Robust integer optimization of turning parameters for cutting tool sustainability and machining economics in discrete production.

Machining optimization is crucial for determining cutting parameters that enhance machining economics. However, few studies address the significant variation in cutting tool wear and the complexities of discrete production, often leading to lower cutting parameters to prevent operational failures. Moreover, variations in part geometries lead to differing contact conditions between the cutting tool and workpiece, as well as variations in material removal. This study employs a robust optimization approach tailored for discrete workpieces to experimentally determine optimal cutting parameters that balance machining efficiency with cutting tool sustainability. An objective function was proposed to integrate tool wear probability and the number of workpieces, with case studies demonstrating its critical role in enhancing efficiency while achieving a tool overuse probability below 2.1 %. Our experiments reveal that processing either too few or too many parts with a single insert can escalate costs due to extreme tool wear or decreased efficiency; notably, the optimal number of parts to be machined was found to be four, yielding an objective function value of 380.6 NTD, which is lower than 394.5 NTD for three parts and 405.4 NTD for five parts in the case study. This research underscores the necessity of considering tool wear distribution and discrete production in machining optimization for sustainable manufacturing applications, providing valuable insights into the impact of cutting parameters and tool wear distribution on the costs for discrete production.