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Quality Control of Ferrite Cores Through Artificial Vision Techniques
Copyright © IFAC Intelligent Components and Instruments for Control Applications. Malaga. Spain . 1992
QUALITY CONTROL OF FERRITE CORES THROUGH ARTIFICIAL VISION TECHNIQUES P. Campoy, J.C. Fernandez, J.M. Sebastian and R. Aracll Department of AUlomatica. DISAM. Poly technical University of Mtuirid. Spajn
Abstract. The present paper describes the system developed for detecting visual flaws in ferrite cores through artificial vision techniques . The physical dimension of the inspected parts, their high variety of types and their low cost have strongly determined the developed system. The whole system is described throughout the paper, analyzing specially the lighting system, the image processing algorithms and the image interpretation criteria. The obtained results are also described at the end of the paper. Keywords .
Artificial vision. Image Processing. Quality Control. Pattern Recognition.
The automation of the visual inspection as the last part of the manufacturing automation has an increasing market in the last years, due to the new techniques in artificial vision and particularly to the more powerful hardware for image processing at reasonable prices (Batchelor; Bolles).
INTRODUCTION The process to be automatized is the fault detection in the visual inspection of ferrite cores. During the manufacturing and manipulation of the parts, they can be totally broken or have some flaws, which make them not acceptable in a quality control inspection, and which have therefore to be detected. The present quality control policy implies a visual inspection of 80% of the production which makes an average amount of 300 millions parts per year. This large amount of inspected parts gives to the quality control inspection an important role in the final cost. Additionally the more competitive market gives to the quality of the product an more relevant importance, where its automation avoids the human faults due to the monotony of the visual inspecting job. Additional advantages of the automation of the visual inspection are (Artley; Fu; Batchelor): -On-line management of the defects. -Automation of statistical and durable data. -Integration to the manufacturing system, in order to obtain an on-line control of the process.
SYSTEM SPECIFICATION The specification of the system are determined by the features of the ferrite cores, by the defects to be detected and by the data of their production. The specifications of the system concerning the two first aspects are (figure 1): -Cylindrical shape of the ferrite cores (with or without internal hole) -Variable size of the parts between some millimeters and a few centimeters -Uniform black tonality all around the ferrite core -Defects may appear in the base or laterally all round the ferrite core close to the base -Defects (flaws of the ferrite cores) don't have a different visual gray level The specifications concerning the production 351
arises from the fact that defects may appear in the base as well as all round the lateral of the ferrite core. The simultaneous inspection of all the defects in one half of the part is solved by positioning the ferrite core within a conic mirror, as shown in figure 3. In this way the image taken from above contains one base of the ferrite core as well as its lateral reflected on the mirror (see figure 4). The lighting system is chosen to enhance the flaws from the background, allowing a visual detection of the defects through image processing techniques. This aim is achieved through a ring of light transmitted by optical fiber like in figure 3. The ring of light is reflected on the upper part of the cone where the ferrite core is positioned, in such a way that the lateral of the core is uniformly lighted but in the flaws which lay in shadow. This lighting system allows a visual detection of defects as shown in figure 4. The cone (containing the mirror and the white surface for light reflection) where the part is inserted is changed accordingly to the different types and sizes of ferrite cores. The distance d to the light source is also changed, in order to adjust the reflecting angle of figure 3 in concordance to the new angle 6. This change of cone and adjustment of the distance to the light source gives the system the desired flexibility for the different types and of ferrite cores.
data are: -High production, which implies a short inspection time per part (about 750 ms) -Low cost of the product and therefore of its quality inspection -High variety of ferrite cores' sizes. This fact implies a high flexibility of the system
Fig. 1. Aspect of the ferrite cores to be inspected.
DESCRIPTION OF THE SYSTEM An overview of the system is shown in figure 2. The main subsystems are: the positioning and lighting system, the image acquisition and digitalizing system and the software structure (including image processing).
camera f: focal distance
white
surface ~ m irror for light for seeing reflection lateral defects
d
ferrite COfe
computer for image processing
Fig. 2. System overview. Fig. 3. Conic mirror and positioned into it.
