基于BP神經(jīng)網(wǎng)絡的鋁合金薄壁件加工變形預測研究
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中圖分類號：TH164 文獻標志碼：A文章編號：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
薄壁零件具有明顯的空間受力特征，與其它結構形式相比，在滿足強度要求的情況下，薄壁結構具有重量輕、強度大、材料利用充分等特點，在航空航天、汽車制造和橋梁制造等領域有廣泛應用[1]。（剩余5426字）