Digital twin-driven surface roughness prediction and process parameter adaptive optimization

机械加工 表面粗糙度 过程(计算) 粒子群优化 可预测性 刀具磨损 人工神经网络 过程变量 计算机科学 工程类 机械工程 人工智能 机器学习 数学 材料科学 操作系统 复合材料 统计
作者
Lilan Liu,Xiangyu Zhang,Xiang Wan,Shuaichang Zhou,Zenggui Gao
出处
期刊:Advanced Engineering Informatics [Elsevier BV]
卷期号:51: 101470-101470 被引量:69
标识
DOI:10.1016/j.aei.2021.101470
摘要

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.
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