Research article

Multi-response optimization of machining characteristics in ultrasonic machining of WC-Co composite through Taguchi method and grey-fuzzy logic

  • Received: 18 November 2017 Accepted: 17 January 2018 Published: 22 January 2018
  • This article addresses the application of grey based fuzzy logic coupled with Taguchi’s approach for optimization of multi performance characteristics in ultrasonic machining of WC-Co composite material. The Taguchi’s L-36 array has been employed to conduct the experimentation and also to observe the influence of different process variables (power rating, cobalt content, tool geometry, thickness of work piece, tool material, abrasive grit size) on machining characteristics. Grey relational fuzzy grade has been computed by converting the multiple responses, i.e., material removal rate and tool wear rate obtained from Taguchi’s approach into a single performance characteristic using grey based fuzzy logic. In addition, analysis of variance (ANOVA) has also been attempted in a view to identify the significant parameters. Results revealed grit size and power rating as leading parameters for optimization of multi performance characteristics. From the microstructure analysis, the mode of material deformation has been observed and the critical parameters (i.e., work material properties, grit size, and power rating) for the deformation mode have been established.

    Citation: Ravi Pratap Singh, Ravinder Kataria, Jatinder Kumar, Jagesvar Verma. Multi-response optimization of machining characteristics in ultrasonic machining of WC-Co composite through Taguchi method and grey-fuzzy logic[J]. AIMS Materials Science, 2018, 5(1): 75-92. doi: 10.3934/matersci.2018.1.75

    Related Papers:

  • This article addresses the application of grey based fuzzy logic coupled with Taguchi’s approach for optimization of multi performance characteristics in ultrasonic machining of WC-Co composite material. The Taguchi’s L-36 array has been employed to conduct the experimentation and also to observe the influence of different process variables (power rating, cobalt content, tool geometry, thickness of work piece, tool material, abrasive grit size) on machining characteristics. Grey relational fuzzy grade has been computed by converting the multiple responses, i.e., material removal rate and tool wear rate obtained from Taguchi’s approach into a single performance characteristic using grey based fuzzy logic. In addition, analysis of variance (ANOVA) has also been attempted in a view to identify the significant parameters. Results revealed grit size and power rating as leading parameters for optimization of multi performance characteristics. From the microstructure analysis, the mode of material deformation has been observed and the critical parameters (i.e., work material properties, grit size, and power rating) for the deformation mode have been established.


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