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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 3/2016

12.03.2014

A weighted-coupled network-based quality control method for improving key features in product manufacturing process

verfasst von: Guangzhou Diao, Liping Zhao, Yiyong Yao

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2016

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Abstract

There are some complicated coupling relations among quality features (QFs) in manufacturing process. Generally, the machining errors of one key feature may cause some errors of other features which are coupled with the key one. Considering the roles of key QFs, the weighted-coupled network-based quality control method for improving key features is proposed in this paper. Firstly, the W-CN model is established by defining the mapping rules of network elements (i.e. node, edge, weight). Secondly, some performance indices are introduced to evaluate the properties of W-CN. The influence index of node is calculated to identify the key nodes representing key features. Thirdly, three coupling modes of nodes are discussed and coupling degrees of key nodes are calculated to describe the coupling strengthen. Then, the decoupling method based on small world optimization algorithm is discussed to analyze the status changes of key nodes accurately. Finally, a case of engine cylinder body is presented to illustrate and verify the proposed method. The results show that the method is able to provide guidance for improving product quality in manufacturing process
Vorheriger Artikel A quantitative estimation technique for welding quality using local mean decomposition and support vector machine
Nächster Artikel Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

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Literatur
Abellan-Nebot, J. V., Liu, J., Subirón, F. R., & Shi, J. J. (2012). State space modeling of variation propagation in multistation machining processes considering machining-induced variations. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 134(2), 021002.CrossRef
Aoki, M., & Aovenner, A. (1991). State space model of multiple time series. Economic Reviews, 10(1), 1–59.CrossRef
Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.CrossRef
Barhak, J., Djurdjanovic, D., Spicer, P., & Katz, R. (2005). Integration of reconfigurable inspection with stream of variations methodology. International Journal of Machine Tools and Manufacture, 45(4–5), 407–419.CrossRef
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308.CrossRef
Capocci, A., Servedio, V. D. P., Caldarelli, G., & Colaiori, F. (2005). Detecting communities in large networks. Physical A, 352(2–4), 669–676.CrossRef
Chen, H., & Chu, X. N. (2012). A network-based assessment approach for change impacts on complex product. Journal of Intelligent Manufacturing, 23(4), 1419–1431.CrossRef
Du, W., Mo, R., Li, S., & Li, B. (2012). Design of product key characteristics management system. Advanced Materials Research, 468–471, 835–838.CrossRef
Gade, P. M., & Hu, C. K. (2000). Synchronous chaos in coupled map lattices with small-world interactions. Physical Review E, 62(5), 6409–6413.CrossRef
Ho, C. Y., & Lin, Z. C. (2003). Analysis and application of grey relation and ANOVA in chemical-mechanical polishing process parameters. International Journal of Advanced Manufacturing Technology, 21(1), 10–14.CrossRef
Hu, S. J. (1997). Stream of variation theory for automotive body assembly. Annals of the CIRP, 46(1), 1–6.CrossRef
Hu, H. S., & Li, Z. W. (2009). Local and global deadlock prevention policies for resource allocation systems using partially generated reachability graphs. Computers and Industrial Engineering, 57(4), 1168–1181.CrossRef
Hu, H. S., & Li, Z. W. (2010). Synthesis of liveness enforcing supervisor for automated manufacturing systems using insufficiently marked siphons. Journal of Intelligent Manufacturing, 21(4), 555–567.CrossRef
Hu, H. S., Zhou, M. C., & Li, Z. W. (2012). iveness and ratio-enforcing supervision of automated manufacturing systems using Petri nets. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 42(2), 2012.CrossRef
Huang, Q., Shi, J., & Yuan, J. (2003). Part dimensional error and its propagation modeling in multi-operational machining processes. ASME Transactions, Journal of Manufacturing Science and Engineering, 125(2), 255–262.CrossRef
Jiao, Y., & Djurdjanovic, D. (2011). Compensability of errors in product quality in multistage manufacturing processes. Journal of Manufacturing Systems, 30(4), 204–213.CrossRef
Jin, M., Li, Y. T., & Tsung, F. (2010). Chart allocation strategy for serial-parallel multistage manufacturing processes. IIE Transactions, 42(8), 577–588.CrossRef
Kam, J. M., Zeng, L., Zhou, Q., Tran, R., & Yang, J. (2013). On assessing spatial uniformity of particle distributions in quality control of manufacturing processes. Journal of Manufacturing Systems, 32(1), 154–166.CrossRef
Keinberg, J. (2000). Navigation in a small world. Nature, 406(6798), 845–845.CrossRef
