Machine learning in manufacturing: Advantages, challenges, and applications

Production and Manufacturing Research

Volumn 4, Issue 1, 2016, Pages 23-45

  Wuest, Thorsten ^a   Weimer, Daniel ^b   Irgens, Christopher ^c   Thoben, Klaus Dieter ^b  

^a West Virginia University   (United States)

^b UNIVERSITY OF BREMEN   (Germany)

^c UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Intelligent manufacturing systems; Machine learning; Manufacturing; Smart manufacturing

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; MANUFACTURE;

CHAOTIC BEHAVIORS; HIGH-QUALITY PRODUCTS; INTELLIGENT MANUFACTURING SYSTEM; MACHINE LEARNING TECHNIQUES; MANUFACTURING ENVIRONMENTS; POTENTIAL BENEFITS; SMART MANUFACTURING;

ENGINEERING EDUCATION;

EID: 84995968996     PISSN: None     EISSN: 21693277     Source Type: Journal    
DOI: 10.1080/21693277.2016.1192517     Document Type: Article

References (114)

1. Akay, D. (2011). Grey relational analysis based on instance based learning approach for classification of risks of occupational low back disorders. Safety Science, 49, 1277–1282. doi:http://dx.doi.org/10.1016/j.ssci.2011.04.018
2. Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.
3. Apte, C., Weiss, S., & Grout, G. (1993). Predicting defects in disk drive manufacturing: A case study in high dimensional classification. In IEEE Annual Computer Science Conference on Artificial Intelligence in Application (pp. 212–218). Los Alamitos, CA.
4. Azadeh, A., Saberi, M., Kazem, A., Ebrahimipour, V., Nourmohammadzadeh, A., & Saberi, Z. (2013). A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization. Applied Soft Computing, 13, 1478–1485. doi:http://dx.doi.org/10.1016/j.asoc.2012.06.020
5. Bar-or, A., Schuster, A., Wolff, R., & Keren, D. (2005). Decision tree induction in high dimensional, hierarchically distributed databases. In Proceedings SI-AM International Data Mining Conference (pp. 466–470). Newport Beach, CA.
6. Ben-hur, A., & Weston, J. (2010). A user’s guide to support vector machines. In Data mining techniques for the life sciences methods in molecular biology (Vol. 609, pp. 223–239). Totowa, NJ: Humana Press. http://dx.doi.org/10.1007/978-1-60327-241-413
7. Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.
8. Borin, A., Ferrão, M. F., Mello, C., Maretto, D. A., & Poppi, R. J. (2006). Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta, 579, 25–32. doi:http://dx.doi.org/10.1016/j.aca.2006.07.008
9. Breiman, L. (2001). Random forest. Machine Learning, 45, 5–32. doi:http://dx.doi.org/10.1023/A:1010933404324
10. Brunato, M., & Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 47, 825–845. doi:http://dx.doi.org/10.1016/j.comnet.2004.09.004
11. Burbidge, R., Trotter, M., Buxton, B., & Holden, S. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computers & Chemistry, 26, 5–14.
12. Carpenter, G. A., & Grossberg, S. (1988). The ART of adaptive pattern recognition by a self-organizing neural network. Computer, 21, 77–88. doi:http://dx.doi.org/10.1109/2.33
13. Çaydas, U., & Ekici, S. (2010). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23, 639–650. doi:http://dx.doi.org/10.1007/s10845-010-0415-2
14. Chand, S., & Davis, J. F. (2010, July). What is smart manufacturing? Time Magazine.
15. Cherkassky, V., & Ma, Y. (2009). Another look at statistical learning theory and regularization. Neural Networks, 22, 958–969. doi:http://dx.doi.org/10.1016/j.neunet.2009.04.005
16. Chinnam, R. B. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40, 4449–4466. doi:http://dx.doi.org/10.1080/00207540210152920
17. Cohn, D. (2011). Active learning (p. 10). Sammut, C. & Webb, G. I. (Eds.) (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8
18. Cook, D. F., Zobel, C. W., & Wolfe, M. L. (2006). Environmental statistical process control using an augmented neural network classification approach. European Journal of Operational Research, 174, 1631–1642. doi:http://dx.doi.org/10.1016/j.ejor.2005.04.035
19. Corne, D., Dhaenens, C., & Jourdan, L. (2012). Synergies between operations research and data mining: The emerging use of multi-objective approaches. European Journal of Operational Research, 221, 469–479. doi:http://dx.doi.org/10.1016/j.ejor.2012.03.039
20. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
21. Davis, J., Edgar, T., Graybill, R., Korambath, P., Schott, B., Swink, D., & Wetzel, J. (2015). Smart manufacturing. Annual Review of Chemical and Biomolecular Engineering, 6, 141–160. doi:http://dx.doi.org/10.1146/annurev-chembioeng-061114-123255
22. Dietterich, T. G. (2000). Ensemble methods in machine learning. Proceedings of Multiple Classifier Systems: First International Workshop (MCS) (pp. 1–15). Berlin: Springer. doi:http://dx.doi.org/10.1007/3-540-45014-91
23. Dingli, D. J. (2012). The manufacturing industry – Coping with challenges (Working Paper No. 2012/05). Maastricht: Maastricht School of Management.
24. Do, T.-N., Lenca, P., Lallich, S., & Pham, N.-K. (2010). Classifying very-high-dimensional data with random forests of oblique decision trees. In F. Guil-let, G. Ritschard, D. Zighed, & H. Briand (Eds.), Advances in knowledge discovery and management (pp. 39–55). Berlin: Springer.
25. Doltsinis, S., Ferreira, P., & Lohse, N. (2012). Reinforcement learning for production ramp-up: A Q-batch learning approach. In 11 th International Conference on Machine Learning and Applications (pp. 610–615). Boca Raton, FL: IEEE. doi:http://dx.doi.org/10.1109/ICMLA.2012.113
26. Dutt, V., & Gonzalez, C. (2012). Making instance-based learning theory usable and understandable: The instance-based learning tool. Computers in Human Behavior, 28, 1227–1240. doi:http://dx.doi.org/10.1016/j.chb.2012.02.006
27. Elangovan, M., Sakthivel, N. R., Saravanamurugan, S., Nair, B. B., & Sugumaran, V. (2015). Machine learning approach to the prediction of surface roughness using statistical features of vibration signal acquired in turning. Procedia Computer Science, 50, 282–288. doi:http://dx.doi.org/10.1016/j.procs.2015.04.047
28. El-naqa, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., & Nishikawa, R. M. (2002). A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging, 21, 1552–1563. doi:http://dx.doi.org/10.1109/TMI.2002.806569
29. European Commission. (2016). Factories for the future. Retrieved from http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html
30. Evgeniou, T., Poggio, T., Pontil, M., & Verri, A. (2002). Regularization and statistical learning theory for data analysis. Computational Statistics & Data Analysis, 38, 421–432. doi:http://dx.doi.org/10.1016/S0167-9473(01)00069-X
31. Evgeniou, T., Pontil, M., & Poggio, T. (2000). Statistical learning theory: A primer. International Journal of Computer Vision, 38, 9–13. doi:http://dx.doi.org/10.1023/A:1008110632619
32. Filipic, B., & Junkar, M. (2000). Using inductive machine learning to support decision making in machining processes. Computers in Industry, 43, 31–41. doi:http://dx.doi.org/10.1016/S0166-3615(00)00056-7
33. Freund, Y., & Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.
34. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M., & Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906–914. doi:http://dx.doi.org/10.1093/bioinformatics/16.10.906
35. Gagliardi, F. (2011). Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction. Artificial Intelligence in Medicine, 52, 123–139. doi:http://dx.doi.org/10.1016/j.artmed.2011.04.002
36. Gardner, R., & Bicker, J. (2000). Using machine learning to solve tough manufacturing problems. International Journal of Industrial Engineering- Theory Applications and Practice, 7, 359–364.
37. Gonzalez, C., Dutt, V., & Lebiere, C. (2013). Validating instance-based learning mechanisms outside of ACT-R. Journal of Computational Science, 4, 262–268. doi:http://dx.doi.org/10.1016/j.jocs.2011.12.001
38. Gordon, J., & Sohal, A. S. (2001). Assessing manufacturing plant competitiveness. International Journal of Operations & Production Management, 21, 233–253.
39. Graham, J. W. (2012). Missing data (p. 322). New York, NY: Springer.
40. Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. (2015). First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technology, 15, 474–483.
