Introspection on parametric effect and multi-objective optimization on machinability indices of fabricated cupola slag-reinforced LM11 matrix composites’ turning have been presented. Low-cost cast composites with grounded waste cupola slag with varying slag content have been fabricated using the stir casting method. The spindle speed, feed rate and weight percentage of cupola slag have been selected as process input for turning following a L9 Taguchi experimental design. The cutting force, surface roughness and material removal rate have been chosen as responses, and they have been optimized with the objective of predicting lower, lower and higher values, respectively. The complex problem of multi-variable multi-objective optimization has been converted to single variable single objective optimization following the grey relational technique. The optimized prediction has been validated by experimentation with an error of around 2%. The results show that with increasing slag content, the cutting force and surface roughness reduce, improving machinability.

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Grey Taguchi-Based Parametric Optimization Studies on Machinability of Cupola Slag-Reinforced LM11 Matrix Composites

  • Soumyabrata Chakravarty,
  • Partha Haldar,
  • Titas Nandi,
  • Goutam Sutradhar

摘要

Introspection on parametric effect and multi-objective optimization on machinability indices of fabricated cupola slag-reinforced LM11 matrix composites’ turning have been presented. Low-cost cast composites with grounded waste cupola slag with varying slag content have been fabricated using the stir casting method. The spindle speed, feed rate and weight percentage of cupola slag have been selected as process input for turning following a L9 Taguchi experimental design. The cutting force, surface roughness and material removal rate have been chosen as responses, and they have been optimized with the objective of predicting lower, lower and higher values, respectively. The complex problem of multi-variable multi-objective optimization has been converted to single variable single objective optimization following the grey relational technique. The optimized prediction has been validated by experimentation with an error of around 2%. The results show that with increasing slag content, the cutting force and surface roughness reduce, improving machinability.