Multi-objective optimization for cost-efficient and resilient machining under tool wear

Abstract

With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost-efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi-objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi-objective formulation. The optimization framework is tested on a high-fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to $15\%$ as compared to the default control scheme.