Special Issue: Artificial Intelligence and the Metal Industry - A Partnership for Progress

 

 

Special Issue Editors

 

Prof. Wilk-Kolodziejczyk Dorota
Department of Applied Computer Science and Modelling,
AGH University of Krakow,
Poland.
Email: dwilk@agh.edu.pl, wilkkdorota@yahoo.com
Google Scholar, Scopus, ORCID

 

Prof. Dr. Krzysztof Jaskowiec
Faculty of Metals Engineering and Industrial Computer Science,
Department of Applied Computer Science and Modelling,
University of Krakow,
Poland.
Email: mailto:Krzysztof.jaskowiec@kit.lulasiewicz.gov.pl
Google Scholar,  Scopus, ORCID

 

Dr. Masoume Jabbarifar
Faculty of Financial Sciences,
Kharazmi University,
Iran.
Email: mailto:jabbarifar@khu.ac.ir
Google Scholar

 

 

Special Issue Information  

Artificial Intelligence (AI) is transforming most metalworking industries, such as metal forming, steel production, and sheet metal cutting. AI resolves longstanding issues like complexity in internal material state tracking and inefficiency in conventional simulation techniques, particularly incremental forming. With the fusion of AI and machine learning and physical models, manufacturers can maximize the process simulation, design optimization, and quality control, which lead to efficiency improvement, cost reduction, and product quality enhancement. In steel production, AI maximizes operational effectiveness by way of predictive maintenance, defect identification and process optimization driven by data. The technologies enable decreased energy consumption, environmental effects, and improved consistency of the product in the face of adversity such as the prohibitively expensive operation, market unpredictability and resistance to digitalization. The investment in AI cannot but grow as the industry transforms into smart manufacturing. Similarly, sheet metal cutting facilities are applying AI for real-time automatic process monitoring, anomaly recognition, waste reduction, and intelligent quote generation. AI automates CAD/CAM and predictive maintenance, reducing cost and production time considerably. AI is improving decision-making speed and accuracy, yet human intelligence continues to be the key to ultimate decision-making as well as AI training, demonstrating the synergistic interaction of AI and humans in modern manufacturing.

In steel production, AI is used to streamline processes, predict maintenance, and identify defects. Real-time surface defects like cracks, dents and scales can be identified using convolutional neural network (CNN)-based vision inspection systems. Predictive maintenance solutions leverage historic and real-time sensor data to predict equipment failure for machines like rolling mills and blast furnaces and cut downtime and repair expenses to a significant extent. One of the emerging technologies that is becoming popular is Reinforcement Learning (RL), which is utilized in creating adaptive process control systems. One of the emerging technologies gaining traction is Reinforcement Learning (RL), which is being used to develop adaptive process control systems. These systems, through learning, develop optimal control policies with time, enhancing cutting speeds, energy consumption, and tool life. Also, Digital Twins virtual images of physical manufacturing systems—powered by AI facilitate real-time monitoring, simulation and predictive adjustment, making smarter and more resilient production environments possible. In addition, Natural Language Processing (NLP) is being researched to pull tacit knowledge out of technical reports, logs and operator comments to boost process knowledge and assist in AI training.

This special issue is on Artificial Intelligence and the Metal Industry: A Partnership for Progress, and the purpose of this special issue is to share the newest studies, innovation and practical applications of AI in different areas of the metal industry, i.e., metal forming, steelmaking, and sheet metal working. This special issue welcomes original research articles, review papers and case studies investigating new AI approaches like machine learning, deep learning, reinforcement learning, computer vision, digital twins and natural language processing in the improvement of process simulation, quality control, predictive maintenance and production optimization.

Topics of interest for the special issue include, but are not limited to, the following:

• Smart Steelmaking: Using Digital Twins for immediate Process Improvement and Energy Efficiency.
• Reinforcement Learning-Driven Reactive Control in Metal Sheet Forming to Increase Productivity.
• Predictive Maintenance in Moving Mills Utilizing Hybrid CNN and Time Series Forecasting Models.
• Using Processing of Natural Language to Extract Tacit Process Information from Operator Logs.
• Computer Vision Systems in Fault Detection in Hot-Rolled Iron: A Deep Learning Approach.
• Intelligent Quote Generation for Sheet Metal Fabrication Employing Generative AI and Operational Data Analytics.
• Combining Physics-Based Simulation with Machine Learning in Predictive Modeling in Steel Forming.
• Self-Optimizing Launch Furnace Operations via Multi-Agent Reinforcement Learning Structures.
• Explainable AI for detecting process anomalies and analyzing root causes in steel manufacturing processes.
• AI-Driven Sustainable development: Minimizing Trash and Emissions for Metal Manufacturing employing Predictive Analytics.
• Enhancing CAD/CAM Productivity with Generative Design & AI-Powered Decision Support Systems.
• Knowledge Retrieval from Technical Reports Using NLP to Speed Up AI Model Training in Startups.

Deadline for manuscript submissions: 30 June 2026.

To submit your manuscript, click here