41. Guo, X., Sun, L., Li, G., & Wang, S. (2008). A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing. Expert Systems with Applications, 35, 415–422. doi:http://dx.doi.org/10.1016/j.eswa.2007.07.052
42. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). A gene selection method for cancer classification using Support Vector Machines. Machine Learning, 46, 389–422. doi:http://dx.doi.org/10.1155/2012/586246
43. Hansson, K., Yella, S., Dougherty, M., & Fleyeh, H. (2016). Machine learning algorithms in heavy process manufacturing. American Journal of Intelligent Systems, 6(1), 1–13. doi:http://dx.doi.org/10.5923/j.ajis.20160601.01
44. Harding, J. A., Shahbaz, M., & Kusiak, A. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128, 969–976. doi:http://dx.doi.org/10.1115/1.2194554
45. Hickey, R. J., & Martin, R. G. (2001). An instance-based approach to pattern association learning with application to the English past tense verb domain. Knowledge-Based Systems, 14, 131–136. doi:http://dx.doi.org/10.1016/S0950-7051(01)00089-2
46. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780. doi:http://dx.doi.org/10.1162/neco.1997.9.8.1735
47. th European Conference on Artificial Intelligence (pp. 345–347). Stockholm, Sweden.
48. Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37, 543–558. doi:http://dx.doi.org/10.1016/S0167-9236(03)00086-1
49. Jain, A. K., Murty, M. N., & Flynn, P. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264–323. doi:http://dx.doi.org/10.1145/331499.331504
50. Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2016). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing. doi:http://dx.doi.org/10.1007/s10845-016-1206-1
51. Kabacoff, R. I. (2011). Advanced methods for missing data. In R. I. Kabacoff (Ed.), R in action: Data analysis and graphics with R (pp. 352–371). Shelter Island, NY: Manning Publications.
52. Kang, P., & Cho, S. (2008). Locally linear reconstruction for instance-based learning. Pattern Recognition, 41, 3507–3518. doi:http://dx.doi.org/10.1016/j.patcog.2008.04.009
53. Kang, P., Kim, D., & Cho, S. (2016). Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Expert Systems with Applications, 51, 85–106. doi:http://dx.doi.org/10.1016/j.eswa.2015.12.027
54. Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15, 1667–1689. doi:http://dx.doi.org/10.1162/089976603321891855
55. Khemchandani, R., & Chandra, S. (2009). Knowledge based proximal support vector machines. European Journal of Operational Research, 195, 914–923. doi:http://dx.doi.org/10.1016/j.ejor.2007.11.023
56. Kim, D., Kang, P., Cho, S., Lee, H., & Doh, S. (2012). Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, 39, 4075–4083. doi:http://dx.doi.org/10.1016/j.eswa.2011.09.088
57. Köksal, G., Batmaz, I., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications, 38, 13448–13467. doi:http://dx.doi.org/10.1016/j.eswa.2011.04.063
58. Koltchinskii, V., Abdallah, C. T., Ariola, M., & Dorato, P. (2001). Statistical learning control of uncertain systems: Theory and algorithms. Applied Mathematics and Computation, 120, 31–43. doi:http://dx.doi.org/10.1016/S0096-3003(99)00283-0
59. Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249–268.
60. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
61. Kwak, D.-S., & Kim, K.-J. (2012). A data mining approach considering missing values for the optimization of semiconductor-manufacturing processes. Expert Systems with Applications, 39, 2590–2596. doi:http://dx.doi.org/10.1016/j.eswa.2011.08.114
62. Lang, S. (2007). Durchgängige Mitarbeiterinformation zur Steigerung von Effizienz und Prozesssicherheit in der Produktion (Dissertation). Universität Erlangen-Nürnberg, Bamberg: Meisenbach Verlag.
63. Larose, D. (2005). Discovering knowledge in data – An introduction to data mining. Hoboken, NJ: Wiley.
64. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. doi:http://dx.doi.org/10.1038/nature14539
65. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1, 541–555.
66. Lee, J., & Ha, S. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179, 844–850. doi:http://dx.doi.org/10.1016/j.ins.2008.11.008
67. Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1, 38–41. doi:http://dx.doi.org/10.1016/j.mfglet.2013.09.005
68. Li, H., Liang, Y., & Xu, Q. (2009). Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95, 188–198. doi:http://dx.doi.org/10.1016/j.chemolab.2008.10.007
69. Li, T.-S., & Huang, C.-L. (2009). Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing. Expert Systems with Applications, 36, 374–385. doi:http://dx.doi.org/10.1016/j.eswa.2007.09.023
70. Loyer, J.-L., Henriques, E., Fontul, M., & Wiseall, S. (2016). Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. International Journal of Production Economics, 178, 109–119. doi:http://dx.doi.org/10.1016/j.ijpe.2016.05.006
71. Lu, S. C.-Y. (1990). Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation. Computers in Industry, 15, 105–120. doi:http://dx.doi.org/10.1016/0166-3615(90)90088-7
72. Manallack, D. T., & Livingstone, D. J. (1999). Neural networks in drug discovery: Have they lived up to their promise? European Journal of Medicinal Chemistry, 34, 95–208.
73. Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval. Cambridge: Cambridge University Press.
74. Margolis, D., Land, W. H., Gottlieb, R., & Qiao, X. (2011). A complex adaptive system using statistical learning theory as an inline preprocess for clinical survival analysis. Procedia Computer Science, 6, 279–284. doi:http://dx.doi.org/10.1016/j.procs.2011.08.052
75. Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183, 1466–1476. doi:http://dx.doi.org/10.1016/j.ejor.2006.04.051
76. Monostori, L. (1993). A step towards intelligent manufacturing: Modelling and monitoring of manufacturing processes through artificial neural networks. CIRP Annals, 42, 485–488. doi:http://dx.doi.org/10.1016/S0007-8506(07)62491-3
77. Monostori, L. (2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Engineering Applications of Artificial Intelligence, 16, 277–291. doi:http://dx.doi.org/10.1016/S0952-1976(03)00078-2
78. Monostori, L., Hornyák, J., Egresits, C., & Viharos, Z. J. (1998). Soft computing and hybrid AI approaches to intelligent manufacturing. Tasks and Methods in Applied Artificial Intelligence Lecture Notes in Computer Science, 1416, 765–774. doi:http://dx.doi.org/10.1007/3-540-64574-8_463
79. Monostori, L., Márkus, A., Van Brussel, H., & Westkämper, E. (1996). Machine learning approaches to manufacturing. CIRP Annals, 45, 675–712.
80. Nilsson, N. J. (2005). Introduction to machine learning. Stanford, CA.
81. Okamoto, S., & Yugami, N. (2003). Effects of domain characteristics on instance-based learning algorithms. Theoretical Computer Science, 298, 207–233. doi:http://dx.doi.org/10.1016/S0304-3975(02)00424-3
82. Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal ofArtificial Intelligence Research, 11, 169–198.
83. Pham, D. T., & Afify, A. A. (2005). Machine-learning techniques and their applications in manufacturing. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture, 219, 395–412. doi:http://dx.doi.org/10.1243/095440505X32274
84. Quadrianto, N., & Buntine, W. L. (2011). Regression (pp. 838–842).
85. Sammut, C. & Webb, G. I. (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8
86. Rejani, Y. I. A., & Selvi, S. T. (2009). Early detection of breast cancer using SVM classifier technique. International Journal on Computer Science and Engineering, 1, 127–130.
87. Ribeiro, B. (2005). Support vector machines for quality monitoring in a plastic injection molding process. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 35, 401–410. doi:http://dx.doi.org/10.1109/TSMCC.2004.843228
88. Salahshoor, K., Kordestani, M., & Khoshro, M. S. (2010). Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy, 35, 5472–5482. doi:http://dx.doi.org/10.1016/j.energy.2010.06.001
89. Sammut, C., & Webb, G. I. (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8
90. Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal, 3, 210–229. doi:http://dx.doi.org/10.1147/rd.33.0210
91. Scheidat, T., Leich, M., Alexander, M., & Vielhauer, C. (2009). Support vector machines for dynamic biometric handwriting classification. In Proceedings of AIAI Workshops (pp. 118–125). Thessaloniki, Greece.
