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Experimental Study and Optimization of Laser Assisted Machining of Particle­ Reinforced Aluminum­ Matrix Composite

Mirshamsi, Mohammad Ali | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 54715 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Movahhedy, Mohammad Reza; Khodaygan, Saeed
  7. Abstract:
  8. In this thesis, the process of laser assisted machining (LAM) of metal matrix composites (MMC) is experimentally studied. The effects of process parameters (cutting speed, feed rate and depth of cut), laser parameters (laser power, laser frequency and laser beam angle), and the percentage of reinforcing particles on the tool wear and the surface roughness are investigated. Furthermore, the effects of these parameters on the built-up edge (BUE), chip shape, and workpiece temperature are explored.The experiments were performed using uncoated tungsten carbide and PCD tools under dry conditions. In order to analyze the sensitivity of the process parameters, the Plackett-Burman method was used for screening of parameters. Then, for experimental single objective optimization of the effective parameters, Taguchi optimization method and analysis of variance (ANOVA) were used. Also, for Multi objective optimization of process parameters in the laser assisted machining of silicon carbide particle-reinforced aluminum matrix composites, the integrated NSGA II - Shannon Entropy-based TOPSIS method were used. The surface roughness of the specimens and tool wear were considered as the main objectives in the optimization procedure. Photographs of the tool tip after cutting showed that the flank wear and BUE on the tool tip were the main wear mechanisms. The experiments revealed that at high feed rates, the BUE could occur at both low and high cutting speeds.As a part of this study, mathematical models for flank wear and surface roughness in terms of the effective parameters have been formulated. Finally, based on the ANOVA study, an optimal parametric combination that minimizes the surface roughness and tool wear were obtained
  9. Keywords:
  10. Metal Matrix Composite (MMC) ; Laser Assisted Machining ; Taguchi Method ; Multiobjective Optimization ; Plackett-Burman Method ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method ; Technique for Order-Perference by Similarity to Ideal Solution (TOPSIS)Method

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