在高科技產業製程中，不同製程參數的設定值會顯著影響產品良率，且製程多半具為多重品質特性問題，各個品質特性之間具有高度相關性，牽一髮而動全身，當製程發生偏移時，工程師難以僅透過經驗快速有效地進行參數調整。另一方面，近年來已經有許多製程參數最佳化方法被提出，但產線工程師必須具備一定的統計分析基礎和程式操作能力才能執行整套分析流程，若轉交資料至其他單位進行分析，亦會失去資料的即時性，當製程時常發生偏移，製程參數需要頻繁的調校時，如何快速的提供最佳製程參數以用於生產，是個重要課題。本研究以實驗設計(Design of Experiemnt)、偏最小平方法(Partial Least Square)和基因演算法(Genetic Algorithm)等機器學習方法為基礎，建構一智慧型代理系統，以提高工程師在產線上的製程參數調整效率，並針對兩種製程進行實證研究。實證對象一為台灣某精密製造廠，該廠商專門製造針測設備中的關鍵零組件，由於精密製造的模具會有隨時間磨耗的特性，因此製程參數需頻繁進行調整使產品維持在規格範圍內，相較於過往工程師使用經驗和試誤法(Trial -and-Error)進行調整，採用本研究提供的智慧型代理系統能大幅提升參數調整效率，提高產能；實證對象二為台灣某積體電路封裝廠的打線接合(Wire Bonding)製程，該廠商過去找尋最佳製程參數的方法分為兩步進行，首先須進行審視實驗設計(Screening DOE)，挑選出對多數品質特性有顯著影響的製程參數後，再進行反應曲面實驗設計(Response Surface Design of Experiment)找出最佳製程參數組合，此方法可能遺漏對某些品質特性有顯著影響的製程參數，且每一步驟的實驗設計都需耗時數日才能完成，實證本研究提供的智慧型代理系統可將兩步驟實驗設計化簡為一個步驟，並且可以找出同時考慮所有品質特性的最佳製程參數組合以提高良率和產能以證明研究方法。

In high-tech industry, product yield is significant affected by setting value of manufacturing process parameters, most of the manufacturing processes have characteristic of multi-response with high correlation, it is hard to conduct parameter tuning with engineers’ experience. Although there are several parameter tuning method being proposed these days, but statistical analysis and programming ability are still required. If the manufacturing process shifts a lot, parameters must be tuned frequently, methods to derive optimized parameters and use in manufacturing process in real-time has become an crucial issue. This research based on design of experiment(DOE) , partial least square and genetic algorithm, construct an intelligent agent system to enhance engineers’ parameter tuning efficiency on-line. Empirical researches are conducted in two types of manufacturing process. The first empirical research is conducted in a precision manufacturing manufacturer in Taiwan. Because of the consuming characteristic of pressing dies, parameters must be tuned frequently in order to keep product inside of spec. Compare to use trial-and-error method and engineers’ experience in the past, the intelligent agent system can increase productivity and the efficiency of parameter tuning significantly. The second empirical research is conducted in wired-bond manufacturing process in an IC packaging company in Taiwan. This company use two steps DOE to derive optimized parameters in the past, first step use screening DOE, second step use response surface method(RSM) DOE. These two steps DOE method may fail to consider some important parameters and time-consuming, the proposed intelligent agent system can reduce the procedure and increase quality and efficiency.

Abdi, H. (2003). Partial least square regression (PLS regression). Encyclopedia for research methods for the social sciences, 6(4), 792-795.
Ahmad, F., Isa, N. A. M., Hussain, Z., Osman, M. K., & Sulaiman, S. N. (2015). A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. Pattern Analysis and Applications, 18(4), 861-870.
Alander, J. T. (1992). On optimal population size of genetic algorithms. Paper presented at the CompEuro'92.'Computer Systems and Software Engineering', Proceedings.
Baş, D., & Boyacı, İ. H. (2007). Modeling and optimization I: Usability of response surface methodology. Journal of food engineering, 78(3), 836-845.
Bellifemine, F. L., Caire, G., & Greenwood, D. (2007). Developing multi-agent systems with JADE (Vol. 7): John Wiley & Sons.
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), 965-977.
Cao, J., Spooner, D. P., Jarvis, S. A., & Nudd, G. R. (2005). Grid load balancing using intelligent agents. Future generation computer systems, 21(1), 135-149.
Carrascal, L. M., Galván, I., & Gordo, O. (2009). Partial least squares regression as an alternative to current regression methods used in ecology. Oikos, 118(5), 681-690.
Chun, H., & Keleş, S. (2010). Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(1), 3-25.
Devarakonda, N., Subhani, S., & Basha, S. A. H. (2014). Outliers detection in regression analysis using partial least square approach. Paper presented at the ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II.
