An Overview on Metal Processing using Machine Learning

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Volume: 12 | Issue: 1 | Year 2026 | Subscription
International Journal of Manufacturing and Materials Processing
Received Date: 03/13/2026
Acceptance Date: 03/14/2026
Published On: 2026-03-26
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By: Heena T. Shaikh and IR. Dr. Kazi Kutubuddin Sayyad Liyakat.

1. Asst. 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

Metalworking, which is an important part of modern industry, includes a wide range of steps, such as shaping and casting the metal, machining it, and riveting it. The industry has always relied on trial and error and other empirical methods, but machine learning (ML) is now causing a big change. It is expected that this change will lead to higher production, better product quality, and better use of resources. The objective of this study is to examine the application of machine learning in metal processing and to highlight the transformative potential of this emerging technology across many phases of the manufacturing process. Machine learning offers a robust framework for analysing complex datasets generated during the manufacturing lifecycle, presenting a possibility for a transformative shift in the metal processing industry. Using machine learning allows for real-time optimisation, finding problems before they happen, and better management of processes. This is done by algorithms that can find patterns and make predictions. There are many benefits to this, including less waste of materials, more productivity, better product quality, and more efficient use of energy. Some of the many uses are finding defects and automating quality control. Other uses include figuring out what materials will be like and making process parameters work better. This article looks at how machine learning can be used in the metal processing industry and how it could change the future of this field.

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How to cite this article: Heena T. Shaikh and IR. Dr. Kazi Kutubuddin Sayyad Liyakat An Overview on Metal Processing using Machine Learning. International Journal of Manufacturing and Materials Processing. 2026; 12(1): -p.

How to cite this URL: Heena T. Shaikh and IR. Dr. Kazi Kutubuddin Sayyad Liyakat, An Overview on Metal Processing using Machine Learning. International Journal of Manufacturing and Materials Processing. 2026; 12(1): -p. Available from:https://journalspub.com/publication/ijmmp/article=26141

Refrences:

