaccurate knowledge of heat sources, thermal boundary condi-tions, mechanisms of heat transfer, etc., precise prediction of thebehaviour of a standard machine tool at the design stage is verydifficult [202]. The research in this field has been summarised inkeynote papers by Bryan [53] and Weck et al. [202]. Some modelsoffer a reliable correlation between thermal load and displace-ment, but the metrological effort as well as the model complexity ishigh.

The correlations between force and displacement are easier tohandle because in general the force acts solely at the tool centrepoint. The measurement of the correlation between force anddisplacement in static and dynamic cases has already been state-of-the-art for a long time. Nowadays it is also possible to simulatethe determination of machine behaviour. The relevant advanceswere noted in 2005 by Altintas et al. [11]. Fig. 4 summarises thepossibilities of the computer-supported analysis, prediction anddesign of a machine tool.

Moreover, in the past few years many new challenges in theanalysis of machine-tool structures have been described. Here, wemust mention in particular the stringent requirements placed onmachine tools by high process speeds [190], the emergence ofparallel-kinematic structure [203], and the increased use ofadaptronic devices [151]. The fields of research mentioned andthe respective advances that have been made provide a solidfoundation with regards to the modelling of machine structures ininteractive systems.

2.2. Cutting and grinding processes

In cutting and grinding, many parallel developments have beencarried out. The move towards faster processes may be one of themost important fields of research. Schulz et al. and Tonshoff et al.have summarised these process developments for cutting andgrinding respectively [177,192].

Fig. 5 shows other important fields of research. Overviews of thevarious modelling approaches are given in [46,191] for grindingand in [139] for cutting.

Fig. 4. Research on machine tool behaviour: virtual prototypes [11].

Fig. 5. Research on cutting and grinding processes.

C. Brecher et al. / CIRP Annals - Manufacturing Technology 58 (2009) 588–607590

As a basis for modelling process–machine interactions, forcesare of high relevance. FEM models are currently used to predictprocess forces. Due to unclear effects at the clearance face ofcutting tools and due to the unknown grain geometry in grinding,the quality of the results is often inadequate. Empirical models givemore exact but less general results.

Thermal effects in grinding influence the quality of themachined part. Refs. [44,140] contain the state-of-the-art withrespect to friction, temperature and cooling in grinding processes.In cutting, dry machining is an option for a large variety ofmaterials. The current state of research has been summarised in[125,204].

In cutting, the material of the workpiece and the tool cancompletely change the process behaviour. New workpiece materials[132,193] and coatings of cutting tools [126] have therefore beenresearched extensively. Byrne et al. give a broad overview of theseand other developments in the cutting process [54].

2.3. Forming processes

Forming processes usually serve as means of mass production.The wide variety of different forming operations can generally beclassified as bulk or sheet metal forming operations.

For sheet and tube forming, a wide variety of processkinematics and their characteristics are summarised in[124,150,176]. Among these are incremental forming processesthat allow very flexible manufacturing of parts and forming withmedia (oil, gas) which can provide tubes with complex geometries.

Bulk metal forming is usually carried out at higher tempera-tures and very high process forces. Incremental forming cansimplify these difficult conditions. The state-of-the-art in this areais summarised in [89]. Modern incremental rolling processkinematics can be seen, for example, in Fig. 6, top right.

The behaviour of the workpiece material during forming is thereal challenge when describing any forming process. Theliterature dealing with this problem (see also Fig. 6) issummarised in [8,19].

Fig. 6. Research on forming processes.

3. Developments describing defined cutting edge phenomena

3.1. Analysis of interaction phenomena

In last two decades the interaction of machine and process indefined cutting edge machining has been studied intensively.Many analyses recognised the need to take into account therelevant interactions in the machine-process system whilecarrying out a simulation of production processes. Vormannoutlined the importance of simulation, including necessaryinteractions for the planning and adjustment of modern produc-tion systems in [198]. He claimed that no production model, i.e.complete simulation of a process and a machine, is possiblewithout a consideration of mutual influences between them. In thearticle by Brecher [41], some important uncertainties during thesimulation of such machine-process systems were highlighted aswell as the demands placed on models for acceptable simulationaccuracy. Witt [212] carried out comprehensive research relatingto the interactions between the components of complex produc-tion machines (Fig. 7). He argued in favour of extending establishedsimulation tools by incorporating the relevant interaction betweenthe machine tool, the workpiece and the cutting process, as this isessential for reliable process planning and optimisation at themachine development stage.

By taking into account the interactions in a machine tool asshown in Fig. 7, the manufacturing process includes, per definition,a workpiece, a manufacturing technology and a tool. The machinetool unites a machine structure, controls and clamping fixtures.These interactions are common to different production machinesand must be regarded as a system when analysing performanceand optimising process parameters. In machine tools such as lathesand milling machines, the direct interaction of the machinestructure with the numerical control system greatly influences thedynamic behaviour of the machine and hence the characteristics ofthe process. This behaviour must be represented very accuratelyfor the integrated simulation of industrial machining processes inorder to minimise errors from machine modelling. Anotherinteraction occurs at the interface of the mechatronic systemand a machining process between the cutting edge and theworkpiece. This interaction determines the quality of a part,possible tolerances and stock removal rate, which might be limitedby process instabilities. In order to achieve a comprehensiveevaluation on the basis of simulation, the influence of machine toolproperties on the process and of the process on the machine toolmust be precisely depicted. Unacceptable simplifications mightlead to significant errors in simulation results and thus to faultyconclusions relating to the process behaviour. Certain criteria areneeded in order to assess and approve cutting processes. On theone hand, process stability and processing time are importantcharacteristics, as they indicate productivity and profitability. Onthe other hand, the surface quality and machining tolerances for amachined part are also of significance when carrying out an

Fig. 7. Interactions and their analysis in machining with defined cutting edges

[212].


evaluation. To assess a production process using simulation thelatter must represent the properties of a process with sufficientaccuracy. In order to carry out this task, all the above-mentionedinteractions must be considered in an integrated simulationsystem. In the literature, there are sources focusing either on themachining process or the machine tool modelling, as well asapproaches analysing the interaction of the two.

3.2. Identification of model parameters

3.2.1. Machine tool

A study of the interactions between a process and a machinerequires appropriate methods to represent all the necessaryproperties of the objects modelled. On the machine tool side, theintegrated models providing data on static and dynamic behaviourof the structure must be appropriately parameterised. According toWitt [212], there have been no approaches to date which enable theparameters of machine elements in multi-body models, i.e. stiffnessand damping coefficients, to be identified in a reliable way withoutsubsequent verification using measured data. The dynamic beha-viour is determined by impacting the machine tool structure with adynamic force and measuring the response in the form of adisplacement. The measured frequency response function (FRF) isused as a basis for the subsequent curve-fitting procedure, whichdecreases the amount of data in FRF by mathematical approximationof the curve. Comprehensive treatment of measurement proceduresis presented in [11,29,107,187,200].

The number of uncertain parameters in complex machine toolmodels is generally very high. A manual correction of the values isextremely complicated and impractical. Considering this, Altinatset al. developed a method for optimising multi-body modelparameters on the basis of measured frequency response functions(Fig. 8) [11]. This method is not suitable, however, for carrying outan optimisation of all the parameters at once. Only the vibrationmodes should be selected which are determined by certaincomponents. For instance, the parameters of the anchoringelements may be optimised by observing that range of thefrequency response function (FRF) where such vibrations mightoccur. Beginning with a certain initial value, the differentparameters can be optimised iteratively. Additional work on thistopic was carried out in [178].

3.2.2. Process

On machining process side, accurate modelling is of greatimportance for achieving a reliable simulation of productionprocesses. Beginning with the pioneering research of Kienzle,many approaches for predicting cutting forces, e.g. mechanistic,linear, etc., were developed [73,121,182]. In recent years, manyanalyses were carried out with the aim of improving force modelsfor different processes. Budak et al. developed orthogonal tooblique cutting model which enables the prediction of the cuttingforce [50].

