考虑J-C本构参数的铝合金铣削温度预测模型OA
Prediction Model for Aluminum Alloy Milling Temperature Considering Johnson-Cook Constitutive Parameters
为准确预测铝合金铣削加工工件温度,减少其对加工质量的影响,基于铝合金 J-C 本构参数修正剪切角模型,并使用 Abaqus 仿真软件和黏结-滑移摩擦模型求取剪切区变形温度和摩擦角,考虑剪切区和犁沟区离散化热源强度,基于移动热源法建立铝合金铣削工件温度预测模型,并通过铝合金正交切削实验验证模型的准确性.研究结果表明:预测温度与实验测量温度平均误差为4.96%,最大误差为10.9%,最小误差为0.47%,各切削参数对工件温度影响规律基本一致,相比现有通用模型精度得到了一定程度的提高,证明温度预测模型在铣削加工中的预测可行性和准确性,可用于实际生产中铝合金结构件加工温度预测与热变形控制,保证零件加工质量.
To accurately predict the workpiece temperature in the milling of aluminum alloy and reduce its adverse effects on machining quality,the shear angle model based on the Johnson-Cook(J-C)constitutive parameters of aluminum alloy is modified.The deformation temperature in the shear zone and the friction angle are obtained by using the Abaqus simulation software and the adhesive-slip friction model.Considering the discretized heat source intensities in both the shear zone and the ploughing zone,a workpiece temperature prediction model for aluminum alloy milling is established based on the moving heat source method,and the accuracy of the model is verified via orthogonal cutting experiments of aluminum alloy.The results show that the average error between the predicted temperature and the experimental is 4.96%,the maximum error is 10.9%,and the minimum error is 0.47%.The influence laws of the cutting parameters on the workpiece temperature are basically consistent.Comparing with the existing models,the prediction accuracy is improved to a certain extent,which verifies the feasibility and accuracy of the present prediction model for milling temperature.The model can be applied to the machining temperature prediction and thermal deformation control of aluminum alloy structural parts in actual production,thus ensuring the machining quality of parts.
王伏林;朱勇旗;唐典雅;聂杰博
湖南大学 机械与运载工程学院,长沙 410082湖南大学 机械与运载工程学院,长沙 410082湖南大学 机械与运载工程学院,长沙 410082湖南大学 机械与运载工程学院,长沙 410082
矿业与冶金
铝合金铣削加工J-C本构参数铣削温度预测模型
aluminum alloymillingJ-C constitutive parametersmilling temperatureprediction model
《机械科学与技术》 2026 (7)
1116-1125,10
云南省重大科技专项(202402AC080005)
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