Automated Quality Control in Ceramic Industry Rafael Guerreiro Baeta1

rafael.baeta@gmail.com Mechanical Engineering Department, Instituto Superior Técnico, Lisboa, Portugal

Abstract - This work has the goal to design an

automatic system to detect flaws and edge

irregularities on ceramic plates. Edge

irregularities are detected as well as black spots

on both sides of the plates. The detection of

defects using photography and image processing

techniques allows image interpretation leading

to a decision to accept or reject the plates. A

special system of lightning was designed. This

lightning does not focus directly on the plates,

therefore avoiding undesirable reflects.

To analyse the data, three algorithms were

developed, the first to detect the black dots on

the upper side of the plate, the second with

identical role for the lower side of the plate and

the third to analyse the edge and the geometry.

Binarization and techniques of morphology

were used to detect flaws on the plates.

The tests performed, with the developed

prototype, concluded that the system has a

reliability of 100%. Tests were performed using

643 photographs of plates without flaws and

with the most common flaws. The plates were

carefully rotated, to place the flaws in different

positions towards the camera. The detection

time varied, for the 3 algorithms, between 2,95

and 3,46 seconds for each operation.

To conclude, it is possible to say that a simple

and functional system was projected. This

system has low construction costs and an

elevated reliability, which can contribute to

enhance the quality of the plates and lower its

costs, increasing, therefore, national

competitively.

Index terms - Quality control of ceramic plates,

CAD, Automation Visual Inspection, Image

Acquision System, Ceramic Defects, Binary

Image, Hough Transform

1. Introduction

In order to attain high quality in Portuguese

porcelain, great care is necessary, as well as a good

technology and, specially, a very thorough

inspection to deliver flawless objects and avoid the

sale of defective pieces. Until a few decades ago,

trained workers performed quality control, as

manual labour was cheap and abundant. Lately, this

situation has become unaffordable, due to its costs

[1].

Therefore, automatic systems of quality control

were developed, but due to their cost [2], most

factories chose to continue to employ manual

inspection, using samples from the lot, instead of a

100% inspection of all pieces [3]. This system is far

from perfect and the competition from emerging

countries, such as Asian countries, with very cheap

manual labour, require from the Portuguese

factories a higher quality and no fails in the final

products.

This situation lead to the idea of developing an

automatic system that allows the detection of the

tiniest defect, reducing simultaneously the costs of

manual labour of sample inspection. To this

purpose, a system of automated transport of plates

was projected. This system, allied to a system of

visual inspection and specific software to interpret

the images and select between good or flawed

plates, gives instructions to the machine to separate

the good from the bad.

In this report it’s shown how the developed

algorithms are able to detect any flaw in the upper

and the lower surfaces of the plates used for this

work, which were kindly provided by the company

Matcerâmica. (Figure  1)

 

Figure 1 - Sample of plate provided for this project

2. Description of the proposed system

The developed system is divided in 5 modules:

1. Centering system;

2. Vision and lighting system;

3. Turning system;

4. Rejection system;

5. Stamping system.

2.1. Centering system

The first problem to arise was how to correct the

random placement of the plates on the feed belt, not

only in what concerns the space between the plates,

but also the correct central placement on the belt.

This perfect alignment with the camera is crucial to

capture the images.

The space between plates was determined taking

under consideration the time frame necessary to

have the plate photographed, in order to have time

for the analysis of the data, and to trigger the

machine that will separate the good plates from the

flawed.

The ideal time is 4 seconds per plate, which means

15 plates per minute or 900 plates per hour.

To solve this problem 2 fed belts were used, one to

receive the plates and the other to take them

sequentially to the centering system (Figure  2).

 

Figure 2 – Centering system

This way, the first belt has a sensor connected to a

system that controls the start and stop of the belt,

allowing the plate to slide into the second fed belt

at 4 second intervals (Figure  2).

To center the plates, a system installed over the belt

was used. This system comprehends 2 steers

connected to a pneumatic cylinder that allows a

pressure equilibrium that will exert some pressing

on the plate, placing it exactly on the center of the

belt. A low-pressure system was used to avoid any

physical damage to the plate. This belt will feed the

next belt where the vision and lighting system is

installed.

