- Email: [email protected]
Applications of Intelligent Systems in Electronics Manufacturing
Copyright ~ IFAC Management and Control of Production and Logistics, Grenoble, France, 2000
APPUCATlONS OF INTELUGENT SYSTEMS IN ELECTRONICS MANUFACIlJRING
Kauko Leiviskli
University ofOulu. Department ofProcess Engineering P. O. Box 4300. F1N-900 14 Oulun yliopisto. FINLAND Phone: +35885532460. Fax: +35885532466 Email: [email protected]
Abstract: Fast growing production in rapidly changing environment has many challenges for decision-making, testing, inspection and control in electronics manufacturing. By combining and comparing the information collected from the production process it is possible to find reasons for some defects, and use this information in process control and development. Intelligent systems are useful in taking into account non-linearity especially in multivariable systems. Intelligent systems can successfully learn the knowledge from human inspectors by analysing on-line data and then use the knowledge to support decision making. These applications provide a good basis for wider use of intelligent systems together with other techniques in improving process design and control. This paper reviews the results gained in Control Engineering Laboratory, University of Oulu. Copyright @ 2000 IFA C Keywords: electronics, fuzzy expert systems, forecasts, testing, inspection
I. INTRODUCTION
industry. Fast growing production in very rapidly changing environment has many challenges for decision-making, testing, inspection and control. Control systems in process industries, on the other hand, have developed during many decades. The concepts of process control and fault diagnosis can be borrowed from process industry to electronics manufacturing industry. This paper concerns with some applications of intelligent systems in electronics manufacturing developed in Control Engineering Laboratory, University of Oulu. Most of them originate from TOOLMET-USTI and TOOLMET-2MODIPRO projects that were carried out during 19951999 (Leiviskii and Juuso, 2000).
A typical electronics manufacturing process consists of paste printing, component placing, re-flow soldering, solder joint inspection and testing. The process is complicated because many parameters in different processes affect the whole process. The relationships between process parameters and quality of final products are many-to-many relationships. It means that one defect could have several root causes originating from different processes; and one problem in a process might result in many different types of defects. Well-established solutions for robust process control are mostly lacking in electronics manufacturing
615
2. INTELLIGENT SYSTEMS
3. APPLICATIONS IN MATERJALS PURCHASING
There are many ways to define intelligent methods. Usually, expert systems, fuzzy set systems, neural networks and genetic algorithms are concerned in this connection. In rule-based expert systems, the domain knowledge is represented as sets of rules that are checked against a collection of facts or knowledge about the current situation. They rely on expert's knowledge and inference mechanisms that in a way simulate the human reasoning. Updating and adaptation of rule-based systems is difficult and they lack methods for uncertainty processing.
Electronic signal transmission devices are composed of hundreds or even thousands of components. Consumption data of various components are presented as discrete time series. An adaptive and hierarchical Fuzzy Logic Advisory Tool (FLAT) has been developed to forecast component purchasing in the electronics manufacturing processes of Nokia Telecommunications, Fixed Access Systems Operations (Frantti, 1995, Frantti and Juuso, 1996). The FLAT model consists of four basic modules (Figure I): membership generation module (MGM), fuzzification module (FM), inference module (IM) and defuzzification module (DM).
Fuzzy sets provide a unified framework for taking into account the gradual or flexible nature of variables, and the representation of incomplete information. Fuzzy control has been one of the most active and fruitful areas for applications of fuzzy set theory. Neural networks are characterised by their learning ability and a parallel distributed structure. Neural networks can be considered as black box modelling methods. Back propagation is probably the most popular method in the supervised learning. Self-organising maps are well known networks utilising unsupervised learning. Genetic algorithms (GA) can be considered as experimenting tools, which produce a satisfactory solution, which is not necessarily optimal. They are useful when model is not known, search space is very large, and data is noisy. The algorithm (selection, crossover, mutation) is a reasonable way for processing population of alternatives.
Membership functions are generated from on-line data using a feature extraction method combining qualitative and quantitative data either in numerical or in linguistic form (Mutka et aI., 1997). Resulting feasible ranges are shown in Figure 2: the reference value is simply a mean value, weighted average, mode or median value of the filtered time series. Also fuzzy clustering methods could be used on this stage. Membership functions are generated from feasible ranges by dividing the distribution into several parts starting from the middle of the distribution. The middle area is so called normal area and areas just outside it are positive small on the right side and negative small on the left side etc. The model is divided into several hierarchical subsystems, and the rule base of each is represented by linguistic relations and the data base by data distributions (Frantti and Juuso, 1996). The inference function in FLAT is based on hierarchical reasoning. Linguistic relations in each subsystem are expressed in equation form and membership functions in point form in order to save resources and speed calculations. As the membership functions depend on the operating point, the system is adaptive.
