Application of TOPSIS Optimization Technique in the Micro-Machining Process

Application of TOPSIS Optimization Technique in the Micro-Machining Process

Boopathi Sampath, Sasikumar C., Sureshkumar Myilsamy
Copyright: © 2023 |Pages: 26
DOI: 10.4018/978-1-6684-5887-7.ch009
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Abstract

Multi-criteria optimization techniques have been applied to predict the best alternative solutions to meet the best results for all attributes. In the micro-machining processes, the various process parameters in the micro-machines, special experimental setup, tool profiles, workpiece materials, and working mediums are considered. Sometimes, the response attributes conflict with each other, making it difficult to estimate the best-influenced process parameters. The multi-criteria optimization methods are applied to solve the abovementioned conditions. In the chapter, the multi-criteria optimization technique: TOPSIS (technique for order of preference by similarity to ideal solution) implementation procedures, flowchart, and result interpretation have been demonstrated to optimize or predict the best micromachining process parameters. A case study on the application of the TOPSIS technique in electrical discharge machining (EDM)) has been illustrated.
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General Optimization Procedure In Micro-Machining Process

The general procedure of optimization in micromachining processes is illustrated in Figure 1. The general steps are explained as follows.(Boopathi, 2021b; Boopathi et al., 2012; Boopathi, Thillaivanan, et al., 2021; Haribalaji, Boopathi, & Asif, 2021a)

  • Step 1: Understand the machining process, and input and output parameters.

  • Step 2: Define the controlled and uncontrolled parameters. The controlled parameters are called measurable parameters, and the uncontrolled parameters are also called noise or non-quantified parameters.

  • Step 3: Select the suitable design of the experiment method (Factorial/ Taguchi /Response surface methods) for the micro-machining processes.

  • Step 4: Define the number of trails or experiments to be performed.

  • Step 5: The experiments are performed.

  • Step 6: The experimentally observed data has been examined.

  • Step 7: To predict the best and optimum process parameters, use single and multi-criteria optimization techniques.

  • Step 8: Predicted results (quality characteristics) have been validated by confirmation experiments.

  • Step 9: Apply/ Utilize the best process parameters in the component manufacturing process.

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