机械

针对铝合金深腔铣削薄壁加工易变形的问题，用涂层硬质合金立铣刀进行铝合金侧铣三因子五水平的正交试验，采用千分尺测量不同切削参数下的加工后铝合金薄壁零件变形量。研究不同切削宽度、转速和进给量对侧铣加工中铝合金薄壁件变形量的影响。在此基础上建立BP神经网络的零件变形量预测模型，将实验数据分为训练集和测试集分别用于训练模型和预测模型。通过验证集数据和真实数据进行对比误差分析来检验模型训练效果。预测结果表明，模型具有很好的预测精度，测试样本相对误差不超过10%。建立的模型可以对不同切削宽度、转速和进给量参数组合下的加工后薄壁零件变形进行预测，为切削用量的合理选择和优化提供了理论依据，对提高薄壁件的加工质量和加工效率有重要意义。

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 three-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 cutting 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 different combinations of cutting width, spindle speed, and feed rate parameters.This provides a theoretical basis for the reasonable selection and optimization of cutting parameters and is of great significance for improving the machining quality and efficiency of thin-walled parts.