Positioning and Lighting System The first problem for the visual inspection 352
ferrite
core
data is stored in the Ferrite Core Data Base for its ulterior on-line use by the module for Defects Detection. The module of Defect Detection is an image processing module which extract the flaws from the image of each inspected ferrite core. The processing of the image is carried out in two regions of interest (ROI) which are defined in the Ferrite Core Data Base and which match respectively to the base and reflected lateral of the part. In these ROIs an image segmentation algorithm based in thresholding is carried out for flaw detection. Next module correspond to the measurement of size and position of the detected defects. In this module close flaws are associated as one defect and the resulting defects are quantify in size and position relative to the base. The size of the area in which all defects are considered as only one is an parameter adjustable by the user. This parameter is set according to the defect criteria of each type of ferrite core. Last software module is the decision module of the quality of the inspected ferrite core. This module take into account the criteria given by the user and stored in the Ferrite Core Data Base, which determines the quality of each type of ferrite core in function of the size and number of defects detected by the prior module. The output of this module is the action over the actuators in order to reject the fault parts. This module also stores statistical data of the production and quality control of ferrite cores .
Fig. 4. Defects detected on a ferrite core.
Acquisition and Digitalizing System The acquisition system is one CCD matrix camera of 756x581 pixels which gives out in the standard black and white video format the image taken to the ferrite core. For the new industrial prototype two cameras are required for taking one image from each side of the ferrite core, once it is positioned in a double cone as shown in figure 5. The digitalizing and hardware processing system is a commercial board (MATROXMVP) placed on a AT compatible computer.This board digitalize the video signal into 512x512 pixels matrix and allows a hardware processing of most of the standard image processing functions.
CP----a::J----t:O
Production
fel"rit e Core
OB
OB
~--------~I
~I____
Ima ge
Fig. 5. Double cone-two cameras system for inspecting both sides.
,-------,~
Fig. 6. Software structure. CONCLUSIONS
SOFTWARE STRUCTURE In this paper a system for an automatic fault detection in the visual inspection of ferrite cores has been presented. The main features of this system are related to the inspected parts, which have radial symmetry, are small, have a low price and there are a high variability of them. The presented system is an on-line, flexible quality control system, which also can be used for other products of the manufacturing industry with similar features.
An overview of the software structure is shown in figure 6. The first module is a user friendly interface for defining the different types and sizes of ferrite cores . This module also helps the user to define and adjust the variable parameters of the positioning and lighting system according to each ferrite core, as well as the rejection criteria according to the detected defects. This 353
Sebastic1n, J.M., Campoy, P., Aracil, F (1989). Artificial vision for automated qualil control in the canned food industry. A European Workshop on Automation in tlJ Food Industry, Dublin, Nov. 1989.
The obtained results for this application are:
*
The developed software allows a high flexibility in inspecting different ferrite types by easily changing of the conic mirror set. Presently up to 50 different types can be inspected.
*
The above mentioned flexibility is achieved by an user friendly interface which easily allows the definition for new ferrite types in terms of physical features and inspection parameters.
*
The system accuracy is about 0.25 mm2 , standard, Imm2 worst case, depending on the ferrite-core size.
*
The inspection time, per part, is mainly restricted by the mechannical device which takes 1 second for positioning each ferrite under the image acquisition system. REFERENCES
Artley, J. W. (1982). Automated visual inspection systems can boost quaility control affordably. Ind. Eng. 14, nO 12, 28-32. Batchelor, B.G., Cotter, S.M., Heywood, P.W., and Moot, D.H. (1982). Recent advances in automated visual inspection. Proc. SPIE, Robot vision and Sensory Controls, 392, 307-326. Batchelor, B.G., Bowman, C.C., Chow, K.W., Goodman, S., McCollum, AJ., and Rowland, S. (1986). Developments in image processing for industrial inspection. Proc. SPIE, Automated Inspection and Measurement, 730. Bolles, R. (1981). An overview of image understanding applications to industrial problems. Proc. SPIE, Techniques and Applications of Image Understanding 281, 134-140. Fu, K.S. (1983). Computer VISIOn for automatic inspection. Proceedings, Robotic Intelligence and Productivity Conf., Detroit, Michigan, Nov. 1983, pp.7-15.
