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On-line model identification for the machining process based on multirate process data
Journal of Manufacturing Systems ( IF 12.1 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.jmsy.2020.04.006
Cheol W. Lee

Abstract Accurate identification of the model parameters of the machining process based on on-line process data is a crucial prerequisite for its model-based control and diagnostics. A typical machining process generates multi-output and multirate data streams. Whereas various sensors provide in-process information about the process, many important process outcomes including product qualities can be only measured in postprocess manner. This paper proposes to improve the identification by using both in-process and postprocess data and by analyzing the identifiability of model parameters. The identification of the model parameters based on multirate output is formulated using the maximum-likelihood estimation and the Fisher information matrix for a multirate-sampled system is derived to study the identifiability of model parameters. A strategy is developed to improve accuracy and robustness of the model identification considering the identifiability. The proposed method is tested on two batches of multirate process data from the cylindrical grinding process. The test results demonstrate using both in-process and postprocess data improves the identifiability and the proposed identification strategy results in improved prediction performance.

中文翻译:

基于多速率过程数据的加工过程在线模型辨识

摘要 基于在线过程数据准确识别加工过程的模型参数是其基于模型的控制和诊断的关键前提。典型的加工过程会生成多输出和多速率数据流。虽然各种传感器提供有关过程的过程信息,但许多重要的过程结果(包括产品质量)只能以后处理方式进行测量。本文建议通过使用过程中和后处理数据并通过分析模型参数的可识别性来改进识别。使用最大似然估计制定基于多速率输出的模型参数的识别,并导出多速率采样系统的 Fisher 信息矩阵以研究模型参数的可识别性。考虑到可识别性,开发了一种策略以提高模型识别的准确性和鲁棒性。所提出的方法在来自外圆磨削过程的两批多速率过程数据上进行了测试。测试结果表明,同时使用过程中和后处理数据可提高可识别性,并且所提出的识别策略可提高预测性能。
更新日期:2020-07-01
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