Fig. 8. Measurement of the dynamic behaviour of a machining centre [11].

Modelling of machining processes was expanded for non-trivialcases like ball-end milling, milling with helical end mills, etc. in[58,61,64,167,175]. Denkena et al. [61] presented a method forprediction of cutting forces in milling on the basis of empiricallydetermined specific cutting forces. The latter were calculated fromthe measured milling forces, which were subsequently related tothe engagement-dependent undeformed chip area. The dynamicsas well as damping of the process is not considered in this model.Schmidt [175] from the same research circle presented a procedurefor predicting cutting forces for helical end mills using ageometrical model considering serrated chips and a parametricforce model. The force model used by Schmidt is described in detailin [62] by Denkena. Riviere-Lorphevre et al. [167] addressed theissue of modelling a helical end mill and concentrated onimplementing a general algorithm for evaluating cutting forceparameters. He proposed an algorithm for retrieval of the cuttingforce model parameters from the measured data set.

Another interesting approach for calculating process forces inmilling is provided by Moreau et al. [147]. The displacement of themilling tool was measured directly during the process by means ofcontact-free sensors. Following this, the frequency response of thetool was determined by means of impulse hammer testing. On thebasis of known displacement in stable and unstable processes andcompliance of the tool, conclusions about the process forces weredrawn.

In plunge milling operations, the force prediction was studied in[58] by D’Acunto et al. The authors used a mechanistic model onthe basis of specific cutting pressure coefficients for predictingforces. The model does not include the influence of processdynamics and tool wear. The latter phenomenon was addressed byOzlu et al. [157], who proposed a model for calculating cuttingforces in an orthogonal cutting process under consideration offriction in primary and secondary shear zones.

Rapid development of new production processes, e.g. HPC (highperformance cutting) machining, is leading researchers to recon-sider some established models that do not cope with the relevantrequirements of modern machining processes. Brecher et al. [35]presented a test bench for experimental identification of theparameters of a complex force model for a milling process based onthe study of orthogonal axial turning. Fig. 9 represents the test set-up. Such a construction allows individual observation of the innerand outer modulation of chip thickness in order to accuratelydetermine dynamic specific cutting-force parameters.

The results of this study are detailed in [42]. The modelcharacteristics obtained in this research will provide higher levelsof accuracy for stability simulations compared to conventionalmethods.

3.3. Overview of simulation approaches

The state-of-the-art simulation of cutting processes underconsideration of machine and process properties can be carried outalong with different methods. When simulating the interactionsbetween a machine, workpiece and process, a distinction isgenerally made between a simulation using a substitute model ofmachine properties and a coupled simulation, as revealed by Witt

Fig. 9. Test bench for analysing the coefficients of dynamic cutting forces [35].

Fig. 11. Analytical simulation of dynamic stability in turning [156].

Fig. 10. Overview of approaches for coupled simulation [212].


[212] in Fig. 10. The substitute model combined with a suitableforce model implies a representation of machine properties. Manyapproaches use some analytical models to represent the cuttingprocess combined with machine properties in the form offrequency response functions, which are either measured orsimulated. By means of curve-fitting, the frequency responsefunction can be represented in a suitable form in the time domain.Such process simulations using measured FRF are carried out in[10,20,72,114,117,183,185,206] in order to simulate the processforces, process stability and resulting workpiece surface in turningand milling, as presented in Fig. 10. A similar procedure is chosen in[221], the only difference being that the machine properties isprovided by means of finite element simulation (Fig. 10, topcentre). Another option for representing the cutting process is thefinite element simulation of chip formation. The approachpresented by Piendl and Aurich [158], considers the structural–mechanical behaviour of a machine and a workpiece. They used asubstitute workpiece and tool integrated into an FE model of thecutting process, which is presented in Fig. 10 (top right). Thisapproach is limited by the number of elements such models caninclude. This means that neither the workpiece surface nor severalrotations of the tool may be simulated. In addition, some commonmethods of representing chip formation in the FE cuttingsimulation do not provide geometrical information about theworkpiece surface behind the cutting edge. Damage criteria areused to delete elements in the area of the chip root. Where cuttingsimulations are carried out with the aid of Arbitrary LagrangianEulerian (ALE) methods, the material flows through a previouslydefined workpiece mesh. Neither of these approaches is suitablefor representing the resulting workpiece surface.

This is required, however, for simulating regenerative vibra-tions [183]. The above-mentioned approach is thus only suitablefor representing chip formation and process forces but not for thesimulation of process instability, e.g. regenerative chatter. Therepresentation of machine and workpiece properties as well asthose of the manufacturing process by suitably coupling differentsimulation environments forms the basis of coupled simulation.Generally, finite element models and flexible multi-body modelsare suitable for representing the structural–mechanical propertiesof a machine and a workpiece.

A comparative survey of coupled systems is provided in [194]by Tonshoff et al., who proposed an approach for elastic-kinematicmodelling of the stiffness of a machine tool structure on the basis ofa multi-body simulation as opposed to a common FEA approach. Inorder to represent a machining process, analytical models aresuitable. So too are finite element models (with some limitations tothe simulation possibilities). In [25], Berkemer proposed that asimplified analytical process force model should be coupled with afinite element model of the machine, as presented in Fig. 10(bottom left). The displacements between workpiece and tool arefed into the force model, where the force components arecalculated. These force components influence the machinestructure. The process model is integrated as a first order delaycomponent. Here, a precise description of the cutting process andan evaluation of the process stability is hard to achieve.

In [174], Schermann et al. carried out some explicit finiteelement calculations, as shown in Fig. 10 (bottom right). Acomplete machine model was coupled with a finite element modelof a turning process. This involved extremely long calculationtimes. Furthermore, it was not possible to represent processstability due to the above mentioned reasons. As shown in Fig. 10(bottom centre), Witt analysed the coupling of a flexible multi-body simulation and an analytical process model [212]. Themachining process was represented by means of suitable models ina digital block simulation, and coupled with a flexible multi-bodymachine model [11,33]. Both the surface properties of theworkpiece for modelling the regenerative effect as well as thegeometrical machining history were calculated in subprograms ofthe digital block simulation. With the aid of this simulationapproach, a comprehensive representation of machining processesis possible, taking into account the process forces, process stabilityand machining result.

3.4. Model integration

3.4.1. Turning

The modelling of production processes is used for predicting anoutcome depending on the chosen settings. Simulation of themachine tool–process system is used in a majority of publishedstudies for analysing the stability of machining. Beyond this, thereare approaches for predicting workpiece surface quality or foranalysing and optimising NC-routines for a machining task, etc.

The stability of turning operations was recently addressed in[52,156,157]. Ozlu and Budak [156] presented an analyticalapproach for modelling the stability of turning operations. Alongwith the multi-dimensional dynamics of the system, the authorsconsidered the true cutter geometry in the turning process model(Fig. 11: stability chart, green square (For interpretation of thereferences to colour, the reader is referred to the web version of thearticle.): stable test trial, red dot: chatter). The study revealed aconsiderable influence of the nose radius on the stability ofturning. This issue was further developed in [157]. The articleprovided a comparison of multidirectional and single-directionalsimulation systems in the context of true geometry modelling. Theconclusion was drawn that single directional systems do notprovide reliable results for inserts with a larger corner radius orinclination angle. Simulation using the multi-dimensional stabilitymodels was recommended for such systems.

Fig. 14. Stability simulation in milling [12].

Fig. 12. Simulation of dynamic stability in boring operations [10].

Fig. 13. Static process machine interaction [64].


