STEP-NC part programs-oriented autonomous machine tool selection using a hybrid multi-criteria decision-making model

Machine tool selection has been a significant challenge in digital manufacturing. However, current research primarily focuses on machine tool procurement prior to machining, with limited attention paid to selecting appropriate machining tools for specific parts during the machining process itself. Efforts should be directed towards optimizing machine tool selection at the part level to enhance manufacturing efficiency, particularly in single-piece and small-batch production modes. To address this gap, this paper proposes a novel approach for in-process, part-level machine tool selection using STEP-NC and hybrid multi-criteria decision-making (MCDM) models. The approach entails two key steps: (1) machinability assessment and (2) optimal selection. Through a comprehensive analysis of the STEP-NC manufacturing resource model, essential machine tool requirements are extracted to establish criteria for both the machinability check and the subsequent evaluation. The optimal selection problem is then formulated as an MCDM problem and solved using a hybrid model integrating DEMATEL and TOPSIS. The feasibility of autonomous part-level machine tool selection is validated through an application case study. The final results and analysis demonstrate the practical implications and application potential of the proposed methodology for machine tool selection.