Call for Papers - Special issue on "Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer"

Details

Published on 31 March 2025

As a cornerstone of industrial advancement, manufacturing is experiencing substantial transformations driven by technological innovations, societal demands, and a heightened focus on sustainability. A vital aspect of this evolution is the integration of Artificial Intelligence (AI) in mechanical manufacturing systems, which form the foundational structures of production processes. The application of AI in manufacturing methodologies has been pivotal in improving efficiency, precision, and adaptability. AI-driven technologies such as machine learning, predictive analytics, generative intelligence, and autonomous systems are being leveraged by researchers and practitioners to redefine traditional manufacturing paradigms and to integrate these technologies into manufacturing systems, such as in the case of lean manufacturing. These innovations facilitate real-time decision-making, optimize production processes, and enable the development of smart factories that can respond dynamically to changing conditions. Sustainability, a crucial issue within the industry, is recurrently addressed through AI applications that promote green manufacturing practices and innovative process designs. AI helps monitor and reduce resource consumption, manage waste, and optimize energy usage, thus supporting regulatory and societal-driven green initiatives.

• Applied AI in manufacturing;
• Sustainable manufacturing enabled by AI;
• Quality control via AI;
• Integration of Lean Six Sigma with AI;
• Waste reduction via AI;
• Integration of lean manufacturing with AI;
• AI in the manufacturing enterprise;
• Integration of supply chain logistics and management with AI.

The Special Issue covers the advancements in artificial intelligence in the field of mechanical manufacturing, tracing its development from statistical learning through discriminative and regression models to generative models. We welcome submissions that explore the application and development of machine learning, deep learning, transfer learning, and generative artificial intelligence approaches. Articles should address algorithmic approaches that are based on data, improving our comprehension in the given domains. All relevant papers will be carefully considered, peer-reviewed by a distinguished team of international experts. The instructions for authors are detailed at: https://www.mechanics-industry.org/author-information/instructions-forauthors

Submission Deadline - 1, Nov 2025 Authors Notification - 15, Dec 2025 Revised Papers Due - 25, Jan 2026 Final notification - 25, Feb 2026

• Name: Xu Zheng (Lead Guest Editor) Email: This email address is being protected from spambots. You need JavaScript enabled to view it. Affiliation: Shanghai Polytechnic University, China
• Name: Jemal H. Abawajy Email: This email address is being protected from spambots. You need JavaScript enabled to view it. Affiliation: Deakin University, Australia
• Name: Haruna Chiroma Email: This email address is being protected from spambots. You need JavaScript enabled to view it. Affiliation: University of Hafr Al Batin, Saudi Arabia
• Name: Shafi’i Muhammad Abdulhamid Email: This email address is being protected from spambots. You need JavaScript enabled to view it. Affiliation: Federal University of Technology (FUT) Minna, Nigeria