참고문헌 (127)

1. Data and Knowledge Engineering Ahmad 63 2 503 2007 10.1016/j.datak.2007.03.016 A k-mean clustering algorithm for mixed numeric and categorical data 

   상세보기

2. Journal of Networks Ahmadinia 9 10 2567 2014 10.4304/jnw.9.10.2567-2573 Data aggregation in wireless sensor networks based on environmental similarity: A learning automata approach 

   상세보기

3. IETE Journal of Research Ahmadinia 59 6 774 2013 10.4103/0377-2063.126958 Energy-efficient and multi-stage clustering algorithm in wireless sensor networks using cellular learning automata 

   상세보기

4. American Journal of Engineering and Applied Sciences Alabi 11 3 1114 2018 10.3844/ajeassp.2018.1114.1124 A survey on recent applications of machine learning with big data in additive manufacturing industry 

   상세보기

5. Acta Polytechnica Hungarica Alfaro-Cortes 17 6 115 2020 10.12700/APH.17.6.2020.6.7 Using random fores to interpret out-of-control signals 

   상세보기

6. 10.1109/IEA.2018.8387124 Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In Proceedings of the 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, 26-28 April 2018 (pp. 355-361). doi:10.1109/IEA.2018.8387124. 

   

7. Sensors Arellano-Espitia 20 14 3929 2020 10.3390/s20143949 Deep-learning-based methodology for fault diagnosis in electromechanical systems 

   상세보기

8. International Journal of Computer Applications Arif 69 22 5 2013 10.5120/12106-8375 A data mining approach for developing quality prediction model in multi- stage manufacturing 

   

9. 10.1109/PHM.2017.8079165 Bai, Y., Li, C., Sun, Z., & Chen, H. (2017). Deep neural network for manufacturing quality prediction. In Proceedings of the 8th IEEE Prognostics and System Health Management Conference, Harbin, China, 9-12 July 2017. doi:10.1109/PHM.2017.8079165. 

   

10. 10.3390/su10010085 Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2018). Manufacturing quality prediction using intelligent learning approaches: A comparative study. Sustainability, 10, article no 85, 1-15. doi:10.3390/su10010085. 

    상세보기

11. Journal of Simulation Bergmann 11 1 38 2017 10.1057/s41273-016-0006-0 Emulation of control strategies through machine learning in manufacturing simulations 

    상세보기

12. Computers and Industrial Engineering Buddhakulsomsiri 57 1 137 2009 10.1016/j.cie.2008.11.006 Sequential pattern mining algorithm for automotive warranty data 

    상세보기

13. Journal of Manufacturing Systems Bustillo 48 Part C 108 2018 10.1016/j.jmsy.2018.06.004 Smart optimization of a friction-drilling process based on boosting ensembles 

    상세보기

14. 10.1109/ICASI.2018.8394336 Cheng, Y., Chen, M., Cheng, F., Cheng, Y., Lin, Y., & Yang, C. (2018). Developing a decision support system (DSS) for a dental manufacturing production line based on data mining. In Proceedings of 4th IEEE International Conference on Applied System Innovation 2018 (ICASI 2018), Chiba, Japan, 13-17 April 2018 (pp. 638-641). doi:10.1109/ICASI.2018.8394336. 

    

15. ICIC Express Letters Part B: Applications Cho 11 5 447 2020 Quality prediction modeling of plastic extrusion process 

16. Journal of Intelligent Manufacturing Choudhary 20 501 2009 10.1007/s10845-008-0145-x Data mining in manufacturing: A review based on the kind of knowledge 

    상세보기

17. International Journal of Computer Integrated Manufacturing Cupek 31 6 537 2018 10.1080/0951192X.2017.1339914 Determination of the machine energy consumption profiles in the mass-customised manufacturing 

    상세보기

18. Journal of Manufacturing Processes Das 27 8 2017 10.1016/j.jmapro.2017.03.012 Torque based defect detection and weld quality modelling in friction stir welding process 

    상세보기

19. Applied Intelligence Deng 50 29 2019 10.1007/s10489-019-01516-2 A high speed D-CART online fault diagnosis algorithm for rotor systems 

