Abstract

Non-Convectional Machining (NCM) processes are used widely to produce accurate and intricate material shape such as titanium, stainless steels and resistant alloy that are of high strength, fiber-based composite, refractories and ceramics. The production of more complex shapes of materials using convectional machining processes is considered to be challenging. This research paper focusses on the section of the most effective NCM process. The ‘choice’ of the most considerable NCM process for a particular application could be seen as a multi-technique for making proper choices for diverse or conflicting approaches. To aid the process of choosing, various NCM techniques have been proposed in this research. This contribution focuses on the usage of unexplored NCM and Multi-Feature Decision-Making (MFDM) ‘choice’ issues.

Highlights

  • Modern machining techniques are known as Non-convectional Machining (NCM) methods

  • Multi-Feature Decision-Making (MFDM) approach is applied to effectively rank these effective NCM process in a descending order based on priority

  • Criteria and attributes, which affect NCM process ‘choice’ decision, incorporate: Tolerant surface finish: It considers the machining capacity of NCM process considering the manner in which NCM process can closely maintain tolerance and attain the essential surface finish on the work materials

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Summary

INTRODUCTION

Modern machining techniques are known as Non-convectional Machining (NCM) methods. For the use of energy principles, the usage of mechanical energy is based on the removal of materials from workpieces With this process, the tools of cutting with sharp edges are not utilized but the materials are eliminated using abrasive actions of high-speed streams of hard, smaller abrasive particles. There have been fundamental developments in use and application of NCM processes, mostly in the machining of harder materials such as stainless steel and titanium These materials are widely used in modern industry because of their developed mechanical features. As there are challenges in the choice of NCM process, the choice is made easier based on the application of the classification code with sixteen digits In this case, the optimization process selection can be accomplished as all the approaches are encrypted in databases of systems.

BACKGROUND
CRITICAL ANALYSI
Findings
CONCLUSION AND DISCUSSION
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