3.4.2. Drilling

A fundamental review of modelling boring and drillingprocesses was provided by Altintas and Weck [10]. Differentmodelling approaches are highlighted in this keynote paper. Inaddition, it also details the challenges arising from the processproperties, e.g. nonlinear dynamics of boring operations and thedependency of cutting forces acting on a drill from its vibrations inall directions (Fig. 12). In [52], the above-mentioned theory of Ozluet al. for turning [156,157] was expanded upon to include boringprocesses. In [26], Biermann et al. present a method of simulationfor a deep-hole drilling process, which enables the calculation ofstability charts for this operation. The results of this research areused in [27], which deals with the development of a controllingsystem for chatter suppression, in particular spiralling in deep-hole drilling. The above mentioned reports are based on someearlier study on related problems carried out in [205]. Some furtherresearch on this topic is presented in contributions of Roukema andAltintas. They modelled the mechanics, dynamical behaviour andchatter stability of drilling operations [164,165].

Jrad et al. [116] studied a drilling process with respect to cuttingforces. The complex geometry of the drill cutting edge wasmodelled. The elementary forces were calculated using a thermo-mechanical model for oblique cutting from Moufki et al. [148] toachieve an elementary partition of the cutting edge. After finalcalculation of the sum, a conclusion about process forces wasdrawn.

An analysis of tapping operations is carried out in the article byLee [137]. A model for the tapping torque is provided underconsideration of friction. Ahn et al. [6] employed this knowledgefor analysing synchronisation errors in ultra-high-speed tapping.To avoid excessive torques on the tap, some recommendations ontolerances for synchronisation errors were elaborated upon resultsof this analysis.

3.4.3. Milling and sawing

Advances in the modelling of milling over the past decades aredetailed in the extensive review of Altintas and Weck in [10].Recently, a vast contribution to this topic was made by manyresearchers relating to various approaches. The latest state-of-the-art is presented in the following papers.

Altintas and Merdol [15] simulated the complete part machin-ing in virtual environment by considering the frequency responsefunction on tool center point as well as mechanics and dynamics ofmilling process for the calculation of process forces.

Fortunato et al. [77] contributed to the simulation of themachining process. He compared analytical and FEM approachesfor calculating cutting forces and identified their pros and cons.

Denkena et al. modelled the stability of the milling system withadaptronic elements [63]. The authors developed a parametricmodel of the spindle unit, taking into account the influence ofactuators on the dynamic behaviour of the system. Together with amachining process model, the developed simulation systemallowed a prediction of stability charts to be made. With the aid

of the model at hand, Denkena et al. were able to study differentcontrol strategies as well as simulate process forces and energyconsumption. Another topic addressed by the scientists of theUniversity of Hannover was the issue of milling of thin-walledworkpieces [64]. The study revealed the correlation between theprocess forces and the induced deformation of the workpiece. Theproposed model includes the feedback on workpiece deflectioninto the process simulation (Fig. 13). The same problem wasstudied by Altintas et al. [16] and Corduan et al. [57]. Altintasproposed a model considering the dynamical behaviour of aflexible thin-wall workpiece and a rigid tool for prediction of athree-dimensional workpiece surface finish form. Corduan et al.proposed a simulation model for predicting the workpiece profile,including data on the macro and micro profile. In [87], Gonzaloet al. describe another approach for simulating thin-walledmachining. In this analysis, a mechanistic model of the millingprocess was coupled with a compliant model of the workpiece,obtained from an FEM-model. The authors pointed to the high levelof consistency of the simulation results for stable processes, andidentified the need to consider process damping for achievingimproved prediction of chatter.

Insperger et al. [114] contributed to the understanding andmodelling of run-out phenomenon for milling operations. The run-out effect is observed when the cutting forces acting on individualcutting teeth are different. Due to this, the main excitationfrequency lies between tooth passing frequency and spindlerotation frequency. The authors modelled the stability of a 2DOFsystem with run-out, and concluded that the stability boundariesremain unchanged, while the chatter frequencies are qualitativelyaffected. A similar task was worked on by Szalai et al. [188], whoanalysed an unstable case due to period-two vibrations.

Altintas and Budak addressed the analytical modelling ofmilling processes [12,48,49] (Fig. 14). The authors deal with ananalytical calculation of the milling force, identification of work-piece and tool deflections and their use in the milling processsimulation. They solved the problem of varying dynamics inmilling by means of Fourier series expansion of periodic terms andFloquet’s theorem. Possible implementation of the theory formodelling other processes, such as milling with variable pitchcutters and five-axis milling, are discussed in [12,49]. Modelling ofthe end-milling process is carried out in [155,195]. The approach ofTunc et al. [195] employs the force model of Ozturk et al. [154] for

Fig. 15. Stability simulation of five-axis milling [185]. Fig. 17. Co-simulation of machine tool and processes [33].


simulative identification of optimum cutting parameters, e.g.spatial cutter positioning or cutting strategy. Ozturk et al. [155]carried out a stability analysis of the five-axis milling process withball end mills. He formulated geometrical interdependencies andcoupled them with the force calculation algorithm in order toderive stability charts.

The issue of process stability and quality of the machinedsurface was addressed by Weinert and Surmann [184,207]. In[184], a simulation model capable of precise machining simulation,i.e. calculation of an arbitrary chip thickness at a random time,along with a dynamics-enabled cutting tool is presented. Thesimulation system predicts the vibration behaviour of the cuttingtool along a path programmed in an NC-program. Since thedynamics of the tool is taken into account, the chatter predictionfunction is achieved as well. An extension to this simulationsystem to include a photorealistic workpiece surface generation isprovided in [185,207]. The surface representation allows tomeasure surface roughness and surface location error (Fig. 15).

This approach was further elaborated by Enk and Surmann in[72,186] for processes with changing tool engagement conditions.The experimental set-up which is described enables the radialdepth of immersion to be changed, resulting in a variation of thetool vibration pattern. The model is capable of both machinedsurface generation as well as the prediction of stability charts forsuch non-stationary processes.

A thorough analysis of sawing processes may be found in [217].A dynamic model of the process includes not only a chatter effectbut also the influence of torsional vibration in sawing (Fig. 16). Inaddition, a concept was presented for adaptive control of the depthof cut for stabilising the process.

3.5. Co-simulation

An overview of simulation approaches was presented in Section3.3. The coupled simulation was defined as a relatively newapproach, permitting simultaneous usage of two different simula-tion environments with data exchange by means of a suitableinterface. This section deals with the latest developments incoupled simulation.

Brecher and Witt presented an approach for simulating amachining process, including interactions between a machine tool

Fig. 16. Regenerative torsional instability in sawing [217].

and a cutting process [33]. They argued in favour of better qualitypredictions relating to process forces and stability boundaries(Fig. 17). Later on, Brecher et al. [36] presented a method for thecoupled simulation of a flexible multi-body machine tool structurewith a three-dimensional FEA-based turning model. The authorsstated that the FE calculation of process forces without furtheroptimisation is very time intensive. A simplified model on the basisof approximated characteristic diagrams was thus used for thecoupled simulation. This approach allowed the surface roughnessof the machined workpiece to be determined.

In [37] three different approaches were used to carry out acoupled simulation of machine and process. The first one dealswith the FE-based process coupled with a substitute model of amachine and workpiece. This coupled system is also referred to in aseparate article by Schermann et al. [174] (Fig. 18). The secondapproach employed an FE model of a machine and the cuttingprocess. The third one incorporated a flexible multi-body model ofa machine structure and an analytical model of the process. Theresults obtained from these approaches were compared, therebyrevealing the limitations and providing suggestions for furtherimprovement.

Hovel [110] contributed to research in cutting processes with adefined cutting edge. She carried out a finite element simulation ofthe cutting process and coupled it with the modelled elasticstructure of a lathe. As a result of this, a more accurate calculationof the cutting forces and more precise chip formation could bemodelled.

