Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes
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.
KeywordsMultistage machining processes Machining error propagation network Process variation trajectory chart Artificial neural network Equipment service performance
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- Hosmer D. W., Lemeshow S. (1989) Applied logistic regression. Wiley, New York, NYGoogle Scholar
- Montgomery D. C. (1996) Introduction to statistical quality control (3rd ed.). Wiley, New YorkGoogle Scholar
- Samrout M., Châtelet E., Kouta R., Chebbo N. (2009) Optimization of maintenance policy using proportional hazard model. Maintenance Modeling and Application 94(1): 44–52Google Scholar
- 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.Google Scholar
- Wade M. R., Woodall W. H (1993) A review and analysis of cause-selecting control charts. Journal of Quality Technology 25(3): 161–169Google 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 and Manufacturing 14(2): 53–67Google Scholar
- 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.Google Scholar