Kim, H. K., Kim, J. K., & Chen, Q. Y. (2012). A product network analysis for extending the market basket analysis. Expert System and Application, 39(8), 7403–7410.CrossRef
Köksoy, O. (2011). A nonlinear programming solution to robust multi-response quality problem. Applied Mathematics and Computation, 196(2), 603–612.CrossRef
Li, J. M., Freheit, T., Hu, S. J., & Koren, Y. (2007). A quality prediction framework for multistage machining process driven by an engineering model and variation propagation model. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 129(6), 1088–1100.CrossRef
Li, M., Liu, F., & Ren, F. Y. (2007). Routing strategy on a two-dimensional small-world network model. Physical Review E, 75(6), 066115.CrossRef
Liu, D. Y., & Jiang, P. Y. (2009). The complexity analysis of a machining error propagation network and its application. Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacturing, 223(6), 623–640.CrossRef
Liu, F. Y., & Qi, G. N. (2008). Research on component relation network of product family and its application. International Journal of Materials and Product Technology, 31(2–4), 186–201.CrossRef
Lv, Y., Lee, K. M., & Wu, Z. (2011). Fuzzy theory applied in quality management of distributed manufacturing system: A literature review and classification. Engineering Applications of Artificial Intelligence, 24(2), 266–277.CrossRef
Mao, W. T., Yan, G. R., Dong, L. L., & Hu, D. K. (2011). Model selection for least squares support vector regressions based on small-world strategy. Expert Systems with Applications, 38(4), 3227–3237.CrossRef
Marsh, R., Jonik, M., Lanham, J., et al. (2010). Modelling an assembly process using a close coupled generative cost model and a discrete event simulation. International Journal of Computer Integrated Manufacturing, 23(3), 257–269.CrossRef
Moreno, Y., Gómez, J. B., & Pacheco, A. F. (2001). Instablity of scal-free networks under node-breaking avalanches. Europhysics Letters, 58(4), 630–636.CrossRef
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.CrossRef
Newman, M. E. J., Moore, C., & Watts, D. J. (2000). Mean-field solution of the small-world network model. Physical Review Letters, 84(14), 3201.CrossRef
Pacella, M., Semeraro, Q., & Anglani, A. (2004). Manufacturing quality control by means of a fuzzy ART network trained on natural process data. Engineering Applications of Artificial Intelligence, 17(1), 83–96.CrossRef
Qin, Y. T., Zhao, L. P., & Yao, Y. Y. (2011a). Multistage machining processes variation propagation analysis based on machining processes weighted network performance. International Journal of Advanced Manufacturing Technology, 55(5–8), 487–499.CrossRef
Qin, Y. T., Zhao, L. P., & Yao, Y. Y. (2011b). Dynamic quality characteristics modeling based on brittleness theory in complex manufacturing processes. International Journal of Computer Integrated Manufacturing, 24(20), 915–926.CrossRef
Quintana, G., Garcia-Romeu, M. L., & Ciurana, J. (2011). Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing, 22(4), 607–617.CrossRef
Rosyidi, C. N., Irianto, D., & Toha, I. S. (2009). Prioritizing key characteristics. Journal of Advanced Manufacturing Systems, 8(1), 57–70.CrossRef
Sanjay, G., & Prabhat, H. (1996). Identification of parameter coupling in turbine design using neural networks. Journal of Propulsion and Power, 12(3), 503–508.CrossRef
Shukla, N., Ceglarek, D., & Tiwari, M. K. (2013). Key characteristics-based sensor distribution in multi-station assembly processes. Journal of intelligent manufacturing. doi:10.​1007/​s10845-013-0759-5.
Wang, N., Xu, J., & Yang, J. (2012). Method of identifying key quality characteristics in multistage manufacturing process based on PLSR. Applied Mechanics and Materials, 217–219, 2580–2584.CrossRef
Wang, X. H., Yang, X. Y., & Su, T. T. (2006). Global numerical optimization based on small-world networks. Lecture Notes in Computer Science, 4222, 194–203.CrossRef
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRef
Waxman, B. M. (1988). Routing of multipoint connections. IEEE Journal on Selected Areas in Communication, 6(9), 1617–1622.CrossRef
Wolbrecht, E., Ambrosio, B. D., Paasch, B., & Kirby, D. (2000). Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 14(1), 53–67.
Yang, Z. M., Djurdjanovic, D., & Ni, J. (2008). Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing, 19(1), 87–98.CrossRef
Yu, K. T., Shen, S. H., & Chen, K. S. (2007). The evaluation of process capability for a machining center. International Journal of Advanced Manufacturing Technology, 33(5–6), 505–510.
Zhang, F. F., Liu, J., & Zuo, C. L. (2012). Research of complex network dynamics evolution. Lecture Notes in Electrical Engineering, 154, 806–815.CrossRef
Metadaten
Titel
A weighted-coupled network-based quality control method for improving key features in product manufacturing process
verfasst von
Guangzhou Diao
Liping Zhao
Yiyong Yao
Publikationsdatum
12.03.2014
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 3/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-014-0887-6

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