    상세보기

20. Procedia Manufacturing Djatnaa 4 336 2015 10.1016/j.promfg.2015.11.049 An application of association rule mining in total productive maintenance strategy: An analysis and modelling in wooden door manufacturing industry 

    상세보기

21. 10.1109/CoDIT.2018.8394807 Djelloul, I., Sari, Z., & Sidibe, I. (2018). Fault diagnosis of manufacturing systems using data mining techniques. In Proceedings of the 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, Thessaloniki, Greece, April 10-13, 2018 (pp. 198-203). doi:10.1109/CoDIT.2018.8394807. 

    

22. IFAC-PapersOnLine Dolgui 51 11 316 2018 10.1016/j.ifacol.2018.08.302 Data mining-based prediction of manufacturing situations 

    상세보기

23. International Journal of Production Economics Dubey 226 2020 10.1016/j.ijpe.2019.107599 Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations 

    상세보기

24. IEEE Transactions on Automation Science and Engineering Ferreira 72 584 2020 10.1109/TASE.2019.2936821 Automatic geometric shape deviation modelling for additive manufacturing systems via Bayesian neural networks 

    상세보기

25. 10.1007/s10921-019-0569-6 Forero-Ramirez, J., Restrepo-Giron, A., & Nope-Rodriguez, S. (2019). Detection of internal defects in carbon fiber reinforced plastic slabs using background thermal compensation by filtering and support vector machines. Journal of Nondestructive Evaluation, 38(1), Article Number 33. doi: 10.1007/s10921-019-0569-6. 

    상세보기

26. Expert Systems with Applications Fuqua 150 2020 10.1016/j.eswa.2020.113275 A cost sensitive convolutional neural network for control chart pattern recognition 

    상세보기

27. Procedia CIRP Gandhi 72 261 2018 10.1016/j.procir.2018.03.076 Towards data mining based decision support in manufacturing maintenance 

    상세보기

28. International Journal of Advanced Manufacturing Technology Gao 105 1-4 217 2019 10.1007/s00170-019-04170-7 A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718 

    상세보기

29. IEEE Access Ge 5 20590 2017 10.1109/ACCESS.2017.2756872 Data mining and analytics in the process industry: The role of machine learning 

    상세보기

30. 10.1109/ICUFN.2016.7536995 Han, J., & Chi, S. (2016). Consideration of manufacturing data to apply machine learning methods for predictive manufacturing. In Proceedings of the Eighth International Conference on Ubiquitous and Future Networks (ICUFN), July 5-8, 2016, Vienna, Austria, (pp. 109-113). doi:10.1109/ICUFN.2016.7536995. 

    

31. Journal of Manufacturing Science Engineering Harding 128 4 969 2005 10.1115/1.2194554 Data mining in manufacturing: A review 

    상세보기

32. International Journal of Advanced Manufacturing Technology Hu 94 9 4317 2018 10.1007/s00170-017-1113-4 Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm 

    상세보기

33. IEEE Transactions on Components, Packaging and Manufacturing Technology Huang 8 4 699 2018 10.1109/TCPMT.2017.2788896 Machine-learning approach in detection and classification for defects in TSV-based 3-DIC 

    상세보기

34. Additive Manufacturing Huang 35 2020 10.1016/j.addma.2020.101197 Unsupervised learning for droplet evolution prediction and process dynamics understanding in inkjet printing 

    상세보기

35. IEEE Transactions on Semiconductor Manufacturing Imoto 32 4 455 2019 10.1109/TSM.2019.2941752 A CNN-based transfer learning method for defect classification in semiconductor manufacturing 

    상세보기

36. Ishizuka, D., Izui, K., Yamada, T., & Nishiwaki, S. (2016). Comparison of data mining techniques for analysis of pareto optimal solutions in layout planning problems in manufacturing systems. In Proceedings of the 46th International Conferences on Computers and Industrial Engineering (CIE 2016), Tianjin, China, 29-31 October, 2016. 

