TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Kiefer, Daniel A1 - Grimm, Florian A1 - Straub, Tim A1 - Bitsch, Günter A1 - Van Dinther, Clemens A1 - Höllig, Jacqueline T1 - Speeding up CNC tool manufacturing: implementing explainable AI for setup time reduction and production agility JF - Procedia CIRP N2 - Long setup times in CNC tool production significantly hinder operational efficiency, characterized by reduced machine utilization, increased planning efforts, and subsequent delivery delays and production bottlenecks. These inefficiencies not only escalate production costs but also tie up capital, compromise order flexibility, augment storage expenses, and prevent the capitalization on market opportunities. This paper explores the application of explainable AI to analyze process data within CNC setups, aiming to identify and elucidate patterns that contribute to prolonged setup durations. By implementing AI models with explanation methods, this research transparently highlights critical improvement points, facilitating targeted interventions to enhance production agility. The outcome is a dual advantage of reducing setup times and operational costs, thereby speeding up overall manufacturing processes. This approach emphasizes innovative manufacturing systems and provides practical insights on using artificial intelligence to enhance efficiency in CNC tool production. KW - explainable artificial intelligence KW - CNC tool manufacturing KW - setup time reduction KW - manufacturing efficiency Y1 - 2024 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54373 SN - 2212-8271 SS - 2212-8271 U6 - https://doi.org/10.1016/j.procir.2024.10.195 DO - https://doi.org/10.1016/j.procir.2024.10.195 VL - 130 SP - 982 EP - 987 S1 - 6 PB - Elsevier CY - Amsterdam ER -