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Neural Network Analysis for High-Speed Machining Predictions: Unraveling Cutting and Process Parameters Correlation

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Anwar Hussain ¹ ,

⁴ M. Tech Student Department of Mechanical Engineering, Nimra College of Engineering, Andhra Pradesh, India

Abstract

In the realm of high-speed machining, achieving optimal cutting and process parameters is paramount for efficiency, precision, and tool longevity. This study employs a neural network approach to unravel the intricate correlation between cutting and process parameters, offering predictive insights that can enhance machining operations. By leveraging machine learning techniques, we explore large datasets encompassing various machining scenarios to establish patterns and relationships between cutting speeds, tool materials, feed rates, and other parameters. The resulting neural network model provides a valuable tool for engineers and manufacturers to optimize their machining processes, reduce waste, and improve productivity in high-speed machining environments.

Keywords

High-Speed Machining, Neural Network Analysis, Machining Predictions, Cutting Parameters

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Neural Network Analysis for High-Speed Machining Predictions: Unraveling Cutting and Process Parameters Correlation

Abstract

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References

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