Artificial Intelligence in Advanced Manufacturing and Materials Processing

Dear Colleagues,

Modern manufacturing systems generate data at exceptional scale and speed through high‑resolution sensors and multiscale simulations. While this data offers significant opportunities for improvement, it often exceeds the capabilities of traditional modeling, limiting the extraction of meaningful insights. Artificial Intelligence (AI) and Machine Learning (ML) have therefore become essential tools for handling this complexity. By identifying nonlinear, high‑dimensional relationships, AI enables advanced modeling, prediction, and optimization, shifting manufacturing from reactive monitoring toward predictive control. This transformation is driven by data‑driven modeling, adaptive control, and predictive analytics, which improve efficiency, reduce defects, and enhance reliability.

A key research direction is the development of hybrid models that integrate data‑driven methods with physics‑based understanding, improving accuracy while capturing multiscale behaviors, from equipment performance to material performance. Emerging methods such as deep learning, reinforcement learning, and physics‑informed intelligent models support real‑time anomaly detection, adaptive optimization, and intelligent failure prediction.

This Special Issue provides a platform for cutting‑edge research, methodological advances, and industrial applications demonstrating the transformative role of AI‑driven manufacturing. We particularly seek contributions that advance fundamental methodologies, provide rigorous validation, and address practical challenges in deploying intelligent systems in real‑world production environments, fostering interdisciplinary collaboration and highlighting emerging innovations in AI‑enabled manufacturing.

Topics of Interest:

This Special Issue welcomes high-quality research articles, reviews, and case studies focused on the development, implementation, and advancement of AI methodologies for advanced manufacturing and materials processing. Emphasis is placed on AI-driven prediction, optimization, monitoring, anomaly detection, and decision-making capabilities that enhance reliability, efficiency, and understanding of complex manufacturing systems.

Specific topics of interest include, but are not limited to, the following:

• AI-Based Predictive Modeling and Process Optimization

Methods that use machine learning and data-driven analysis to predict process behavior, improve process efficiency, and support the design of high-performance materials and components.

• AI-Enabled Forecasting of Material Processing and Manufacturing Results

AI models that predict final process outcomes, such as product quality,
dimensional accuracy, mechanical properties, microstructural features, and performance characteristics, based on in‑process data, sensor signals, and historical records. 

• Real-Time Anomaly Detection and Process Monitoring

AI-enabled systems for continuous monitoring, early detection of process deviations, and real-time identification of abnormalities to improve reliability and product quality.

• Failure Prediction, System Health Monitoring, and Predictive Maintenance

Data-driven tools that forecast failures, estimate remaining useful life, and enhance equipment
reliability within intelligent production environments.

• Hybrid and Physics-Informed Modeling Approaches

Techniques that combine data‑driven methods with physics-based principles to generate accurate and explainable models for complex manufacturing and materials phenomena.

Dr. Ebrahim Seidi
Dr. Scott F. Miller
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• machine learning
• advanced manufacturing
• process optimization
• predictive modeling
• smart manufacturing
• deep learning
• data-driven decision-making
• real-time monitoring
• physics-informed intelligent models