工業4.0使智慧製造的概念與其相關技術開始被應用於產業中，其核心的大數據分析方法能將大量資料轉換為有意義的資訊，同時結合領域知識與專業經驗，發展為協助決策者的工具。半導體產業是台灣高科技產業最重要的支柱，然而在創造龐大經濟效益的同時，消耗的電力也較其他產業還要多，身處能源議題受到全球關注的時代，半導體業應當致力於發展節能策略，為環境發展盡一份心力。若能提升機台的運作效率與降低設備系統總耗電，就能確保機台穩定運作，並節能減碳以降低成本。本研究應用智慧製造概念與大數據分析手法，以資料挖礦方法為研究架構，探索機台各項參數的重要性以及參數對於機台效率的影響，使用逐步迴歸法與主成分迴歸建立機台效率的預測模型；經數學驗證與領域專家評估後，將不同機台的模型進行組合，發展為匹配節能機台配置的工具，能計算出滿足需求同時消耗能量最低的機台組合。本研究以某半導體公司的空壓機系統為實證案例，證明此流程能協助決策者掌控設備的效率與耗能，並能完整地將使用者輸入資料自動轉換為可實際應用之模型，達成效率提升、耗能下降與節省成本的目標。

Industry 4.0, smart manufacturing and its related technologies are now becoming the leading trend in the development of manufacturing industry. The core of Industry 4.0 is big data analysis method, which can transform a large amount of data into useful information, and combines domain knowledge to develop decision-making strategies. Semiconductor industry is the most important high-tech industry in Taiwan, but it is also one of the most energy-consuming industry in the country. In the era where energy issues are gaining global attention, semiconductor industry should be committed to develop energy-saving strategies and contribute to the sustainability of environment. Therefore, it is critical to improve the efficiency of machines and reduce overall energy consumption of facility systems. This study applies the concept of smart manufacturing and big data analysis, using data mining methods as the research framework. By exploring the importance of various parameters of machines, an efficiency prediction model is built using stepwise regression and principal component regression. After mathematical verification and evaluation from domain experts, the models are applied to develop a tool that can construct a combination of machines, which can meet the production demand while consuming the least amount of energy. A case study is conducted in a semiconductor company in Tainan, targeting at the air compressor system in its factory. This study proves that the research framework is able to assist decision makers to manage the efficiency and energy consumption of facilities. In addition, it can transform input data from users into a model that can achieve the goals of increasing operation efficiency, reducing energy consumption, and saving total costs.

[1] Yuquan Meng, Yuhang Yang, Haseung Chung, Pil-Ho Lee and Chenhui Shao, “Enhancing sustainability and energy efficiency in smart factories: A review,” Sustainability 2018.

[2] Sameer Mittal, Muztoba Ahmad Khan, David Romero, and ThorstenWuest, “Smart manufacturing: Characteristics, technologies and enabling factors,” Engineering Manufacture, vol. 233, pp. 1342-1361, 2019.

[3] Benny Suryajaya, Chao-Chun Chen, Min-Hsiung Hung, Yu-Yang Liu, Jia-Xuan Liu, Yu-Chuan Lin, “A fast large-size production data transformation scheme for supporting smart manufacturing in semiconductor industry,” IEEE Conference on Automation Science and Engineering (CASE), August 2017.

[4] Kiwook Jung, Katherine Morris, Kevin W. Lyons, Swee Leong, Hyunbo Cho, “Performance challenges identification method for smart manufacturing systems,” National Institute of Standards and Technology, U.S. Department of Commerce.

[5] Manar Jaradat, Moath Jarrah, Abdelkader Bousselham, Yaser Jararweh, Mahmoud Al-Ayyoub, “The internet of energy: Smart sensor networks and big data management for smart grid,” Procedia Computer Science, pp. 592 – 597, 2015.

[6] Jingcheng Gao, Jing Liu, Bharat Rajan, Rahul Nori, Bo Fu, Yang Xiao, Wei Liang and C. L. Philip Chen, “SCADA communication and security issues,” Security and Communication Networks, 2014, 7:175–194.

[7] Andrew Kusiak, “Smart manufacturing must embrace big data,” NATURE, vol. 544, 6 April 2017.

[8] Min Chen, Shiwen Mao, Yunhao Liu, “Big data: A survey,” Mobile Networks and Applications, vol. 19, No. 2, pp. 171-209, April 2014.

[9] Qinglin Qi, Fei Tao, “Digital twin and big data towards smart manufacturing and Industry 4.0: 360 Degree comparison,” IEEE Access, vol. 6, pp.3585-3593, January 2018.

[10] Marzieh Khakifirooz, Mahdi Fathi, and Kan Wu, “Development of smart semiconductor manufacturing: Operations research and data science perspectives,” IEEE Access, vol. 7, 5 August 2019.

[11] Chen-Fu Chien, Jin-Tang Peng, Hsing-Chih Yu, “Building energy saving performance indices for cleaner semiconductor manufacturing and an empirical study,” Computers & Industrial Engineering, vol. 99, pp. 448-457, September 2016.

