Analysis of Machine Learning in Metal Processing: A Novel Prospect

Year : 2026 | Volume : 16 | Issue : 01 | Page : 40 51
    By

    Heena T. Shaikh,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor, and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

Metal is processed by a wide range of procedures, from forming and casting to machining and riveting. Metal processing is a crucial part of modern manufacturing. The application of machine learning (ML) is driving a significant change in the sector, which has historically depended on empirical knowledge and trial-and-error techniques. Increased production, improved product quality, and resource optimization are expected outcomes of this action. This study aims to explore the application of machine learning to metal processing and highlight how this cutting-edge technology has the potential to completely transform a number of production steps. Machine learning offers a chance for a paradigm change in the metal processing sector by offering a strong framework for the analysis of intricate information produced throughout the manufacturing lifecycle. Real-time optimization, preemptive problem detection, and enhanced process management are all made possible by the application of machine learning. Algorithms capable of pattern recognition and predictive modeling are used to achieve this. Reduced material waste, increased productivity, better product quality, and optimized energy use are just a few of the significant advantages that can result from this. The automation of quality control and defect detection are just two of the numerous uses. Predicting material properties and optimizing process parameters are two further uses. This article examines the different applications of machine learning in the metal processing industry, emphasizing how this technology has the potential to revolutionize the industry’s future.

Keywords: Machine learning, material design, material selection, metal processing, predictive maintenance

[This article belongs to Journal of Materials & Metallurgical Engineering ]

How to cite this article:
Heena T. Shaikh, Kazi Kutubuddin Sayyad Liyakat. Analysis of Machine Learning in Metal Processing: A Novel Prospect. Journal of Materials & Metallurgical Engineering. 2026; 16(01):40-51.
How to cite this URL:
Heena T. Shaikh, Kazi Kutubuddin Sayyad Liyakat. Analysis of Machine Learning in Metal Processing: A Novel Prospect. Journal of Materials & Metallurgical Engineering. 2026; 16(01):40-51. Available from: https://journals.stmjournals.com/jomme/article=2026/view=241291