2.2. Vision and lighting system

This is the true innovation of our project, as all the

other parts, in spite of being important for the

overall functioning, are nothing more than a

mechanical control of simple robotics.

The plate will enter a special dome, in the shape of

a half cylinder (Figure  3), illuminated by leds, as

suggested by Ahmed Patel [4]. This shape ensures

a diffuse illumination where there are no light

reflexions on the plates whatsoever. In spite of

being known that the lighting system usually

employed in this kind of work is a dome, in our

project this system was used, with better results.

 

Figure 3 – Vision and lightning system

This system has the capacity to receive plates until

450 mm of diameter and is composed by a lateral

wood base and a dome in metallic plate of 1,5 mm,

painted in white matte to avoid any reflections. A

rail with all the lighting system was placed inside,

so that that light would not shine directly on the

plates, but indirectly through the reflection on the

dome.

The illumination was installed through a led

system, with a ribbon with 5m and 150 leds/m type

SMD5630 (cold white 6000k), cut and distributed

in an even way through the rails. The power of the

ribbon (40w) requires an adequate power source

and the choice was one from the brand MeanWell,

model 100F12, and giving 8,5A at 12 VDC.

The plate stops 1 second under the camera to be

photographed and here the choice was a DSLR

digital single lens reflex camera with a sensor type

CMOS (complementary metal-oxide-

semiconductor). In spite of not being an industrial

equipment, it fills perfectly our needs and it’s

cheaper, as it has a CMOS sensor and not a CCD

(charge coupled device), which would allow for an

image of better quality, but this is not necessary

thanks to the developed software.

During the rehearsals the camera used was the

Canon 600D with 18 megapixels and a Canon lens

EF-S 15-85mmR/3,5.5,6 IS USN, which, of course,

kept the same configurations during all the tests.

The configurations used were as follows:

• Focal distance 15 cm;

• Index of sensibility (ISO) of 100;

• Opening shutter speed 1/100;

• Shutter opening 5,6.

2.3. Turning system

This module will allow an 180º rotation of the

plate, in order for it to go through a second module

of vision and lightning to detect flaws in the other

side of the plate.

This system is completely mechanical and is made

of two belts (a feeding belt and a leaving belt) and

the turning mechanism itself. Using V-belts, the

first belt will take the plate to the turning

mechanism. The belt has an opening that allows

three fingers to pass through it. These capitation

fingers will take the plate from the first belt and

lead it to the second belt in identical conditions, in

order to go into the second vision lightning system.

There are two supports that keep the plate always

centered (for the second inspection), preventing the

plate from moving (Figure  4).

 

Figure 4 – Turning system and detail of the griping system

2.4. Rejection system

This module is rather simple, composed by a

pneumatic system using a fixed cylindrical

backstop which, when activated, pushes the plate

out of the belt.

The developed software, when detecting a flawed

plate, triggers an electric valve firing the cylinder,

pushing automatically the plate outside the belt

(Figure  5).

Special attention must be given to the belt, which

cannot be made of traditional covering, because

this will cause friction with the moving plate, so, in

this project, a motorized roller belt was used to

transport the plates.

When exiting this module, the flawless plates

remain on the main belt and proceed for stamping.

The flawed plates are steered to a parallel line,

where they are inspected manually and can be

recycled if the flaws are deemed acceptable.

Figure 5 – Rejection system

2.5. Stamping system

This is a simple mechanical system composed by a

belt and stamping equipment.

The plate, after being inspected, enters this belt and

stops under the stamping equipment for one

second, which is the time allotted to this function

and the logo and the batch are placed unto the plate

(Figure  6). This takes us to the end of the line and

now the plates only need to be packed and prepared

for expedition.

Figure 6 – Stamping system

The assembled complete system is shown in Figure  

7.

 

Figure 7 - Complete system top view and side view

3. Implemented methods

3.1. Default types of plates

For our work 3 types of plates were used: white

plates with worked rim, white plates with simple

rim and plates with decal and simple rim.

From the lot of plates received from Matcerâmica,

some had the kind of flaws that are currently found

on their production line. The flaws found on those

plates are black dots, clear and dark granules,

bubbles, decal and broken edges.

