Prediction of Flank Wear in Turning of Monel K500 by Using Machine Learning Model in Comparison With Experimental Analysis

This study uses machine learning algorithms to estimate flank wear in Monel K500, a high-performance nickel-copper alloy, turning. Experimental findings are compared. Tool life, machining quality, and process efficiency depend on flank wear. Traditional flank wear predictions use empirical models based on experimental data, which may be inaccurate and unreliable. This work investigates an alternate flank wear prediction method utilizing machine learning algorithms. Support vector machines, random forests, and neural networks are used to create prediction models using cutting parameters, tool attributes, and workpiece qualities. Experimental data from Monel K500 turning experiments trains and validates the models. Machine learning models are tested against experimental analysis findings to determine accuracy and dependability. The study evaluates machine learning’s flank wear prediction accuracy and finds the best model for Monel K500 turning operations. The models’ study also reveals Monel K500 machining flank wear variables. This research improves flank wear prediction in Monel K500 turning operations and sheds light on machine learning in machining optimization. The findings can help manufacturers and researchers improve Monel K500 and comparable alloy machining techniques, reduce tool wear, and boost productivity.