A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling

  • 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.

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Metadaten
Author:Eddi Miller, Anna-Maria Schmitt, Tobias Kaupp, Rafael Batres, Andreas Schiffler, Jan Schmitt
DOI:https://doi.org/10.1007/978-3-031-77429-4_59
ISBN:9783031774287
ISSN:2195-4356
Parent Title (English):Lecture Notes in Mechanical Engineering
Publisher:Springer Nature Switzerland
Place of publication:Cham
Document Type:Part of a Book
Language:English
Year of publication:2025
Release Date:2025/01/17
Pages/Size:9
First Page:535
Last Page:543
Institutes and faculty:Fakultäten / Fakultät Wirtschaftsingenieurwesen
Institute / Institut Digital Engineering (IDEE)
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