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

机译：加工状态智能评价方法对工艺质量控制的智能评价方法研究

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摘要

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 time-varying, 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.
机译：流程质量的动态控制具有重要意义，可以提高制造过程的智能化。加工状态的实时监控和评估为工艺质量的智能控制提供了支持。鉴于监测参数的时变，耦合和动态特性，以及处理状态与产品质量之间的实时动态相关性和非线性关系，本文采用堆叠的自动编码器（SAE）来优化加工过程中的多维实时监测参数。通过使用SAE-BP神经网络的混合模型，多维监控参数和处理状态之间的非线性映射关系的特征自适应地和在智能制造过程中的加工状态的动态智能评价实现。以实验平台为例，验证了处理状态下使用的SAE-BP神经网络的动态智能评估的有效性。该方法提供了对加工状态的实时动态评估的支持。

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