Linguistic equations provide various methods for combining expertise with simulation experiments and experimental data from real systems. Linguistic equations were introduced to managerial decision making in Juuso et al. (1993). The first application in electronics manufacturing was developed for materials purchasing (Frantti and Juuso, 1996). Later the approach was extended to fault diagnosis with an application to functional testing 4(omulainen et aI., 1997). Automatic system generation with pipelined analysis methods can be based on normal process data.
Various methods have been studied for forecasting the component consumption on the basis of history data by calculating coefficients describing information content, complexity, non-linear autocorrelation and entropy (Mutka et al., 1997). The time series of component consumption was preprocessed by filtering the raw data using various filters and wavelet analysis following from the filter bank methods and finally calculating the correlation dimension, which is used as an input value for a Fuzzy Self-Organising Map (FSOM). As modelling with non-linear dynamics differentiates noise from chaos, it can be used in filtering noise from the raw data.
Unfortunately the handling methods of uncertain and vague information itself are usually not enough for real-world applications. Uncertain data has to be extracted from data sources avoiding noise or at least avoiding to increase it. In most cases this is the crucial point for applications development's success. Weak extraction method might lead to unsuccessful uncertainty handling methods, such good they might be in theory. Several feature extraction methods are represented and discussed in Mutka et al. (1997).
616
Rule Base
Knowledge Base
MGM
FM
Membership Generation
Fuzzificalion
on • line data
I....
IM
I
Oefuzzificalion
Fuzzy Inference
fuzzy measurement
crisp measurement
OM
fuzzy control
~ crisp results
Figure I. The Fuzzy Logic Advisory Tool for Component Purchasing (Frantti, 1995). The aim of the study was also to use intelligent methods and to integrate various types of knowledge (fuzzy expert knowledge, exact test results, conventional rules and statistic probability), in solving this problem.
normalized grade
I.
cl membership
min
~Iwlu.
Plug-in units are made in the production line by setting surface-mounted components automatically in the pick and place machines and lead-through components manually. In one plug-in unit there might be hundreds of components, which are soldered by reflow or wave soldering.
quantity
Figure 2. The nonnal value of a fuzzy distribution. (Mutka et aI., 1997). The FSOM classifies preprocessed consumption time series into three categories: volume components (class I), forecastable components (class 2) and unforecastable components (class 3). Membership functions are generated through on-line time series data with developed feature extraction method for class 2 components.
The automated test equipment checks the functioning of the units. Failure infonnation is transferred to the Windows-based program. Measurements have been divided into about 20 measurement groups, each of which has further been divided to one to four measurement steps. These steps are divided to measurement channels, but some measurements are also dealing with common functions of all channels (e.g. system power). The measurement report includes hundreds of individual measurements.
The comparison between actual consumption, consumption defined from storage level and FLAT model was given by Mutka et al. (1997). The time period in simulation was 304 days and number of components was 37. The actual consumption varied in chaotic manner without periodicity. Consumption defined from storage levels oscillated strongly. It was not able to anticipate actual consumption and caused continuously too high storage levels. Consumption prediction by the FLAT model followed actual demand more accurately and kept the storage level lower without any shortages.
A fuzzy rule base has been generated by personnel interviews and data collected from databases. A typical rule is if 285.201 IS OK and 285.202 IS XS and 285.203 IS S then COMPONENT IS ICI, The numbers refer to the measurement groups and measurements steps. The first three numbers (285) tell the measurement group and the last three nunbers (e.g..20 I) the measurement step.
4. APPLICATIONS IN FUNCTIONAL TESTING An adaptive and intelligent system has been developed utilising process failure infonnation from the functional testing of plug-in units (Komulainen et aI., 1997). The system was aimed for the easier, faster and more effective and reliable failure localisation and also for faster recovering of damaged set of components.
The rule base is divided into three hierarchical levels: •
•
617
channel level. At this level a failed channel or channels are found and the reasoning is directed to some failure type common to all channels. group level. The system consists of about 20
•
measurement groups that further include from one to four measurement steps and 3-50 rules. The total number of rules is more than 300. The rule above is an example of rules for the group 285. step level. Measurement steps are the actual variables of the system. Each of them has from three to five membership functions
The fuzzy rule base has been tested using FuzzyCon, which is a Windows application developed in Control Engineering Laboratory (Juuso, Myllyneva and Leiviska, 1994, Juuso and Leiviska, 1995). Testing has been done manually using real test data. The reliability of the rules has been verified and membership functions have been tuned during the testing. The FuzzEqu-toolbox (Juuso, 1996) working in Matlab® environment has been used to convert rules into linguistic equations. Generation of linguistic equations from linguistic relations is pipelined for measurement groups. Each group produces I - 3 equations.
