TY - CHAP A1 - Miller, Eddi A1 - Schmitt, Anna-Maria A1 - Kaupp, Tobias A1 - Batres, Rafael A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling T2 - Lecture Notes in Mechanical Engineering N2 - Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense. Y1 - 2025 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/5739 SN - 9783031774287 SN - 2195-4356 SP - 535 EP - 543 PB - Springer Nature Switzerland CY - Cham ER -