Britz and Ulbrich [43] carried out a coupled simulation of a lathestructure and a turning process. Both rigid and flexible multi-bodymodels were used to represent the machine tool structure. A face-milling process was simulated and the process forces werecalculated by means of the model. In [32], the modelling of aturning process coupled with a machine structure is described.Brandt et al. developed a coupled model to simulate an unbalancedspindle and its influence on the workpiece surface quality.

Zah and Schwarz [218] presented an approach for using virtualmachine tool models for design tasks. They described a method foridentifying the optimum characteristics of the structural compo-nents and controls as well as for analysing the influence of the

Fig. 18. Coupled model, forces and quasi-static displacements in turning [174].

Fig. 19. Dynamic co-simulation of the machine tool and processes [180].Fig. 21. Co-simulation of spindle systems and HSC processes [33].


machining process. Mense and Uhlmann [142] proposed aninnovative use of the coupled simulation. They drew conclusionsabout necessary design changes, e.g. damping and mass distribu-tion, for improving machining stability on the basis of calculatedstability charts.

The connection of a coupled simulation system with a PC-basedNC system was achieved by Hamm et al. [100]. Zah and Siedl[219,220] were confronted with the challenge of simulatingconsiderable machine axes movement. The proposed solutionemploys coupled multi-body simulation that takes into account aflexible model of machine tool components together with feeddrive controls. A similar approach is described in [146] by Mohringet al. Schwarz [180] continued investigations in this area andpublished a study of the interactions during a turning process witha lathe structure. He employed the multi-body simulation underconsideration of the feed drive controls. Furthermore, someimprovements on the machining process side were proposed inorder to consider all the relevant interdependencies in the model(Fig. 19).

In [75], Fleischer et al. identified the importance of anappropriate and accurate machining process model for coupledsimulation and discussed several parameters that influence theaccuracy of the process model.

Abele et al. [2] presented a new approach and discussed somepractical issues on performing milling operations using anindustrial robot. Abele et al. [3] considers the static behaviour ofa robotic structure and [4] presents a method for the analyticalidentification of the stiffness of the robot structure in the entireworking space. A simulation system that includes a flexiblemulti-body model of the robot structure and a model of themilling process is presented in [5]. The developed modelpermitted an agreeable prediction of tool displacement due toprocess load in the settled boundary conditions of experiment(Fig. 20).

A peculiar approach that considers thermal effects in a co-simulation system was presented in [68]. Two modellingenvironments for machine and process were employed for thecoupled simulation of the Charpy impact test. The temperature-dependent interactions in the system were described by means ofanalytical models.

Fig. 20. Milling with industrial robots—co-simulation of robot and process [5].

3.6. Spindle-speed effects

The dynamic behaviour of milling machines is influenced to agreat extent by the properties of the spindle-bearing system. Thisis valid especially for HPC milling operations. Brecher et al. [33]presented a study of the influence of spindle rotation on thedynamic behaviour of the system.

The authors developed a simulation system allowing the crucialelements of a spindle unit, i.e. spindle shaft, bearings and toolinterface, to be modelled precisely (Fig. 21). A non-lineardependency of the bearing stiffness on the spindle speed wasoutlined. Dynamic properties dependent on spindle rotationpermitted an increase in the accuracy of the calculated stabilitychart by means of the coupled simulation.

A similar study was carried out by Abele and Fiedler [1], whopointed out the discrepancies in the stability chart calculatedwithout consideration of the real behaviour of the spindle-bearingsystem (Fig. 22).

Altintas and Cao developed a dynamic model of spindle thatconsiders speed and preload dependent stiffness of angular contactbearings. The effect of gyroscopic and centrifugal forces on thestiffness and damping matrices are considered in the Timoshenko-based nonlinear finite element model of the complete spindlesystem. The authors coupled the spindle design, analysis andmilling process dynamics in a virtual environment to achieve anoptimal spindle construction [13,55].

3.7. Tool wear and process damping

Tool wear is often claimed to be the reason for somediscrepancies in the modelled and real characteristics of machin-ing processes, e.g. process forces and damping, since this non-linear effect requires non-trivial modelling approaches. A numberof recent scientific articles propose different methods for model-ling tool wear and process damping.

The research by Budak and Kayhan [51] deals with a practicalidentification of the influence of vibratory cutting conditions ontool life. The results indicated significant reduction of tool life dueto chatter (Fig. 23). Elbestawi et al. [70] modelled an interferencebetween the tool flank and the wavy surface generated. The

Fig. 22. Stability simulation of HSC processes [1].

Fig. 23. Chatter effects on tool life [51].

Fig. 25. Slope and surface curvature dependency of process damping [14].


resulting damping due to the tool flank wear was modelled and anincrease in the stability boundaries was observed.

Zah and Schwarz [222] presented an innovative force modelwhich considers dynamic material behaviour, nonlinear frictionratios as well as dynamic tool wear (Fig. 24).

A further approach is presented by Altintas et al. [14]. Theauthors considered the regenerative vibrations x(t), slope (dx/dt � 1/V) and curvature (d2x/dt2 � 1/V2) terms of the inner waveswhich effect the process damping, and noted an increase in chatterstability with progressive tool wear (Fig. 25). The implementationof the new force model allowed a more accurate calculation ofstability boundaries for low-speed machining.

Filice et al. [74] studied different friction modelling approachesand compared the results. The authors argued in favour ofincluding thermal influences in the force model validation.

4. Developments describing undefined cutting edgephenomena


As a high-precision machining process, grinding determines thesurface roughness, the shape and the dimensional accuracy ofworkpieces, and directly influences the quality of the finishedparts. Due to the fact that grinding processes are generally placedat the end of the whole process chain, grinding inaccuracy oftenresults in rejects and high costs. On the one hand, grindingirregularities occur as burning, grinding commas, hairline crackson the workpiece surface or geometrical deviations. On the otherhand, the causes of these grinding irregularities can includedynamic instabilities in the machine–grinding wheel–workpiecesystem, such as chatter vibrations or dynamic deflections [101].

In the case of regenerative chatter, the vibration is related to thesurface waviness that was generated earlier during the processitself, re-entering the grinding area. A special characteristic of thegrinding process in comparison to the other metal-cuttingprocesses described earlier is that not only the workpiece butalso the grinding wheel can be the bearer of the regenerative effect.The regenerative effect on the workpiece side is characterised by arapidly increasing vibration amplitude, which can be seen andmeasured on the workpiece. In contrast to this, the vibrationscaused by the effect on the wheel side increase much more slowly.

Fig. 24. Consideration of tool wear [222].

The regenerative effect is of special interest for this paper as itoccurs due to the process–machine interaction.

The analysis of the grinding process always has to considerthe process and machine behaviour. The process–machineinteraction studied in different papers may be summarised ina meta-model that has been described in a similar form byYounis [214], Inasaki [111], Schiefer [173], Klotz [130], Folkerts[76] and Schutte [179].

Fig. 26 represents the closed-loop description by Schiefer [173].In the upper part, the dynamic system compliance is described. Itconsists of the dynamic machine behaviour [102], the contactcompliance [131,76] as well as the grinding wheel and workpiecebehaviour [66,160]. The calculation of the process models, such asmaterial removal, wheel wear, workpiece surface and grindingforces, is shown in the lower part of Fig. 26. In macroscopic modelapproaches, those calculations are usually carried out separately,whereas in microscopic model approaches, the calculation of somephysical effects may be combined, for example material removaland surface generation with single-grain models.

The system shown can become unstable and chatter can occurdue to the re-entering workpiece and the grinding-wheel surfacein the contact kinematics [113]. Alternatively, an abrupt change inmaterial removal in speed-stroke grinding leads to a dynamicdeflection in the grinding zone and thus to marks on the workpiecesurface [129].

Fig. 26. Process—machine interaction in grinding [173].

Fig. 27. Measurement of the dynamic behaviour of machine tool and grinding wheel

[38,101].



The accumulated compliance of the machine tool and theprocess models will be described separately in the followingsection.

