基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

Research on CNC Milling Machine Cutting Power Prediction Model Considering Tool Wear Based on VMD-SSA-LSTM

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。

Traditional researches of cutting process powers required complex cutting power models and often neglected the influences of tool wear,so a CNC milling machine cutting power prediction model considering tool wear was designed based on VMD,SSA,and LSTM neural network.This model did not require the deconstruction of the energy consumption mechanism during the operation of CNC milling machines,and achieved high-precision prediction of cutting process powers based on historical experimental data.Firstly,artificial intelligence machine vision technology was used to analyze and process images of the tool wear,obtaining digital features of the worn tools and determining the maximum wear.Then,the VMD-SSA-LSTM model was established,which considered tool wear in the prediction of CNC milling machine cutting powers.VMD was used to decompose the operational data of CNC milling machines,and then the SSA algorithm optimized the hyperparameters of the LSTM neural network.The decomposed milling machine data components were input into the LSTM neural network,and the predicted values of each component were summed to obtain the cutting power prediction value.Taking face milling as an example,the proposed prediction model was compared and analyzed against BP neural networks,LSTM neural networks,and traditional models,which validated the effectiveness and superiority of the proposed model.