A material-general energy prediction model for milling machine tools

Hannah Budinoff, Raunak Bhinge, David Dornfeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Increasing awareness of energy consumption and its environmental impacts has prompted a need to better predict the energy consumption of various industrial processes, including manufacturing. Modeling can allow manufacturers to optimize the efficiency of their manufacturing processes. Highly accurate, data-driven models of energy consumption of CNC milling have been developed but these models are generated from experimental data and are not generally applicable. If any conditions are varied beyond the experimental parameter ranges, a data-driven model faces challenges in maintaining its prediction accuracy. In this work, two models based on the non-cutting power demand of the CNC machine and the specific cutting energy of the workpiece material are analyzed. These models are then used to predict milling energy consumption of several experimental parts. Both models predicted the total energy consumption of the experimental parts with an average relative total error of less than 3%, which is comparable to datadriven models. Unlike most models, the proposed models presented here can be applied to most workpiece materials.

Original languageEnglish (US)
Title of host publicationInternational Symposium on Flexible Automation, ISFA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages161-164
Number of pages4
ISBN (Electronic)9781509034673
DOIs
StatePublished - Dec 16 2016
Externally publishedYes
EventInternational Symposium on Flexible Automation, ISFA 2016 - Cleveland, United States
Duration: Aug 1 2016Aug 3 2016

Publication series

NameInternational Symposium on Flexible Automation, ISFA 2016

Conference

ConferenceInternational Symposium on Flexible Automation, ISFA 2016
Country/TerritoryUnited States
CityCleveland
Period8/1/168/3/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'A material-general energy prediction model for milling machine tools'. Together they form a unique fingerprint.

Cite this