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2021 | OriginalPaper | Chapter

Workpiece Quality Prediction Research Based on Multi-source Heterogeneous Industrial Big Data

Author : Huiteng Cao

Published in: Big Data Analytics for Cyber-Physical System in Smart City

Publisher: Springer Singapore

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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.

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Metadata
Title
Workpiece Quality Prediction Research Based on Multi-source Heterogeneous Industrial Big Data
Author
Huiteng Cao
Copyright Year
2021
Publisher
Springer Singapore
Book
Big Data Analytics for Cyber-Physical System in Smart City

Print ISBN: 978-981-334-573-7

Electronic ISBN: 978-981-334-572-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4572-0_44

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