Versatile CNC Machine for Tabletop Use Enhanced with Machine Learning Integration

Open Access

Year : 2023 | 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 under section in Journal of Polymer and Composites (jopc)]

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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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References

  1. Li, X., Wang, L., & Zhang, Y. (2020). Machine learning in CNC machining: Recent advances and future perspectives. International Journal of Machine Tools and Manufacture, 156, 103562.
  2. Brown, A., & Johnson, R. (2019). Machine learning applications in manufacturing: A review. Journal of Industrial Engineering and Management, 12(2), 227–249.
  3. Singh, A., & Verma, S. (2017). Machine learning for predictive modeling of tool wear in CNC machining. International Journal of Advanced Manufacturing Technology, 89(9-12), 3139–3151.
  4. Chen, J., Yang, H., & Liu, Y. (2021). A survey of machine learning techniques in CNC machining. Journal of Manufacturing Systems, 61, 43–56.
  5. Wang, Y., Zhang, L., & Li, H. (2020). Deep learning-based quality control and inspection in CNC machining. International Journal of Advanced Manufacturing Technology, 108(7-8), 2761v2777.
  6. Wu, X., Wang, L., Chen, Z., Sun, L., & Du, Q. (2018). Machine Learning-Based Approaches for CNC Machining: A Review. International Journal of Advanced Manufacturing Technology, 98(1-4), 1039–1052.
  7. Sastry, P. S. (2013). Unsupervised Learning Algorithms: An Overview. International Journal of Computer Science and Information Technologies, 4(3), 431–435.
  8. Gao, X., Li, W., Zhao, S., Xu, L., & Li, W. (2021). Intelligent optimization of CNC machining parameters based on reinforcement learning. Journal of Intelligent Manufacturing, 32(2),
    397–410.
  9. Anuar, S. N., Ibrahim, M. H. I., Bahari, A. R., & Rasid, M. F. A. (2020). Integration of machine learning techniques into CNC machining for surface roughness prediction: A review. International Journal of Advanced Manufacturing Technology, 104(1-4), 133–149.
  10. Padhi, S. N., Rout, T., Raghu Ram, K. S., & International Journal of Recent Technology and Engineering (IJRTE). (2019, May). Parametric Instability and Property Variation Analysis of a Rotating Cantilever FGO Beam. International Journal of Recent Technology and Engineering, 8(1). ISSN: 2277-3878.
  11. Jagadale, V. S., Padhi, S. N., & International Journal of Innovative Technology and Exploring Engineering (IJITEE). (2019, November). Mechanical Behavior of Coir Fiber Reinforced Polymer Resin Composites with Saturated Ash Particles. International Journal of Innovative Technology and Exploring Engineering, 9(1). ISSN: 2278-3075.
Special Issue Open Access Review Article
Volume 11
Special Issue 08
Received 27/11/2023
Accepted 05/01/2024
Published 15/03/2024
Publication Time 109 Days
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