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

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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

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Abbreviations

Symbols/ parameters :

\(\mathbf {Meaning}\)

\(f_{i}\) :

\(i\)th quality feature of product

\(q- f_{i}\) :

output quality of \(i\)th feature

\(F\) :

set of quality feature \(f_{i}\)

\(n\) :

total number of features

\(s_{i }\) :

\(i\)th machining stage

\(S \) :

set of machining stage \(s_{i}\)

\(m \) :

total number of machining stages

\(l_{ij }\) :

coupling relation between \(f_{i}\) and \(f_{j}\)

\(L \) :

set of coupling relation

\(p_{i}\) :

importance degree of \(f_{i}\)

\(P\) :

set of importance degree

\(N_{i}\) :

\(i\)th network node

\(N\) :

node set

\(e_{ij}\) :

network edge between \(N_{i}\) and \(N_{j}\)

\(E \) :

edge set

\(w_{i}\) :

\(i\)th node weight

\(W \) :

weight set

\(D_{i}\) :

degree of \(i\)th node

\(C_{i }\) :

clustering coefficient of \(i\)th node

\(E_{i}\) :

sensitivity degree of \(i\)th node

\(Q_{i}\) :

influence index of \(i\)th node

\(\varphi \) :

node strength coefficient

\(\rho \) :

Weibull coefficient

\(c_{ij}\) :

coupling degree of \(N_{i}\) and\( N_{j}\)

\(\alpha \) :

number of produced new nodes

\(\beta \) :

possible maximum edges

\(U\) :

time dimension of node state

\(x_{t}\), \(y_{t}\) :

element of node state at time \(t\)

\(\gamma \) :

the resolution ratio

\(N_{key}\) :

key node

\(c_{key}\) :

coupling degree of key nodes

\(\lambda \) :

number of nodes coupled with key nodes

\(X_{key}\) :

state set of key nodes

\(X_{ckey}\) :

coupling data set of key nodes

\(c_{ckey, j}\) :

coupling degree between key node and \(j\)th node

\(b_{0}\),\(b_{1}\),\(b_{2}\),\(b_{3}\) :

coefficient of status equation

\(B \) :

initial solution

\(T \) :

target solution

\(Q \) :

number of total targets

\(u \) :

start region

\(v \) :

end region

\(\delta \) :

objects in one search region

\(\sigma \) :

region size

\(q\) :

number of search regions

\(\sigma \) :

search parameters

\(T_{l}\) :

global random search steps

\(\psi \) :

global search index

\(C_{\sigma }\) :

global search constant

\(T_{s}\) :

local walk search steps

\(r\) :

neighbor points of \(B\)

\(\mu \) :

local search coefficient

\(E\) :

minimum accumulated error

References

  • 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.

    Article  Google Scholar 

  • Aoki, M., & Aovenner, A. (1991). State space model of multiple time series. Economic Reviews, 10(1), 1–59.

    Article  Google Scholar 

  • Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308.

    Article  Google Scholar 

  • Capocci, A., Servedio, V. D. P., Caldarelli, G., & Colaiori, F. (2005). Detecting communities in large networks. Physical A, 352(2–4), 669–676.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Du, W., Mo, R., Li, S., & Li, B. (2012). Design of product key characteristics management system. Advanced Materials Research, 468–471, 835–838.

    Article  Google Scholar 

  • Gade, P. M., & Hu, C. K. (2000). Synchronous chaos in coupled map lattices with small-world interactions. Physical Review E, 62(5), 6409–6413.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hu, S. J. (1997). Stream of variation theory for automotive body assembly. Annals of the CIRP, 46(1), 1–6.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jiao, Y., & Djurdjanovic, D. (2011). Compensability of errors in product quality in multistage manufacturing processes. Journal of Manufacturing Systems, 30(4), 204–213.

    Article  Google Scholar 

  • Jin, M., Li, Y. T., & Tsung, F. (2010). Chart allocation strategy for serial-parallel multistage manufacturing processes. IIE Transactions, 42(8), 577–588.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Keinberg, J. (2000). Navigation in a small world. Nature, 406(6798), 845–845.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Köksoy, O. (2011). A nonlinear programming solution to robust multi-response quality problem. Applied Mathematics and Computation, 196(2), 603–612.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Li, M., Liu, F., & Ren, F. Y. (2007). Routing strategy on a two-dimensional small-world network model. Physical Review E, 75(6), 066115.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Moreno, Y., Gómez, J. B., & Pacheco, A. F. (2001). Instablity of scal-free networks under node-breaking avalanches. Europhysics Letters, 58(4), 630–636.

    Article  Google Scholar 

  • Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rosyidi, C. N., Irianto, D., & Toha, I. S. (2009). Prioritizing key characteristics. Journal of Advanced Manufacturing Systems, 8(1), 57–70.

    Article  Google Scholar 

  • Sanjay, G., & Prabhat, H. (1996). Identification of parameter coupling in turbine design using neural networks. Journal of Propulsion and Power, 12(3), 503–508.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Waxman, B. M. (1988). Routing of multipoint connections. IEEE Journal on Selected Areas in Communication, 6(9), 1617–1622.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Zhang, F. F., Liu, J., & Zuo, C. L. (2012). Research of complex network dynamics evolution. Lecture Notes in Electrical Engineering, 154, 806–815.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by grant no. 51275399 from the National Natural Science Foundation.

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Guangzhou Diao & Liping Zhao

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Yiyong Yao

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  1. Guangzhou Diao
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  2. Liping Zhao
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Correspondence to Guangzhou Diao.

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Diao, G., Zhao, L. & Yao, Y. A weighted-coupled network-based quality control method for improving key features in product manufacturing process. J Intell Manuf 27, 535–548 (2016). https://doi.org/10.1007/s10845-014-0887-6

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