37. Ismail, R., Othman, Z., & Abu Bakar, A. (2012). A production schedule generator framework for pattern sequential mining. In Proceedings of the 7th International Conference on Computing and Convergence Technology (ICCIT 2012), Seoul, South Korea, 3-5 December 2012 (pp. 784-788). 

38. IEEE Transactions on Semiconductor Manufacturing Jin 32 3 286 2019 10.1109/TSM.2019.2916835 A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map 

    상세보기

39. 10.1109/COASE.2017.8256319 Jong de, W. A., Rubrico, J., Adachi, M., Nakamura, T., & Ota, J. (2017). Big data in automation: Towards generalized makespan estimation in shop scheduling problems. In Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE), Xi'an, China, August 20-23, 2017 (pp. 1516-1521). doi:10.1109/COASE.2017.8256319. 

    

40. ICIC Express Letters, Part B: Applications Jung 9 6 559 2018 Text mining based online news analysis about smart factory 

41. Expert Systems with Applications Kamsu-Foguem 40 4 1034 2013 10.1016/j.eswa.2012.08.039 Mining association rules for the quality improvement of the production process 

    상세보기

42. Mechanical Systems and Signal Processing Kannatey-Asibu 85 651 2017 10.1016/j.ymssp.2016.08.035 Monitoring tool wear using classifier fusion 

    상세보기

43. 10.1051/matecconf/201712300029 Kao, H., Hsieh, Y., Chen, C., & Lee, J. (2017). Quality prediction modeling for multistage manufacturing based on classification and association rule mining. In Proceedings of the 2nd International Conference on Precision Machinery and Manufacturing Technology, ICPMMT 2017, Pingtung, Taiwan, 19-21 May 2017, MATEC Web of Conferences, 123, 21 September 2017, Article number 00029, 1-6. doi:10.1051/matecconf/201712300029. 

    상세보기

44. International Journal of Mechanics Kerdrasop 5 4 336 2011 A data mining approach to automate fault detection model development in the semiconductor manufacturing process 

45. Advanced Science and Technology Letters Kim 133 85 2016 10.14257/astl.2016.133.16 Euclidean distance based feature selection for fault detection prediction model in semiconductor manufacturing process 

    

46. Concurrency and Computation Practice and Experience Kim 29 1 2017 10.1002/cpe.4128 Particle swarm optimization-deep belief network-based rare class prediction model for highly class imbalance problem 

    상세보기

47. Expert Systems with Applications Kim 39 4 4075 2012 10.1016/j.eswa.2011.09.088 Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing 

    상세보기

48. International Journal of Computer Integrated Manufacturing Kim 31 8 701 2018 10.1080/0951192X.2017.1407447 Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles 

    상세보기

49. Industrial Management and Data Systems Ko 117 5 927 2017 10.1108/IMDS-06-2016-0195 Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data 

    상세보기

50. Expert Systems with Applications Koksal 38 10 13448 2011 10.1016/j.eswa.2011.04.063 A review of data mining applications for quality improvement in manufacturing industry 

    상세보기

51. Smart Technology Kujawinska 10-20 2018 10.1007/978-3-319-73323-4_2 Application of cluster analysis in making decision about purchase of additional materials for welding process 

    

52. Expert Systems with Applications Lee 40 7 2435 2013 10.1016/j.eswa.2012.10.057 A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry 

    상세보기

53. 10.1109/TSM.2017.2676245 Lee, K., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 30(2), May 2017, 135-142. doi:10.1109/TSM.2017.2676245. 

    상세보기

54. 10.1109/TSM.2016.2628865 Lee, H., Kim, Y., & Kim, C. (2017). A deep learning model for robust wafer fault monitoring with sensor measurement noise. IEEE Transactions on Semiconductor Manufacturing, 30(1), February 2017, Article number 7744687, 23-31. doi:10.1109/TSM.2016.2628865. 

