Topic: Energy-efficient and High-quality Laser Processing Assisted by Artificial Intelligence Technology and Simulation
A Special Issue of Journal of Materials Informatics
ISSN 2770-372X (Online)
Submission deadline: 30 Sep 2025
Guest Editors
Prof. Dazhong Wu
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA.
Prof. Yu Huang
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Prof. Hui Chen
State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
Special Issue Introduction
As the demand for energy efficiency and high-quality manufacturing increases, Artificial Intelligence (AI) technology, along with numerical simulation, offers innovative solutions for optimizing energy efficiency and improving process quality in laser processing. This Special Issue aims to explore the fusion of AI technologies and simulation models in laser processing to optimize both energy efficiency and processing quality. AI technologies, such as machine learning, deep learning, and simulation models, play a crucial role in real-time process monitoring, adaptive control, energy management, and quality optimization. Simulation models containing mechanistic information are essential for predicting and optimizing laser processing parameters, helping to ensure energy-efficient, high-quality results. Contributions to this issue will cover AI algorithms for process optimization, simulation-based process modeling, intelligent defect detection, and high-quality system design. By focusing on the intersection of AI, simulation technologies, and laser processing, this Special Issue aims to present the latest advancements and future directions in this rapidly evolving field.
This Special Issue aims to highlight the latest research on highly energy-efficient laser processing technologies driven by AI. We invite high-quality papers that explore innovative approaches to improve energy efficiency, optimize processing parameters, and leverage material information for better outcomes. Specific topics of interest include, but are not limited to:
● AI-driven optimization of laser processing parameters for energy efficiency and quality;
● Optimization strategies for energy efficiency in laser additive manufacturing;
● Predictive models for laser processing based on simulation and AI;
● Real-time monitoring and adaptive control of laser processes;
● Analysis and simulation of heat and mass transfer mechanisms in laser processes;
● Material composition screening, design, and optimization for laser processing;
● Carbon emission modeling and life cycle assessment of laser manufacturing systems.
This Special Issue aims to highlight the latest research on highly energy-efficient laser processing technologies driven by AI. We invite high-quality papers that explore innovative approaches to improve energy efficiency, optimize processing parameters, and leverage material information for better outcomes. Specific topics of interest include, but are not limited to:
● AI-driven optimization of laser processing parameters for energy efficiency and quality;
● Optimization strategies for energy efficiency in laser additive manufacturing;
● Predictive models for laser processing based on simulation and AI;
● Real-time monitoring and adaptive control of laser processes;
● Analysis and simulation of heat and mass transfer mechanisms in laser processes;
● Material composition screening, design, and optimization for laser processing;
● Carbon emission modeling and life cycle assessment of laser manufacturing systems.
Keywords
Machine learning, additive manufacturing, process optimization, physics-informed modeling
Submission Deadline
30 Sep 2025
Submission Information
For Author Instructions, please refer to https://www.oaepublish.com/jmi/author_instructions
For Online Submission, please login at https://www.oaecenter.com/login?JournalId=JMI&IssueId=jmi2509302401
Submission Deadline: 30 Sep 2025
Contacts: Mengyu Yang, Assistant Editor, [email protected]