The developed software allowed the detection of

any irregularities on the edge of the plates as well

as any dots on the white plates.  

3.2. Pre-processing

Now that the types of flaws that have to be detected

are identified, it is possible to proceed to the

description of the algorithms implemented in this

project. These were developed using the software

Matlab version R2009b 64bit Mac OSx.

Three algorithms were developed:

• One algorithm to detect black dots on the

upper side of the plate;

• One algorithm to detect black dots on the

lower side of the plate;

• One algorithm to analyse the edge and

geometry of the plate.

In the pre-processing stage there will always be the

following steps (Figure  8):

Image opening - the image, obtained from the

vision and lightning system, is read by the

algorithm with its original name. The algorithm is

able to read the image where it is saved, without

having to be introduced in the program by an

operator.

Image resizing – the original size of the image is

reduced.

Binarization – a value of threshold is defined and

the initial image is transformed into a binary image.

New image resizing – the binary image is again

reduced.

Application of the Hough transform – with the

reduced image, the algorithm of the Hough

transform is introduced, to calculate the outline of

the plate. The outputs obtained are the radius of the

figure and the coordinates of its center.

Crop – this allows us to cut the image and leave

only the plate with a small contour. This cut is only

possible thanks to the coordinates of the center of

the plate, as well as the radius obtained earlier.

 

Figure 8 – Pre-processing stages

3.3. Algorithm for the detection of flaws on

the upper side of the plate

Binarization and morphology techniques were used

to calculate this algorithm. This is a simpler

Image opening Image resizing Binarization

New image resizing

Application of the Hough transform

Crop

approach with simple implementation and fast

processing (Figure  9).

In this process the critical point will be the

threshold value to be used. The camera used in this

project, in spite of not being considered a high-

speed camera, is considered a high-resolution

camera. This fact leads to image detection of

details of the plate that are practically invisible to

the human eye. This way, when proceeding to

binarization, if the threshold value is too high,

beyond the flaws of the plate, there will be the

detection of undesirable details, when the objective

is to have detection only of the black dots

considered as flaws. So it was decided to use a

manual threshold, with this value perfected for the

slightest flaw (approximately 1 mm).

To guaranty only the presence of flaws in the

image, the algorithm was strengthened,

implementing erosion, eliminating undesirable

details.

After choosing a threshold and applying erosion,

the algorithm proceeds to the inversion of the

colour of all the pixels and counts the number of

defects (set of white dots) existing in the image.

 

Figure 9 - Algorithm for the detection of flaws on the upper side of the plate

3.4. Algorithm for the detection of flaws on

the lower side of the plate

Concerning the lower part of the plate, the

algorithm used is practically the same (Figure  10).

However, it is necessary to eliminate the image of

the edge of support of the plate, since there may be

some irregularities on the support that would distort

the results.

Figure 10 - Algorithm for the detection of flaws on the lower side of the plate

3.5. Algorithm to analyse the edge and

geometry of the plate

After the pre-processing phase, the perimeter of all

the components of the image is detected. Therefore

an image with edge of the plate is showed, as well

as the edges of the flaws, if they exist.

The limit lines of the image are analysed. After the

detection of the first and the last lines, the exact

coordinates are obtained in the spacing between

them. The same process is applied for columns and

the exact center of the plate is obtained.

Subsequently, all internal and external pixels are

removed and a survey for all the pixels of the edge

is conducted.

The (x,y) position of all the pixels allows, using the

center, to know the distance of each to the center.

After obtaining all the distances, the mean

distances and the standard deviation are calculated.

The detection of a flaw on the edge is made using

the minimal distance detected on the table of

distances of the several pixels to the center. If this

value is inferior to the mean distance calculated,

minus the standard deviation, a flaw is signalled on

that spot.

To analyse a bulge on the edge, the process of

detection is identical, and the maximal distance

value found is used as reference.

Threshold value (pre-processing)

Color inversion Defects count

Threshold value (pre-processing)

Lower rim elimination Color inversion

Defects count

4. Results

4.1. Illumination

The use of a diffuse illumination system proved to

be a good choice as, allied to a black base, it

eliminates parasite glows and allows the camera to

visualize all characteristics of the plate, including

rim and decal, as well as all the flaws.