    상세보기

55. Sensors Lee 18 5 1 2018 10.3390/s18051428 Implementation of cyber-physical production systems for quality prediction and operation control in metal casting 

    상세보기

56. International Journal of Online Engineering Lei 13 6 22 2017 10.3991/ijoe.v13i06.6812 Online monitoring of manufacturing process based on autoCEP 

    상세보기

57. Journal of Environmental Management Li 246 564 2019 10.1016/j.jenvman.2019.06.022 Mining of association rules between industrialization level and air quality to inform high-quality development in China 

    상세보기

58. 10.1016/j.procir.2013.05.033 Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. In Proceedings of the Forty Sixth CIRP Conference on Manufacturing Systems 2013, Procedia CIRP, 7, 193-198. doi:10.1016/j.procir.2013.05.033. 

    상세보기

59. IEEE Transactions on Semiconductor Manufacturing Lim 30 3 285 2017 10.1109/TSM.2017.2721820 Failure prediction using sequential pattern mining in the wire bonding process 

    상세보기

60. Procedia CIRP Lingitz 72 1051 2018 10.1016/j.procir.2018.03.148 Lead time prediction using machine learning algorithms: A case study by asemiconductor manufacturer 

    상세보기

61. 10.1016/j.ifacol.2018.03.075 Liukkonen, M., & Hiltunen, Y. (2018). Recognition of systematic spatial patterns in silicon wafers based on SOM and k-means. IFAC-PapersOnLine, 51(2), 1 January 2018, 439-444. doi:10.1016/j.ifacol.2018.03.075. 

    상세보기

62. 10.1016/j.cie.2015.04.008 Luo, M., Yan, H., Hu, B., Zhou, J., & Pang, C. (2015). A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Computers and Industrial Engineering, 85, 1 September 2015, Article number 4006, 414-422. doi:10.1016/j.cie.2015.04.008. 

    상세보기

63. Applied Sciences Lv 8 4 582 2018 10.3390/app8040582 A review of data mining with big data towards its applications in the electronics industry 

    상세보기

64. 10.1109/BigData.2016.7840826 Mangal, A., & Kumar, N. (2016). Using big data to enhance the bosch production line performance: A Kaggle challenge. In Proceedings of the IEEE International Conference on Big Data. December 5-8, Washington D.C, USA (pp. 2029-2035). doi:10.1109/BigData.2016.7840826. 

    

65. IEEE Transactions on Semiconductor Manufacturing Meidan 24 2 237 2011 10.1109/TSM.2011.2118775 Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining 

    상세보기

66. 10.1007/978-3-642-23957-1_27 Minaei-Bidgoli, B., Asadi, M., & Parvin, H. (2011). An ensemble based approach for feature selection. In L. Iliadis, & C. Jayne (Eds), Engineering Applications of Neural Networks. Advances in Information and Communication Technology, 363, 240-246. doi:10.1007/978-3-642-23957-1_27. 

    

67. Artificial Intelligence Review Minaei-Bidgoli 41 27 2014 10.1007/s10462-011-9295-x Effects of resampling method and adaptation on clustering ensemble efficacy 

    상세보기

68. 10.1109/CCECE.2016.7726783 Mohammadi, P., & Wang, Z. (2016). Machine learning for quality prediction in abrasion- resistant material manufacturing process. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering. May 15-18, 2016, Vancouver, Canada (pp.1-4). doi:10.1109/CCECE.2016.7726783. 

    

69. 10.1109/ICCP.2017.8116997 Moldovan, D., Cioara, T., Anghel, I., & Salomie, I. (2017). Machine learning for sensor-based manufacturing processes. In Proceedings of the 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 7-9 Sept. 2017 (pp. 147-154). doi:10.1109/ICCP.2017.8116997. 

    

70. 10.7763/IJET.2016.V8.898 Munirathinam, S., & Ramadoss, B. (2016). Predictive models for equipment fault detection in the semiconductor manufacturing process. IACSIT International Journal of Engineering and Technology, 8(4), August 2016. 273-285. doi:10.7763/IJET.2016.V8.898. 

