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Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters

Published

Author(s)

Seungjun Shin, Jungyub Woo, Sudarsan Rachuri, Seungjun Shin

Abstract

Energy consumption is a major sustainability focus in the metal cutting industry. As a result, process planning is increasingly concerned with reducing energy consumption in machine tools. The relevant literature has been categorized into two research areas. The first includes energy prediction models, which characterize the relationships between cutting parameters - the main outputs of process planning - and energy consumption. The second involves energy-consumption optimization, which uses the prediction models to find the cutting parameters that minimize energy use. However, previous energy prediction models are limited to predict energy for tool paths coded in a Numerical Control (NC) program. Previous energy optimization methods typically do not use online optimization, which enables fast optimization decision-making for supporting on-demand process planning and real-time machine control. This paper presents a component-based energy-modeling methodology to implement the online optimization needed for real-time control. Models that can predict energy up to the tool path-level at specific machining configurations are called component-models in this paper. These component-models are created using historical data that includes process plans, NC programs, and machine-monitoring data. The online optimization is implemented using a dynamic composition of component-models together with a divide-and-conquer technique. The feasibility and effectiveness of our methodology has been demonstrated in a milling-machine example.
Citation
Journal of Cleaner Production
Volume
161

Keywords

Metal cutting, Predictive modeling, Optimization, Energy efficiency, STEP-NC, MTConnectMTConnect

Citation

Shin, S. , Woo, J. , Rachuri, S. and Shin, S. (2017), Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters, Journal of Cleaner Production, [online], https://doi.org/10.1016/j.jclepro.2017.05.013 (Accessed December 4, 2023)
Created May 5, 2017, Updated December 9, 2022