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Review on Application of Data Mining in Product Design and Manufacturing

Keqin Wang Northwestern Polytechnical

University [email protected]

Shurong Tong Northwestern Polytechnical

University [email protected]

Benoît Eynard University of Technology of

Troyes [email protected]

Lionel Roucoules

University of Technology of Troyes

[email protected]

Nada Matta University of Technology of

Troyes [email protected]

Abstract

Implementation of data mining technology in some areas such as banking, finance, marketing, insurance, health care, etc. has given very good results. However, applications of data mining in product design and manufacturing are not as broad as expected and many challenges are still ahead. This work analyzes the reasons for the limitation of data mining application in manufacturing industry and focuses on reviewing the state-of-the-art of the applications of data mining in product design and manufacturing. Some points of views are also discussed and finally this paper is concluded. 1. Introduction

In the digital age, large amount of data has been generated and collected during the product manufacturing process, the production data are available to extract relevant information/knowledge for quality improvement and other purposes. Accessing and distilling the valuable knowledge hidden in this vast amount of information/data is crucial [2]. Unfortunately, with the improved data acquisition ability, new obstacles have emerged making it difficult to discover valuable knowledge resident in production data. For example: huge volume of industrial data cannot be handled efficiently; industrial data are typically noisy, highly correlated, and very often, they are randomly missing due to various reasons such as faulty sensors and computer communication errors. Using traditional data analysis and modeling tools to analyze such industrial data can be very time consuming, and even generate misleading results. Because of these obstacles, many of the industrial data are in fact not used, or at least, not used efficiently, which leads to an undesirable situation - data rich, but information poor, also known as “drowning in data yet starving for knowledge”. This means that a significant amount of valuable process knowledge is lost, diminishing the return on data infrastructure investments.

Implementation of data mining (DM) technology in some areas such as banking, finance, marketing, insurance, telecommunication, health care, etc. has given very good results [3,4] and people are already benefiting from the knowledge discovered in these fields. Although some successful DM applications in design and manufacturing have been reported, however, many challenges are still ahead. It is very difficult to design a data-supported manufacturing enterprise that can benefit from its historical or legacy systems as well as from its current databases. To date, little has been done to exploit the potentially viable DM technology in manufacturing industries, and indeed literature on the application of DM in manufacturing has only recently appeared [5-10].

To understand the state-of-the-art of DM applications in product design and manufacturing, this work makes a literature review. Due to the paper length limitation and some related review works have already appeared [28,53,54], and their works particularly focused on production processes, fault detection, maintenance, decision support, and product quality improvement, information integration, and standardization aspects. To avoid the overlap with their works and consider more new papers, here we particularly focus on product design, quality related area, etc., but not all manufacturing areas.

This paper is organized as follows. Section 2 analyzes reasons for the limitation of DM in manufacturing. Section 3 reviews the DM applications in manufacturing problem-solving, quality data, and other areas. Section 4 reviews DM application in product design. Finally some viewpoints are discussed and this paper is concluded. 2. Reasons for not widely applications of DM in manufacturing

We believe that the knowledge /information extracted from existing data warehouses, or from current product and production processes by DM methodologies, can be used to improve the quality of product design and the performance of manufacturing systems.

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)0-7695-2874-0/07 $25.00 © 2007

Since large stores of data exist in manufacturing enterprises, it is not clear why DM is not commonly used in the engineering industry. It might be because of the long time scales and expense involved in introducing these new techniques, or it could be because of the uncertainty of payback values. An alternative possible reason might be the complexity and diversity of different manufacturing processes, as these can make it extremely difficult to devise a generic DM process that can be used for all kinds of manufacturing processes and can tackle all types of manufacturing problems [6].