4.2.1. Machine tool

Machine tools react with dynamic deformation to the forcesapplied to them by the process. Different approaches havetherefore been developed to measure, model and thus predictthose deformations.

The measurement can either be carried out during the process[209] or offline, tracing compliance-response functions or, ifnecessary, conducting a detailed modal analysis [107]. Forceinduction and measurement in grinding machines can becomplicated because of the restricted accessibility, form andcontact conditions at the grinding wheel and the regulating wheelin the case of centreless grinding. In spite of this, the conventionalcompliance-response measurement procedure and the modalanalysis for grinding machines as shown in Fig. 27 on the leftside are common knowledge. However, the specifics of grindingdemand more adequate measurements. The motion of the wheelsis usually measured at the spindle or at an unloaded part of thewheel. The contact deflection and the wheel deformation are oftenneglected. Hannig developed a tool for displacement measurementin the force-path that combines all sensors and actors in onedevice. Using an adapter in the form of a workpiece, he was able tomeasure the system deflection, taking all the effects into account,as shown in the upper left section of Fig. 27 [101]. Contactdeflections have also been measured in grinding tests carried outby Konig et al. [131] and in measurement comparisons in theloaded and unloaded part of a wheel by Folkerts [76]. Folkertsdemonstrated that contact deflections are nearly constant at allfrequencies. Hannig revealed that contact compliance only affectsthe real part of the compliancy-response function, whereas theimaginary part does not change.

Different approaches for the modelling of machine tools areprovided in [11]. Simplifications of the machine model for grindinggreatly depend on the grinding process under consideration. Themain modelling methods for grinding machines are finite element,multi-body simulation, boundary element and analytical methods[28]. Generally speaking, a distinction is made between fullmachine models and a selective approach, where only small partsof the machine are modelled in greater detail.

Hoffmann used a flexible multi-body simulation to develop afull machine model for a speed-stroke grinding machine, andintegrated a drive control model in his simulation [109].Approaches using finite element methods have been describedby Denkena et al. [65] and Herzenstiel et al. [106], for example. Inthe past, simple analytical models were often used to approximatethe machine, for example with a single mass oscillator represent-ing the main resonance. Research by Alldieck [7], Salje and Dietrich[169], Hannig [102], Weck et al. [201] and Michels [144] uses a

more complex analytical formula in simulation to analyse themeasured compliance-response (Fig. 27, lower right corner).

In the selective approach, the parts of the machine that aremodelled in great detail are usually the grinding wheel and theworkpiece, sometimes the centre lathe and, for centerless grinding,the regulating wheel and the support blade. Herzenstiel et al.presented an FE model of the grinding wheel [106]. Jansendiscretised the grinding wheel and its spindle with finite elementsand concentrated on the compliance of the grinding machinestructure in elastic bearings [115]. On the workpiece side, FEsimulations have been carried out in cases where the complianceof the workpiece cannot be neglected. Denkena et al. built an FEMmodel of a drill to simulate workpiece deflection during a toolgrinding process, and compared the simulated deflections with themeasured ones [60]. Schutte concentrated on dynamic deflectionin particular. He calculated in FEM and measured naturaloscillation forms and frequencies of both the grinding wheeland the workpiece [179].

4.2.2. Process

Due to the large number of abrasive grains with unknown time-dependent geometry and distribution, grinding is a complexmaterial-removal operation. Different approaches for modellingthe grinding process were presented in the Annals of the CIRP [46].The models are generally used to predict grinding forces,temperatures, grinding energies, surface integrity, etc., dependingon the purpose [199]. Within the kinematic-geometrical approach,a distinction can be made between microscopic and macroscopicmodels [28,46]. On the one hand, the complex microscopic modelstypically allow a detailed view into process behaviour. On the otherhand, the macroscopic models are used for simplified and rapidsimulations of the grinding process.

Microscopic approaches consider different complex or simpli-fied 2D and 3D shapes of single grains and their statistical ormeasured distribution on the grinding wheel [112,120,191,227].Much of the research relates to the extensive classification formeasured grain shapes as presented in [18,213]. Early 2Dapproaches for micro simulation were made in the 1960s and1970s by Yoshikawa [215,216], Kassen [118] and Law [136]. Theyconcentrated on the prediction of surface quality by tracing single-grain passes through the workpiece. 3D approaches emerged withthe increasing power of computer systems [46]. These models takeinto account machine, material and process parameters, and allowthe calculation of single grain forces, stress distributions, chipthickness, as well as the number of static and kinematic cuttingedges. More generally, they assisted in understanding the grindingprocess in a greater detail.

There exist numerous kinematic-geometrical models, Refs.[28,46,168] present detailed summaries. Most of the mentionedmodels assume ideal total material removal by solid grains. On onehand, the loads and stresses can be calculated in FEM [60] for everysingle grain by using material models [65]. On the other hand, theforces that are generated can be calculated using modified Kienzleequations together with the knowledge of the accumulatedmachined material and the chip cross-section [28,121,122]. A2D single-grain scratching model in FEM was developed by Klockeet al. [127] (Fig. 31, upper left corner) and Brinksmeier et al. [45].This created fundamental insights into the relationship betweencutting speed, chip thickness and cutting efficiency that correlatewell to results obtained from experiments. Denkena et al. [65] arecurrently carrying out a scratching simulation for integration into aprocess–machine interaction model that is described in Section4.5. 3D FE scratch simulations have been presented in [128](Fig. 31, lower left corner).

Macroscopic approaches describe the geometry of the tool-workpiece penetration zone without detailed specifications ofthe grinding wheel topography or modelling single grains.Macroscopic parameters, such as the total material removal rate[101], are generally used to determinate grinding forces incombination with modified Kienzle equations (Fig. 28). In this

Fig. 28. Identification and modelling of the grinding process behaviour [101].

Fig. 30. Kinematic 3D simulation of surface generation, taking into account the

plastic and elastic behaviour of the workpiece [168].


case, extensive empirical data is necessary to calculate grindingforces [28,209].


The machine model and the process model can be combined intwo different ways for grinding to generate a complete interactivesimulation approach, as shown in Fig. 29.

In the model integration technique, both parts are modelled inthe same simulation environment. In this type of simulation, asimplified process or machine model is usually integrated into anexisting enhanced model of its counterpart.

If both models run simultaneously in different simulationenvironments and communicate with each other in synchronisedcycles, the technique is called co-simulation. In this case, complexmachine and complex process models that might have beendeveloped independently may be combined.


A process–machine interaction using a microscopic approachwas carried out by Sakakura et al., who investigated the interactionbetween the grinding grains and the workpiece surface [168](Fig. 30).

Unlike traditional models where the surface evaporates out ofthe way of a rigid grain [191,227], Sakakura et al. consider both theelastic deflection of the grains, which can be measured, and elasticand plastic deformation of the workpiece surface before the actualcut is made. Through the simulated deformation and the pile upzone, the resulting workpiece surface is much closer to the realmeasured one.

Herzenstiel et al. [106] present a concept for a comprehensivegrinding model that combines machine and process models(Fig. 31 right). The process model is based on the kinematic-geometrical simulation by Zitt [227]. It takes into account the

Fig. 29. Simulation approaches for process machine interaction. According to [97].

micro- and macro-geometry of the grinding wheel and calculatesthe forces of every single grain. The sum thereof represents thetotal cutting force over time. This force serves as an input value forthe machine model, which calculates the displacements of thedifferent machine system components over time, including themacro-geometrical deflection of the grinding wheel as presentedin Fig. 31 (right). Based on the new contact conditions, the processmodel recalculates the grinding forces. The loop is repeated in eachtime step until forces and displacements converge [106].

Numerous 2D models have been developed using modelintegration over the past decade to simulate the chatterphenomena in cylindrical plunge grinding. The models are usuallybased on the chatter loop described in Section 4.1, focusing ondifferent parts of the whole model.