5. APPLICAnONS IN X-RAY INSPECTION X-ray inspection is used in detecting soldering defects on printed circuit boards. The decision making in X-ray inspection is usually based on conventional deterministic methods. There are typically 5-10 measurements taken from each solder joint and the acceptance is based on threshold limits. Due to tight acceptance windows this approach tends to give 'false alarms'; i.e. an indication of a defect even when the solder joint actually is acceptable. This results from keeping the number of escape defects low.
reducing the number of false alarms (Xu Wei et aI., 1998). The present software package of the industrial prototype called Fuzzy Expert System for X-ray Inspection (FESXI) contains the human experts' knowledge and has the ability of retrieving human knowledge from on-line data. This fuzzy expert system can successfully learn the knowledge from human experts and then use the knowledge to support decision-making. The software package consists of six subsystems: X-ray operator interface subsystem, inspection data acquisition subsystem, board status monitoring subsystem, decision making subsystem, historical data collecting subsystem and membership function generation subsystem. The fuzzy reasoning module is the critical part of the expert system. The fuzzy reasoning module processes defect candidates and their measurement values and makes decisions to separate good and defective joints. The fuzzy reasoning module consists of three sub modules for fuzzification, inference and defuzzification. The automatic generation of membership functions is done in the same way as in the FLAT system described above (Xu Wei et aI., 1999). The quality of data is improved by removing noise: those measurement values that appear only few times are considered as noise data (Figure 4). The FESXI is coded by object Pascal in Borland Delphi 3.0 environment and C++ in Borland C++ 5.0 environment and runs in the Microsoft NT Windows. The graphic user interfaces allow user to select the package type and the defect types, and it shows the calculation results graphically. Results show that automatic tuning of X-ray inspection process and fewer false alarms can be achieved by this fuzzy expert system. The field tests show that the new algorithm eliminates 40 % - 50 % of false alarms. The first on-line field tests were performed on a mass production line, which has few product types with large lot sizes (XU Wei et aI., 1999). From 23 981 530 solder joints inspected by X-ray, 103 538 defective joints were reported during the three months testing. The fuzzy expert system processed 103 538 defective joints data, and reported that 39 682 joints are acceptable. After human checking, 13 435 real defects were found. The false alarms were reduced from 90 103 to 50 42 I meaning that about 44% of the false alarms were reduced by the fuzzy expert system. The escape defect PPM was 0.42.
In the test case factory, the PCBs are inspected by Xray inspection machine after soldering, and the inspection results are sent to the factory database. In the paperless rework station, rework personnel retrieves data of defect joints from database and sends re-inspection results to the database after checking the joints carefully. Parts Per Million (PPM) for false alarms and escape defects is calculated every month in order to measure the performance of the Xray inspection. If the number of false alarms would be reduced, the rework time would be shortened and the production costs would be significantly reduced. A real-time fuzzy expert system (Figure 3) was proposed for improving X-ray inspection results and
618
,-----------Knowledge B..e
Function Generation Module
~embersbip
•...
Fuzzy Reasoning Module
Verification
Secondary Decision making
Primary Decision making
Figure 3. Fuzzy expert system environment (Xu Wei et aI., 1998).
Measurement Value
...~
I . .l---..-~ .1 '.
