A statistical approach to the optimization of a laser-assisted micromachining process

The objective of this study is to optimize a laser-assisted micro-grooving process designed for micromachining of difficult-to-machine materials such as hard mold/die steels and ceramics. The process uses a relatively low power continuous wave laser beam focused directly in front of a micro-grooving tool to thermally soften the material thereby lowering the cutting forces and associated machine and tool deflections. However, the use of laser heating can produce a detrimental heat-affected zone (HAZ) in the workpiece surface layers. Consequently, the laser and micro-grooving parameters need to be optimized in order to achieve the desired thermal softening effect while minimizing the formation of a HAZ in the material. Although thermal and force models for the hybrid process have been developed for possible use in process optimization, they are computationally intensive and are not accurate enough to produce reliable results. We overcome these deficiencies using a statistical approach. First, easy-to-evaluate metamodels are developed to approximate the complex engineering models. Then, the metamodels are statistically adjusted using real data from the process to make more accurate predictions. The optimization is then carried out on this statistically adjusted metamodels. The optimization strategy is experimentally verified and shown to yield good results.