Assessment of Energy Efficiency and Productivity of CNC Machining Processes

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Computer Numerically Controlled (CNC) machines are the base energy consuming devices in manufacturing systems. At utmost concern, the energy efficiency of CNC machining processes is a challenging goal across the industry, government and academia. Feed rate is one of the machining process parameters defined during the process planning (CAPP) which greatly impacts on the machining performance (i.e., productivity and energy efficiency). Hence, the performance of two machining strategies: i) the feed rate being traditionally defined by the machinists based on experience and tooling handbook, ii) the feed rate being defined by an optimisation method, when producing a multi-feature part design will be analysed and compared. For that, several experimental trials are carried out for data collection of the energy consumption and machining time using different machine tools. The results reveal that drilling and counter boring features represent the least energy-efficient features in both strategies, in addition, the amount of energy per machining state (e.g., basic, actual cutting, standby, coolant) highly depends on the machine tool, where the amount of non-productive energy is greater on the more powerful machine. Also, the machining strategy using the optimised feed rates is more energy-efficient and productive when compared to the traditional processes, however, such improvements are highly affected by the machine tool capabilities. Such effects are further analysed, quantified and discussed.
Original languageEnglish
Title of host publicationIEEE Workshop & SENSORICA 2019
PublisherIEEE
Publication statusPublished - 7 Jun 2019

Keywords

  • machining
  • modelling
  • optimisation
  • Operational performance
  • predictice analytics

Fingerprint Dive into the research topics of 'Assessment of Energy Efficiency and Productivity of CNC Machining Processes'. Together they form a unique fingerprint.

Cite this