r~:~
12.00
•
0,00
Quantity
1.00
2.00
3.00
'.00
s,OO
6.00
3.00
',00
s.oo
6.00
.0 70 60
so .0 30 2. 10 0
- --
----1
~
I-
0,00
1.00.4
NB
NS ZE PS
2,00
PB
J{jj;£ Figure 4. Generating membership functions from on-line data (Xu Wei et aI., 1999). linguistic equations are better and more accurate decision-making due to the model-based approach and systematic knowledge management for materials purchasing. For functional testing, large fuzzy expert systems were systematically constructed from expert knowledge. For most cases, the rule base was transformed into a compact set of linguistic
6. CONCLUSIONS Intelligent systems are useful for taking into account non-linearity especially in multivariable systems. The potential benefits of a fuzzy set system combined with
619
equations. The final application is running as a fuzzy expert system. In the X-ray inspection application, the resulting fuzzy expert system can successfully learn the knowledge from human inspectors by analysing on-line data and then use the knowledge to support decision making. These applications provide a good basis for wider use of intelligent systems together with other techniques in improving process design and control. There are several other possibilities to use intelligent methods in electronics manufacturing. Manufacturing problems and product defects should be detected as automatically as possible in order to take the corrective actions in time. Statistical Process Control (SPC) is a good technique to find out the quality parameters, which can be measured and analysed. Traditionally in the electronics manufacturing industry, each production machine and control system origins from a different supplier, and therefore, the production line consists of separate, individual and independent automation islands without real time information exchange. Even though there is a lot of information available, it has neither been continuously collected nor analysed. Intelligent systems enhance the performance of traditional SPC in production lines with small lot sizes. Bringing new product into the market is an important asset to a company (Jokinen et al., 1999). In addition to considerable costs, detecting and reworking of manufacturing defects causes long delivery times. Forecasting the quality should be done in the earliest possible phase of the design, and the design feedback would also improve the monitoring of the production. Intelligent systems are essential in this.
REFERENCES Frantti T. (1995). An Adaptive, Hierarchical Fuzzy Expert System for Materials Purchasing in an Electronics Manufacturing Environment. Licentiate Thesis. University of Qulu, Department of Process Engineering. Qulu, Finland. Frantti T. and E. K. Juuso (1996). An Adaptive, Hierarchical Fuzzy Logic Advisory Tool (FLAT) for Anticipating the Demand of Transmission Products. 4 th International Conference on Soji Computing, Iizuka '96. Vol I, pp. 410-413, 1996, Fukuoka, Japan. Jokinen T., E. Juuso, S. Lotvonen, and K. Ronkii. Quality Forecasting Tool for Electronics Production. Automaatio 1999, Helsinki, September 14- I6, 1999, pp. 320-325. (In Finnish)
Juuso E. K. (1996) Linguistic Equations in System Development for Computational Intelligence. Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing EUFIT'96, Aachen, September 2 - 5, 1996 (H.-J. Zimmermannn, ed.), Aachen, 1996. Verlag Mainz. 1996, Volume 2, pp. 1127-113 I. Juuso E. K, J.c. Bennavail and M.G. Singh (1993). Hybrid Knowledge-Based System for Managerial Decision Making in Uncertainty Environment. In: Qualitative Reasoning and Decision Technologies, Proceedings of the IMACS International Workshop on Qualitative Reasoning and Decision Technologies -QUARDET'93, Barcelona, June 1618, 1993 (N. Piera Carrete and M. G. Singh, eds.), CIMNE, Barcelona 1993, pp. 234-243. Juuso E. K. and K. Leiviskii (1995). A Development Environment for Industrial Applications of Fuzzy Control. In Proceedings of the Third European Congress on Intelligent Technoiques and Soft Computing -EUFIT'95, Aachen, August 28 - 31, 1995 (H.-J. Zimmermannn, ed.), volume 2, pp. 796803, Aachen, 1995. Verlag und Druck Mainz Juuso E. K, J. Myllyneva and K.Leiviskii (1994). Fuzzy Logic Controller and Adaptive TIming. Proceedings of the 1994 European Simulation Multiconference, June 1-3, 1994, Barcelona, Spain (A. Guash & R. Huber, eds.), pp. 475-479, San Diego, 1994, SCSI. Komulainen, K., M. Heikkinen, T. Frantti, E. Juuso and K Leiviskii (1997). Utilization of Failure Information in Functional Testing. Proceedings of TOOLMET'97 Workshop. Qulu, Finland, 109-115. Leiviskii K. and E. Juuso (2000). Tool environments and development methods. Final Report. University ofQulu, 2000. (in print) Mutka P., E. K. Juuso, K. Leiviskii and T. Frantti (1997). Uncertainty Modelling in Chaotic Environment. Proceedings of European Symposium on Intelligent Techniques, Bari, Italy 20-21.3.1997, 40... 44. Tervahartiala P., and K. Leiviskii. Statistical Process Control. Comparision of Existing Programs. Report B No 9, Control Engineering Laboratory, University of Qulu, August 1999, 38 p. (In Finnish) Xu Wei, T. Frantti, M. Verkasalo, K. Lappalainen, E. Juuso and K. Leiviskii (1998). A Real Time Fuzzy Expert System For X-Ray Solder Joint Inspection. Proceedings of TOOLMET '98 Symposium. Qulu, Finland, 114-120. . Xu Wei, M. Verkasalo, K. Lappalainen, E. Juuso and K. Leiviskii (1999). Building a Fuzzy Expert System For Solder Joint X-Ray Inspection. Proceedings of
EUFIT'99.
