Energy prediction for CNC machining with machine learning. (November 2021)

• Record Type: Journal Article
• Authors: Brillinger, Markus; Wuwer, Marcel; Abdul Hadi, Muaaz; Haas, Franz
• Is Part Of: CIRP journal of manufacturing science and technology. Volume 35(2021)
• Page Start: 715
• Page End: 723
• Publication Date: 2021-11
• Subjects: Energy prediction -- CNC machine tools -- Machine learning -- CNC machining -- NC code; Manufacturing processes -- Periodicals; 670.5
• DOI: 10.1016/j.cirpj.2021.07.014
• Languages: English
• ISSNs: 1755-5817

Abstract

Highlights: Machine learning based energy prediction for a machining process. Validation based on complex geometries. Usage of high-frequency data. Abstract: Nowadays, the reduction of CO2 emissions by moving from fossil to renewable energy sources is on the policy of many governments. At the same time, these governments are forcing the reduction of energy consumption. Since large industries have been in the focus for the last decade, today also small and medium enterprises with production lot size one are increasingly being obliged to reduce their energy requirements in production. Energy-efficient CNC machine tools contribute to this goal. In machining processes, the machining strategy also has a significant influence on energy demand. For manufacturing of lot size one, the prediction of the energy demand of a machining strategy, before a part is manufactured plays a decisive role. In numerous previous studies, analytical models between the energy demand and the machining strategy have been developed. However, their accuracy depends largely on the parameterization of these models by dedicated experiments. In this paper, different machine learning algorithms, especially variations of the decision tree ('DecisionTree', 'RandomForest', boosted 'RandomForest') are investigated for their ability to predict the energy demand of CNC machining operations based on real production data, without the need for dedicated experiments. As shown in this paper, the most accurate energy demand predictions can be achieved with the 'RandomForest' algorithm.