Abstract
To ensure the machining processes stability of multistage machining processes (MMPs) and improve the quality of machining processes, a real-time quality monitoring and predicting model based on error propagation networks for MMPs is proposed in this paper. As there are some complicated interactions among different stages in MMPs, a machining error propagation network (MEPN) is proposed and its complexity is discussed to analyze the correlation among different stages in MMPs. Based on these, a real-time quality-monitoring model based on process variation trajectory chart is proposed to monitor the key machining stages extracted by MEPN. Due to the complexity of the correlation in MEPN, it is important and necessary to explore the variation propagation mechanism in MEPN. As for this issue, a machining error propagation model of machining form feature nodes in MEPN is established with the neuron model, which is solved with back-propagation neural network. The mapping relationship among machining errors of quality attributes is described through this node model. Furthermore, a novel equipment synthetic failure probability exponent of machining status nodes in MEPN is established to synthesize equipment’s parameters by using logistic regression to quantitatively analyze the potential-failure and forecast the equipment degradation trend. At last, the machining process of a connecting rod is used to verify the proposed method.
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References
Akaike H. (1969) Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 21(1): 243–247
Barabasi A. L., Albert R. (1999) Emergence of scaling in random networks. Science 286: 509–512
Barhak J., Djurdjanovic D., Spicer P., Katz R. (2005) Integration of reconfigurable inspection with stream of variations methodology. International Journal of Machine Tools & Manufacture 45: 407–419
Chen W. C., Tseng S. S., Wang C. Y. (2005) A novel manufacturing defect detection method using association rule mining techniques. Expert Systems with Applications 29(4): 807–815
Ding F., He Z., Zi Y., Chen X., Cao H., Tan J. (2009) Reliability assessment based on equipment condition vibration feature using proportional hazards model. Chinese Journal of Mechanical Engineering 45(12): 89–94
Ertugrul I., Aytaç E. (2009) Construction of quality control charts by using probability and fuzzy approaches and an application in a textile company. Journal of Intelligent Manufacturing 20: 139–149
Feng J., Jiang P. Y. (2009) Method of change management based on dynamic machining error propagation. Science in China Series E: Technological Sciences 52(7): 1811–1820
Guo Y., Dooley K. J. (1992) Identification of change structure in statistical process control. International Journal of Production Research 30(7): 1655–1669
Hosmer D. W., Lemeshow S. (1989) Applied logistic regression. Wiley, New York, NY
Hu S. J. (1997) Stream of variation theory for automotive body assembly. Annals of CIRP 46(1): 1–6
Huang Q., Shi J., Yuan J. (2003) Part dimensional error ant its propagation modeling in multi-operational machining processes. Journal of Manufacturing Science and Engineering 125(2): 255–262
Jiang P., Liu D., Zeng Z. (2009) Recognizing control chart patterns with neural network and numerical fitting. Journal of Intelligent Manufacturing 20: 625–635
Jin M., Li Y. T., Tsung F. (2010) Chart allocation strategy for serial-parallel multistage manufacturing processes. IIE Transactions 42(8): 577–588
Liu D., Jiang P. (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 Manufacture 223: 623–640
Luxhoj J. T., Shyur H.-J. (1997) Comparison of proportional hazards models and neural networks for reliability estimation. Journal of Intelligent Manufacturing 8(3): 227–234
Montgomery D. C. (1996) Introduction to statistical quality control (3rd ed.). Wiley, New York
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: 607–617
Samrout M., Châtelet E., Kouta R., Chebbo N. (2009) Optimization of maintenance policy using proportional hazard model. Maintenance Modeling and Application 94(1): 44–52
Tian Z. (2012) An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing 32(2): 227–237
Väyrynen, J., Mattila, J., Vilenius, M., Ali, M., Valkama, P., Siuko M., & Semeraro, L. (2011). Predicting the runtime reliability of ITER remote handling maintenance equipment. In Proceedings of the 26th symposium of fusion technology (SOFT-26), Vol. 86, no. 9–11, pp. 2012–2015.
Wade M. R., Woodall W. H (1993) A review and analysis of cause-selecting control charts. Journal of Quality Technology 25(3): 161–169
Watts D. J., Strogatz S. H (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442
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 and Manufacturing 14(2): 53–67
Yang S.-F. (2009) Process control using VSI cause selecting control charts. Journal of Intelligent Manufacturing 21: 853–867
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
Zhang, G. X. New type of quality control charts – cause-selecting control charts and a theory of diagnosis with control charts. In Proceedings of the World Quality Congress ’84, Brighton, England 1984, 175-185.
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Jiang, P., Jia, F., Wang, Y. et al. Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. J Intell Manuf 25, 521–538 (2014). https://doi.org/10.1007/s10845-012-0703-0
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DOI: https://doi.org/10.1007/s10845-012-0703-0