Model-Based Optimisation of the Energy Efficiency of Machining Processes

EasyChair Preprint 16028

Abstract

In this work, a hybrid modelling approach combining physical relationships with empirical data is proposed to estimate the electrical energy consumption of machining processes. Measurements indicate that only 5–10 % of the total energy consumed by the machine during the processing of the designed reference part is used for material removal. This
share is estimated using the cutting force model proposed by Kienzle. The overall model, consisting of a machine model and a process model, is validated using data from an energy measurement system and a force measurement system. Validation is carried out for three different grades of steels (mild steel and tool steels), using material-dependent cutting parameters as well as dry machining and emulsion-based cooling. The results
show that the proposed model achieves a deviation of less than 10 % compared to the measured energy demand. These findings demonstrate the potential of the approach as a basis for further refinements, such as incorporating tool wear, additional cooling strategies and non-electrical consumers. In addition, the proposed approach enables the integration of energy consumption into production planning and process optimisation, while maintaining a high degree of transferability to different machines, materials and process conditions.

Keyphrases: Machining Processes, energy efficiency, energy modelling

@booklet{EasyChair:16028,
  author    = {Doris Bernroider and Fabian Spitzer and Jochen Giedenbacher and Holger Groening},
  title     = {Model-Based Optimisation of the Energy Efficiency of Machining Processes},
  howpublished = {EasyChair Preprint 16028},
  year      = {EasyChair, 2026}}