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The usage of data mining techniques for quality improvement in manufacturing industry

Year 2010, Issue: 2, 47 - 65, 01.03.2010

Abstract

Quality is a major requirement of competition in today's world markets. Organizations give much more importance to quality design, control and improvement of products and processes, and accomplish these with the participation of all employees. As a result, it is aimed to achieve customer satisfaction along with reduction in cost and increase in productivity and profitability. In quality improvement (QI) studies, a variety of analyses are performed by collecting data from the field, customer and manufacturing. In these analyses, an increasing number of data mining (DM) approaches are being used, especially for large datasets with too many and mixed type of input and output variables. However, DM is still not widely known and utilized by people practicing QI, and there is no sufficient research into the possible contributions of it to QI. In this study, first of all, the DM process is defined, and then selected DM applications on certain QI problems in manufacturing industry, published in 1997-2007, are examined. Among the QI problems, the followings are studied: description of product and process quality, prediction of quality, classification of quality, and optimization of quality parameters. Moreover, a case study is presented, which utilizes a commonly used and effective DM technique called decision trees for identifying influential process variables and their levels that cause casting defects in a casting company.

References

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İmalat sektöründe kalite iyileştirmede veri madenciliği tekniklerinin kullanımı

Year 2010, Issue: 2, 47 - 65, 01.03.2010

Abstract

Günümüzde kalite dünya pazarında rekabetin ana unsurlarından biri haline gelmiştir. İşletmeler artık ürün ve süreçlerin kalite tasarım, kontrol ve iyileştirme çalışmalarına daha fazla önem vermekte, bu çalışmaları da tüm çalışanların katılımıyla gerçekleştirmektedir. Sonuç olarak müşteri memnuniyetinin kazanılmasıyla birlikte maliyetlerin düşürülmesi, verimlilik ve kârlılığın artırılması istenmektedir. Kalite iyileştirme çalışmalarında sıklıkla sahadan, müşteriden ve üretimden veriler toplamak yoluyla çeşitli analizler yapılmaktadır. Bu analizlerde, özellikle karışık tipte ve çok sayıda girdi ve çıktı değişkenine sahip büyük miktardaki veri kümeleri için giderek daha fazla veri madenciliği (VM) yaklaşımları kullanılmaktadır. Ancak VM, kalite iyileştirme çalışmalarında bulunanlar tarafından hâlâ yeterince tanınmayan ve kalite iyileştirmeye olası katkıları yeterince araştırılmamış bir alandır. Bu çalışmada, öncelikle VM süreci tanımlanmış ve ardından 1997-2007 yılları arasını kapsayan literatürden seçilen, imalat sektöründe belirli kalite iyileştirme problemlerine uygulanmış VM çalışmaları değerlendirilmiştir. Kalite iyileştirme problemlerinden süreç ve ürün kalitesinin tanımlanması, kalitenin tahmini, kalitenin sınıflandırılması ve kalite parametrelerinin optimizasyonu üzerinde durulmuştur. Çalışmada ayrıca, en yaygın kullanılan ve etkili VM tekniklerinden karar ağaçlarının bir döküm fabrikasında döküm hatalarına neden olan değişkenleri ve seviyelerini belirlemek amacıyla yapılan uygulamaya yer verilmiştir.