[12] 台灣電力公司電力行業別用電(98~107年資料)
https://www.taipower.com.tw/upload/43/43_04/歷年行業別(98-107).pdf

[13] The U.S. Energy Information Administration. Annual energy review. Available online: https://www.eia.gov/totalenergy/data/annual/#consumption

[14] Chen-Fu Chien, Yun-Ju Chen, Jin-Tang Peng, “Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle,” International Journal of Production Economics, vol. 128, pp. 496-509 December 2010.

[15] Marzieh Khakifirooz, Chen-Fu Chien, Ying-Jen Chen, “Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower Industry 4.0,” Applied Soft Computing, vol. 68, pp. 990-999, July 2018.

[16] Shao-Chung Hsu, Chen-Fu Chien, “Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing,” International Journal of Production Economics, vol. 107, pp. 88-103, May 2007.

[17] Russ M. Dabbas, Hung-Nan Chen, “Mining semiconductor manufacturing data for productivity improvement – An integrated relational database approach,” Computers in Industry, vol. 45, pp. 29-44, May 2001.

[18] Chung-Jen Kuo, Chen-Fu Chien, and Jan-Daw Chen, “Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time,” IEEE Transactions on Automation Science and Engineering, vol. 8, No. 1, January 2011.

[19] Shiyong Wang, Jiafu Wan, Di Li, and Chunhua Zhang, “Implementing smart factory of Industrie 4.0: An outlook,” International Journal of Distributed Sensor Networks, January 19, 2016.

[20] Fei Tao, Ying Zuo, Li Da Xu, Lin Zhang, “IoT-Based intelligent perception and access of manufacturing resource toward cloud manufacturing,” IEEE Transactions on Industrial Informatics, vol. 10, No. 2, May 2014.

[21] Lidong Wang, Guanghui Wang, “Big data in cyber-physical systems, digital manufacturing and Industry 4.0,” International Journal of Engineering and Manufacturing(IJEM), vol. 6, July 2016.

[22] Keliang Zhou, Taigang Liu, Lifeng Zhou, “Industry 4.0: Towards future industrial opportunities and challenges,” International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015.

[23] Mario Hermann, Tobias Pentek, Boris Otto, “Design principles for Industrie 4.0 scenarios,” Hawaii International Conference on System Sciences, 2016.

[24] Rainer Drath, Alexander Horch, “Industrie 4.0: Hit or hype?” IEEE Industrial Electronics Magazine, pp. 56-58, 2014.

[25] Henning Kagermann, Wolfgang Wahlster and Johanned Helbig, Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group. Frankfurt, 2013.

[26] Thomas Bauernhansl, Michael ten Hompel, Birgit Vogel-Heuser, Industrie 4.0 in Produktion, Automatisierung und Logistik. Springer, Wiesbaden, 2014.

[27] F. Shrouf, J. Ordieres, G. Miragliotta, “Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm,” IEEE International Conference on Industrial Engineering and Engineering Management, 2014.

[28] Baotong Chen, Jiafu Wan, Lei Shu, Peng Li, Mithun Mukherjee, Boxing Yin, “Smart factory of Industry 4.0: Key technologies, application case, and challenges,” IEEE Access, 2017.
[29] Jonny Herwan, Seisuke Kano, Ryabov Oleg, Hiroyuki Sawada, Nagayoshi Kasashima, “Cyber-Physical System architecture for machining production line,” IEEE Industrial Cyber-Physical Systems (ICPS), 2018.

[30] Edward A. Lee, “Cyber-Physical Systems - Are computing foundations adequate?” NSF Workshop On Cyber-Physical Systems, 2006.

[31] Edward A. Lee, “Cyber Physical Systems: Design challenges,” IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC), pp. 363-369, 2008.

[32] F. Gamboa Quintanilla, O. Cardin, A. L'Anton, P. Castagna, “Implementation framework for cloud-based holonic control of cyber-physical production systems,” IEEE International Conference on Industrial Informatics (INDIN), 2016.

[33] Fang Li, Jiafu Wan, Ping Zhang, Di Li, Daqiang Zhang, Keliang Zhou, “Usage-specic semantic integration for cyber-physical robot systems,” ACM Transactions on Embedded Computing Systems, vol. 15, No. 3, May 2016.

[34] Cheng-Chen Yang, Min-Hao Lee, Cheng-Tso Tsai, and Ta-Chih Hung, “Introduction of smart factory communication protocol,” Journal of Mechanical Engineering, vol. 352, pp. 83-93, 2012.

[35] Yi-Chih Fan, Jen-Yuan Chang, “Equipment communication architecture for smart manufacturing,” IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE), 2018.

[36] Sangkeun Yoo, Yong-woon Kim, Hoon Choi, “An assessment framework for smart manufacturing,” International Conference on Advanced Communications Technology(ICACT), 2017.

[37] Kee-Young Shin, Nam-Ho Shin, Sang-Woo Choi, Se-Ho Choi, “Systems engineering approach to designing smart condition monitoring systems for smart manufacturing,” International Conference on Control, Automation and Systems (ICCAS), 2016.

