An Integrated Experimental and Machine Learning Approach for Machinability Assessment and Tool Life Prediction in Drilling of 14NiCr10 Alloy Using AlTiN-Coated Carbide Tools

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Adik M. Takale, Uday A. Dabade, Manjunath G. Avalappa, Uttam U. Deshpande, Mukesh Kumar
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Abstract

To maximize tool life and process efficiency in high-performance drilling applications, it is necessary to examine the machinability of 14NiCr10 alloy using AlTiN-coated carbide tools with varying cobalt compositions. We propose a method to evaluate tool wear, cutting force, temperature, and the number of holes drilled before resharpening during experimental trials in dry cutting settings. In comparison to normal tools, carbide tools with a higher cobalt content demonstrated better wear resistance, lower thermal load, and a 51% increase in tool life during experiments. To validate the measured mechanical and thermal loads, we carried out simulation tests using Finite Element Analysis (FEA) on temperature distribution, torque, and stress behavior loads. For normal and increased cobalt content tool versions, the values stayed within safe operating limits. We trained the sensor-acquired force and temperature data using the Decision Tree Regressor to create a machine learning-based predictive model, further improving process reliability. With more than 90% of tool life estimates falling within an acceptable error range of ±10%, the model exhibited excellent predictive accuracy. Our method provides a comprehensive hybrid framework for machining high-strength alloys by utilizing a combination of simulation, machine learning, experimental analysis, and enhancing tool performance. Thus, it facilitates predictive maintenance and supports the development of smart manufacturing processes.

Abstract Image

基于实验和机器学习的14NiCr10合金altin涂层硬质合金切削性能评估和刀具寿命预测方法
为了在高性能钻井应用中最大限度地提高刀具寿命和工艺效率,有必要使用不同钴成分的altin涂层硬质合金刀具来研究14NiCr10合金的可切削性。我们提出了一种方法来评估工具磨损、切削力、温度和在干切削环境下重新锐化之前钻的孔数。与普通刀具相比,高钴含量的硬质合金刀具在实验中表现出更好的耐磨性,更低的热负荷,刀具寿命延长51%。为了验证测量的机械和热载荷,我们使用有限元分析(FEA)对温度分布、扭矩和应力行为载荷进行了模拟测试。对于正常和增加钴含量的工具版本,值保持在安全操作范围内。我们使用决策树回归器训练传感器获取的力和温度数据,以创建基于机器学习的预测模型,进一步提高过程可靠性。超过90%的刀具寿命估计在±10%的可接受误差范围内,该模型显示出出色的预测精度。我们的方法通过结合仿真、机器学习、实验分析和提高刀具性能,为加工高强度合金提供了一个综合的混合框架。因此,它促进了预测性维护并支持智能制造流程的开发。
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CiteScore
5.10
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审稿时长
19 weeks
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