UNLOCKING HIGH-SPEED MACHINING INSIGHTS: A NEURAL NETWORK APPROACH TO PREDICTING CUTTING AND PROCESS PARAMETERS CORRELATION

Authors

Razia Begum M. Tech Student Department of Mechanical Engineering, Nimra College of Engineering and Technology (Ncet) Ibrahimpatnam, Vijayawada, Andhra Pradesh, India

Keywords:

High-speed machining, neural network, cutting parameters

Abstract

This research delves into the realm of high-speed machining by employing a neural network approach to predict the correlation between cutting and process parameters. High-speed machining is a critical manufacturing technique that offers enhanced efficiency and precision. Understanding the intricate relationships between cutting parameters (e.g., cutting speed, feed rate) and process parameters (e.g., tool wear, surface finish) is essential for optimizing machining operations. Through the utilization of neural networks, this study develops predictive models that elucidate these correlations. By analyzing extensive datasets and training neural networks, insights are uncovered that facilitate informed decision-making in high-speed machining, leading to improved quality and productivity.

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Published

2015-04-04

How to Cite

Razia Begum. (2015). UNLOCKING HIGH-SPEED MACHINING INSIGHTS: A NEURAL NETWORK APPROACH TO PREDICTING CUTTING AND PROCESS PARAMETERS CORRELATION. Ethiopian International Journal of Multidisciplinary Research, 2(04), 01–05. Retrieved from https://eijmr.org/index.php/eijmr/article/view/54