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Surface roughness modeling in Laser-assisted End Milling of Inconel 718

机译:Inconel的激光辅助端铣削表面粗糙度建模718

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摘要

Inconel 718 is a difficult-to-machine material while products of this material require good surface finish. Therefore, it is essential for the evaluation and prediction of surface roughness of machined Inconel 718 workpiece to be developed. An analytical model for the prediction of surface roughness under laser-assisted end milling of Inconel 718 is proposed based on kinematics of tool movement and elastic response of workpiece. The actual tool trajectory is first predicted with the consideration of overall tool movement, elastic deformation of tool, and the tool tip profile. The tool movements include the translation in feed direction and the rotation along its axis. The elastic deformation is calculated based on the previously established milling force prediction model. The tool tip profile is predicted based on the tool tip radius and angle. The machined surface profile is simulated based on the tool trajectory with elastic recovery, which is considered through the comparison between the minimum thickness and actual cutting thickness. Experiments are conducted in both conventional and laser-assisted milling under seven different sets of cutting parameters. Through the comparison between the analytical predictions and experimental measurements, the proposed model has high accuracy with the maximum error less than 27%, which is more accurate for lower feed rate with error less than 3%. The proposed analytical model is valuable for providing a fast, credible, and physics-based method for the prediction of surface roughness in milling process.
机译:Inconel 718是一种难以加入的材料,而这种材料的产品需要良好的表面光洁度。因此,对于待开发的机加工的inconel 718工件的表面粗糙度的评估和预测是必要的。基于工具运动的运动学和工件弹性响应,提出了一种用于预测Inconel 718的激光辅助端铣削表面粗糙度的分析模型。首先考虑到整体工具运动,工具弹性变形以及工具尖端轮廓的实际刀具轨迹。刀具运动包括馈线方向的平移和沿其轴的旋转。基于先前建立的研磨力预测模型计算弹性变形。基于工具尖端半径和角度来预测工具尖端轮廓。基于具有弹性恢复的刀具轨迹模拟加工表面轮廓,这是通过比较的最小厚度和实际切割厚度之间的比较来模拟。在七种不同切削参数下的常规和激光辅助研磨中进行实验。通过对分析预测和实验测量的比较,所提出的模型具有高精度,最大误差小于27%,这对于误差小于3%的进料速率更准确。所提出的分析模型对于提供一种快速,可信和基于物理的方法,可以用于预测铣削过程中的表面粗糙度。

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