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Product manufacturing reliability modeling paper has been published by Sensors journal

发布时间:2020年10月07日

Title:Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Fulltext:https://doi.org/10.3390/s20195677
Authors:Anqi Zhang, Yihai He, Xiao Han, Yao Li, Xiuzhen Yang,Zixuan Zhang.

Journal :Sensors.
SCI Impact factor:3.275(Q1) .
Published date:October  5
,2020.

Abstract:
For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.