Next-Generation Material Designs and Processes for Additive Manufacturing

Special Issue Editors


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Guest Editor
Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Interests: metal additive manufacturing; extrusion printing; digital light processing; polymer nanocomposites; biocomposites; smart materials; liquid metal; stretchable/flexible electronics; 3D printing of regolith materials; curable resins; design for additive manufacturing (DfAM); robotic 3D printing systems

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Guest Editor
Manufacturing and Mechanical Engineering Technology, Rochester Institute of Technology, Rochester, NY, USA
Interests: bioink; rheology; bioprintability; biofabrication; robotics and automation; machine learning; biomechatronics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Manufacturing and Industrial Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
Interests: 3D and 2D additive manufacturing processes; integration of sensors in additive processes for quality control and process monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of additive manufacturing is undergoing rapid evolution, demanding the development of new materials and innovative processes to fully harness its potential. The integration of additive manufacturing with emerging technologies such as machine learning, artificial intelligence, and process monitoring is driving significant advancements in this domain. These technologies are transforming material design, enabling real-time process control and enhancing the quality and efficiency of additive manufacturing.

Progress is being made not only in metals and polymers but also in ceramics, biological materials, and composites, pushing the boundaries of traditional additive manufacturing technologies. Machine learning is playing a pivotal role in designing new materials, while AI-powered machine vision is facilitating on-the-fly adjustments to process parameters to ensure an optimal print quality. Furthermore, additive manufacturing is being adapted to accommodate unconventional materials, expanding its applications across various industries such as aerospace, wearable personalized devices, and tissue engineering.

Given these advancements, we are pleased to invite researchers to contribute to this Special Issue, which will focus on next-generation material design techniques and processes in additive manufacturing. We encourage submissions that explore the additive manufacturing of metals, polymers, ceramics, composites, bio-inks, and other advanced materials via theoretical, computational, or experimental approaches, either separately or in combination.

We look forward to receiving your groundbreaking research, shaping the future of additive manufacturing.

Dr. Mohammad Khondoker
Dr. Md Ahasan Habib
Dr. Farid Ahmed
Guest Editors

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Keywords

  • additive manufacturing or 3D printing
  • machine learning
  • artificial intelligence
  • material design
  • process monitoring

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Published Papers (1 paper)

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Research

12 pages, 10766 KiB  
Article
Molecular Dynamics-Based Two-Dimensional Simulation of Powder Bed Additive Manufacturing Process for Unimodal and Bimodal Systems
by Yeasir Mohammad Akib, Ehsan Marzbanrad and Farid Ahmed
J. Manuf. Mater. Process. 2025, 9(1), 9; https://doi.org/10.3390/jmmp9010009 - 1 Jan 2025
Viewed by 917
Abstract
The trend of adapting powder bed fusion (PBF) for product manufacturing continues to grow as this process is highly capable of producing functional 3D components with micro-scale precision. The powder bed’s properties (e.g., powder packing, material properties, flowability, etc.) and thermal energy deposition [...] Read more.
The trend of adapting powder bed fusion (PBF) for product manufacturing continues to grow as this process is highly capable of producing functional 3D components with micro-scale precision. The powder bed’s properties (e.g., powder packing, material properties, flowability, etc.) and thermal energy deposition heavily influence the build quality in the PBF process. The packing density in the powder bed dictates the bulk powder behavior and in-process performance and, therefore, significantly impacts the mechanical and physical properties of the printed components. Numerical modeling of the powder bed process helps to understand the powder spreading process and predict experimental outcomes. A two-dimensional powder bed was developed in this work using the LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) package to better understand the effect of bimodal and unimodal particle size distribution on powder bed packing. A cloud-based pouring of powders with varying volume fractions and different initialization velocities was adopted, where a blade-type recoater was used to spread the powders. The packing fraction was investigated for both bimodal and unimodal systems. The simulation results showed that the average packing fraction for bimodal and unimodal systems was 76.53% and 71.56%, respectively. A particle-size distribution-based spatially varying powder agglomeration was observed in the simulated powder bed. Powder segregation was also studied in this work, and it appeared less likely in the unimodal system compared to the bimodal system with a higher percentage of bigger particles. Full article
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