Since the data accumulated in manufacturing plants has unique characteristics, conventional DM methods are ineffective. Rokach and Maimon [5] considered that the “imbalanced distribution, curse of dimensionality, and mixed-type of data” are problematic properties. 3. DM in manufacturing

DM applications in manufacturing spread across many fields of research, for example, product design, quality control, job-shop scheduling, lead time estimation, etc. Each field of application need different kind of raw data and each can extract different kinds of knowledge for further decision in manufacturing. This section focuses on the DM applications in problem-solving in manufacturing process control especially on quality related problems. 3.1. DM for problem-solving in manufacturing

Most researches on the DM for problem-solving in manufacturing focus on the yield enhancement, failure analysis, process capacity, quality improvement, etc.

“DM for Design and Manufacturing: Methods and Applications” [8] is the first book that brings together researches and applications of DM within design and manufacturing. The aim of the book is 1) to clarify the integration of DM in engineering design and manufacturing, 2) to present a wide range of domains to which DM can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, DM, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments.

The experiments in [11] have demonstrated that manufacturing data can be mined to produce effective decision theoretic control methods. Furthermore, these methods can produce substantial improvements in the die-level functional test stage of VLSI IC manufacturing. Kittler [12] explore how DM using decision trees and the use of a comprehensive engineering database can be used to help solve several yield and failure analysis problems. Kittler and Wang [13] discuss practical issues associated with algorithm selection and use on problems common to yield enhancement in IC manufacturing. Shahbaz et al. [6] examines the application of association rules to

manufacturing databases to extract useful information about a manufacturing system’s capabilities and its constraints. The final set of extracted rules contains very interesting information relating to the geometry of the product and also indicates where limitations exist for improvement of the manufacturing processes involved in the production of complex geometric shapes. Zhang and Dudzic [14] describe a state-of-the-art online monitoring system using multivariate statistical technologies for continuous steel casting process.

The most popular DM applications in manufacturing focus on the field of semiconductor manufacturing. Braha and Shmilovici [24] present an application of DM methodologies to the refinement of a new dry cleaning technology. Baek et al. [25] presents a comprehensive and successful application of DM methodologies to improve wafer yield in a semiconductor wafer fabrication system. A combination of self-organizing neural networks and rule induction is used to identify the critical poor yield factors from normally collected wafer manufacturing data by Gardner and Bieker [26]. Wafer yield problems were solved 10x faster than standard approaches; yield increases ranged from 3% to 15%; endangered customer product deliveries were saved. This approach is flexible, easy to use, and can be appropriate for a number of complex manufacturing processes.

Machine learning (ML) techniques can be useful tools for discovering valuable patterns in manufacturing data. Several research [16,22,23].shows that ML algorithms can be used for novel problem solving in engineering design and manufacturing. Some results showed that ML can be powerful tools for continuous quality improvement in complex process. Pham and Afify [20,21] discuss some applications of ML techniques in manufacturing with special emphasis on inductive learning.

Other works also present several studies that examine the implementation of DM tools in manufacturing problem solving, such as [17,18,27,28,29,42,43]. 3.2. DM in data related with quality

Presently process quality control still remains the main issue for manufacturing industries to tackle. An increasing number of production data are available to extract relevant knowledge for quality improvement. To manage these large data sets and to address the new issues of off-line process analysis, DM seems to be the adequate statistical framework [1]. The patterns extracted in manufacturing processes can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, increasing safety, etc. Some DM applications focusing on quality related data have been emerging. Rokach and Maimon [5] focus on mining quality-related data in manufacturing. Their goal was to find the relation between the quality measure and the input attributes (the manufacturing process data).


Maki and Teranishi [7] developed an automated DM system designed for quality control in manufacturing. It has three features; periodical-analysis, storing the result, and extracting temporal-variances of the result. They found that the system is useful for the rapid recovery from problems of the production process. Lou and Huang [32] introduce a proactive quality control (QC) approach and the approach is developed for solving a class of engineering problems for which conventional reactive QC approaches are feeble due to system complexity and uncertainties. This system is capable of evaluating process performance, and providing various valuable decision-supports for defect prevention in different stages of a topcoat application process. Da Cunha et al. [33] focused on DM for improvement of product quality. Production data can contain errors, e.g. due to data entry, faulty material or a non-adapted equipment. One of their aims is to determine whether DM techniques can perform with a non-negligible ‘error rate’. The main goal is to improve the quality of the assembly process with DM. The challenge is to discern any associations between faults and assembly sequence in the presence of parasite noise.