Schutte developed the 2D simulation model represented inFig. 32 [179]. The goal of this work was to analyse nonlinearoscillation in external cylindrical grinding. He thus created a modelin a single simulation environment that integrated the simulationof the grinding contact and system dynamics. He carried outextensive dynamic FEM and experimental studies of the workpieceand the grinding wheel. The contact and abrasion simulation is aconventional macroscopic approach with a process model fromregression analysis of grinding tests. The roughness of the grindingwheel contour is generated via random radius variation across theperimeter. However, abrasive and possibly irregular wear of thegrinding wheel is not considered. The abrasion of the workpiece iscalculated from the material evaporation in the penetration zone.The calculated forces from the penetration zone provide the inputvalues for the dynamic calculation of workpiece and machine,leading to the dynamic movement of machine parts. From the newdynamic position, the new contact zone is recalculated which, inturn, provides new grinding forces.

Fig. 31. Small and large scale of FEA in grinding [106,128].

Fig. 32. Chatter simulation and suppression in external grinding [179].

Fig. 34. Co-simulation of spindle and process for NC-shape grinding [115,209].


Alldieck uses an elaborated approach for the simulation ofplunge grinding in the time domain [7]. The 2D simulation tooltakes into account detailed parameters on the process part andincludes static and dynamic compliance on the machine part.Alldieck simulates the penetration zone with a macroscopic modelthat includes the material removal rate, and calculates abrasion,grinding wheel wear as well as the response force of the lathecentre. These forces are applied in the machine model thatcalculates the actual dynamic compliance for the three machineparts via fitted compliance-response functions. Weck and Hennes[201] developed an advanced simulation model of the traversegrinding process based on Alldieck’s model.

Centreless grinding occupies a special position among thegrinding processes for rotationally symmetric workpieces due toits highly complex geometry. As the workpiece is not centered by alathe centre but is supported at its outer surface, processinstabilities are possible even in absence of machine dynamics.As the very first approach, Dall [59] developed a mathematicaldescription of the rounding process. Rowe [166] implemented thefirst geometric computer simulations. Later, simulation advanceswhere achieved by Friedrich [83]. Gurney [88] was the first todefine the term ‘geometrical instability’. Furukawa et al. [84]developed an integrative analytic approach based on Gurney that isstill used and has been improved by Miyashita [145], Zhou[225,226], Epureanu [69], Lizarralde [138] and Gallego [85,86].Gurney [88], Reeka [161] as well as Harrison and Pearce [103]developed the today most commonly known geometric stabilitymaps for centreless grinding machines.

In more recent developments, Guo et al. [99] and Kim [123]represented the workpiece contour in same manner as Schutte[179]. They used optimum round grinding and regulating wheelgeometries as well as optimum flat support blade geometries withsingle point contact of the workpiece to the tools. Lizarralde [138]and Gallego [85] represent an analytical approach to centrelessgrinding, incorporating contact compliance into their calculations.Transforming the equation into the frequency domain, they derive

Fig. 33. Centreless grinding: simulation of process–machine interaction [101,102].

an analytical solution for the occurrence of geometrical lobes. Thetransformation back into the time domain enhances the simulationof real workpiece surface development.

Hannig transfers Alldieck’s [7] simulation algorithm to centre-less grinding with its much more complex geometry andinteraction, as presented in Fig. 33 [101,102]. He calculates thecurrent workpiece dynamics and the position of the workpiece atthe regulating wheel, as well as on the support blade in thecomplex grinding gap geometry. His advanced surface penetrationalgorithm takes into account the microscopic compliance of theroughness peaks, while allowing a loss of contact in wavinessvalleys of the workpiece surface.

4.5. Co-simulation

NC shape grinding has been researched by Jansen, Weinert et al.in a co-simulation [115,208,209]. Some representative results arepresented in Fig. 34. The purpose of this research is the simulationof free-form surface grinding with toroid grinding wheels. Themain challenge in this process is the complex and varying contactarea between the grinding wheel and workpiece. Hence, themachine structure undergoes varying loads from the process,leading to significant shape errors, as described by Okuyama et al.[153].

Jansen’s simulation consists of three distinguishable parts indifferent simulation environments. The geometric-kinematic simu-lation discretises the workpiece and the kinematics of the grindingmachine. It is also able to predict the grinding forces in anapproximate way. The finite element simulation describes theresponse of the grinding machine to the contact between thegrinding wheel and the workpiece, taking dynamic friction intoaccount. The removal predictor uses the contact forces toapproximately calculate the real infeed. The global outcome is thereal workpiece form, which deviates from the programmed one.

Denkena et al. presents a combined simulation approach fortool grinding in [65], as presented in Fig. 35. The purpose is tosimulate the grinding process and the resulting shape and surfacequality of drills and end mills. The grinding process under analysis

Fig. 35. Modelling and co-simulation of the process–machine interaction in tool

grinding [65].


is characterized by complex three-dimensional engagementconditions, high material removal rates, yielding to greatly varyingstatic and dynamic workpiece properties, and a complex interac-tion of process, grinding wheel, workpiece and machine. Denkenaet al. take an integrated approach that comprises multiscalemodelling and experimental aspects. A macroscopic materialremoval model based on surface/surface intersection has beendeveloped to calculate the current workpiece geometry [66].

A microscopic material removal model has been implementedas an FEM single grain scratch to predict grinding forces in a laterstate. Analysis of static and dynamic grinding wheel and workpiececompliance has been carried out using FEM. Empirical data fromtool grinding experiments has also been collected [60].

Klocke et al. likewise use an elaborated approach to simulatespeed stroke grinding [129]. Combining the works of Hoffmann onflexible multi-body simulation with moving machine parts[108,109] and Zeppenfeld’s research on speed-stroke grinding[223,224], they generate an integrative tool that is able to simulateboth the grinding process and the dynamic and static machinebehaviour. In addition, the machine model integrates virtual drivecontrol loops as mentioned in Section 4.2.

5. Developments describing forming phenomena

The numerical computation of forming processes, based on thefinite element analysis (FEA), has been established as an efficienttool within process development. By using simulation, importantaspects specific to forming can be analysed prior to initial formingtests and the dies can be optimised accordingly [133]. This leads toa decrease in manufacturing and development time as well as to anincrease in quality and productivity [152].

The benefit of the simulation for the user greatly depends on thequality of the results. Only those computation results are usefulwhich provide a highly precise representation of the real situation[149]. While this is already the case for material flow computation,the dimensions of formed workpiece still cannot be determined ina satisfactory manner. The main reason for the insufficientaccuracy of the simulation results is the non-realistic representa-tion of the machine behaviour within the simulation of the formingprocess [94,172].


The high flow stress of the workpiece material causes extremeloads on the machine and tool system, resulting in considerabledeflections of these components. This, in turn, influences theworkpiece dimensions and accuracy [31]. Thus there are interac-tions between the process, the machine and the tool system(Fig. 36). These interactions complicate the optimisation of tooland process design that should lead to high workpiece accuracy.

Increasing workpiece accuracy by taking into account theinteractions of machine and process when simulating formingprocesses is thus an important objective in recent researchactivities from Behrens [24], Brecher [40], Engel and Geiger

Fig. 36. Interaction of the press and the forming process [71].

et al. [135], Franzke and Hirt [80], Großmann [96], Meier [141],Schapp [172] and Wiemer [211].


5.2.1. Machine tool

The accuracy of a forming process is defined by the deflectionand tilting behaviour of the machine and the tool system under theprocess load. To characterise the accuracy of the machine, the axialand tilting stiffness is used. For presses, the standardisedmeasurement of these machine parameters is carried out at thebottom dead centre position of the ram and with a static loadaccording e.g. to DIN 55189 [67], VDI 3193 [196] and VDI 3194[197]. A continuative approach to identify the press stiffness as a6 � 6 flexibility matrix is shown in [17,56]. Furthermore, in [22], anapproach for the dynamic measurement of axial and tiltingstiffness of press machines is given.

