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Tool Material Selection System for CNC Turning
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Abstract: This study presents the development of a tool material selection system designed to improve decision-making in machining operations through systematic evaluation of tool-workpiece compatibility, tool life, and production economics. Conventional cutting tool selection often depends on operator experience, handbook references, and trial- based methods, which can lead to inconsistent results, premature tool wear, and higher manufacturing costs. The proposed system addresses these limitations by integrating materials engineering principles with computational analysis in an interactive recommendation platform.
A structured database containing 62 industrial work piece materials and 14 cutting tool grades was developed, covering major ISO material groups. The system applies a multi-parameter compatibility model based on hardness margin, thermal resistance, wear behaviour, edge strength, and chip control to generate a performance score for each tool-material combination. An enhanced tool life prediction model and material removal rate calculations were incorporated to estimate productivity outcomes. In addition, a manufacturing economics module evaluates tool cost, machine cost, labour cost, and profitability indices to recommend the most efficient tooling option.
Validation against industrial machining guidelines and handbook data showed strong agreement in recommended grades and cutting conditions. The system achieved rapid response time and enabled comparison of multiple tool grades within seconds. Results indicate that optimized tool selection can significantly reduce decision time, improve tool utilization, lower production cost, and increase process productivity. The developed framework demonstrates how intelligent engineering systems can support practical manufacturing optimization while remaining transparent, explainable, and scalable for future industrial applications.
Keywords: Machining, Tool Selection, Manufacturing Optimization, Tool Life, Artificial Intelligence
A structured database containing 62 industrial work piece materials and 14 cutting tool grades was developed, covering major ISO material groups. The system applies a multi-parameter compatibility model based on hardness margin, thermal resistance, wear behaviour, edge strength, and chip control to generate a performance score for each tool-material combination. An enhanced tool life prediction model and material removal rate calculations were incorporated to estimate productivity outcomes. In addition, a manufacturing economics module evaluates tool cost, machine cost, labour cost, and profitability indices to recommend the most efficient tooling option.
Validation against industrial machining guidelines and handbook data showed strong agreement in recommended grades and cutting conditions. The system achieved rapid response time and enabled comparison of multiple tool grades within seconds. Results indicate that optimized tool selection can significantly reduce decision time, improve tool utilization, lower production cost, and increase process productivity. The developed framework demonstrates how intelligent engineering systems can support practical manufacturing optimization while remaining transparent, explainable, and scalable for future industrial applications.
Keywords: Machining, Tool Selection, Manufacturing Optimization, Tool Life, Artificial Intelligence
How to Cite:
[1] Vidit Jain, Bhavesh Goel, Tanmay Misra, M.S. Niranjan, “Tool Material Selection System for CNC Turning,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2026.134119
This work is licensed under a Creative Commons Attribution 4.0 International License.