Risk Analysis and Management in CNC Machining Operations: A Comprehensive Framework for Hazard Identification, Mitigation, and Process Safety Enhancement
DOI:
https://doi.org/10.67308/irjist.053Keywords:
CNC Machining, Risk Analysis, Risk Management, Failure Mode and Effects Analysis (FMEA), Hazard and Operability Study (HAZOP), Fault Tree Analysis (FTA), Process SafetyAbstract
Computer Numerical Control (CNC) machining plays a vital role in modern manufacturing by enabling high precision, productivity, and repeatability. However, CNC operations involve various mechanical, electrical, thermal, and operational hazards that require systematic risk management to ensure workplace safety and process reliability. This review presents a comprehensive overview of risk analysis and management approaches applicable to CNC machining, focusing on widely adopted methodologies such as Failure Mode and Effects Analysis (FMEA), Hazard and Operability Study (HAZOP), and Fault Tree Analysis (FTA). The paper examines their applications, strengths, and limitations in hazard identification, risk assessment, and implementation of effective mitigation strategies. It also discusses recent developments in predictive maintenance, Industry 4.0, and integrated safety management for enhancing manufacturing performance. The review highlights the importance of combining multiple risk assessment techniques to improve safety, reduce operational risks, minimize downtime, and support sustainable manufacturing practices. The findings provide a valuable reference for researchers, manufacturing engineers, and industrial practitioners seeking effective strategies for improving safety and reliability in CNC machining operations.
Downloads
References
[1] IEC 60812, Failure Modes and Effects Analysis (FMEA and FMECA), International Electrotechnical Commission, Geneva, Switzerland, 2018.
[2] ISO 31000:2018, Risk Management – Guidelines, International Organization for Standardization, Geneva, Switzerland, 2018.
[3] IEC 61882, Hazard and Operability Studies (HAZOP Studies): Application Guide, International Electrotechnical Commission, Geneva, Switzerland, 2016.
[4] E. N. Bowles, Failure Mode and Effect Analysis: A Practical Approach, 2nd ed., CRC Press, Boca Raton, FL, USA, 2003.
[5] D. H. Stamatis, Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd ed., ASQ Quality Press, Milwaukee, WI, USA, 2003.
[6] H. A. Watson, “Launch Control Safety Study,” Bell Telephone Laboratories, Murray Hill, NJ, USA, 1961.
[7] J. A. McDermott, R. J. Mikulak, and M. R. Beauregard, The Basics of FMEA, 2nd ed., Productivity Press, New York, NY, USA, 2009.
[8] S. Kalpakjian and S. R. Schmid, Manufacturing Engineering and Technology, 7th ed., Pearson Education, Boston, MA, USA, 2014.
[9] Y. Altintas, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2nd ed., Cambridge University Press, Cambridge, UK, 2012.
[10] M. P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 4th ed., Pearson, New York, NY, USA, 2021.
[11] Y. Peng, X. Wang, and Z. Li, “Risk assessment and safety management for intelligent CNC machining systems,” Journal of Manufacturing Systems, vol. 71, pp. 140–153, 2024.
[12] L. Chen, H. Zhang, and J. Liu, “Artificial intelligence-assisted predictive risk analysis in smart manufacturing,” Journal of Intelligent Manufacturing, vol. 35, no. 2, pp. 421–438, 2024.
[13] A. Kumar and R. Singh, “Digital twin-enabled risk monitoring for CNC machining operations,” Procedia CIRP, vol. 122, pp. 395–401, 2023.
[14] S. Patel, V. Shah, and K. Mehta, “Integrated FMEA and machine learning framework for predictive maintenance in manufacturing systems,” Machines, vol. 12, no. 5, Art. no. 311, 2024.
[15] J. Lee, B. Bagheri, and H. Kao, “Recent advances in smart manufacturing and predictive maintenance: A review,” Annual Reviews in Control, vol. 58, pp. 100–118, 2025.
[16] K. S. Chaudhari, N. B. More, and A. Somatkar, “Risk Assessment in Automated Manufacturing Systems: A Hybrid Framework for Industry 4.0 and Beyond,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 2, pp. 54–63, 2026.
[17] S. Mhatre and A. Somatkar, “Risk Analysis and Process Optimization in Toy Manufacturing Using FMEA, Six Sigma, and Statistical Process Control,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 2, pp. 39–46, 2026.
[18] G. Sanap, V. Sanap, T. Patil, and A. Kadu, “Integrated Analytical Modeling of Material Removal Rate and Surface Roughness in Magnetic Levitation EDM of Ti-6Al-4V,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 2, pp. 32–38, 2026.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jay Lapurkar, Siddhant Diwan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in the International Research Journal of Innovation in Science and Technology (IRJIST) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors retain the copyright of their work and grant the journal the right of first publication. Proper attribution to the authors and the journal is required for any reuse of the published content.