Knowledge of the press parameters for stiffness and clearance isnot sufficient to draw direct conclusions regarding machinebehaviour during the forming process. This is because, in mostcases, the loads during the process are unknown. Measurementsduring the forming process are therefore necessary. Freiherr[81,82] developed an optical system with two laser light sourcesand two gauge heads for measuring ram deflection and tilting of apress. The system is mounted on a press table and ram within theworking room of the machine which could be difficult formeasurement during warm forming processes. Schapp used anoptical laser measurement system (Lasertracker) for the measure-ment of press behaviour during a forging process [34,170,172]. Theanalyses reveal that this kind of approach can be used to measurethe machine behaviour under process load with only negligibleinterruption to the production process. The measurement can beused for presses which have a rigid connection into the ground.

Wiemer [211] gives a comprehensive overview of differentapproaches for modelling and simulating mechanical pressmachines. In recent research activities from Blau et al. [30],Großmann et al. [90–92], Krimm [134], Schapp [172], Wiemer et al.[211] particular attention has been paid to modelling the nonlinearelastic load–deflection behaviour of the ram guiding system, as thishas a crucial effect on the machine behaviour under load.

Schapp [40,172] compared the simulation accuracy of machinebehaviour under process load by calculating with different types ofpress models and carrying out measurements during real forgingtests (Fig. 37). The press models, which allow nonlinear elasticcomputation of machine behaviour, demonstrate a high level ofsimulation accuracy compared to the forging tests. Furthermore,the work reveals that an analytical, nonlinear elastic machinemodel programmed in a high-level-language is sufficient for thesimulation of the machine behaviour of forging presses.

5.2.2. Process

In metal forming, process modelling and simulation is used topredict material flow, stress and temperature distributions,stresses and forces exerted on tools, and potential sources ofdefects and failures. It is even possible to predict product

Fig. 37. Coupled simulation of forging process and nonlinear elastic machine

models [40].

Fig. 39. Offline coupling of a deep-drawing process and machine simulation [23,24].


microstructure and properties as well as elastic recovery andresidual stresses [8]. Approaches and possibilities for thenumerical simulation of forming processes are presented in[8,21,105,162,163,172,210].

The accuracy of FE process simulation depends on reliable inputdata, namely on CAD data relating to the die geometry, the speedand force characteristics of the press used for forming, the flowstress of the deforming material as a function of strain, the strainrate and temperature in the range relevant to the process, as wellas friction characteristics at the interface between the deformingmaterial and the die.

A broad overview of modelling and testing approaches andtechniques for predicting material response with the latestdevelopments in research laboratories and industrial applicationsis provided in [9,19,150].


Comprehensive modelling of the forming process demands thecoupling of subsystems, as the machine, tool and workpiece allinfluence the forming process. Principle concepts coupling the FEworkpiece model and machine model are presented in [98]. Fig. 38gives an overview of coupling variants which are classifiedaccording to their method of integration.

In case of offline coupling, process force progressions of theentire forming process are computed with the workpiece model.With these process force progressions, the machine behaviour iscomputed separately in the machine simulation. So one simulationtypically uses the complete results from an entire run of the othersimulation. The cycle will be repeated until convergence of thesimulations is reached.

In the model integration approach, the workpiece model istypically extended by a simplified machine model within the samesimulation environment (usually FEA). The integrated modelallows direct interaction between the process load and toolposition as a result of the machine behaviour.

In co-simulation, both simulations run simultaneously indifferent simulation environments and communicate to eachother in synchronised cycles. In this case, detailed machine models,e.g. from multi-body simulation, can be coupled with FE workpiecemodels. The synchronisation and exchange of simulation data iscarried out by a special coupling tool.

In the following the results of recent research activities areoutlined with respect to the interactions between machine andprocess in the simulation of forming processes using the differentsimulation approaches.

5.4. Offline coupling of process and machine simulations

In [23,24] Behrens et al. presented an approach for theoptimised simulation of metal forming processes by means ofthe FE method that takes into account machine properties. Theapproach is an offline coupling of FE process simulation and amachine simulation in a higher programming language. It isexemplified by a three-stage multi-engaged deep-drawing pro-

Fig. 38. Simulation approaches for process–machine interaction according to [97].

cess. A phenomenological approach is used to generate themachine model, which is based on the results of experimentalmeasurements. The machine simulation takes into account thenonlinear stiffness characteristic of the connecting rod, ram guidesand machine frame, as well as the deflection of the bolster plateand the ram collective. The clearances in the bearings and ramguides are likewise considered by the measuring data selected asinput values. In order to consider the mutual influence between theprocess simulation and the machine simulation, an iterativecombined simulation was developed. The cycle comprises foursteps that are carried out one by one (Fig. 39).

Meier et al. [141] presented a robot-based forming process inwhich the path design of the robot was computed using offlinecoupling of FEA and multi-body simulation (MBS). Roboforming isa method in incremental sheet-metal forming for a low number ofpieces. The principle is based on flexible shaping by means of twoindustrial robots. The compliances of the machine structuresinvolved and the springback effects of the workpiece are the maininfluencing factors on dimensional accuracy in incremental sheet-metal forming. This is evident in roboforming, where the robots’stiffness is low compared to a conventional machine tool. Thedriven path deviates significantly from the planned path, and theshapes produced are thus of insufficient quality. To predict thesedeviations and to compensate the tool path, a simulation modelfocusing on the interaction between the forming process and therobot structure was developed. By coupling both models itera-tively, an adjusted TCP path is generated that compensates thepath deviations. To validate the coupled simulation model, ameasurement using an external optical coordinate measuringmachine was carried out. The computed tool-tip positions werevalidated by comparing them with the measured positions. Theresults demonstrate a high level of consistency.


An example of model integration when modelling the process–tool-machine interactions in cold forging is presented in [71,135]by Engel, Geiger, Kroiß and Volkl. The process under analysis is anaxially symmetric forward extrusion process on a stroke-con-trolled press. Cold-forged parts are produced with stroke-controlled presses in many cases. The deflection of thesemechanical presses caused by high loading during the forgingprocess directly influences the actual punch stroke. However, thedeflection does not only affect the press itself but also the toolsystem up to the punch. In addition, the prestressed die is alsodeformed elastically during the forging process. The deflection ofthese components, which leads to a change in the punch stroke,influences the workpiece dimensions.

This is used to determine the stiffness of the press indirectlyusing the evolution of the workpiece length depending on theactual punch stroke (Fig. 40). For the stiffness calculation,experiments and an FE simulation of the example process wereapplied. However, the behaviour of the whole press is not linear

Fig. 40. Consideration of the resulting press stiffness in the FE process simulation

[135].Fig. 42. Simulation of sheet-metal forming process with advanced forming process

model [96,97].


due to initial effects caused by the press drive system. These non-linear effects at low forming loads are constant and can bemodelled by a shift factor in the FE simulation. At high formingloads, the deflection of the whole press, including the tool system,reveals a linear behaviour and can be modelled by a springelement. The model consisting of a shift factor and a springelement with the stiffness of press and tool system is ultimatelyintegrated into the FE process simulation. Based on this model, anoptimisation of the influencing parameters and thus the workpieceaccuracy was made.

In [93], Großmann et al. presented an approach for modelintegration that reveals how interactions between the formingpress, the tool and the sheet-metal forming process can bemodelled by enhancing conventional FE process models. Sheet-metal forming processes are commonly described by shellelements with simple constitutive equations. The workpiece-dieinterface is represented by friction and contact law with constantcoefficients. Within the concept of an advanced forming processmodel, as described, static effects of the press, such as vertical andhorizontal total stiffness and the tilting stiffnesses, are considered.For simulation with a rigid ram these parameters are connected asconcentrated stiffnesses to the centre of gravity of the ram. In asecond step the model is enhanced by elastic tool models thatdemand elastic embedding in the machine. This implies that thepress has to be modelled by an elastic ram and an elastic presstable. Similar to real press structures, the bearing of the elastic ramin the press is determined by the drive and ram guidance. Theadvanced forming process model is thus extended by distributedspring elements (Fig. 41, left). This advanced forming processmodel allows the deflection and tilting of the ram and the resultinginfluence on the sheet-metal flow and sheet thickness to becomputed.