620
APPUCATlONS OF INTELUGENT SYSTEMS IN ELECTRONICS MANUFACIlJRING
Kauko Leiviskli
University ofOulu. Department ofProcess Engineering P. O. Box 4300. F1N-900 14 Oulun yliopisto. FINLAND Phone: +35885532460. Fax: +35885532466 Email: [email protected]
Abstract: Fast growing production in rapidly changing environment has many challenges for decision-making, testing, inspection and control in electronics manufacturing. By combining and comparing the information collected from the production process it is possible to find reasons for some defects, and use this information in process control and development. Intelligent systems are useful in taking into account non-linearity especially in multivariable systems. Intelligent systems can successfully learn the knowledge from human inspectors by analysing on-line data and then use the knowledge to support decision making. These applications provide a good basis for wider use of intelligent systems together with other techniques in improving process design and control. This paper reviews the results gained in Control Engineering Laboratory, University of Oulu. Copyright @ 2000 IFA C Keywords: electronics, fuzzy expert systems, forecasts, testing, inspection
I. INTRODUCTION
industry. Fast growing production in very rapidly changing environment has many challenges for decision-making, testing, inspection and control. Control systems in process industries, on the other hand, have developed during many decades. The concepts of process control and fault diagnosis can be borrowed from process industry to electronics manufacturing industry. This paper concerns with some applications of intelligent systems in electronics manufacturing developed in Control Engineering Laboratory, University of Oulu. Most of them originate from TOOLMET-USTI and TOOLMET-2MODIPRO projects that were carried out during 19951999 (Leiviskii and Juuso, 2000).
A typical electronics manufacturing process consists of paste printing, component placing, re-flow soldering, solder joint inspection and testing. The process is complicated because many parameters in different processes affect the whole process. The relationships between process parameters and quality of final products are many-to-many relationships. It means that one defect could have several root causes originating from different processes; and one problem in a process might result in many different types of defects. Well-established solutions for robust process control are mostly lacking in electronics manufacturing
615
2. INTELLIGENT SYSTEMS
3. APPLICATIONS IN MATERJALS PURCHASING
There are many ways to define intelligent methods. Usually, expert systems, fuzzy set systems, neural networks and genetic algorithms are concerned in this connection. In rule-based expert systems, the domain knowledge is represented as sets of rules that are checked against a collection of facts or knowledge about the current situation. They rely on expert's knowledge and inference mechanisms that in a way simulate the human reasoning. Updating and adaptation of rule-based systems is difficult and they lack methods for uncertainty processing.
Electronic signal transmission devices are composed of hundreds or even thousands of components. Consumption data of various components are presented as discrete time series. An adaptive and hierarchical Fuzzy Logic Advisory Tool (FLAT) has been developed to forecast component purchasing in the electronics manufacturing processes of Nokia Telecommunications, Fixed Access Systems Operations (Frantti, 1995, Frantti and Juuso, 1996). The FLAT model consists of four basic modules (Figure I): membership generation module (MGM), fuzzification module (FM), inference module (IM) and defuzzification module (DM).
Fuzzy sets provide a unified framework for taking into account the gradual or flexible nature of variables, and the representation of incomplete information. Fuzzy control has been one of the most active and fruitful areas for applications of fuzzy set theory. Neural networks are characterised by their learning ability and a parallel distributed structure. Neural networks can be considered as black box modelling methods. Back propagation is probably the most popular method in the supervised learning. Self-organising maps are well known networks utilising unsupervised learning. Genetic algorithms (GA) can be considered as experimenting tools, which produce a satisfactory solution, which is not necessarily optimal. They are useful when model is not known, search space is very large, and data is noisy. The algorithm (selection, crossover, mutation) is a reasonable way for processing population of alternatives.
Membership functions are generated from on-line data using a feature extraction method combining qualitative and quantitative data either in numerical or in linguistic form (Mutka et aI., 1997). Resulting feasible ranges are shown in Figure 2: the reference value is simply a mean value, weighted average, mode or median value of the filtered time series. Also fuzzy clustering methods could be used on this stage. Membership functions are generated from feasible ranges by dividing the distribution into several parts starting from the middle of the distribution. The middle area is so called normal area and areas just outside it are positive small on the right side and negative small on the left side etc. The model is divided into several hierarchical subsystems, and the rule base of each is represented by linguistic relations and the data base by data distributions (Frantti and Juuso, 1996). The inference function in FLAT is based on hierarchical reasoning. Linguistic relations in each subsystem are expressed in equation form and membership functions in point form in order to save resources and speed calculations. As the membership functions depend on the operating point, the system is adaptive.