    

71. Journal of Manufacturing Systems Mutlu 53 169 2019 10.1016/j.jmsy.2019.09.008 Assessment of occupational risks in Turkish manufacturing systems with data-driven models 

    상세보기

72. International Journal of Communications, Network and System Sciences Nagorny 10 3 31 2017 10.4236/ijcns.2017.103003 Big data analysis in smart manufacturing: A review 

    상세보기

73. IEEE Transactions on Semiconductor Manufacturing Nakata 30 4 339 2017 10.1109/TSM.2017.2753251 A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing 

    상세보기

74. 10.1109/ICCA.2017.8003037 Nedelkoski, S., & Stojanovski, G. (2017). Machine learning for large scale manufacturing data with limited information. In Proceedings of the 13the IEEE International Conference on Control and Automation, 3-6 July 2017, Ohrid, Macedonia (pp. 70-75). doi:10.1109/ICCA.2017.8003037. 

    

75. Sensors Niitsoo 19 5 1 2019 10.3390/s19051064 A deep learning approach to position estimation from channel impulse responses 

    상세보기

76. Solar Energy Materials and Solar Cells Odabasi 205 2020 10.1016/j.solmat.2019.110284 Machine learning analysis on stability of perovskite solar cells 

    상세보기

77. PLoS ONE Onel 14 10 2019 10.1371/journal.pone.0223517 Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization 

    상세보기

78. Jurnal Teknologi Ong 77 18 103 2015 10.11113/jt.v77.6496 A manufacturing failure root cause analysis in imbalance data set using PCA weighted association rule mining 

    상세보기

79. Procedia CIRP Packianather 62 123 2017 10.1016/j.procir.2016.06.120 Data mining techniques applied to a manufacturing SME 

    상세보기

80. 10.1016/j.eswa.2019.112950 Park, H. & Jung, J. (2020). SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining. Expert Systyems with Applications, 141, Article Number: 112950. doi:10.1016/j.eswa.2019.112950. 

    상세보기

81. Journal of Experimental and Theoretical Artificial Intelligence Parvin 25 2 227 2013 10.1080/0952813X.2012.715683 A new classifier ensemble methodology based on subspace learning 

    상세보기

82. International Journal of Learning Management Systems Parvin 1 2 37 2013 10.12785/ijlms/010204 A classifier ensemble of binary classifier ensembles 

    

83. International Journal of Physical Sciences Parvin 6 14 3419 2011 Linkage learning based on differences in local optimums of building blocks with one optima 

84. Journal of Bionanoscience Parvin 7 6 673 2013 10.1166/jbns.2013.1162 A new imbalanced learning and dictions tree method for breast cancer diagnosis 

    

85. Computers and Electrical Engineering Parvin 39 5 1433 2013 10.1016/j.compeleceng.2013.02.004 Data weighing mechanisms for clustering ensembles 

    상세보기

86. Engineering Applications of Artificial Intelligence Parvin 37 34 2015 10.1016/j.engappai.2014.08.005 Proposing a classifier ensemble framework based on classifier selection and decision tree 

    상세보기

87. 10.1109/BigData.2016.7840828 Pavlyshenko, B. (2016). Machine learning, linear and bayesian models for logistic regression in failure detection problems. In Proceedings of the 2016 IEEE International Conference on Big Data, 5-8 Dec. 2016, Washington, DC, USA (pp. 2046-2050). doi:10.1109/BigData.2016.7840828. 

    

88. 10.1243/095440505X32274 Pham, D., & Afify A. (2005). Machine-learning techniques and their applications in manufacturing. In The Proceedings of the Institution of Mechanical Engineers Part B, Journal of Engineering Manufacture, 219(5), 395-412. doi:10.1243/095440505X32274. 

    상세보기

89. 10.1109/SSCI.2016.7849923 Pospisil, M., Bartik, V., & Hruska, T. (2016). Analyzing machine performance using data mining. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, 6-9 Dec. 2016 (pp. 1-7). doi:10.1109/SSCI.2016.7849923. 