354
QUALITY CONTROL OF FERRITE CORES THROUGH ARTIFICIAL VISION TECHNIQUES P. Campoy, J.C. Fernandez, J.M. Sebastian and R. Aracll Department of AUlomatica. DISAM. Poly technical University of Mtuirid. Spajn
Abstract. The present paper describes the system developed for detecting visual flaws in ferrite cores through artificial vision techniques . The physical dimension of the inspected parts, their high variety of types and their low cost have strongly determined the developed system. The whole system is described throughout the paper, analyzing specially the lighting system, the image processing algorithms and the image interpretation criteria. The obtained results are also described at the end of the paper. Keywords .
Artificial vision. Image Processing. Quality Control. Pattern Recognition.
The automation of the visual inspection as the last part of the manufacturing automation has an increasing market in the last years, due to the new techniques in artificial vision and particularly to the more powerful hardware for image processing at reasonable prices (Batchelor; Bolles).
INTRODUCTION The process to be automatized is the fault detection in the visual inspection of ferrite cores. During the manufacturing and manipulation of the parts, they can be totally broken or have some flaws, which make them not acceptable in a quality control inspection, and which have therefore to be detected. The present quality control policy implies a visual inspection of 80% of the production which makes an average amount of 300 millions parts per year. This large amount of inspected parts gives to the quality control inspection an important role in the final cost. Additionally the more competitive market gives to the quality of the product an more relevant importance, where its automation avoids the human faults due to the monotony of the visual inspecting job. Additional advantages of the automation of the visual inspection are (Artley; Fu; Batchelor): -On-line management of the defects. -Automation of statistical and durable data. -Integration to the manufacturing system, in order to obtain an on-line control of the process.
SYSTEM SPECIFICATION The specification of the system are determined by the features of the ferrite cores, by the defects to be detected and by the data of their production. The specifications of the system concerning the two first aspects are (figure 1): -Cylindrical shape of the ferrite cores (with or without internal hole) -Variable size of the parts between some millimeters and a few centimeters -Uniform black tonality all around the ferrite core -Defects may appear in the base or laterally all round the ferrite core close to the base -Defects (flaws of the ferrite cores) don't have a different visual gray level The specifications concerning the production 351
arises from the fact that defects may appear in the base as well as all round the lateral of the ferrite core. The simultaneous inspection of all the defects in one half of the part is solved by positioning the ferrite core within a conic mirror, as shown in figure 3. In this way the image taken from above contains one base of the ferrite core as well as its lateral reflected on the mirror (see figure 4). The lighting system is chosen to enhance the flaws from the background, allowing a visual detection of the defects through image processing techniques. This aim is achieved through a ring of light transmitted by optical fiber like in figure 3. The ring of light is reflected on the upper part of the cone where the ferrite core is positioned, in such a way that the lateral of the core is uniformly lighted but in the flaws which lay in shadow. This lighting system allows a visual detection of defects as shown in figure 4. The cone (containing the mirror and the white surface for light reflection) where the part is inserted is changed accordingly to the different types and sizes of ferrite cores. The distance d to the light source is also changed, in order to adjust the reflecting angle of figure 3 in concordance to the new angle 6. This change of cone and adjustment of the distance to the light source gives the system the desired flexibility for the different types and of ferrite cores.
data are: -High production, which implies a short inspection time per part (about 750 ms) -Low cost of the product and therefore of its quality inspection -High variety of ferrite cores' sizes. This fact implies a high flexibility of the system
Fig. 1. Aspect of the ferrite cores to be inspected.
DESCRIPTION OF THE SYSTEM An overview of the system is shown in figure 2. The main subsystems are: the positioning and lighting system, the image acquisition and digitalizing system and the software structure (including image processing).
camera f: focal distance
white
surface ~ m irror for light for seeing reflection lateral defects
d
ferrite COfe
computer for image processing
Fig. 2. System overview. Fig. 3. Conic mirror and positioned into it.
