Research On Intelligent Evaluation Method For Machining State Oriented To Process Quality Control

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI:10.1109/ICMLC.2018.8526989

Liping Zhao, Feng-Xian Yan, Lu Wang, Yiyong Yao

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引用次数: 2

Abstract

The dynamic control of process quality is of great significance to improve the intellectualization of manufacturing process. The real-time monitoring and evaluation of machining state provides support for the intelligent control of process quality. In view of the timevarying, coupling and dynamic characteristics of monitoring parameters, as well as the real-time dynamic correlation and nonlinear relationship between the processing state and the product quality, this paper uses the Stacked Auto-encoder (SAE) to optimize the multidimensional real-time monitoring parameters in the machining process. By using the hybrid model of SAE-BP neural network, the nonlinear mapping relation between multidimensional monitoring parameters and the processing state is characterized adaptively and the dynamic intelligent evaluation of the machining state in the intelligent manufacturing process is realized. Taking an experimental platform as an example, the validity of the dynamic intelligent evaluation of the SAE-BP neural network used in processing state is verified. The proposed method provides support for the real-time dynamic evaluation of machining state.

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面向加工质量控制的加工状态智能评价方法研究

过程质量的动态控制对提高制造过程的智能化水平具有重要意义。加工状态的实时监测和评估为过程质量的智能控制提供了支持。针对监控参数的时变、耦合和动态特性，以及加工状态与产品质量之间的实时动态相关性和非线性关系，本文采用叠置自编码器(SAE)对加工过程中的多维实时监控参数进行优化。采用SAE-BP神经网络混合模型，自适应表征了多维监测参数与加工状态之间的非线性映射关系，实现了智能制造过程中加工状态的动态智能评价。以实验平台为例，验证了SAE-BP神经网络在处理状态下动态智能评估的有效性。该方法为加工状态的实时动态评估提供了支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 International Conference on Machine Learning and Cybernetics (ICMLC)

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