Linguistic equations provide various methods for combining expertise with simulation experiments and experimental data from real systems. Linguistic equations were introduced to managerial decision making in Juuso et al. (1993). The first application in electronics manufacturing was developed for materials purchasing (Frantti and Juuso, 1996). Later the approach was extended to fault diagnosis with an application to functional testing 4(omulainen et aI., 1997). Automatic system generation with pipelined analysis methods can be based on normal process data.
Various methods have been studied for forecasting the component consumption on the basis of history data by calculating coefficients describing information content, complexity, non-linear autocorrelation and entropy (Mutka et al., 1997). The time series of component consumption was preprocessed by filtering the raw data using various filters and wavelet analysis following from the filter bank methods and finally calculating the correlation dimension, which is used as an input value for a Fuzzy Self-Organising Map (FSOM). As modelling with non-linear dynamics differentiates noise from chaos, it can be used in filtering noise from the raw data.
Unfortunately the handling methods of uncertain and vague information itself are usually not enough for real-world applications. Uncertain data has to be extracted from data sources avoiding noise or at least avoiding to increase it. In most cases this is the crucial point for applications development's success. Weak extraction method might lead to unsuccessful uncertainty handling methods, such good they might be in theory. Several feature extraction methods are represented and discussed in Mutka et al. (1997).
616
Rule Base
Knowledge Base
MGM
FM
Membership Generation
Fuzzificalion
on • line data
I....
IM
I
Oefuzzificalion
Fuzzy Inference
fuzzy measurement
crisp measurement
OM
fuzzy control
~ crisp results
Figure I. The Fuzzy Logic Advisory Tool for Component Purchasing (Frantti, 1995). The aim of the study was also to use intelligent methods and to integrate various types of knowledge (fuzzy expert knowledge, exact test results, conventional rules and statistic probability), in solving this problem.
normalized grade
I.
cl membership
min
~Iwlu.
Plug-in units are made in the production line by setting surface-mounted components automatically in the pick and place machines and lead-through components manually. In one plug-in unit there might be hundreds of components, which are soldered by reflow or wave soldering.
quantity
Figure 2. The nonnal value of a fuzzy distribution. (Mutka et aI., 1997). The FSOM classifies preprocessed consumption time series into three categories: volume components (class I), forecastable components (class 2) and unforecastable components (class 3). Membership functions are generated through on-line time series data with developed feature extraction method for class 2 components.
The automated test equipment checks the functioning of the units. Failure infonnation is transferred to the Windows-based program. Measurements have been divided into about 20 measurement groups, each of which has further been divided to one to four measurement steps. These steps are divided to measurement channels, but some measurements are also dealing with common functions of all channels (e.g. system power). The measurement report includes hundreds of individual measurements.
The comparison between actual consumption, consumption defined from storage level and FLAT model was given by Mutka et al. (1997). The time period in simulation was 304 days and number of components was 37. The actual consumption varied in chaotic manner without periodicity. Consumption defined from storage levels oscillated strongly. It was not able to anticipate actual consumption and caused continuously too high storage levels. Consumption prediction by the FLAT model followed actual demand more accurately and kept the storage level lower without any shortages.
A fuzzy rule base has been generated by personnel interviews and data collected from databases. A typical rule is if 285.201 IS OK and 285.202 IS XS and 285.203 IS S then COMPONENT IS ICI, The numbers refer to the measurement groups and measurements steps. The first three numbers (285) tell the measurement group and the last three nunbers (e.g..20 I) the measurement step.
4. APPLICATIONS IN FUNCTIONAL TESTING An adaptive and intelligent system has been developed utilising process failure infonnation from the functional testing of plug-in units (Komulainen et aI., 1997). The system was aimed for the easier, faster and more effective and reliable failure localisation and also for faster recovering of damaged set of components.
The rule base is divided into three hierarchical levels: •
•
617
channel level. At this level a failed channel or channels are found and the reasoning is directed to some failure type common to all channels. group level. The system consists of about 20
•
measurement groups that further include from one to four measurement steps and 3-50 rules. The total number of rules is more than 300. The rule above is an example of rules for the group 285. step level. Measurement steps are the actual variables of the system. Each of them has from three to five membership functions
The fuzzy rule base has been tested using FuzzyCon, which is a Windows application developed in Control Engineering Laboratory (Juuso, Myllyneva and Leiviska, 1994, Juuso and Leiviska, 1995). Testing has been done manually using real test data. The reliability of the rules has been verified and membership functions have been tuned during the testing. The FuzzEqu-toolbox (Juuso, 1996) working in Matlab® environment has been used to convert rules into linguistic equations. Generation of linguistic equations from linguistic relations is pipelined for measurement groups. Each group produces I - 3 equations.