Phase I analysis aims at identifying the data from an in-control process as accurately as possible so that quality engineers can have a good reference to establish the control charts for a future process. Ding et al. [31] focused on phase I analysis for monitoring nonlinear profiles in manufacturing processes. They presents a strategy that consists of two major components: a data-reduction component that projects the original data into a lower dimension subspace while preserving the data-clustering structure and a data-separation technique that can detect single and multiple shifts as well as outliers in the data.

A project which uses DM techniques for quality problem-solving is run in a large chemical company in Europe to analyze a production process in a plant for polymeric plastics. Another example is the CASSIOPEE troubleshooting system, developed by a joint venture of General Electric and SNECMA using the KATE discovery tool. The system is applied by three major European airlines to diagnose and predict problems for BOEING 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European first prize for innovative applications [34]. 3.3. DM in other manufacturing fields

This subsection will discuss some DM applications in other manufacturing fields such as job shop scheduling, lead time estimation, etc. Due to the limited length of the paper, here we only focus on the DM applications in job shop scheduling (JSS) problem. JSS is an important and complex activity in manufacturing and has been an important DM application field.

Genetic algorithm (GA) is one of the most widespread DM methodologies in resolving JSS problem. Koonce and Tsai [19,47] apply DM to explore the patterns in data generated in scheduling operation. An attribute-oriented induction methodology was used to develop a set of rules. These rules can duplicate the GA’s performance on an identical problem and provide solutions that are generally superior to a simple dispatching rule for similar problems. Rabbani et al. [48] proposed one algorithm combines GA and an attribute-oriented induction algorithm. This algorithm helps GA to find the optimal or near-optimal solution much quicker than previous methods. Harrath et al [50,51] propose a new method based on GA and DM. The developed GA generates a learning population of good solutions. The mining step produces decision rules which are transformed into a meta-heuristic allowing the affectation of operations on machines.

Besides GA applications, Li and Olafsson [45,46] discuss how DM can be used to capture both explicit and implicit knowledge that is used to create production schedules. Their DM process starts by transforming historic schedules into appropriate data files, which can be mined effectively. They then learn which past scheduling decisions correspond to best practices and use the corresponding data to learn new dispatching rules. These dispatching rules can then be used to automate these best practices. Yoshida and Touzaki [52] propose a way to evaluate the effectiveness of such dispatching rules in the environment of complex manufacturing systems in the sense that there exist two performance measures.

Besides DM applications in JSS problem, there are also some other DM applications. Liu and Sin [49] outlines a comparative investigation of machine maintenance planning through the approach of DM, case-based reasoning, neural networks and their integration. C.-F. Chien et al. [30] aims to develop a methodology for predicting cycle time based on domain knowledge and DM algorithms given production status including WIP, throughput. Ozturk et al. [44] explore use of DM for lead time estimation in make-to-order manufacturing. DM with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that the DM approach outperforms these methods. 4. DM applications in engineering design

This section will focus on DM applications in manufacturing related with engineering design. In fact, product design and manufacturing should be integrated as iterative and interactive process. Some applications of DM in manufacturing have tried to improve the product quality. However, the patterns extracted from the raw data are only used by decision makers in manufacturing. In fact, this knowledge may include useful ones for decision


makers in product design process. Decision makers may potentially use the information buried in the raw data to assist their decisions. It has become an important topic to effectively transfer complex manufacturing data into valuable information and knowledge for improvements of product design. The extracted information and knowledge can assist the designers as their reference and basis for similar product design.

The activities around material, parts, and products across the market analysis, design engineering, manufacturing, and service cycle create a data trail [36]. This data is of growing importance in modern systems. DM algorithms extract knowledge from this large volume of data leading to significant improvements in the next generation of products and services.