92. Shiang, L. E., & Nagaraj, S. (2011). Impediments to innovation: Evidence from Malaysian manufacturing firms. Asia Pacific Business Review, 17, 209–223. doi:http://dx.doi.org/10.1080/13602381.2011.533502
93. Simon, H. A. (1983). Why should machines learn? In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning (pp. 25–37). Charlotte, NC: Tioga Press.
94. Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to machine learning. Cambridge: Cambridge University Press.
95. Steel, D. (2011). Testability and statistical learning theory. In P. S. Bandyopadhyay & M. R. Forster (Eds.), Handbook of the philosophy of science (Vol. 7, pp. 849–861). Amsterdam: Elsevier. doi:http://dx.doi.org/10.1016/B978-0-444-51862-0.50028-9
96. Stone, P. (2011). Reinforcement learning (pp. 849–851). Sammut, C., & Webb, G. I. (Eds.) (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8
97. Sun, J., Rahman, M., Wong, Y., & Hong, G. (2004). Multiclassification of tool wear with support vector machine by manufacturing loss consideration. International Journal of Machine Tools and Manufacture, 44, 1179–1187. doi:http://dx.doi.org/10.1016/j.ijmachtools.2004.04.003
98. Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11, 812–820. doi:http://dx.doi.org/10.1109/TII.2014.2349359
99. Sutton, R. S., & Barto, A. G. (2012). Reinforcement learning: An introduction (2nd ed.). Cambridge, MA: MIT Press.
100. Tay, F. E. H., & Cao, L. J. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48, 847–861. doi:http://dx.doi.org/10.1016/S0925-2312(01)00676-2
101. Thomas, A. J., Byard, P., & Evans, R. (2012). Identifying the UK’s manufacturing challenges as a benchmark for future growth. Journal of Manufacturing Technology Management, 23, 142–156. doi:http://dx.doi.org/10.1108/17410381211202160
102. Wang, K.-J., Chen, J. C., & Lin, Y.-S. (2005). A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory. Production Planning & Control, 16, 665–680. doi:http://dx.doi.org/10.1080/09537280500213757
103. White House. (2014, October 27). FACT SHEET: President Obama announces new actions to further strengthen U.S. manufacturing. Retrieved from https://www.whitehouse.gov/the-pressoffice/2014/10/27/fact-sheet-president-obama-announces-new-actions-further-strengthen-us-m
104. Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21, 2560–2574. doi:http://dx.doi.org/10.1016/j.ymssp.2006.12.007
105. Wiendahl, H.-P., & Scholtissek, P. (1994). Management and control of complexity in manufacturing. CIRP Annals, 43, 533–540. doi:http://dx.doi.org/10.1016/S0007-8506(07)60499-5
106. Wiering, M., & Van Otterlo, M. (2012). Reinforcement learning: State-of-the-art. New York, NY: Springer.
107. Wu, Q. (2010). Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system. Journal of Computational and Applied Mathematics, 233, 2481–2491. doi:http://dx.doi.org/10.1016/j.cam.2009.10.030
108. Wuest, T. (2015). Identifying product and process state drivers in manufacturing systems using supervised machine learning (Springer theses). New York, NY: Springer Verlag.
109. Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25, 1167–1180. doi:http://dx.doi.org/10.1007/s10845-013-0761-y
110. Wuest, T., Liu, A., Lu, S. C.-Y., & Thoben, K.-D. (2014). Application of the stage gate model in production supporting quality management. Procedia CIRP, 17, 32–37. doi:http://dx.doi.org/10.1016/j.procir.2014.01.071
111. Yang, K., & Trewn, J. (2004). Multivariate statistical methods in quality management. New York, NY: McGraw-Hill.
112. Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the Twentieth International Conference on Machine Learning (ICML2003) (pp. 8). Washington, DC.
113. Zheng, Y., Li, S., & Wang, X. (2010). An approach to model building for accelerated cooling process using instance-based learning. Expert Systems with Applications, 37, 5364–5371. doi:http://dx.doi.org/10.1016/j.eswa.2010.01.020
114. Zhou,Z.-H.(2012).Ensemblemethods–Foundationsandalgorithms,MachineLearning&PatternRecognitionSeries.Florida,FL:Chapman&Hall/CRC.ISBN:978-1-4398-3003-1.