  1. Sunil B. Mishra (2024a). AI-Driven-IoT (AIIoT) Based Decision-Making in Molten Metal Processing, Journal of Industrial Mechanics, 9(2), 45-56.
  2. Sunil B. Mishra (2024). AI-Driven-IoT (AIIoT)-Based Decision Making in Manufacturing Processes in Mechanical Engineering, Journal of Mechanical Robotics, 9(2), 27-38.
  3. Sunil Dhanve, (2025). Machine Learning Forges a New Future for metal Processing: A Study, International Journal of Artificial Intelligence in Mechanical Engineering, Vol 1, Issue 1, pp. 1- 12.
  4. Khadake, S., Kawade, S., Moholkar, S., Pawar, M. (2024). A Review of 6G Technologies and Its Advantages Over 5G Technology. In: Pawar, P.M., et al. Techno-societal 2022. ICATSA 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-34644-6_107.
  5. J. Patil, S. B. Khadake, D. A. Tamboli, H. M. Mallad, S. M. Takpere and V. A. Sawant, “Review of AI in Power Electronics and Drive Systems,” 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 2024, pp. 94-99, doi: 10.1109/PARC59193.2024.10486488
  6. J. Patil, S. B. Khadake, D. A. Tamboli, H. M. Mallad, S. M. Takpere and V. A. Sawant, “A Comprehensive Analysis of Artificial Intelligence Integration in Electrical Engineering,” 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Lalitpur, Nepal, 2024, pp. 484-491, doi: 10.1109/ICMCSI61536.2024.00076
  7. Suhas B. Khadake, Sudarshan P. Dolli, K.S. Rathod, O.P. Waghmare and A.V. Deshpande, “An Overview Of Intelligent Traffic Control System Using Plc And Use Of Current Data Of Vehicle Travels”, JournalNX, pp. 1-4, Jan. 2021.
  8. Shraddha S Magar, Archana S Sugandhi, Shweta H Pawar, Suhas B Khadake, H. M. Mallad,“Harnessing Wind Vibration, a Novel Approach towards Electric Energy Generation- Review”, IJARSCT, Volume 4, Issue 2, October 2024, pp. 73-82. DOI: 10.48175/IJARSCT-19811.
  9. Khadake, S. B., Padavale, P. V., Dhere, P. M., & Lingade, B. M., “Automatic hand dispenser and temperature scanner for Covid-19 prevention”, International Journal of Advanced Research in Science, Communication and Technology, 3(2), 362-367. DOI: 10.48175/IJARSCT-11364. https://ijarsct.co.in/A11364.pdf
  10. Ameykumar Balkrishna Dudgikar, Adnan Ahmad Akbar Ingalgi, Abhishek Gensidha Jamadar, Onkar Rameshchandra Swami, Suhas Balram Khadake and Shreya Vikram Moholkar, “Intelligent Battery Swapping System for Electric Vehicles with Charging Stations Locator on IoT and Cloud Platform”, IJARSCT, vol. 3, no. 1, January-2023, pp. 204-208, DOI: 10.48175/IJARSCT-7867
  11. B. Khadake and V. J. Patil, “Prototype Design & Development of Solar Based Electric Vehicle,” 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2023, pp. 1-7, doi: 10.1109/SMARTGENCON60755.2023.10442455.
  12. Seema S Landage, Sonali R Chavan, Pooja A Kokate, Sonal P Lohar, M. K. Pawar, Suhas B Khadake.,“Solar Outdoor Air Purifier With Air Quality Monitoring System”, Synergies Of Innovation: Proceedings Of Ncstem 2023, Pp. 260-266, September, 2024. Available At: https://www.researchgate.net/publication/383631190_Solar_Outdoor_Air_Purifier_with_Air_Quality_Monitoring_System
  13. Suhas B. Khadake. (2021). Detecting Salient Objects Of Natural Scene In A Video’s Using Spatio-Temporal Saliency &Amp; Colour Map. Journalnx – A Multidisciplinary Peer Reviewed Journal, 2(08), 30–35. Retrieved From Https://Repo.Journalnx.Com/Index.Php/Nx/Article/View/1070 .
  14. Khadake Suhas .B. (2021). Detecting Salient Objects In A Video’s By Using spatio-Temporal Saliency & Colour Map. International Journal Of Innovations In Engineering Research And Technology, 3(8), 1-9. Https://Repo.Ijiert.Org/Index.Php/Ijiert/Article/View/910.
  15. Prachi S Bhosale, Pallavi D Kokare, Dipali S Potdar, Shrutika D Waghmode, V A Sawant, Suhas B Khadake.,“DTMF Based Irrigation Water Pump Control System”, Synergies Of Innovation: Proceedings Of NCSTEM 2023, Pp. 267-273, September, 2024. Available At: https://www.researchgate.net/publication/383629320_DTMF_Based_Irrigation_Water_Pump_Control_System
  16. Pramod Korake, Harshwardhan Murade, Rushikesh Doke, Vikas Narale, Suhas B. Khadake, Aniket S Chavan., “Automatic Load Sharing of Distribution Transformer using PLC”, Synergies Of Innovation: Proceedings Of NCSTEM 2023, Pp. 253-259, September, 2024. Available At: https://www.researchgate.net/publication/383628063_Automatic_Load_Sharing_of_Distribution_Transformer_using_PLC
  17. Suhas B khadake, Pranita J Kashid, Asmita M Kawade, Santoshi V Khedekar, H. M. Mallad .,“Electric Vehicle Technology Battery Management –Review”, IJARSCT, Volume 3, Issue 2, Septeber 2023,pp. 319-325. DOI: 10.48175/IJARSCT-13048.Available at: https://www.researchgate.net/publication/374263508_Electric_Vehicle_Technology_Battery_Management_-_Review