In [35], a prediction problem encountered in engineering design is considered. The problem is solved with a DM approach. Kusiak et al. [37] presents an approach to analyze events leading to pluggage of a boiler. Two independent DM algorithms have been applied to detect both static and dynamic relationships among the process parameters. A methodology using DM algorithms in the design of product families was introduced by Agard and Kusiak [38]. Gertosio and Dussauchoy [39] discussed knowledge discovery from industrial databases. Zhai et al. [40] summarizes a study leading to the establishment of a prototype feature extraction system to simplify the process for product quality evaluation. 5. Discussions and conclusions

With the increasing generation of large volumes of industrial data, new technologies need to be put into use for more efficient processing of these data. DM is the prospective one. However, the current situation of the application of DM in manufacturing industry is not optimistic. This work analyzes some reasons for the situation and makes a literature review on the applications of DM in manufacturing industry. Some viewpoints and the further research directions are proposed as follows:

(1) Broad application of DM in manufacturing is the coming trend. At present large volumes of data generated during a production process are often only poorly exploited. Also, the relations between the control, process, and quality variables are not completely understood by the engineers. In addition, time and space constraints, which play an especially important role in manufacturing, are not well handled by most DM tools.

With the reorganization of the benefit from DM applications in some manufacturing, it will be the trend that there will be more and more applications of DM throughout the different sectors in manufacturing.

(2)The discovered knowledge should be integrated with decision making. The growing interest in DM has led to the development of many algorithms that extract

knowledge and features from large data sets. However, as Kusiak [28] pointed out, these impressive developments in DM have not been matched by the progress in decision making based on the extracted knowledge. The latter may be explained by the fact that most DM results have been developed by the non-engineering community and focus on the knowledge extraction process rather than decision making.

Using DM techniques, we can find very useful patterns to support decisions. The rules are much easier for humans to understand than the rough data since the rules are extracted from large, otherwise incomprehensible data sets. Decisions based upon the extracted rules will be more reliable.

(3) Application of DM in manufacturing should be integrated with product design. Some researches on DM in manufacturing have tried to improve the quality of the product. However, the patterns extracted from the raw data are only used by decision makers in manufacturing process. In fact, this knowledge may include useful ones for decision makers in product design. Decision makers may potentially use the information buried in the raw data to assist their decisions. It is an important topic to effectively transfer complex manufacturing data into valuable information and knowledge for product design. The extracted information/knowledge can assist designers as their reference and basis for similar product design. As shown in [6] [41], we should investigate manufacturing data for use within design through DM techniques.

(4) The successful development of DM-based knowledge base is the key issue to achieve the ultimate objective of decision support to product design and manufacturing problem-solving. More works should be further done to find appropriate knowledge representation methods to represent the different kinds of knowledge which is extracted by different DM methods from different kinds of raw data. Then this knowledge should be input into the knowledge base. With the accumulation of extracted knowledge, the ability of the knowledge base will become stronger for product design improvement and manufacturing problem-solving. 6. Acknowledgments

This paper has been supported by National Natural Science Foundation of China (NSFC, Grant 70462066) and the Youth for NPU teachers Scientific and Technological Innovation Foundation. 7. References [1] G.C. Porzio1 and G. Ragozini, “Visually Mining Off-line

Data for Quality Improvement”, Quality and Reliability Engineering International, Wiley InterScience, New Jersy, USA, 19(4), 2003, pp. 273-283.


[2] C.J. Romanowski, R. Nagi, and M. Sudit, “Data mining in an engineering design environment: OR applications from graph matching”, Computers & Operations Research, Elsevier, Oxford, UK, 33(11), 2006, pp. 3150-3160.

[3] K. Hashimot, K. Matsumoto, and M. Terabe, “Applying data mining techniques to telecommunication services”, Jap. Soc. Artif. Intell., Tokyo, Japan, 17(3), 2002, pp. 320-325.

