Tool performance optimization while machining aluminium-based metal matrix composite

Usama Umer, Mustufa Haider Abidi, Jaber Abu Qudeiri, Hisham Alkhalefah, Hossam Kishawy

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC).

Original languageEnglish
Article number835
Pages (from-to)1-16
Number of pages16
Issue number6
Publication statusPublished - Jun 2020


  • Cutting tools
  • Finite element model (FEM)
  • Metal matrix composite (MMC)

ASJC Scopus subject areas

  • General Materials Science


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