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首页> 外文期刊>Journal of Intelligent Manufacturing >Machine learning-based instantaneous cutting force model for end milling operation
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Machine learning-based instantaneous cutting force model for end milling operation

机译：基于机器学习的瞬时切削力模型，用于端铣手

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摘要

Cutting force is the fundamental parameter determining the productivity and quality of the milling operation. The development of a generic cutting force model for end milling operation necessitates a large number of experiments. The experimental data contains multiple outliers due to noise and process disturbances lowering prediction accuracy of the model. This paper presents a novel approach combining the mechanistic model and the supervised neural network (NN) model to predict instantaneous cutting force variation during the end milling operation. The approach proposes training of an NN model using datasets generated from the mechanistic force model instead of using experimental data. The methodology generates a large number of datasets for the training of an NN model without conducting rigorous experimentation. A set of NN architectures were developed, and an appropriate network was derived by comparing performance parameters. A series of end milling experiments were conducted to examine the efficacy of the proposed approach in predicting cutting forces over a wide range of cutting conditions.
机译：切割力是确定铣削操作的生产率和质量的基本参数。用于端铣床的通用切割力模型的开发需要大量的实验。由于噪声和过程干扰降低了模型预测精度，实验数据包含多个异常值。本文提出了一种组合机械模型和监督神经网络（NN）模型的新方法，以预测端部研磨操作期间的瞬时切割力变化。该方法建议使用从机械力模型产生的数据集来训练NN模型，而不是使用实验数据。该方法产生大量数据集，用于训练NN模型，而无需进行严格的实验。开发了一组NN架构，通过比较性能参数来得出适当的网络。进行了一系列最终研磨实验，以检查提出的方法在广泛的切削条件下预测切削力的疗效。

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