[4] F.A. Omer, K. Ertan, and M. Piero, “Data Mining for Database Marketing at Garanti Bank”, Management Information Systems, WIT Press, Southampton, UK, Vol. 2, 2000, pp. 93-107.

[5] L. Rokach and O. Maimon, “Data mining for improving the quality of manufacturing: a feature set decomposition approach”, Journal of Intelligent Manufacturing, Kluwer Academic Publishers, Dordrecht, Netherlands, 17(3), 2006, pp. 285-299.

[6] M. Shahbaz et al., “Product design and manufacturing process improvement using association rules”, Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., Professional Engineering Publishing Ltd., London, UK, 220(2), 2006, pp. 243-254.

[7] H. Maki and Y. Teranishi, “Development of automated data mining system for quality control in manufacturing”, Lect. Notes Comput. Sci., Springer-Verlag, Berlin Heidelberg, Germany, Vol. 2114, 2001, pp. 93-100.

[8] Braha D. (Ed.), Data mining for design and manufacturing: methods and applications, Kluwer Academic Publishers, Norwell, USA, 2001.

[9] C. Gertosio and A.Dussauchoy, “Knowledge discovery from industrial databases”, Journal of Intelligent Manufacturing, Kluwer Academic Publishers, Dordrecht, Netherlands, 15(1), Feb. 2004, pp. 29-37.

[10] H. Maki et al., “Applying data mining to data analysis in manufacturing”, Proceedings of 1999 International Conference on Advances in production management systems, Kluwer Academic Publishers, Norwell, USA., 2000, pp. 324-331.

[11] T. Fountain, T. Dietterich, and B. Sudyka, “Mining IC test data to optimize VLSI testing”, Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, USA., 2000, pp. 18-25.

[12] R. Kittler, “Advanced Statistical Tools for Improving Yield and Reliability”, Proceedings of the 25th International Symposium for Testing and Failure Analysis, ASM International, USA., 1999, pp. 233-238.

[13] R. Kittler and W. Wang, “Data Mining for Yield Improvements”, Proc. Intl Conf. on Modeling and Analysis of Semiconductor Manufacturing, Tempe, USA. May, 2000.

[14] Y. Zhang and M.S. Dudzic, “Online monitoring of steel casting processes using multivariate statistical technologies: From continuous to transitional operations”, Journal of Process Control, Elsevier, Oxford, UK, 16(8), 2006, pp.819-829.

[15] B.G. Kingsman et al., “Responding to customer inquiries in make-to-order companies: Problems and solutions”, International Journal of Production Economics, Elsevier, Oxford, UK, 46-47, 1996, pp. 219-231.

[16] A. Kusiak, “Non-Traditional Applications of Data Mining”, In D. Braha (Ed.), Data Mining for Design and

Manufacturing, Kluwer Academic Publishers, Norwell, USA, 2001, pp. 401-416.

[17] C. Apte, S.M. Weiss, and G. Grout, “Predicting defects in disk drive manufacturing: A case study in high dimensional classification”, Proceedings of the 9th Conference on Artificial Intelligence for Applications, IEEE, Piscataway, USA., 1993, pp.212-218.

[18] I.S. Markham, R.G. Mathieu, and B.A. Wray, “A rule induction approach for determining the number of kanbans in a just-in-time production system”, Computers and Industrial Engineering, Elsevier Sci. Ltd., Exeter, Engl., 34(4), 1998, pp. 717-727.

[19] D.A. Koonce and S.C. Tsai, “Using data mining to find patterns in genetic algorithm solutions to a job shop schedule”, Computers and Industrial Engineering, Elsevier Sci. Ltd., Exeter, Engl., 38(3), 2000, pp. 361-374.

[20] D.T. Pham and A.A. Afify, “Applications of machine learning in manufacturing”, Proc. of the 1st Virtual Intl Conference on Intelligent Production Machines and Systems, Elsevier, Oxford, UK, 2005, pp. 225-230.

[21] D.T. Pham and A.A. Afify, “Machine-learning techniques and their applications in manufacturing”, Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., Professional Engineering Publishing Ltd., London, UK, 219(5), 2005, pp.395-412.

