5739 2025 eng 535 543 9 bookpart Springer Nature Switzerland Cham 1 -- -- -- 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. Lecture Notes in Mechanical Engineering 9783031774287 2195-4356 10.1007/978-3-031-77429-4_59 true book-chapter https://creativecommons.org/licenses/by/4.0 crossref PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PublisherName,PublisherPlace,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence Global Conference on Sustainable Manufacturing Buenos Aires, Argentinien doi-import Eddi Miller Anna-Maria Schmitt Tobias Kaupp Rafael Batres Andreas Schiffler Jan Schmitt Fakultät Wirtschaftsingenieurwesen Institut Digital Engineering (IDEE)