    

90. Computers and Industrial Engineering Priore 126 282 2018 10.1016/j.cie.2018.09.034 Learning-based scheduling of flexible manufacturing systems using ensemble methods 

    상세보기

91. International Journal of Control and Automation Purnama 8 12 47 2015 10.14257/ijca.2015.8.12.05 Mining interesting least association rules in manufacturing industry: A case study in MODENAS 

    상세보기

92. Journal of Manufacturing Systems Quatrini 56 117 2020 10.1016/j.jmsy.2020.05.013 Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities 

    상세보기

93. 10.1109/ICSSEM.2011.6081155 Raktham, T., & Piromsopa K. (2011). Development of workload models for CNC machines from 3 - Phase current consumption using ensemble method. In Proceedings of the International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2011, Guiyang, China, 22-23 October 2011 (pp. 102-105). doi:10.1109/ICSSEM.2011.6081155. 

    

94. 10.1016/j.jmsy.2018.04.008 Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48(Part C), July 2018, 71-77. doi:10.1016/j.jmsy.2018.04.008. 

    상세보기

95. 10.1145/3093742.3095099 Rivetti, N., Busnel, Y., & Gal, A. (2017). FlinkMan : Anomaly Detection in manufacturing equipment with Apache Flink. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS’17), Barcelona, Spain. 19-23 June, 2017 (pp. 274-279). doi:10.1145/3093742.3095099. 

    

96. Sensors Rodriguez-Martin 20 14 3982 2020 10.3390/s20143982 Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods 

    상세보기

97. Artificial Intelligence Review Rokach 33 1 2010 10.1007/s10462-009-9124-7 Ensemble-based classifiers 

    상세보기

98. International Journal of Metrology and Quality Engineering Rostami 6 1 2015 10.1051/ijmqe/2015023 Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines 

    상세보기

99. 10.1109/ICMLA.2016.0094 Rostami, H., Blue, J., & Yugma, C. (2016). Equipment condition diagnosis and fault fingerprint extraction in semiconductor manufacturing. In Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA, 18-20 December 2016 (pp. 534-539). doi:10.1109/ICMLA.2016.0094. 

    

100. 10.1109/EDPC.2016.7851317 Sand, C., Kunz, S., Hubbert, H., & Franke, J. (2016). Towards an inline quick reaction system for actuator manufacturing using data mining. In Proceedings of the 6th International Electric Drives Production Conference (EDPC), Nuremberg, Germany, 30 November-1 December, 2016. doi:10.1109/EDPC.2016.7851317. 

     

101. International Journal of Basic Sciences and Applied Research Seyedaghaee 2 5 500 2013 An optimized model for the university strategic planning 

102. Chinese Journal of Mechanical Engineering Shao 30 6 1347 2017 10.1007/s10033-017-0189-y a deep learning approach for fault diagnosis of induction motors in manufacturing 

     상세보기

103. 10.1007/978-3-319-51133-7_91 Shotorbani, P., Ameri, F., Kulvatunyou, B., & Ivezic, N. (2016). A hybrid method for manufacturing text mining based on document clustering and topic modeling techniques. In Proceedings of the International Conference on Advances in Production Management System (APMS 2016) (pp. 777-786). doi:10.1007/978-3-319-51133-7_91. 

     

104. Stanisavljevic, D., & Spitzer, M. (2016). A review of related work on machine learning in semiconductor manufacturing and assembly lines. In Proceedings of the 16th International Conference on Knowledge Technologies and Data Driven Business, Graz, Austria, 18-19 October 2016. 

105. Procedia Manufacturing Susto 11 2018 2017 10.1016/j.promfg.2017.07.353 Anomaly detection approaches for semiconductor manufacturing 

     상세보기

106. Sensors Syafrudin 18 9 2018 10.3390/s18092946 Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing 

     상세보기

107. Neurocomputing Tian 151 1 296 2015 10.1016/j.neucom.2014.09.036 Steel plates fault diagnosis on the basis of support vector machines 

     상세보기

108. Journal of Manufacturing Science and Engineering, Transactions of the ASME Tootooni 139 9 2017 Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches 

     상세보기

109. IFAC-PapersOnline Traini 52 13 177 2019 10.1016/j.ifacol.2019.11.172 Machine learning framework for predictive maintenance in milling 

     상세보기

110. Journal of Intelligent Manufacturing Wang 18 4 487 2007 10.1007/s10845-007-0053-5 Applying data mining to manufacturing: The nature and implications 

     상세보기

111. Advances in Manufacturing Wang 1 1 62 2013 10.1007/s40436-013-0010-9 Towards zero-defect manufacturing (ZDM)-A data mining approach 

     상세보기

112. 10.1109/FSKD.2007.482 Wang, K., Tong, S., & Eynard, B. (2007). Review on application of data mining in product design and manufacturing. In Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Haikou, China, 24-27 August 2007. doi:10.1109/FSKD.2007.482. 