[22] L. Monostori et al., “Machine learning approaches to manufacturing”, CIRP Annals, Int Inst for Production Engineering Research, Berne, Switzerland, 45(2), 1996, pp. 675-712.

[23] L. Monostori, “AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing”, Engng Appl. Artific. Intell., Elsevier, Oxford, UK, 16(4), 2003, pp. 277-291.

[24] D. Braha and A. Shmilovici, “Data Mining for Improving a Cleaning Process in the Semiconductor Industry”, IEEE Transactions on Semiconductor Manufacturing, IEEE, Piscataway, USA, 15(1), 2002, pp. 91-100.

[25] D.H. Baek, I.J. Jeong, and C.H. Han, “Application of Data Mining for Improving Yield in Wafer Fabrication System”, Lecture Notes in Computer Science. Springer Verlag, Heidelberg, Germany, 3483(IV), 2005, pp. 222-231.

[26] M. Gardner and J. Bieker, “Data mining solves tough semiconductor manufacturing problems”, Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, USA, 2000, pp. 376-383.

[27] A. Kusiak, “Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing”, IEEE Transactions on Electronics Packaging Manufacturing, IEEE, Piscataway, NJ, USA, 24(1), 2001, pp. 44-50.

[28] A. Kusiak, “Data mining: manufacturing and service applications”, International Journal of Production Research, Taylor and Francis, Abingdon, Oxfordshire, UK, 44(18-19), 2006, pp. 4175-4191.

[29] M. Last and A. Kandel, “Data mining for process and quality control in the semiconductor industry”, In D. Braha (Ed.), Data Mining for Design and Manufacturing, Kluwer Academic Publishers, Norwell, USA, 2001, pp. 207-234.

[30] C.-F. Chien et al., “Cycle time prediction and control based on production line status and manufacturing data mining”, Proceedings of 2005 IEEE International Symposium on Semiconductor Manufacturing, IEEE, Piscataway, NJ, USA, 13-15 Sept. 2005, pp. 327 – 330.


[31] Y. Ding, L. Zeng, and S.Y. Zhou, “Phase I analysis for monitoring nonlinear profiles in manufacturing processes”, Journal of Quality Technology, ASQ, Milwaukee, USA, 38(3), 2006, pp. 199-216.

[32] H.H. Lou and Y.L. Huang, “Hierarchical decision making for proactive quality control: system development for defect reduction in automotive coating operations”, Engineering Applications of Artificial Intelligence, Elsevier, Oxford, UK, 16(3), 2003, pp. 237-250.

[33] C. Da Cunha, B. Agard, and A. Kusiak, “Data mining for improvement of product quality”, International Journal of Production Research, Taylor and Francis, Abingdon, Oxfordshire, UK, 44(18-19), 2006, pp. 4027-4041.

[34] M. Manago and M. Auriol, “Mining for OR”, OR/MS Today, Lionheart Publishing, Marietta, USA, Feb. 1996, pp. 28-32.

[35]A. Kusiak and T.L. Tseng, “Data mining in engineering design: a case study”, Proc. IEEE Int. Conf. Rob. Autom., IEEE, Piscataway, USA, Vol. 1, 2000, pp. 206-211.

[36] A. Kusiak. “Data Mining in Design of Products and Production Systems”, Proceedings of 12th IFAC Symposium on Control Problems in Manufacturing, Elsevier Sci. Ltd., Exeter, Engl., Vol.1, 2006, pp. 49-53.

[37] A. Kusiak et al., “Detection of Events Causing Pluggage of a Coal-fired Boiler: A Data Mining Approach”, Combust Science and Technology, Taylor and Francis, Abingdon, Oxfordshire, UK, 177(12), 2005, pp. 2327-2348.

[38] B. Agard and A. Kusiak, “Data-mining-based methodology for the design of product families”, International Journal of Production Research, Taylor and Francis, Abingdon, Oxfordshire, UK, 42(15), 2004, pp. 2955-2969.