5. APPLICAnONS IN X-RAY INSPECTION X-ray inspection is used in detecting soldering defects on printed circuit boards. The decision making in X-ray inspection is usually based on conventional deterministic methods. There are typically 5-10 measurements taken from each solder joint and the acceptance is based on threshold limits. Due to tight acceptance windows this approach tends to give 'false alarms'; i.e. an indication of a defect even when the solder joint actually is acceptable. This results from keeping the number of escape defects low.
reducing the number of false alarms (Xu Wei et aI., 1998). The present software package of the industrial prototype called Fuzzy Expert System for X-ray Inspection (FESXI) contains the human experts' knowledge and has the ability of retrieving human knowledge from on-line data. This fuzzy expert system can successfully learn the knowledge from human experts and then use the knowledge to support decision-making. The software package consists of six subsystems: X-ray operator interface subsystem, inspection data acquisition subsystem, board status monitoring subsystem, decision making subsystem, historical data collecting subsystem and membership function generation subsystem. The fuzzy reasoning module is the critical part of the expert system. The fuzzy reasoning module processes defect candidates and their measurement values and makes decisions to separate good and defective joints. The fuzzy reasoning module consists of three sub modules for fuzzification, inference and defuzzification. The automatic generation of membership functions is done in the same way as in the FLAT system described above (Xu Wei et aI., 1999). The quality of data is improved by removing noise: those measurement values that appear only few times are considered as noise data (Figure 4). The FESXI is coded by object Pascal in Borland Delphi 3.0 environment and C++ in Borland C++ 5.0 environment and runs in the Microsoft NT Windows. The graphic user interfaces allow user to select the package type and the defect types, and it shows the calculation results graphically. Results show that automatic tuning of X-ray inspection process and fewer false alarms can be achieved by this fuzzy expert system. The field tests show that the new algorithm eliminates 40 % - 50 % of false alarms. The first on-line field tests were performed on a mass production line, which has few product types with large lot sizes (XU Wei et aI., 1999). From 23 981 530 solder joints inspected by X-ray, 103 538 defective joints were reported during the three months testing. The fuzzy expert system processed 103 538 defective joints data, and reported that 39 682 joints are acceptable. After human checking, 13 435 real defects were found. The false alarms were reduced from 90 103 to 50 42 I meaning that about 44% of the false alarms were reduced by the fuzzy expert system. The escape defect PPM was 0.42.
In the test case factory, the PCBs are inspected by Xray inspection machine after soldering, and the inspection results are sent to the factory database. In the paperless rework station, rework personnel retrieves data of defect joints from database and sends re-inspection results to the database after checking the joints carefully. Parts Per Million (PPM) for false alarms and escape defects is calculated every month in order to measure the performance of the Xray inspection. If the number of false alarms would be reduced, the rework time would be shortened and the production costs would be significantly reduced. A real-time fuzzy expert system (Figure 3) was proposed for improving X-ray inspection results and
618
,-----------Knowledge B..e
Function Generation Module
~embersbip
•...
Fuzzy Reasoning Module
Verification
Secondary Decision making
Primary Decision making
Figure 3. Fuzzy expert system environment (Xu Wei et aI., 1998).
Measurement Value
...~
I . .l---..-~ .1 '.
r~:~
12.00
•
0,00
Quantity
1.00
2.00
3.00
'.00
s,OO
6.00
3.00
',00
s.oo
6.00
.0 70 60
so .0 30 2. 10 0
- --
----1
~
I-
0,00
1.00.4
NB
NS ZE PS
2,00
PB
J{jj;£ Figure 4. Generating membership functions from on-line data (Xu Wei et aI., 1999). linguistic equations are better and more accurate decision-making due to the model-based approach and systematic knowledge management for materials purchasing. For functional testing, large fuzzy expert systems were systematically constructed from expert knowledge. For most cases, the rule base was transformed into a compact set of linguistic
6. CONCLUSIONS Intelligent systems are useful for taking into account non-linearity especially in multivariable systems. The potential benefits of a fuzzy set system combined with
619
equations. The final application is running as a fuzzy expert system. In the X-ray inspection application, the resulting fuzzy expert system can successfully learn the knowledge from human inspectors by analysing on-line data and then use the knowledge to support decision making. These applications provide a good basis for wider use of intelligent systems together with other techniques in improving process design and control. There are several other possibilities to use intelligent methods in electronics manufacturing. Manufacturing problems and product defects should be detected as automatically as possible in order to take the corrective actions in time. Statistical Process Control (SPC) is a good technique to find out the quality parameters, which can be measured and analysed. Traditionally in the electronics manufacturing industry, each production machine and control system origins from a different supplier, and therefore, the production line consists of separate, individual and independent automation islands without real time information exchange. Even though there is a lot of information available, it has neither been continuously collected nor analysed. Intelligent systems enhance the performance of traditional SPC in production lines with small lot sizes. Bringing new product into the market is an important asset to a company (Jokinen et al., 1999). In addition to considerable costs, detecting and reworking of manufacturing defects causes long delivery times. Forecasting the quality should be done in the earliest possible phase of the design, and the design feedback would also improve the monitoring of the production. Intelligent systems are essential in this.
