Versatile CNC Machine for Tabletop Use Enhanced with Machine Learning Integration

Open Access

Year : 2024 | Volume :11 | Special Issue : 08 | Page : 353-361
By

Ashwini Kumar Baluguri

Srinivasa Rao Seeram

  1. Research Scholar Department of Mechanical Engineering, KLEF University Andhra Pradesh India
  2. Professor Department of Mechanical Engineering, KLEF University Andhra Pradesh India

Abstract

In the realm of tabletop multipurpose CNC machines, the integration of machine learning represents a groundbreaking advancement potentially revolutionary in the field of desktop manufacturing. This research explores the seamless incorporation of machine learning algorithms into tabletop CNC machines to enhance their capabilities, performance, and user experience. Through case studies and examples, we demonstrate the profound impact of machine learning integration in key areas of CNC machining, such as accurate tool wear prediction, real-time error detection and compensation, and precise quality control and inspection. The findings highlight the transformative power of machine learning in expanding the horizons of tabletop CNC machines, empowering users with unprecedented precision, efficiency, and versatility. By unraveling the challenges and opportunities associated with machine learning integration, this research paves the way for future developments that will propel tabletop multipurpose CNC machines into an era of intelligent, autonomous, and user-centric manufacturing systems. In addition, real-time feedback and monitoring during the machining process are made possible by the machine learning integration. The system can detect anomalies, such as tool deflection or material inconsistencies, and autonomously make corrective adjustments, ensuring a higher quality output. Additionally, the model continually learns from each machining operation, contributing to an evolving database of best practices for different materials and machining scenarios.

Keywords: Machine learning, user-centric manufacturing, CNC machining, tabletop, algorithms, error detection.

This article belongs to Special Issue Conference International Conference on Innovative Concepts in Mechanical Engineering (ICICME – 2023)

How to cite this article: Ashwini Kumar Baluguri, Srinivasa Rao Seeram. Versatile CNC Machine for Tabletop Use Enhanced with Machine Learning Integration. Journal of Polymer and Composites. 2024; 11(08):353-361.
How to cite this URL: Ashwini Kumar Baluguri, Srinivasa Rao Seeram. Versatile CNC Machine for Tabletop Use Enhanced with Machine Learning Integration. Journal of Polymer and Composites. 2024; 11(08):353-361. Available from: https://journals.stmjournals.com/jopc/article=2024/view=134898

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Conference Open Access Review Article
Volume 11
Special Issue 08
Received November 27, 2023
Accepted January 5, 2024
Published March 15, 2024
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