Analytical performance analysis in laser-assisted and ultrasonic vibration-assisted milling

Abstract

The performance of machining can be evaluated through milling forces, temperature field, residual stress profile of machined surface, surface roughness, and tool wear. On one hand, it is of practice interest to be able to predict the performance under designed process parameters such as tool geometry, laser power, vibration amplitude, feed rate, and cutting depth. For this study, the analytical models are built to predict the force, temperature, residual stress, surface roughness, and tool wear in laser-assisted and ultrasonic vibration-assisted milling. On the other hand, people want to know the possible combination of process parameters to achieve required target performance. Therefore, inverse analysis is proposed on milling forces, residual stress, surface roughness, and tool life, in laser-assisted milling. The method uses the analytical model to solve the direct problem and applies a variance-based recursive method to guide the inverse analysis. The forward problem methodology is valuable in terms of providing an accurate and reliable reference for the prediction of milling forces, temperature, residual stress, tool wear, and surface roughness. The inverse problem methodology is valuable in terms of guiding the selection of process parameters based on desired target performances. The effects of laser preheating and grain growth are considered in laser-assisted milling, while the intermittent tool-workpiece separation is considered in ultrasonic vibration-assisted milling. All proposed models are validated through experiments with high accuracy, and the effect of each process parameter on the target performance is presented through the sensitivity analysis. A computing platform is also built to incorporate all algorithms consisting of several graphical user interfaces.