REFERENCES Frantti T. (1995). An Adaptive, Hierarchical Fuzzy Expert System for Materials Purchasing in an Electronics Manufacturing Environment. Licentiate Thesis. University of Qulu, Department of Process Engineering. Qulu, Finland. Frantti T. and E. K. Juuso (1996). An Adaptive, Hierarchical Fuzzy Logic Advisory Tool (FLAT) for Anticipating the Demand of Transmission Products. 4 th International Conference on Soji Computing, Iizuka '96. Vol I, pp. 410-413, 1996, Fukuoka, Japan. Jokinen T., E. Juuso, S. Lotvonen, and K. Ronkii. Quality Forecasting Tool for Electronics Production. Automaatio 1999, Helsinki, September 14- I6, 1999, pp. 320-325. (In Finnish)
Juuso E. K. (1996) Linguistic Equations in System Development for Computational Intelligence. Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing EUFIT'96, Aachen, September 2 - 5, 1996 (H.-J. Zimmermannn, ed.), Aachen, 1996. Verlag Mainz. 1996, Volume 2, pp. 1127-113 I. Juuso E. K, J.c. Bennavail and M.G. Singh (1993). Hybrid Knowledge-Based System for Managerial Decision Making in Uncertainty Environment. In: Qualitative Reasoning and Decision Technologies, Proceedings of the IMACS International Workshop on Qualitative Reasoning and Decision Technologies -QUARDET'93, Barcelona, June 1618, 1993 (N. Piera Carrete and M. G. Singh, eds.), CIMNE, Barcelona 1993, pp. 234-243. Juuso E. K. and K. Leiviskii (1995). A Development Environment for Industrial Applications of Fuzzy Control. In Proceedings of the Third European Congress on Intelligent Technoiques and Soft Computing -EUFIT'95, Aachen, August 28 - 31, 1995 (H.-J. Zimmermannn, ed.), volume 2, pp. 796803, Aachen, 1995. Verlag und Druck Mainz Juuso E. K, J. Myllyneva and K.Leiviskii (1994). Fuzzy Logic Controller and Adaptive TIming. Proceedings of the 1994 European Simulation Multiconference, June 1-3, 1994, Barcelona, Spain (A. Guash & R. Huber, eds.), pp. 475-479, San Diego, 1994, SCSI. Komulainen, K., M. Heikkinen, T. Frantti, E. Juuso and K Leiviskii (1997). Utilization of Failure Information in Functional Testing. Proceedings of TOOLMET'97 Workshop. Qulu, Finland, 109-115. Leiviskii K. and E. Juuso (2000). Tool environments and development methods. Final Report. University ofQulu, 2000. (in print) Mutka P., E. K. Juuso, K. Leiviskii and T. Frantti (1997). Uncertainty Modelling in Chaotic Environment. Proceedings of European Symposium on Intelligent Techniques, Bari, Italy 20-21.3.1997, 40... 44. Tervahartiala P., and K. Leiviskii. Statistical Process Control. Comparision of Existing Programs. Report B No 9, Control Engineering Laboratory, University of Qulu, August 1999, 38 p. (In Finnish) Xu Wei, T. Frantti, M. Verkasalo, K. Lappalainen, E. Juuso and K. Leiviskii (1998). A Real Time Fuzzy Expert System For X-Ray Solder Joint Inspection. Proceedings of TOOLMET '98 Symposium. Qulu, Finland, 114-120. . Xu Wei, M. Verkasalo, K. Lappalainen, E. Juuso and K. Leiviskii (1999). Building a Fuzzy Expert System For Solder Joint X-Ray Inspection. Proceedings of
EUFIT'99.
620