     

113. 10.1016/j.ifacol.2018.09.316 Wang Y., Li K., & Gan, S. (2018). A kernel connectivity-based outlier factor algorithm for rare data detection in a baking process. IFAC-PapersOnLine, 51(18), 1 January 2018, 297-302. doi:10.1016/j.ifacol.2018.09.316. 

     상세보기

114. 10.1007/s12652-018-0794-3 Wang, J., Wang, K., Wang, Y., Huang, Z., & Xue, R. (2018). Deep Boltzmann machine based condition prediction for smart manufacturing. Journal of Ambient Intelligence and Humanized Computing, 21 April 2018, 1-11. doi:10.1007/s12652-018-0794-3. 

     상세보기

115. IEEE Robotics and Automation Letters Wang 4 3 195775490 2019 10.1109/LRA.2019.2921928 Modeling of human Welders’ operations in virtual reality human-robot interaction 

     상세보기

116. 10.1109/ASMC.2018.8373191 Waschneck, B., Reichstaller, A., Belzner, L., Altenmuller, T., Bauernhansl, T., Knapp, A., & Kyek, A. (2018). Deep reinforcement learning for semiconductor production scheduling. In Proceedings of the 29th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018, Saratoga Springs, United States, 30 April-3 May, 2018 (pp. 301-306). doi:10.1109/ASMC.2018.8373191. 

     

117. The International Journal of Advanced Manufacturing Technology Weichert 104 1889 2019 10.1007/s00170-019-03988-5 A review of machine learning for the optimization of production processes 

     상세보기

118. IEEE Transactions on Systems, Man, and Cybernetics: Systems Wen 49 1 136 2019 10.1109/TSMC.2017.2754287 A new deep transfer learning based on sparse auto-encoder for fault diagnosis 

     상세보기

119. International Journal of Production Research Wu 58 9 2822 2020 10.1080/00207543.2020.1727041 A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant producton systems 

     상세보기

120. Journal of Intelligent Manufacturing Wuest 25 5 1167 2014 10.1007/s10845-013-0761-y An approach to monitoring quality in manufacturing using supervised machine learning on product state data 

     상세보기

121. Production & Manufacturing Research Wuest 4 1 23 2016 10.1080/21693277.2016.1192517 Machine learning in manufacturing: Advantages, challenges, and applications 

     상세보기

122. IEEE Robotics and Automation Letters Yasutomi 5 2 2921 2020 10.1109/LRA.2020.2974709 Localization of inspection device along belt conveyors with multiple branches using deep neural networks 

     상세보기

123. Control Engineering Practice Zhang 95 2020 10.1016/j.conengprac.2019.104258 Prediction and causal analysis of defects in steel products: Handling nonnegative and highly overdispersed count data 

     상세보기

124. Journal of Cleaner Production Zhang 142 2 626 2017 10.1016/j.jclepro.2016.07.123 A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products 

     상세보기

125. Mechanical Systems and Signal Processing Zhao 115 213 2019 10.1016/j.ymssp.2018.05.050 Deep learning and its applications to machine health monitoring 

     상세보기

126. Advances in Manufacturing Zhou 5 4 321 2017 10.1007/s40436-017-0205-6 Thinking process rules extraction for manufacturing process design 

     상세보기

127. International Journal of Advanced Robotic Systems Zidek 13 5 1 2016 10.1177/1729881416664901 Embedded vision equipment of industrial robot for inline detection of product errors by clustering-classification algorithms 

     상세보기