基于BP神经网络的铝合金薄壁件加工变形预测研究

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中图分类号：TH164 文献标志码：A文章编号：1006-0316（2025）06-0068-06

doi：10.3969/j.issn.1006-0316.2025.06.010

Deformation Prediction of Aluminum Alloy Thin-Walled Parts Processing BasedonBPNeuralNetwork

WANG Jue

（Xiamen Golden Egret Special Alloy Co.， Ltd.， Xiamen 361100， China ）

Abstract ： To address the issue of deformation in deep cavity milling of aluminum alloy thin-walled parts， a coated carbide end mill was used to conduct a thre-factor， five-level orthogonal experiment on aluminum alloy side milling. A micrometer was used to measure the deformation after machining under different cutting parameters.The effects of different cuting widths，spindle speeds，and feed rates on the deformation during side milling were studied. Based on this，a BP neural network prediction model for part deformation was established. The experimental data were divided into training and testing sets， which were used to train and predict the model respectively. The training effect of the model was tested by comparing the errors between the validation set data and the actual data. The prediction results show that the model has good prediction accuracy， with the relative error of the test samples not exceeding 10% .The established model can predict the deformation of thin-walled parts after machining under diffrent combinations of cutting width，spindle speed，and feed rate parameters. This provides a theoretical basis for the reasonable selection and optimization of cuting parameters and is of great significance for improving the machining quality and efficiency of thin-walled parts.

Key words ∵ thin-walled parts ; side milling ;workpiece deformation ; neural network ; prediction mode

薄壁零件具有明显的空间受力特征，与其它结构形式相比，在满足强度要求的情况下，薄壁结构具有重量轻、强度大、材料利用充分等特点，在航空航天、汽车制造和桥梁制造等领域有广泛应用[1]。（剩余5426字）