Laser Powder Bed Fusion Additive Manufacturing: Experimental, Simulation, and Machine Learning
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 1472
Special Issue Editors
Interests: laser powder bed fusion; additive friction stir deposition; thermophysical property; thermodynamics modeling; alloy design; machine learning
Interests: additive manufacturing; zinc ion batteries; lithium ion batteries
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Laser powder bed fusion (LPBF) additive manufacturing represents a cutting-edge frontier in the field of advanced manufacturing technologies. Distinguished for its precision and versatility, LPBF continues to revolutionize how we approach design and production across various industries. This Special Issue is dedicated to exploring the expansive and dynamic realm of LPBF, highlighting the synergy between experimental methods, simulation techniques, and the burgeoning field of machine learning.
Key focus areas include the following: (i) Experimental Advancements: This section explores the experimental studies pushing the boundaries of LPBF technology. It covers the latest findings in material properties, process optimization, and novel applications of LPBF in diverse fields. (ii) Simulation Techniques: Recognizing the complexity of LPBF processes, this issue explores advanced simulation models that aid in predicting outcomes, optimizing process parameters, and understanding the intricate thermal and mechanical behaviors during printing. (iii) Machine Learning Integration: At the intersection of LPBF and digital transformation, machine learning emerges as a powerful tool. This section examines how AI algorithms are revolutionizing LPBF processes, from predictive maintenance and quality control to real-time adjustments and process optimization.
We invite researchers, academics, and industry professionals to contribute their latest research papers, communications, and reviews on the experimental, simulation, and machine learning aspects of LPBF. This issue aims to cover a wide spectrum of topics, including, but not limited to, alloy development, process parameter optimization, microstructure analysis, thermal modeling, and data-driven process control in LPBF.
Dr. Congyuan Zeng
Dr. Wangwang Xu
Dr. Shafiqur Rahman
Guest Editors
Manuscript Submission Information
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Keywords
laser powder bed fusion
additive manufacturing
experimental study
simulation
machine learningBenefits of Publishing in a Special Issue
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