4.2. Database

Three sets of plates received from Matcerâmica

were tested: white plates with worked rim, white

plates with simple rim and plates with decal and

simple rim. In those sets of plates we had all the

commonly found flaws.

Due to the limitation of the number of samples and

to increase the credibility of the test, each plate was

photographed 5 to 6 times, rotating the plate in

order for the flaws would not be located always on

the same spot, towards the camera.

All the results were registered in Excel and the

flowchart of the database is given in Figure  11.

Figure 11 – Database Flowchart

Independently of the position of the plates, the

camera always identified the flaw in the same

manner.

4.3. Results of Implemented methods

For the upper side algorithm we used 98 plates that

include flawless plates and plates with flaws. For

the lower side, we used 67 plates with the same

conditions. All these plates are included in the 643

images database. The main results for all 3

algorithms are presented on Table  1.

Table 1 – Results for the implemented 3 algorithms

Plates correctly classified

Precision of the number of defaults

Average processing

time Upper side algorithm

100% 98,97% 3,46s

Lower side algorithm

100% 74,63% 3,04s

Edge and geography algorithm

100% - 4,8s

It is safe to say the first 2 algorithms have the same

ability of correctly classifying all kinds of plates

(flawless and with flaws). However, the first

algorithm has a higher percentage of accuracy.

Both algorithms have good processing times, witch

means they can be implemented almost directly

into the automated inspection system. On Figure  

12 and Figure  13 we can see defects detected on

the upper side of the plate and the lower side,

respectively.

 

Figure 12 - Plate with default on the upper side

 

Figure 13 - Plate with default on the lower side

Using the third algorithm, all 11 places with

defective rims were signalled as having flaws

(Figure   14). This algorithm has a slightly bigger

processing time. This algorithm was used to check

the geometry of 98 plates and no size defects were

detected. All plates were deemed acceptable. This

algorithm has also a good processing time.

Figure 14 – Plates with defective rims

5. Conclusions

With the development of this project, the main goal

is to show that it is possible to use a low cost and

low energy expenditure equipment to install an

automatic system to select plates produced by a

ceramic factory.

This system not only optimizes labour but also

allows the inspection of plates using a reliable and

quality system that replaces the inspection by

samples of a batch [5].

On the other side, the advantage of not falling in

the routine of fatigue that befalls the manual

inspectors leads to an elimination of human errors.

Several authors describe this and some defend that

the inspection of samples from a lot is superior to

the inspections of 100% of items, to avoid fatigue

and human error.

This project focus in the automatic inspection

system of the flawed plates, but the assembly line

can easily be completed with an automatic feeding

system, with a cleaning system by air jet to clean

the plates, considering that the materials used in

ceramics cause respiratory problems [6] and,

finally, with an automatic packing system.

The described equipment is 100% reliable,

economically profitable, and can make a

contribution for the development of Portuguese

industry, giving it conditions to compete in the

global world market.

6. References

[1] Timothy S. Newman and Anil K. Jay, "A survey of Automated Visual Inspection," Computer Vision and Image Understanding, vol. 61, no. 2, pp. 231-262, March 1995

[2] P. Wambacq. and A. Oosterlinck L. Van Gool, "Intelligent Robotic Visions Systems," Inteligent Robotic Systems, pp. 457-507, 1991

[3] José Queirós, Cerâmica Portuguesa e Outros Estudos, 2nd ed., Editorial Presença, Ed. Lisboa, Portugal, 2002.

[4] Ahmed Patel, Leila Yazdi, Anton Satria Prabuwono Ehsan Golkar, "Ceramic Tile Border Defect Detection Algorithms in Automated Visual Inspection System," Journal Of American Science, vol. 7, pp. 542-550, 2011

[5] Timothy S. Newman and Anil K. Jay, "A survey of Automated Visual Inspection," Computer Vision and Image Understanding, vol. 61, no. 2, pp. 231-262, March 1995

[6] "Guia de Boas Práticas para a Redução da Exposição à Sílica Cristalina Respirável na Indústria Cerâmica," Centro Tecnológico da Cerâmica e do Vidro, Coimbra, Relatório de trabalho nº: 333.18146-4/10 2012