Abstract
In modern manufacturing environment, multi-source heterogeneous data in the process of workshop processing are collected on a large scale, which reflects the information of processing equipment and workpiece quality. In order predict the workpiece quality by making full use of the collected big data, the SC-GA-RBF model is established. The parameters of RBF neural network are optimized by using SC algorithm and GA algorithm. The four quality prediction algorithms are simulated on the simulation platform, and the experiment result shows that SC-GA-RBF model is the best, BP model is the second, SC-RBF model is the third, and RBF model is the worst in term of RMSE, and the RMSE of SC-GA-RBF model is 0.093. The requirements of quality control can be satisfied by the application of the SC-GA-RBF model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Costa, A., Claro, F.: Double sampling, X̄, control chart for a first-order autoregressive moving average process model. Int. J. Adv. Manuf. Technol. 39(5–6), 521–542 (2008)
Chang, C.C., Pan, T.H., Wong, S.H., et al.: A G&P EWMA algorithm for high-mix semiconductor manufacturing processes. J. Process Control 21(1), 28–35 (2011)
Lowry, C., Woodall, W., Champ, C., et al.: A multivariate exponentially weighted moving average control chart. Technometrics 34(1), 8 (1992)
Zantek, P., Wright, G.P., Plante, R.: A self-starting procedure for monitoring process quality in multistage manufacturing systems. A I IE Trans. 38(4), 16 (2006)
Jun, C., Chao, Y., Fei, L., et al.: Research and application on dynamic statistical process control of key process in multi–varieties and small–batch machining workshop. China Mech. Eng. 22(23), 2822–2827 (2011). (in Chinese)
Rui, M., Xiao-ming, S., Shu-gang, L., Dong, Y., et al.: Research on statistical process quality control based on low volume manufacturing. Comput. Integr. Manuf. Syst. 11(11), 1633–1635 (2005). (in Chinese)
Qiu, P., Zhang, J.: On phase II SPC in cases when normality is invalid. Qual. Reliab. Eng. 31(1), 27–35 (2014)
Farooq, A., Cherif, C.: Development of prediction system using artificial neural networks for the optimization of spinning process. Fibers Polym. 13(2), 253–257 (2012)
Peng, K., Yu, S.: Prediction and control of strip thickness based on bayesian neural networks. J. Univ. Sci. Technol. Beijing 32(2), 256–262 (2010). (in Chinese)
Sun, L., Yang, S.: Intelligent prediction for process quality of flexible manufacturing system based on SVM. Syst. Eng. Theor. Pract. 29(6), 139–146 (2009). (in Chinese)
Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2), 281–294 (1989)
Adem, K., Seref, S.: Elman network with embedded memory for system identification. Inf. Sci. Eng. 22(6), 1555–1568 (2006)
Da-peng, X., Jian-zhong, Z., Hua-feng, G., Xia, J.: Quality prediction of laser cladding layer based on improved neural network. Laser Technol. 31(5), 511–514 (2007). (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cao, H. (2021). Workpiece Quality Prediction Research Based on Multi-source Heterogeneous Industrial Big Data. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_44
Download citation
DOI: https://doi.org/10.1007/978-981-33-4572-0_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4573-7
Online ISBN: 978-981-33-4572-0
eBook Packages: Computer ScienceComputer Science (R0)