[38] Wu-Jeng Li, Jung-Chieh Wang, Chiaming Yen, You-Sheng Lin, Shu-Chu Tung, “Cloud supervisory control system based on JustIoT,” IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE), 2018.
[39] Jehn-Ruey Jiang, “An improved Cyber-Physical Systems architecture for Industry 4.0 smart factories,” IEEE International Conference on Applied System Innovation, 2017.

[40] Chunyang Yu, Xun Xu, Yuqian Lu, “Computer-Integrated manufacturing, Cyber-Physical Systems and cloud manufacturing – Concepts and relationships,” Manufacturing Letters, vol. 6, pp. 5-9, October 2015.

[41] Roberto Saracco, “Digital Twins: Bridging physical space and cyberspace,” Computer, vol. 52, pp. 58-64, November 2019.

[42] Hao Zhang, Qiang Liu, Xin Chen, Ding Zhang, Jiewu Leng, “A digital twin-based approach for designing and multi-objective optimization of hollow glass production line,” IEEE Access, vol. 5, pp. 26901-26911, October 2017.

[43] Edward H. Glaessgen, David S. Stargel, “The digital twin paradigm for future NASA and U.S. Air Force vehicles,” Structures, Structural Dynamics, and Materials Conference, April 2012.

[44] Fei Tao, Meng Zhang, “Digital Twin shop-floor: A new shop-floor paradigm towards smart manufacturing,” IEEE Access, vol. 5, pp. 20418-20427, September 2017.

[45] Michael Schluse, Marc Priggemeyer, Linus Atorf, Juergen Rossmann, “Experimentable digital twins—Streamlining simulation-based systems engineering for Industry 4.0,” IEEE Transactions on Industrial Informatics, vol. 14, N. 4, April 2018.

[46] Usama Fayyad, Gregory Piatetsky-shapiro, Padhraic Smyth, Terry Widener, “The KDD process for extracting useful knowledge from volumes of data,” Communications of the ACM, vol. 39, No.11, November 1996.

[47] Michael J.A. Berry and Gordon S. Linoff, Data Mining Techniques for Marketing, Sales, and Customer Support, John Wiley and Sons, New York, 1997.

[48] C. Kleissner, “Data mining for the enterprise,” IEEE Proceedings 31st Annual Hawaii International Conference on System Sciences, 1998.

[49] 簡禎富、許嘉裕 (2014)，「資料挖礦與大數據分析」，前程文化事業有限公司。

[50] Dan Braha and Armin Shmilovici, “Data mining for improving a cleaning process in the semiconductor industry,” IEEE Transactions on Semiconductor Manufacturing, vol. 15, No. 1, February 2002.

[51] Chen-Fu Chien, Li-Fei Chen, “Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing,” IEEE Transactions on Electronics Packaging Manufacturing, vol. 20, No. 4, November 2007.

[52] Li-Fei Chen, Chen-Fu Chien, “Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries,” Flexible Services and Manufacturing Journal, pp.263-289, January 2011.

[53] 簡禎富、游智閔、徐紹鐘 (2009)，「紫式決策分析以建構半導體晶圓廠人力規劃決策模型」，管理與系統，第十六卷，第二期，157-180頁。

[54] Andrew Kusiak, “Rough Set Theory: A data mining tool for semiconductor manufacturing,” IEEE Transactions on Electronics Packaging Manufacturing, vol. 24, No. 1, January 2001.
[55] Geoffrey K.F Tso, Kelvin K.W Yau, “Predicting electricity energy consumption: A comparison of regression, analysis, decision tree and neural networks” Energy, vol. 32, pp. 1761-1768, September 2007.
[56] Fazil Kaytez, M. Cengiz Taplamacioglu, Ertugrul Cam, Firat Hardalac, “Forecasting electricity consumption: A comparison of regression, analysis, neural networks and least squares support vector machines” International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431-438, May 2015.
[57] Kangji Li, Chenglei Hu, Guohai Liu, Wenping Xue, “Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis,” Energy and Buildings, vol. 108, pp. 106-113, December 2015.
[58] Hsiao-Tien Pao, Chung-Ming Tsai, “Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil,” Energy, vol. 36, pp. 2450-2458, May 2011.
[59] L. Suganthi, Anand A. Samuel, “Energy models for demand forecasting—A review,” Renewable and Sustainable Energy Reviews, vol. 16, pp.1223-1240, February 2012.

[60] Kenneth P. Burnham, David R. Anderson, “Multimodel Inference: Understanding AIC and BIC in model selection,” Sociological Methods & Research, vol. 33, pp. 261-304, November 1, 2004.

[61] Kenneth P. Burnham, David R. Anderson, Model Selection and Multimodel Inference. Springer-Verlag New York, 2002.

[62] Trevor Hastie, Robert Tibshirani, Jerome Friedman, The elements of statistical learning. Springer-Verlag New York, 2009.

[63] Yi-Jyun Sun, Chi-An Lai, Kuo-Hao Chang, Li-Der Chen, Chih-Hung Wang, Chung-Jung Chen and Chih-Ming Lin, “Finding an Optimal Configuration for Air Compressors – A Case Study of a Taiwan Semiconductor Company,” APIEMS, Kanazawa, Japan, 2019.