Krause, ArturArturKrauseDannerbauer, TobiasTobiasDannerbauerWagenmann, SteffenSteffenWagenmannTjaden, GretaGretaTjadenStröbel, RobinRobinStröbelFleischer, JürgenJürgenFleischerAlbers, AlbertAlbertAlbersBursac, NikolaNikolaBursac2025-01-142025-01-142024-05Procedia CIRP 58 (27): 1674-1679 (2024)https://tore.tuhh.de/handle/11420/53111This paper investigates the integration of sustainability and environmental performance in the development of machine tools. Through a literature review and a case study involving a fully automated solid-state laser-cutting machine tool, this research explores the potential of a data-driven, explainable machine learning (XML) approach to optimize machine tool operations for sustainability. Specifically, it addresses modeling resource consumption in laser-cutting machines, identifies the most significant factors influencing these models, and examines their contributions toward sustainable machine operation practices. Employing a combination of correlation analysis and stepwise linear regression for feature selection, and utilizing random forest regression models for predictive analysis, this study reveals that operational duration significantly impacts resource consumption levels, more than the effects of machine and laser configuration settings. The utilization of Shapely Additive Explanations (SHAP) further elucidates the predictive behaviors of these models, emphasizing the critical role of part contour length in program run duration and resource consumption. The findings suggest that optimizing machine speed and production planning, as well as incorporating part contour length as a sustainability Key Performance Indicator (KPI) during the design phase, can enhance the environmental performance of laser-cutting machines. Further, the analysis of resource consumption patterns of machine tools also offers actionable strategies to improve their sustainability during operation. It underscores the importance of integrating sustainability considerations into the development and operational phases of machine tools, contributing valuable insights to the field.en2212-827120242716741679Elsevierhttps://creativecommons.org/licenses/by-nd/4.0/Data-driven Sustainability | Explainable Machine Learning | Industrial Energy | Machine Tool OptimizationTechnology::621: Applied PhysicsTechnology::658: Marketing::658.5: Of ProductionSocial Sciences::333: Economics of Land and Energy::333.7: Natural Resources, Energy and EnvironmentComputer Science, Information and General Works::004: Computer SciencesTechnology::620: EngineeringConference Paperhttps://doi.org/10.15480/882.1428810.1016/j.procir.2024.10.29910.15480/882.14288Conference Paper