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IJAT Vol.14 No.6 pp. 951-958
doi: 10.20965/ijat.2020.p0951
(2020)

Paper:

Estimation of In-Process Power Consumption in Face Milling by Specific Energy Consumption Models

Tetsuo Samukawa, Kazuki Shimomoto, and Haruhiko Suwa

Faculty of Science and Engineering, Setsunan University
17-8 Ikeda-nakamachi, Neyagawa, Osaka 572-8508, Japan

Corresponding author

Received:
March 27, 2020
Accepted:
September 10, 2020
Published:
November 5, 2020
Keywords:
energy-efficient machining, face milling, machine tools, electrical power consumption, specific energy consumption
Abstract

Prediction of energy consumption in the entire production system is crucial for managing production and pursuing environmentally friendly manufacturing. One critical issue that must be addressed to realize green manufacturing is to construct a method for predicting the electric power consumed by each manufacturing device. To address this problem, we have proposed a regression-based power consumption model to predict in-process power consumption based on the strong correlation between MRR and SEC. This study is an extension of our previous work, and here, we conducted face milling experiments by utilizing ten different materials to demonstrate the applicability and generalization capability of the model. We focused on the face milling process and measured the power consumption of the machine tool during the milling process. We also determined the characteristics of the in-process power consumption in face milling from the viewpoint of SEC and MRR and the influence of the work material on SEC. The prediction accuracy of the proposed model is demonstrated by comparison with a conventional model. It was revealed that the proposed model can describe the influence of the entire machine tool on power consumption depending on the characteristics of the work materials.

Cite this article as:
Tetsuo Samukawa, Kazuki Shimomoto, and Haruhiko Suwa, “Estimation of In-Process Power Consumption in Face Milling by Specific Energy Consumption Models,” Int. J. Automation Technol., Vol.14, No.6, pp. 951-958, 2020.
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