  18. Suhas B. khadake, Amol Chounde, Buddhapriy B. Gopnarayan, Karan Babaso Patil, Shashikant S Kamble. (2024). Human Health Care System: A New Approach towards Life, 15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024, 2024, 2, pp. 5487-5494.
  19. Suhas B. khadake, Vijay J Patil, H. M. Mallad, Buddhapriy B. Gopnarayan, Karan Babaso Patil. (2024). Maximize Farming Productivity through Agriculture 4.0 based Intelligence, with use of Agri Tech Sense Advanced Crop Monitoring System, 15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024, 2024,2, pp. 5127-5134.
  20. Veena, M. Sridevi, K. K. S. Liyakat, B. Saha, S. R. Reddy and N. Shirisha,(2023). HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems, 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India, 2023, pp. 407-410, doi: 10.1109/ICIIP61524.2023.10537627. Available at: https://ieeexplore.ieee.org/document/10537627
  21. Kazi, S. (2024a). Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications. In M. Garcia & R. de Almeida (Eds.), Transformative Approaches to Patient Literacy and Healthcare Innovation(pp. 112-135). IGI Global. https://doi.org/10.4018/979-8-3693-3661-8.ch006  Available at: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823
  22. (2025d). AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. In L. Özgür Polat & O. Polat (Eds.), AI-Driven Innovation in Healthcare Data Analytics(pp. 277-306). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7277-7.ch009
  23. M Pradeepa, et al. (2022). Student Health Detection using a Machine Learning Approach and IoT, 2022 IEEE 2nd Mysore sub section International Conference (MysuruCon), Available at: https://ieeexplore.ieee.org/document/9972445
  24. Mahant, M. A. (2025). Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System. In N. Wickramasinghe (Ed.), Digitalization and the Transformation of the Healthcare Sector(pp. 205-236). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9641-4.ch007
  25. Mulani AO, Liyakat KKS, Warade NS, et al (2025). . ML-powered Internet of Medical Things Structure for Heart Disease Prediction. Journal of Pharmacology and Pharmacotherapeutics. 2025; 0(0). doi:1177/0976500X241306184
  26. Odnala, S., Shanthy, R., Bharathi, B., Pandey, C., Rachapalli, A., & Liyakat, K. K. S. (2025). Artificial Intelligence and Cloud-Enabled E-Vehicle Design with Wireless Sensor Integration. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5107242
  27. Neeraja, R. G. Kumar, M. S. Kumar, K. K. S. Liyakat and M. S. Vani. (2024), DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Greater Noida, India, 2024, pp. 589-594, doi: 10.1109/IC2PCT60090.2024.10486714. Available at: https://ieeexplore.ieee.org/document/10486714
  28. Santoshi V Khedekar, Asmita M Kawade, Shradhha Shivaji Vyavahare, Pranita J Kashid, Chounde Amol B, H. M. Mallad., “Solar Based Electric Vehicle Charging System-Review”, IJARSCT, vol. 4, Issue 2, December 2024, pp. 42-57, DOI: 10.48175/IJARSCT-22705
  29. Akshay B Randive , Sneha Kiran Gaikwad , Suhas B Khadake , Mallad H. M., “Biodiesel: A Renewable Source of Fuel”, IJARSCT, vol. 4, Issue 3, December 2024, pp. 225-240,  DOI: 10.48175/IJARSCT-22836 Available at https://www.researchgate.net/publication/387352609_Biodiesel_A_Renewable_Source_of_Fuel
  30. Priya Mangesh Nerkar, Bhagyarekha Ujjwalganesh Dhaware. (2023). Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning, Journal of Advanced Zoology, 2023, Volume 44, Special Issue -2, Page 3673:3686.  Available at: https://jazindia.com/index.php/jaz/article/view/1695
  31. Priya Nerkar and Sultanabanu, (2024). IoT-Based Skin Health Monitoring System, International Journal of Biology, Pharmacy and Allied Sciences (IJBPAS). 2024, 13(11): 5937-5950. https://doi.org/10.31032/IJBPAS/2024/13.11.8488
  32. B. Khadake, A. B. Chounde, A. A. Suryagan, M. H. M. and M. R. Khadatare, (2024). AI-Driven-IoT(AIIoT) Based Decision Making System for High-Blood Pressure Patient Healthcare Monitoring, 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2024, pp. 96-102, doi: 10.1109/ICSCNA63714.2024.10863954.
  33. Sayyad Liyakat, S. B. Khadake, A. B. Chounde, A. A. Suryagan, M. H. M. and M. R. Khadatare, (2024). AI-Driven-IoT(AIIoT) Based Decision Making System for High-Blood Pressure Patient Healthcare Monitoring, 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2024, pp. 96-102, doi: 10.1109/ICSCNA63714.2024.10863954.

 

 

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