SUMMARY

The control of machining is critical to the quality of final product, while the evaluation of advanced manufacturing process including laser-assisted and ultrasonic vibration-assisted milling becomes more challenging. The performance of machining can be evaluated through several aspects. In situ parameters including force, temperature, and tool wear indicate if the machining is conducted within allowable range of equipment. Force and temperature in shear zone is the results of both mechanical and thermal loads during milling. Moreover, tool wear describes the gradual failure of cutting tools due to regular operation. Residual stress and surface roughness reflect the machining process and are directly related to fatigue performance and surface quality of the product. Surface roughness characterizes the surface texture in terms of deviations. Residual stress is created under mechanical load, thermal gradient, and phase change, which significantly affects the damage tolerance and fatigue performance of product. All these quantities are selected due to their importance in the evaluation of laser-assisted and ultrasonic vibration-assisted milling process. 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 milling forces, temperature field, residual stress profile of machined surface, 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.