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Inverse analysis of the residual stress in laser-assisted milling
摘要： In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti–6Al–4 V and Si3N4. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.
关键词： Residual stress,Ti–6Al–4V,Si3N4,Laser-assisted milling,Inverse analysis,Iterative gradient search
更新于2025-09-16 10:30:52
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A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel
摘要： Laser-assisted milling (LAM) represents an innovative process to enhance productivity in comparison with conventional milling. The workpiece temperature in LAM not only affects the cutting performance of materials, but also the machined surface quality of the part. This paper presents a 3D transient finite element (FE) model for workpiece temperature prediction in LAM. A moving Gaussian laser heat source model is implemented as a user-defined subroutine and linked to ABAQUS. The thermal model is validated by machining AerMet100 steel under different process parameters (laser power, spindle speed and feed per tooth). Good agreement between predicted and measured workpiece temperatures indicates that the FE model is feasible. In addition, the effects of laser spot size and incident angle on workpiece temperature are analyzed based on the proposed model. This work can be further applied to optimize process parameters for controlling the machined surface quality in LAM.
关键词： workpiece temperature,AerMet100 steel,finite element model,laser-assisted milling
更新于2025-09-16 10:30:52