Surface roughness prediction for Ti-48Al-2Cr-2Nb micro-milling based on 1DCNN-LSTM neural network
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摘要: 表面粗糙度是衡量微细加工零件表面质量的主要指标,为提高微铣削加工表面粗糙度预测的精准性,提出一种一维卷积-长短期记忆(1DCNN-LSTM)的深度神经网络预测模型。利用一维卷积网络高效的数据处理机制和长短期记忆网络精准的预测能力,有效解决了批量序列数据处理、样本关键特征学习以及小样本数据的表面粗糙度预测精确问题。以主轴转速、进给速度、铣削深度和微铣刀螺旋角作为控制变量,用实验数据对微铣削表面粗糙度预测模型进行训练并对该模型验证。结果表明:相比于传统机器学习模型,1DCNN-LSTM神经网络平均预测误差仅为5.9%,验证了该模型基于小样本数据的高精度预测性能,为微铣削表面粗糙度的预测研究提供了一种新的方法。Abstract: Surface roughness is the main index to measure the surface quality of micro machined parts. In order to improve the accuracy of surface roughness prediction in micro milling, a deep neural network prediction model based on one-dimensional convolution-long short-term memory (1DCNN-LSTM) is proposed. Using the efficient data processing mechanism of one-dimensional convolution network and the accurate prediction ability of long-term and short-term memory network, the problems of batch sequence data processing, sample key feature learning and small sample data surface roughness prediction are effectively solved. Taking spindle speed, feed speed, milling depth and micro milling cutter spiral angle as control variables, the prediction model of micro milling surface roughness is trained and verified by experimental data. The results show that compared with the traditional machine learning model, the average prediction error of 1DCNN-LSTM neural network is only 5.9%, which verifies the high-precision prediction performance of the model based on small sample data, and provides a new method for the prediction of micro-milling surface roughness.
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表 1 高温合金与γ-TiAl基合金力学性能对比
力学性能 高温合金 γ-TiAl基合金 密度/(g/cm3) 7.9~8.5 3.7~4.3 熔点/℃ 1 453 1 460 弹性模量/GPa 160~180 206 室温塑性/(%) 2.2~25 0.5~4 高温塑性/(%) 21~80(870 ℃) 9~600(870 ℃) 抗拉强度/MPa 1 251~1 453 451~702 屈服强度/MPa 803~1 205 400~633 热膨胀系数/(×10−6/℃) 14.8 10.8 热导率/(W/(m·K)) 11 22 表 2 微槽铣削正交参数因素水平表
水平 因素 主轴转速
n/(r/min)进给速度
vf /(mm/min)铣削深度
ap /μm螺旋角
β/(°)水平1 5 1.5 10 25 水平2 15 3.0 15 30 水平3 25 4.5 20 35 水平4 35 6.0 25 40 水平5 45 7.5 30 45 表 3 切削参数范围
主轴转速
n/(r/min)进给速度
vf /(mm/min)铣削深度
ap/μm螺旋角
β/(°)[5 000, 75 000] [1.5, 100] [6, 100] [25, 30, 35, 40, 45] 表 4 表面粗糙度预测结果
序号 主轴转速
n/(×103r/min)进给速度
vf /(mm/min)铣削深度
ap/μm螺旋角
β/(°)Ra测量值/
μm1DCNN-LSTM神经网络 BP神经网络 Ra预测值/μm 误差百分比/(%) Ra预测值/μm 误差百分比/(%) 1 30 40 15 25 0.192 8 0.168 3 9.65 0.214 3 11.15 2 50 30 30 25 0.087 1 0.088 0 0.92 0.072 6 16.65 3 70 20 45 25 0.020 6 0.019 2 4.85 0.016 2 21.36 4 30 40 15 30 0.049 6 0.052 4 4.44 0.058 6 18.15 5 50 30 30 30 0.048 6 0.052 4 7.82 0.058 8 20.99 6 70 20 45 30 0.044 2 0.049 0 10.41 0.046 3 4.75 7 30 40 15 35 0.021 6 0.020 7 4.17 0.023 0 6.48 8 50 30 30 35 0.061 6 0.059 8 4.71 0.067 3 9.25 9 70 20 45 35 0.073 4 0.077 3 6.27 0.076 2 3.81 10 30 40 15 40 0.107 6 0.109 0 1.49 0.086 8 19.33 11 50 30 30 40 0.074 8 0.078 6 2.81 0.087 7 17.25 12 70 20 45 40 0.074 4 0.080 8 8.60 0.084 7 13.84 13 30 40 15 45 0.105 9 0.112 1 5.10 0.080 1 24.36 14 50 30 30 45 0.082 5 0.091 3 10.18 0.094 0 13.94 15 70 20 45 45 0.130 6 0.139 6 7.04 0.101 2 22.51 -
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