References

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  • •• Bakır, B., Batmaz, I., Güntürkün, F. A., Ipekci, İ. A., Köksal, G. and Özdemirel, N. E. (2006), Defect cause modeling with decision tree and regression analysis
  • In: Proceedings of XVII. International Conference on Computer and Information Science and Engineering, 8-10 December 2006 Cairo, Cairo: World Enformatica Society, 16, 266-269
  • •• Batmaz, I. (2007), Data mining applications on manufacturing data: a casting quality improvement case, In: Ayhan, H.O. and Batmaz, I. (eds.), Recent Advances in Statistics, Ankara, Turkey: TUIK, 197-206
  • •• Bertino, E., Catania, B. and Caglio, E. (1999), Applying data mining techniques to wafer manufacturing, Principles of Data Mining and Knowledge Discovery, 1704, 41-50
  • •• Brinksmeier, E., Toe Nshoff, H. K., Czenkusch C., Heinzel C. (1998). Modeling and Optimization of Grinding Processes, Journal of Intelligent Manufacturing, 9, 303-314
  • •• Chen, W. C., Lee, A. H. I., Deng, W. J. and Liu, K. Y. (2007), The implementation of neural network for semiconductor PECVD process, Expert Systems with Applications, 32 (4), 1148–1153
  • •• Chiang, T.L., Su, C.T., Li, T.S. and Huang, R.C.C. (2002), Improvement of process capability through neural networks and robust design: A case study, Quality Engineering, 14 (2), 313-318
  • •• Chien, C., Li H. and Jeang, A. (2006), Data mining for improving the solder bumping process in the semiconductor packaging industry, Intelligent Systems in Accounting, Finance and Management, 14 (1-2), 43-57
  • •• Chien, C.F., Wang, W.C. and Cheng, J.C. (2007), Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Expert Systems with Applications, 33 (1), 192-198
  • •• Cook, D.F., Ragsdale, C.T., Major, R.L. (2000), Combining a Neural Network with a Genetic Algorithm for Process Parameter Optimization, Engineering Applications of Artificial Intelligence, 13, 391-396
  • •• Cool, T., Bhadeshia, H.K.D.H., MacKay, D.J.C. (1997), The Yield and Ultimate Tensile Strength of Steel Welds, Materials Science and Engineering, A223, 186-200
  • •• Cser, L., Gulyas, J., Szucs, L., Horvath, A., Arvai, L. and Baross, B. (2001), Different kinds of neural networks in control and monitoring of hot rolling mill. In: Monostori, L., Vancza, J. and Ali, M. (eds.), Proceedings of the 14th International conference on industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems, IEA/AIE 2001, LectureNotes In Computer Science, 4-7 June 2001 Budapest Hungary. London: Springer-Verlag, 2070, 791 – 796
  • •• Cus, F., Balic, J. (2003), Optimization of Cutting Process by GA Approach, Robotics and Computer Integrated Manufacturing, 19, 113–121
  • •• De Abajo, N., Diez, A. B., Lobato, V. and Cuesta, S. R. (2004), ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study. In: Kohavi, R. et al. (eds.), KDD-2004: proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 22-25 August 2004 Seattle Washington, New York: ACM Press, 799-804
  • •• Deng, B. and Liu, X. (2002), Data mining in quality improvement, SUGI27: Proceedings of the Twenty-Seventh Annual SAS® Users Group International Conference, 14-17 April 2002 Orlando, Florida [online]http://www2.sas.com/ proceedings/sugi27/Proceed27.pdf [23 Nisan 2008 tarihinde erişilmiştir]
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  • •• Erzurumlu, T., Öktem, H. (2003), Comparison of response surface model with neural network in determining the surface quality of moulded parts, Materials and Design, 28, 459–465
  • •• Gardner, M., Bieker, J. (2000), Data Mining Solves Tough Semiconductor Manufacturing Problems, Proceedings of the Conference on Knowledge Discovery and Data Mining, Boston, MA USA, 376-383
  • •• Georgilakis, P., Hatziargyriou, N. (2002), On the Application of Artificial Intelligence Techniques to the Quality Improvement of Industrial Processes, Vlahavas, I.P. and Spyropoulos, C.D. (eds.), Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence, Lecture Notes In Computer Science, 11-12, Nisan, Thessaloniki, Greece,London: Springer-Verlag, 2308, 473 – 484
  • •• Guessasma, S., Salhi, Z., Montavon, G., Gougeon, P., Coddet, C. (2004), Artificial Intelligence Implementation in the APS Process Diagnostic, Materials Science and Engineering B, 110, 285–295
  • •• Hamedi, M., Shariatpanahi, M., and Mansourzadeh, A. (2007), Optimizing spot welding parameters in a sheet metal assembly by neural networks and genetic algorithm, Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 221 (7), 1175-1184
  • •• Harding, J.A., Shahbaz, M., Srinivas, S. and Kusiak, A. (2006), Data mining in manufacturing: A review, Journal of Manufacturing Science and Engineering-Transactions of ASME, 128 (4), 969-976
  • •• Holena, M., Baerns, M. (2003), Feedforward neural networks in catalysis - A Tool for the Approximation of the Dependency of Yield on Catalyst Composition, and for Knowledge Extraction, Catalysis Today, 81, 485–494
  • •• Hsieh, K., Tong, L. (2001), Optimization of Multiple Quality Responses Involving Qualitative and Quantitative Characteristics in IC Manufacturing Using Neural Networks, Computers in Industry, 46, 1-12
  • •• Hu, C., Su, S. (2004), Hierarchical Clustering Methods for Semiconductor Manufacturing Data, Proceedings of the IEEE International Conference on Networking, 21-23 Mart 2004 Taipei, Taiwan. IEEE, 2, 1063 – 1068Huang, C., Li, T., Peng, T. (2006). Attribute Selection Based on Rough Set Theory for Electromagnetic Interference (EMI) Fault Diagnosis, Quality Engineering, 18, 161–171
  • •• Huang, H., Wu, D. (2005), Product Quality Improvement Analysis Using Data Mining : A Case Study in Ultra-Precision Manufacturing Industry, Proceedings of the Conference on Fuzzy Systems and Knowledge Discovery, Changsha , CHINE, 577-580
  • •• Hung, Y. H. (2007), Optimal process parameters design for a wire bonding of ultra-thin CSP package based on hybrid methods of artificial intelligence, Microelectronics International, 24 (3), 3-10
  • •• Hsu, S.C. and Chien, C.F. (2007), Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing, International Journal of Production Economics, 107 (1), 88-103
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Details

Primary Language Turkish
Journal Section Articles
Authors

Gülser Köksal This is me

İnci Batmaz This is me

Murat Caner Testik This is me

Fatma Güntürkün This is me

Publication Date March 1, 2010
Submission Date August 16, 2014
Published in Issue Year 2010 Issue: 2

Cite

APA Köksal, G., Batmaz, İ., Testik, M. C., Güntürkün, F. (2010). İmalat sektöründe kalite iyileştirmede veri madenciliği tekniklerinin kullanımı. Verimlilik Dergisi(2), 47-65.

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