Trade-off analysis of tool wear, machining quality and energy efficiency of alloy cast iron milling process

Xiaona Luan, Song Zhang, Jianfeng Li, Gamini Mendis, Fu Zhao, John W. Sutherland

Research output: Contribution to journalConference articlepeer-review

17 Scopus citations

Abstract

For many manufacturing companies, maximizing profit is a primary goal. However, the environmental impact of the manufacturing process should be considered due to government and public pressure. Cutting tool costs and electricity costs are the two main costs of manufacturing process. Tool wear conditions can be used to measure the cutting tool cost indirectly. The objective of this research is to increase the profit of an industry by identifying trade-offs between cutting tool cost, electricity cost, and machining quality. First, an on-line tool wear prediction model was designed to study the change in cutting power due to the change in tool wear. Experimental data were collected to examine the relationships between tool wear, cutting power and surface roughness. Second, multi-objective optimization functions were established to solve trade-off analysis problems. Real-time cutting power data, surface roughness, and flank tool wear data from the experiments were analyzed in the models. Finally, grey relational analysis was applied to obtain the grey relational grade of the cutting parameters on the three optimization objectives. The relationships between these parameters were evaluated using trade-off analysis. The tool wear-cutting power-surface roughness trade-off surface was used to visualize the trade-offs among the parameters. The results of this work can help decision makers or operators design cutting parameters for different goals.

Original languageEnglish (US)
Pages (from-to)383-393
Number of pages11
JournalProcedia Manufacturing
Volume26
DOIs
StatePublished - 2018
Event46th SME North American Manufacturing Research Conference, NAMRC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

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