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Machine learning in SMED

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and their comparison are presented in the paper. Machine learning methods are used to suggest the method of time measurement which should be applied in a particular case of workstation reorganization. A training set is developed and an example of classification is presented. Time and motion study is one of important methods of estimating machine changeover time. In the field of time study, researchers present the obtained results by using (linear) multi-linear regression models (MLR), and (non-linear) multi-layer perceptrons (MLP). The presented approach is particularly important for the enterprises which offer make-to-order products. Development of the SMED method can influence manufacturing cost reduction of customized products. In variety oriented manufacturing, SMED supports flexibility and adaptability of the manufacturing system.
Rocznik
Strony
31--40
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
  • University of Bielsko-Biala, Bielsko-Biala, Poland
Bibliografia
  • [1] GORSKI F., ZAWADZKI P., HAMROL A., 2016, Knowledge based engineering as a condition of effective mass production of configurable products by design automation, Journal of Machine Engineering, 16/4.
  • [2] UHLMANN E., HOHWIELER E., GEISERT C., 2017, Intelligent production systems in the era of industrie 4.0 – changing mindsets and business models, Journal of Machine Engineering, 17/2.
  • [3] JEDRZEJEWSKI J., KWASNY W., 2015, Development of Machine Tool Operational Properties, Journal of Machine Engineering, 15/1.
  • [4] GEST G., CULLEY S.J., MCINTOSH R.I., MILEHAM A.R., OWEN G.W., 1995, Review of fast tool change systems, Computer Integrated Manufacturing Systems, 8, 205-210.
  • [5] COIMBRA E.A., 2009, Total Flow Management: Achieving Excellence with Kaizen and Lean Supply Chains, Kaizen Institute.
  • [6] FERRADÁS P.G., SALONITIS K., 2013, Improving changeover time: a tailored SMED approach for welding cells, Forty Sixth CIRP Conference on Manufacturing Systems 2013, Procedia CIRP, 7, 598-603, Published by Elsevier.
  • [7] GOUBERGEN D.V., LANDEGHEM H.V., 2002, Rules for integrating fast changeover capabilities into new equipment design, Robotics and Computer Integrated Manufacturing, 18, 205-214.
  • [8] SHINGO S., 1985, A revolution in manufacturing: The SMED system, Productivity Press, Stanford, CT.
  • [9] SAKAMOTO S., 2010, Beyond World-Class Productivity, Springer London.
  • [10] ARN E.A., 1975, Group Technology, Springer-Verlag, Berlin Heidelberg.
  • [11] GIRONIMO G., MARTINO C., LANZOTTI A., MARZANO A., RUSSO G., 2012, Improving MTM-UAS to predetermine automotive maintenance time, International Journal on Interactive Design and Manufacturing, 6, 265-273.
  • [12] CARAGNANO G., FISCHER H., 2010, First time right. MTM-prawidłowo od samego początku, Polskie Stowarzyszenie MTM.
  • [13] KUO C.F., WANG M.J., 2009, Motion generation from MTM semantics, Computers in Industry, 60, 339-348.
  • [14] LARINGA J., FORSMANB M., KADEFORSA R., ORTENGRENA R., 2002, MTM-based ergonomic workload analysis, International Journal of Industrial Ergonomics, 30, 135-148.
  • [15] BRAMLEY A., KNIGHT W., et al., 2011, Dictionary of Production Engineering/Wörterbuch der Fertigungstechnik.
  • [16] ZHOU J., DUAN B., HUANG J., CAO H., 2014, Data-driven modelling and optimization for cavity filters using linear programming support vector regression, Neural Computing and Application, 24, 1771-1783.
  • [17] ROY R., KOEPPEN M., OVASKA S., FURUHASHI T., HOFFMANN F., 2002, Soft Computing and Industry, Springer-Verlag London.
  • [18] YANG Q., WU D.L., ZHU H.M., BAO J.S., WEI Z.H., 2013, Assembly operation process planning by mapping a virtual assembly simulation to real operation, Computers in Industry, 64, 869-879.
  • [19] ZHA X.F., LIM S.Y.E., FOK S.C., 1998, Integrated intelligent design and assembly planning: a survey, The International Journal of Advanced Manufacturing Technology, 14, 664-685.
  • [20] SHIUE Y.-R., GUH R.-S., 2006, The optimization of attribute selection in decision tree-based production control systems, International Journal of Advanced Manufacturing Technology, 28, 737-746.
  • [21] PRIORE P., DE LA FUENTE D., GOMEZ A., PUENTE J., 2001, A review of machine learning in dynamic scheduling of flexible manufacturing systems, Artif. Intell. Eng. Des. Anal. Manuf., 15/3, 251-263.
  • [22] YOUNG H.T., TSAI D.H., 1994, An integrated expert operation planning system with a feature- based design model, The International Journal of Advanced Manufacturing Technology, 9, 305-310.
  • [23] FERNANDEZ-DELGADO M., REBOREDA M., CERNADAS E., BARRO S., 2010, A comparison of several neural networks to predict the execution, Neural Computing and Applications, 19/5, 741-754.
  • [24] CHEN T., WANG Y.-C., TSAI H.-R., 2009, Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization, International Journal of Advanced Manufacturing Technology, 42, 1206-1216.
  • [25] CHEN T., 2008, A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction, International Journal of Advanced Manufacturing Technology, 37, 782–792.
  • [26] CHEN T., 2012, A job-classifying and data-mining approach for estimating job cycle time in a wafer fabrication factor, Journal of Advanced Manufacturing Technology, 62, 317-328.
  • [27] KUTSCHENREITER-PRASZKIEWICZ I., 2013, Application of neural network in QFD matrix, Journal of Intelligent Manufacturing, 24, 397-404.
  • [28] RENUGA DEVI S., ARULMOZHIVARMAN P., VENKATESH C., AGARWAL P., 2016, Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction, International Journal of Automation and Computing, 13/5, 417-42.
  • [29] WANG X.Z., 1999, Data mining and knowledge discovery for process monitoring and control, Springer-Verlag London Limited.
  • [30] HAYASHI Y., SETINO R., AZCARRAGA A., 2016, Neural network training and rule extraction with augmented discretized input, Neurocomputing, 207, 610-622.
  • [31] JIANG C., JIANG M., XU Q., HUANG X., 2017, Expectile regression neural network model with applications. Neurocomputing, 247, 73-86.
  • [32] LIU W., WANG Z., LIU X., ZENG N., LIU Y., ALSAADI F., 2017, A survey of deep neural network architectures and their applications, Neurocomputing, 234, 11-26.
  • [33] COOK D., SHANNON R., 1991, A sensitivity analysis of a back-propagation neural network for manufacturing process parameters, Journal of Intelligent Manufacturing, 2, 155-163.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7c442852-25c6-4f9b-a783-0f2e117133fb
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