References

  1. Du Y, Mukherjee T, Li R, Hou Z, Dutta S, Arnold CB, Elwany A, Kung S, Tang J, DebRoy T. A review of deep learning in metal additive manufacturing: Impact on process, structure, and properties. Progress in Materials Science. 2025 Oct 6:101587.
  2. Mishra SB, Liyakat KK. AI-Driven-IoT (AIIoT) Based Decision-Making in Molten Metal Processing. Journal of Industrial Mechanics. 2024;9(2):45-56.
  3. Balasubramanian S, Scholar II. Integration of artificial intelligence in the manufacturing sector: A systematic review of applications and implications. International Journal of Production Technology and Management (IJPTM). 2023;14(1):01-11.
  4. Khadake S, Kawade S, Moholkar S, Pawar M. A review of 6G technologies and its advantages over 5G technology. InTechno-Societal 2016, International Conference on Advanced Technologies for Societal Applications 2022 Dec 9 (pp. 1043-1051). Cham: Springer International Publishing.
  5. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. Review of AI in power electronics and drive systems. In2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC) 2024 Feb 23 (pp. 94-99). IEEE.
  6. Dudgikar AB, Ingalgi AA, Jamadar AG, Swami OR, Khadake SB, Moholkar SV. Intelligent battery swapping system for electric vehicles with charging stations locator on IoT and cloud platform. International Journal of Advanced Research in Science, Communication and Technology. 2023 Jan;3(1):204-8.
  7. Khadake SB, Patil VJ. Prototype design & development of solar based electric vehicle. In2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) 2023 Dec 29 (pp. 1-7). IEEE.
  8. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. A comprehensive analysis of artificial intelligence integration in electrical engineering. In2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) 2024 Jan 18 (pp. 484-491). IEEE.
  9. Khadake SB, Dolli SP, Rathod KS, Waghmare MO, Deshpande MA. An overview of intelligent traffic control system using PLC and use of current data of vehicle travels. JournalNX-A Multidisciplinary Peer Reviewed Journal. 2016 Feb 12.
  10. Tandel R, Shah S, Tripathi S. A state-of-art review on Bladeless Wind Turbine. InJournal of Physics: Conference Series 2021 Aug 1 (Vol. 1950, No. 1, p. 012058). IOP Publishing.
  11. Rusimamto PW, Nurhayati N, Yundra E, Rahmadian R, Widodo A, Dermawan MA. Automatic hand sanitizer container to prevent the spread of corona virus disease. InInternational Joint Conference on Science and Engineering (IJCSE 2020) 2020 Nov 24 (pp. 60
  12. Collado E, Harish G, Sáez Y. Design of a solar-powered air pollution monitoring system under tropical climate environments. In2022 IEEE 40th Central America and Panama Convention (CONCAPAN) 2022 Nov 9 (pp. 1-6). IEEE.
  13. Azaza A, Kabbai L, Abdellaoui M, Douik A. Salient regions detection method inspired from human visual system anatomy. In2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2016 Mar 21 (pp. 155-160). IEEE.
  14. Fouad H, Hassanein AS, Soliman AM, Al-Feel H. Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction. Measurement. 2020 Jul 15;159:107757.
  15. Majidov T, Mirzaev S, Nazir I, Buvabekov B, Berdiev M, Turdiev A. Software for automating the complex system of drip irrigation. InAIP Conference Proceedings 2025 Jul 15 (Vol. 3256, No. 1, p. 020045). AIP Publishing LLC.
  16. De Carne G, Buticchi G, Liserre M, Yoon C, Blaabjerg F. Voltage and current balancing in low and medium voltage grid by means of smart transformer. In2015 IEEE Power & Energy Society General Meeting 2015 Jul 26 (pp. 1-5). IEEE.
  17. Sundaram Y, Nanjan S. A Short Review on Battery Management System for Electric Vehicle. Journal of Pharmaceutical Negative Results. 2022 Oct 4;13.
  18. Chounde A, Gopnarayan BB, Patil KB, Kamble SS. Human Health Care System: A New Approach towards Life. Grenze International Journal of Engineering & Technology (GIJET). 2024 Jun 15;10.
  19. Arvanitis KG, Symeonaki EG. Agriculture 4.0: The role of innovative smart technologies towards sustainable farm management. The Open Agriculture Journal. 2020 Aug 19;14(1).
  20. Arulious JA, Earlina D, Harish D, Priya PS, Rexy AI, Mary JN. Design of solar powered electric vehicle. InJournal of Physics: Conference Series 2021 Nov 1 (Vol. 2070, No. 1, p. 012105). IOP Publishing.
  21. Atabani AE, Silitonga AS, Badruddin IA, Mahlia TM, Masjuki H, Mekhilef S. A comprehensive review on biodiesel as an alternative energy resource and its characteristics. Renewable and sustainable energy reviews. 2012 May 1;16(4):2070-93.
  22. Suma MR, Madhumathy P. Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption cryptography for privacy preserved data access in cloud assisted Internet of Things services using glow‐worm swarm optimization. Transactions on Emerging Telecommunications Technologies. 2022 Dec;33(12):e4641.
  23. Huang X, Wang H, She C, Feng J, Liu X, Hu X, Chen L, Tao Y. Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy. Frontiers in endocrinology. 2022 Sep 29;13:946915.
  24. Moreno-Sanchez PA. Chronic kidney disease early diagnosis enhancing by using data mining classification and features selection. InInternational Conference on IoT Technologies for HealthCare 2020 Dec 3 (pp. 61-76). Cham: Springer International Publishing.
  25. Shanthakumari R, Nalini C, Vinothkumar S, Roopadevi EM, Govindaraj B. Multi disease prediction system using random forest algorithm in healthcare system. In2022 International Mobile and Embedded Technology Conference (MECON) 2022 Mar 10 (pp. 242-247). IEEE.
  26. Bharadwaj HK, Agarwal A, Chamola V, Lakkaniga NR, Hassija V, Guizani M, Sikdar B. A review on the role of machine learning in enabling IoT based healthcare applications. IEEE Access. 2021 Feb 16;9:38859-90.
  27. Pandey V, Lilhore UK, Walia R, Alroobaea R, Alsafyani M, Baqasah AM, Algarni S. Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy. Scientific Reports. 2024 Oct 16;14(1):24221.
  28. SRINIVAS O, Shanthy R, Bharathi B, Pandey C, Rachapalli A, Liyakat KK. Artificial Intelligence and Cloud-Enabled E-Vehicle Design with Wireless Sensor Integration. InProceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering
  29. Neeraja P, Kumar RG, Kumar MS, Liyakat KK, Vani MS. DL-based somnolence detection for improved driver safety and alertness monitoring. In2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) 2024 Feb 9 (Vol. 5, pp. 589-594). IEEE.
  30. Maini E, Venkateswarlu B, Maini B, Marwaha D. Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India. medical journal armed forces india. 2021 Jul 1;77(3):302-11.
  31. Alavi AH, Jiao P, Buttlar WG, Lajnef N. Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement. 2018 Dec 1;129:589-606.
  32. Vimalnath S, Dharshini R, Archana S, Gowsikadevi M, Harini S. Next-Generation Health Monitoring Using IoT. In2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) 2024 Mar 15 (pp. 1-5). IEEE.
  33. Fang K, Wang W, Woźniak M, Zhang Q, Yu K, Chen J, Tolba A, Zhang L. Guest Editorial AI-Empowered Internet of Things for Data-Driven Psychophysiological Computing and Patient Monitoring. IEEE Journal of Biomedical and Health Informatics. 2024 May 6;28(5):2496-9.
Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 27/01/2026
Accepted 28/01/2026
Published 21/02/2026
Publication Time 25 Days
12

Login


My IP

PlumX Metrics