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Artificial neural networks back propagation algorithm for cutting force components predictions

机译：人工神经网络反向传播算法用于切削力分量预测

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摘要

Reinforced Poly Ether Ether Ketone with 30% of Carbon Fiber (PEEK CF30) offer several thermo-mechanical advantages over standard materials and alloys which make them better candidates in different applications. However, the hard and abrasive nature of the reinforcement fiber is responsible for rapid tool wear and high machining costs. It is very important to find highly effective ways to machine that material. Accordingly, it is important to predict forces when machining fiber matrix composites because this will help to choose perfect tools for machining and ultimately save both money and time. In this study, Artificial Neural Network (ANN) was applied to predict the cutting force components in turning operations of PEEK CF30 using TiN coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design experiments (DOEs) on CNC turning machine. The results indicated that the well-trained (ANN) model could be able to predict the cutting force components in turning of Carbon Fiber Reinforcement Polymer (CFRP) composites. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions.
机译：碳纤维含量为30％的增强型聚醚醚酮（PEEK CF30）与标准材料和合金相比，在热机械方面具有多项优势，这使其在不同应用中更适合使用。然而，增强纤维的坚硬和磨蚀性导致工具快速磨损和高加工成本。找到加工该材料的高效方法非常重要。因此，在加工纤维基复合材料时预测力很重要，因为这将有助于选择完美的加工工具，并最终节省金钱和时间。在这项研究中，使用人工神经网络（ANN）预测在干燥条件下使用TiN涂层切削工具在PEEK CF30车削过程中切削力分量，切削条件为切削速度范围，进给速度和切削深度。对于本研究，已在CNC车床上使用全因子设计实验（DOE）进行了实验。结果表明，训练有素的（ANN）模型可以预测碳纤维增强聚合物（CFRP）复合材料车削过程中的切削力分量。在ANN模型推导过程中未使用的补充结果使人们能够评估所获得预测的有效性。

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