Positioning and Lighting System The first problem for the visual inspection 352
ferrite
core
data is stored in the Ferrite Core Data Base for its ulterior on-line use by the module for Defects Detection. The module of Defect Detection is an image processing module which extract the flaws from the image of each inspected ferrite core. The processing of the image is carried out in two regions of interest (ROI) which are defined in the Ferrite Core Data Base and which match respectively to the base and reflected lateral of the part. In these ROIs an image segmentation algorithm based in thresholding is carried out for flaw detection. Next module correspond to the measurement of size and position of the detected defects. In this module close flaws are associated as one defect and the resulting defects are quantify in size and position relative to the base. The size of the area in which all defects are considered as only one is an parameter adjustable by the user. This parameter is set according to the defect criteria of each type of ferrite core. Last software module is the decision module of the quality of the inspected ferrite core. This module take into account the criteria given by the user and stored in the Ferrite Core Data Base, which determines the quality of each type of ferrite core in function of the size and number of defects detected by the prior module. The output of this module is the action over the actuators in order to reject the fault parts. This module also stores statistical data of the production and quality control of ferrite cores .
Fig. 4. Defects detected on a ferrite core.
Acquisition and Digitalizing System The acquisition system is one CCD matrix camera of 756x581 pixels which gives out in the standard black and white video format the image taken to the ferrite core. For the new industrial prototype two cameras are required for taking one image from each side of the ferrite core, once it is positioned in a double cone as shown in figure 5. The digitalizing and hardware processing system is a commercial board (MATROXMVP) placed on a AT compatible computer.This board digitalize the video signal into 512x512 pixels matrix and allows a hardware processing of most of the standard image processing functions.
CP----a::J----t:O
Production
fel"rit e Core
OB
OB
~--------~I
~I____
Ima ge
Fig. 5. Double cone-two cameras system for inspecting both sides.
,-------,~
Fig. 6. Software structure. CONCLUSIONS
SOFTWARE STRUCTURE In this paper a system for an automatic fault detection in the visual inspection of ferrite cores has been presented. The main features of this system are related to the inspected parts, which have radial symmetry, are small, have a low price and there are a high variability of them. The presented system is an on-line, flexible quality control system, which also can be used for other products of the manufacturing industry with similar features.
An overview of the software structure is shown in figure 6. The first module is a user friendly interface for defining the different types and sizes of ferrite cores . This module also helps the user to define and adjust the variable parameters of the positioning and lighting system according to each ferrite core, as well as the rejection criteria according to the detected defects. This 353
Sebastic1n, J.M., Campoy, P., Aracil, F (1989). Artificial vision for automated qualil control in the canned food industry. A European Workshop on Automation in tlJ Food Industry, Dublin, Nov. 1989.
The obtained results for this application are:
*
The developed software allows a high flexibility in inspecting different ferrite types by easily changing of the conic mirror set. Presently up to 50 different types can be inspected.
*
The above mentioned flexibility is achieved by an user friendly interface which easily allows the definition for new ferrite types in terms of physical features and inspection parameters.
*
The system accuracy is about 0.25 mm2 , standard, Imm2 worst case, depending on the ferrite-core size.
*
The inspection time, per part, is mainly restricted by the mechannical device which takes 1 second for positioning each ferrite under the image acquisition system. REFERENCES
Artley, J. W. (1982). Automated visual inspection systems can boost quaility control affordably. Ind. Eng. 14, nO 12, 28-32. Batchelor, B.G., Cotter, S.M., Heywood, P.W., and Moot, D.H. (1982). Recent advances in automated visual inspection. Proc. SPIE, Robot vision and Sensory Controls, 392, 307-326. Batchelor, B.G., Bowman, C.C., Chow, K.W., Goodman, S., McCollum, AJ., and Rowland, S. (1986). Developments in image processing for industrial inspection. Proc. SPIE, Automated Inspection and Measurement, 730. Bolles, R. (1981). An overview of image understanding applications to industrial problems. Proc. SPIE, Techniques and Applications of Image Understanding 281, 134-140. Fu, K.S. (1983). Computer VISIOn for automatic inspection. Proceedings, Robotic Intelligence and Productivity Conf., Detroit, Michigan, Nov. 1983, pp.7-15.
354