In [94,95], the advanced forming process model is extended toinclude elastic die-cushion effects. In reality, the distributed loadon the blankholder surface results from the equilibrium betweenthe process load, which is variable in time and location, theblankholder deflection and deformation. In forming simulations,this demands the extension of the model to include thedeformation and deflection of the blankholder. The implementa-tion of the die-cushion model for an elastic blankholder is similar

Fig. 41. Modelling of press and die cushion in the advanced forming process model

[97].

to the implementation of the press behaviour. The stiffnesses of thedie-cushion drive, the guidance, the pressure pin, and the pinguidance are represented by substitutional springs (Fig. 41, right).The analyses demonstrate that the effect of the blankholderdeflection on the example drawn part is greater than the influenceof ram tilting.

In [96] the process simulation with the advanced formingprocess model is verified with experiments on a single-actionhydraulic press. During these experiments, the static tilting andstiffness behaviour of the press were determined by measurementand taken into consideration during the FE forming process modelwith non-linear springs. The elastic properties of the die cushionand forming tool were also considered. The simulation corre-sponds well to the experimental results. Furthermore, theexperimental results confirm the prediction of wrinkles and cracksmade using a forming limit diagram (Fig. 42).

5.6. Co-simulation

Brecher and Schapp presented in [39,171,172] a method for thecoupled simulation of a forging process with external machine toolsimulation systems and nonlinear elastic press models. Theapproach of co-simulation allows all interactions between thepress and the process to be considered in order to improve theaccuracy of the forging simulation. Fig. 43 describes the method forincorporating the press behaviour into the forging simulation byenabling data exchange between the process and the machinesimulation.

In forging simulations, the entire computation process isdivided into several steps. When using coupled simulation, thecurrent process load is computed at the end of each step andtransferred from the forging simulation to the machine simulation,which then determines the nonlinear elastic relative displacementand tilting of the upper and lower dies. Subsequently, the pressdeflection information is transferred back to the forging simula-tion, where the dies are repositioned accordingly. By using thisprocedure, the externally computed press behaviour is fullyintegrated into the computation of the forging process.

The comparison between the simulation results from thecoupled simulation with nonlinear elastic machine models and

Fig. 43. Co-simulation of the machine and process in forging [172].

Fig. 44. Co-simulation of multi-staged forging processes and nonlinear elastic press

models [40].

Fig. 45. Modelling of the interaction effects between the roll and the process in flat

rolling [79,80].


forging tests reveals a high correlation. The press behaviourtransfers onto the shape and the dimensions of the workpiece inreality and in the coupled simulation. With these more precisecomputation results of the forging simulation, simulation-basedtool optimisation may be achieved and machine individual tooladjustments can already be done in the design stage of the tools[39,172]. Furthermore, the coupled simulation allows the devel-opment of forging errors caused by the press to be reconstructed.

Brecher et al. [40] presented an extended approach for thecoupled simulation of multi-engaged, multi-staged forging pro-cesses (Fig. 44). In addition to the workpiece-based interaction ofthe different forging stages in single-engaged, multi-stageprocesses, coupled simulation also takes into account themachine-based interaction. It is because every single die stagecontributes to the machine behaviour that a change in one of thedie stages results in a different process load of the press machineand so to retroactive effects on all die stages. Especially these twointeractions make the optimisation of multi-engaged multi-stageprocesses very demanding. The results show that even suchcomplex interactions can be simulated correctly, thereby formingthe basis for a simulation-aided optimisation of multi-engagedforging processes.

In cold rolling processes, the strip-flatness and the strip-thickness profile are highly influenced by the interaction betweenthe process (strip) and the machine (rolls). These influencesinclude changes in pressure distribution in the roll gap, rolldeformation phenomena, and strip tension distribution, amongother things. Modelling such processes using a single FE model thattakes these influences into account is not only difficult to carry outbut it also complicates the convergence criteria. This situation iseven worse if the contact situation between elastic bodies (workrolls and backup rolls) has to be considered. Franzke, Puchhala,Dackweiler and Hirt in [78,79,80] presented a concept that meetsthe above-mentioned requirements efficiently by separating thecomputation of tool elastic effects from the process simulation(Fig. 45).

Helduser and Lohse described an approach for the co-simulation of a deep-drawing process and a hydraulic pressmachine in [104]. Here, the FE simulation of the sheet-metalforming process is coupled with the mechanical model of the press

machine that contains the controlled subsystems of the hydraulicdrives. The objective of this analysis is to optimise the hydraulicdrive control of the deep-drawing press and to achieve bettersimulation results with respect to the influence of machinebehaviour on the workpiece dimensions.

6. Summary and conclusion

For a wide variety of metal-working processes, the state-of-the-art and the state of ongoing research in process–machineinteractions have been presented. The large number of projectsdemonstrates the strong interest in the related research.

In cutting and grinding, there is a long tradition of viewing theprocess and the machine tool as an interacting system. Researchersof forming processes have started to consider press behaviour overthe last few years. In all disciplines, there are more or less fourimportant steps in the research of process–machine interaction.

First comes the understanding of the modes of interaction.What are the relevant aspects of behaviour? How can theinteraction be measured and assessed? Secondly, a concept formodelling the interaction is needed. Where are the interfacesbetween the process and the machine tool? How can all modes ofinteraction be included? Thirdly, abstraction of the models isnecessary. What are the relevant interaction phenomena? How canexisting process and machine models be used for the modelling ofthese phenomena? Lastly, modelling leads to methods ofsimulating process–machine interactions, which is generally thegoal of the research projects. Prediction of interaction resultsthrough simulations can help to improve machines and processesin order to obtain a more efficient production system.

As in a feedback loop, the simulation results can be comparedwith observed experimental data. This enables the researcher togain a closer understanding of the details of process–machineinteractions, starting the four steps again.

Currently and in future projects, this approach will besupplemented with cross-functional research. How can mechan-ical and mathematical methods be used to describe interactionphenomena independently of the individual process? What are thecommon grounds for forming, grinding and cutting experts? Thesolution to these questions will be found by intensifyinginterdisciplinary communication. The CIRP research group ‘‘Pro-cess Machine Interaction (PMI)’’ has made the first steps in thisdirection.

Acknowledgments

The authors would like to thank Stephan Baumler, AlexanderGuralnik, Marco Tannert and Yuri Trofimov for their efforts incompiling the state-of-the-art in the field of process-machineinteractions for this paper.

The authors are grateful to the following persons for theircontributions to the preparation of this paper: Professor Altintas,University of British Columbia, Canada; Professors Biermann andWeinert, Technical University Dortmund; Professor Denkena and


Mr. Deichmuller, Leibnitz Universitat Hannover; ProfessorGroßmann and Dr. Wiemer, Technical University Dresden; ProfessorSmith, University of North Carolina at Charlotte; Professor Zah,Technical University Munich, Germany, Professor Abele, TechnicalUniversity Darmstadt, Germany; Professor Ahn, Pusan NationalUniversity, Korea; Professor Aurich, Technical University Kaisers-lautern, Germany; Professor Budak, Sabanci University, Turkey;Professor Engel, University of Erlangen-Nurnberg, Germany; Pro-fessor Fleischer, Karlsruhe Technical University, Germany; ProfessorInasaki, Keio University, Japan; Professor Seliger, Technical Uni-versity Berlin, Germany; Dr. Zatarain, Tekniker, Spain. We wouldalso like to thank the industrial companies who provided theirsupport in preparing this paper.

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