[39] C. Gertosio and A. Dussauchoy, “Knowledge discovery from industrial databases”, Journal of Intelligent Manufacturing, Kluwer Academic Publishers, Dordrecht, Netherlands, 15(1), 2004, pp. 29-37.

[40] L.Y. Zhai, L.P. Khoo, and S.C. Fok, “Feature extraction using rough set theory and genetic algorithms-an application for the simplification of product quality evaluation”, Computers and Industrial Engineering, Elsevier Sci. Ltd., Exeter, Engl., 43(4), 2002, pp. 661 - 676.

[41] M.D. Giess and S.J. Culley, “Investigating manufacturing data for use within design”, Proceedings of International Conference on Engineering Design(ICED 03), Stockholm, August 19-21, 2003.

[42] C.F. Chien, W.C. Wang, and J.C. Cheng, “Data mining for yield enhancement in semiconductor manufacturing and an empirical study”, Expert Systems with Applications, Elsevier, Oxford, UK, 33(1), 2007, pp. 192-198.

[43] T. Wuenscher, D.G. Feldmann, and D. Krause, “Product and process improvements based on data mining of shop-floor information”, Proceedings of International Design

Conference, DESIGN 2006, Dubrovnik-Croatia, May 15-18, 2006, pp. 1319-1326.

[44] A. Ozturk, S. Kayaligil, and N.E. Ozdemirel, “Manufacturing lead time estimation using data mining”, European Journal of Operational Research, Elsevier, Amsterdam, Netherlands, 173(2), 2006, pp. 683-700.

[45] X. Li and S. Olafsson, “Data mining for best practices in scheduling data”, 2004 IIE Annual Conference and Exhibition, IIE, Norcross, USA, 2004, pp. 853.

[46] X. Li and S. Olafsson, “Discovering Dispatching Rules Using Data Mining”, Journal of Scheduling, Springer Science and Business Media, Berlin, Germany, 8(6), 2005, pp. 515 – 527.

[47] D. A. Koonce, C.-H. Fang, and S.-C. Tsai, “A data mining tool for learning from manufacturing systems”, Computers and Industrial Engineering, Elsevier Sci. Ltd., Exeter, Engl., 33(1-2), 1997, pp. 27-30.

[48] M. Rabbani, et al., “An efficient algorithm for solving bi-objective fuzzy job-shop scheduling problems by genetic algorithms and data mining”, Journal of Science and Technology, Amirkabir University of Technology, Teheran, Iran, 15(58), 2004, pp. 570-591.

[49] J.N.K. Liu and D.K.Y. Sin, “Integrated data mining approach for maintenance scheduling”, International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, CRL Publishing, Leicestershire, Engl., 8(2), 2000, pp. 119-126.

[50] Y. Harrath, B. Chebel-Morello, and N. Zerhouni, “A genetic algorithm and data mining to resolve a job shop schedule”, IEEE Symposium on Emerging Technologies and Factory Automation, IEEE, Piscataway, USA, Vol. 2, 2001, pp. 727-728.

[51] Y. Harrath, B. Chebel-Morello, and N. Zerhouni, “A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem”, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, USA., Vol. 7, 2002, pp. 280-285.

[52] T. Yoshida and H. Touzaki, “A study on association among dispatching rules in manufacturing scheduling problems”, Proceedings of 7th IEEE International Conference on Emerging Technologies and Factory Automation, IEEE, Piscataway, USA., Vol. 2, 18-21 Oct. 1999, pp. 1355-1360.

[53] J. A. Harding et al., “Data Mining in Manufacturing: A Review”, Transactions of the ASME on Journal of Manufacturing Science and Engineering, ASME, New York, USA, 128(4), 2006, pp. 969-976.

[54] J.C.-X. Feng and A. Kusiak, “Data mining applications in engineering design, manufacturing and logistics”, International Journal of Production Research, Taylor and Francis, Abingdon, UK, 14(4), 2006, pp.2689-2694.


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