Ding, B., & Gentleman, R. (2005). Classification using generalized partial least squares. Journal of Computational and Graphical Statistics, 14(2), 280-298.
Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational behavior and human decision processes, 60(3), 306-325.
Ferber, J. (1999). Multi-agent systems: an introduction to distributed artificial intelligence (Vol. 1): Addison-Wesley Reading.
Fort, G., & Lambert-Lacroix, S. (2004). Classification using partial least squares with penalized logistic regression. Bioinformatics, 21(7), 1104-1111.
Garthwaite, P. H. (1994). An interpretation of partial least squares. Journal of the American Statistical Association, 89(425), 122-127.
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica chimica acta, 185, 1-17.
Genesereth, M. R., & Ketchpel, S. P. (1994). Software agents. Commun. ACM, 37(7), 48-53.
Höskuldsson, A. (2001). Variable and subset selection in PLS regression. Chemometrics and intelligent laboratory systems, 55(1), 23-38.
Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms: John Wiley & Sons.
Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
Indahl, U. G., Liland, K. H., & Næs, T. (2009). Canonical partial least squares—a unified PLS approach to classification and regression problems. Journal of Chemometrics, 23(9), 495-504.
Julong, D. (1989). Introduction to grey system theory. The Journal of grey system, 1(1), 1-24.
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group: Forschungsunion.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
Lu, H., Chang, C., Hwang, N., & Chung, C. (2009). Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. Journal of Materials Processing Technology, 209(8), 3808-3817.
Maes, P. (1994). Agents that reduce work and information overload. Communications of the ACM, 37(7), 30-40.
Magnier, L., & Haghighat, F. (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 45(3), 739-746.
Man, K.-F., Tang, K.-S., & Kwong, S. (2012). Genetic algorithms: Concepts and designs: Springer Science & Business Media.
Martens, H., & Naes, T. (1992). Multivariate calibration: John Wiley & Sons.
McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Readings in artificial intelligence, 431-450.
Mehmood, T., Liland, K. H., Snipen, L., & Sæbø, S. (2012). A review of variable selection methods in partial least squares regression. Chemometrics and intelligent laboratory systems, 118, 62-69.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (2013). Machine learning: An artificial intelligence approach: Springer Science & Business Media.
Mishra, A., & Desai, V. (2006). Drought forecasting using feed-forward recursive neural network. ecological modelling, 198(1-2), 127-138.
Peckover, D. L. (2000). Intelligent agents for electronic commerce. In: U.S. Patent No. 6,119,101.
Rich, E., & Knight, K. (1991). Artificial intelligence. McGraw-Hill, New York.
Roeva, O., Fidanova, S., & Paprzycki, M. (2013). Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. Paper presented at the Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on.
Rosipal, R., & Krämer, N. (2006). Overview and recent advances in partial least squares. Lecture notes in computer science, 3940, 34.
Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 25, 27.
Santos, C. A., Spim Jr, J. A., Ierardi, M. C., & Garcia, A. (2002). The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel. Applied Mathematical Modelling, 26(11), 1077-1092.
Schutzer, D., Forster Jr, W. H., Hu, H., Lee, W., Stolfo, S. J., & Fan, W. (1999). Method and system for using intelligent agents for financial transactions, services, accounting, and advice. In: U.S. Patent No. 5,920,848.
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and brain sciences, 3(3), 417-424.
Shen, C., Wang, L., & Li, Q. (2007). Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Materials Processing Technology, 183(2-3), 412-418.
Shen, W. (2002). Distributed manufacturing scheduling using intelligent agents. IEEE intelligent systems, 17(1), 88-94.
Sibalija, T. V., & Majstorovic, V. D. (2012). An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing, 23(5), 1511-1528.
Socher, R., Lin, C. C., Manning, C., & Ng, A. Y. (2011). Parsing natural scenes and natural language with recursive neural networks. Paper presented at the Proceedings of the 28th international conference on machine learning (ICML-11).
Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 24(4), 656-667.
Sycara, K., Pannu, A., Willamson, M., Zeng, D., & Decker, K. (1996). Distributed intelligent agents. IEEE expert, 11(6), 36-46.
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11), 1225-1231.
Valderrama, P., Braga, J. W. B., & Poppi, R. J. (2007). Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy. Journal of agricultural and food chemistry, 55(21), 8331-8338.
von Solms, S. H. (1998). Electronic commerce with secure intelligent trade agents. computers & security, 17(5), 435-446.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65-85.
Wold, H. (1975). Soft modeling by latent variables: the nonlinear iterative partial least squares approach. Perspectives in probability and statistics, papers in honour of MS Bartlett, 520-540.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.
Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115-152.
簡禎富, 林國義, 許鉅秉, & 吳政鴻. (2016). 台灣生產與作業管理之相關期刊文獻回顧與前瞻: 從工業 3.0 到工業 3.5. In 管理學報 (Vol. 33, pp. 87-103).