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Open-Source Framework for Automated Milling Experiments: G-Code Adaptation and NC Data Acquisition with Siemens Industrial Edge

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Názov: Open-Source Framework for Automated Milling Experiments: G-Code Adaptation and NC Data Acquisition with Siemens Industrial Edge
Autori: Uhlmann, Eckart, Polte, Mitchel, Heper, Martin
Informácie o vydavateľovi: Technische Universität Berlin, 2025.
Rok vydania: 2025
Predmety: Siemens Industrial Edge, edge device, automated milling experiments, numerical control, 600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung::671 Metallverarbeitung und Rohprodukte aus Metall, smart manufacturing, G-Code adaptation, NC Data acquisition
Popis: In modern manufacturing, precise monitoring and control of milling processes are critical for ensuring process stability and efficiency. This paper introduces an open-source framework leveraging Siemens Industrial Edge technology to automate complex milling experiments while simultaneously acquiring NC signal data. A Python-based application enables dynamic G-code adaptation, facilitating autonomous stability investigations and beyond. Stability lobe diagrams are generated as an exemplary case study, demonstrating the feasibility of automated experiments. The open-source availability of G-code and Python scripts empowers users to adapt the solution, laying the foundation for deploying AI models on machine controls and support scalable smart manufacturing solutions.
Druh dokumentu: Conference object
Jazyk: English
DOI: 10.14279/depositonce-24413
Rights: CC BY NC ND
Prístupové číslo: edsair.doi...........8df6f3c3bcf0bac32e5922ea7e905c0c
Databáza: OpenAIRE
Popis
Abstrakt:In modern manufacturing, precise monitoring and control of milling processes are critical for ensuring process stability and efficiency. This paper introduces an open-source framework leveraging Siemens Industrial Edge technology to automate complex milling experiments while simultaneously acquiring NC signal data. A Python-based application enables dynamic G-code adaptation, facilitating autonomous stability investigations and beyond. Stability lobe diagrams are generated as an exemplary case study, demonstrating the feasibility of automated experiments. The open-source availability of G-code and Python scripts empowers users to adapt the solution, laying the foundation for deploying AI models on machine controls and support scalable smart manufacturing solutions.
DOI:10.14279/depositonce-24413