Page 8 - IJAMD-1-1

International Journal of AI
for Material and Design Editorial

creating intelligent manufacturing ecosystems. Ng and References
Tan examined the role of ML in enhancing the efficiency 1. Makridakis S. The forthcoming Artificial Intelligence
and quality of 3D-bioprinted cultivated meat, focusing (AI) revolution: Its impact on society and firms. Futures.
on optimizing production processes, improving meat 2017;90:46-60.
printability, characterizing flavor, and ensuring quality
control. Zhang et al. review the application of ML to doi: 10.1016/j.futures.2017.03.006
7
enhance quality control in laser powder bed fusion, 2. Popenici SAD, Kerr S. Exploring the impact of artificial
focusing on optimizing parameters and in situ monitoring intelligence on teaching and learning in higher education.
to address the method’s challenges and improve part Res Pract Technol Enhanc Learn. 2017;12(1):1-13.
8
reliability. Wong et al. detail the enhancement of a deep doi: 10.1186/s41039-017-0062-8
learning model’s predictive accuracy for the directed 3. Wolff J, Pauling J, Keck A, Baumbach J. The economic
energy deposition process by integrating in-process impact of artificial intelligence in health care: Systematic
physics-based simulation data with experimental review. J Med Internet Res. 2020;22(2):e16866.
datasets, specifically focusing on the impact of sulfur
content in stainless steel on the final track geometry. doi: 10.2196/16866
9
Goh et al. demonstrated the usage of a predictive 4. Hardian R, Liang Z, Zhang X, Szekely G. Artificial
model for controlling bending angles in soft grippers intelligence: The silver bullet for sustainable materials
with variable stiffness, utilizing a data-driven approach development. Green Chem. 2020;22(21):7521-7528.
that combines numerical modeling and ML, achieving doi: 10.1039/D0GC02956D
significant accuracy in angle prediction for delicate 5. Badini S, Regondi S, Pugliese R. Unleashing the power of
10
automation tasks. An et al. introduce the manufacturing artificial intelligence in materials design. Materials (Basel).
multi-organs database, a real-time updated database that 2023;16(17):5927.
utilizes ML to enhance biofabrication by standardizing doi: 10.3390/ma16175927
the analysis of material properties and manufacturing
processes, for optimizing experimental designs and 6. Huang JS, Liew JX, Ademiloye A, Liew KM. Artificial
11
improving the efficiency of organ manufacturing. The intelligence in materials modeling and design. Arch Comput
featured articles not only demonstrate the technical Methods Eng. 2021;28:3399-3413.
prowess of AI and ML techniques but also underscore doi: 10.1007/s11831-020-09506-1
their transformative impact on material innovation, 7. Ng WL, Tan JS. Application of machine learning in
energy efficiency, and the sustainability of manufacturing 3D bioprinting of cultivated meat. Int J AI Mater Des.
practices. 2024;1(1):2279.
As the editorial members of IJAMD, we are enthusiastic doi: 10.36922/ijamd.2279
about the journal’s role in shaping the future of materials 8. Zhang J, Yin C, Xu Y, Sing SL. Machine learning applications
science and design through AI technologies. We invite for quality improvement in laser powder bed fusion: A state-
researchers, practitioners, and innovators to contribute of-the-art review. Int J AI Mater Des. 2024;1(1):2301.
their insights and discoveries, fostering a collaborative doi: 10.36922/ijamd.2301
environment that propels the field forward. We look
forward to engaging with the community through this 9. Wong SJL, Chen C, Tan EZE, Li H. Integration of physics-
platform for idea exchange, and promoting research based data in deep learning model training for predicting
that bridges AI with practical and theoretical aspects of the effect of sulfur content in the directed energy deposition
process. Int J AI Mater Des. 2024;1(1):2355.
materials and design.
doi: 10.36922/ijamd.2355
We are excited to embark on this journey with our
readers and contributors, exploring the vast possibilities 10. Goh GL, Huang X, Toh W, et al. Joint angle prediction for
that AI and ML hold for the future of materials science and a cable-driven gripper with variable joint stiffness through
numerical modeling and machine learning. Int J AI Mater
design. Join us in navigating this evolving landscape, where Des. 2024;1(1):2328.
the convergence of AI, materials, and design is crafting the
blueprint for a smarter, more sustainable, and innovative doi: 10.36922/ijamd.2328
future. 11. An J, Cui W, Chen H, et al. Manufacturing multi-organs
database: A comprehensive, predictive, and analytical
Conflict of interest biofabrication database. Int J AI Mater Des. 2024;1(1):2420.
The author declares no competing interests. doi: 10.36922/ijamd.2420

Volume 1 Issue 1 (2024) 2 https://doi.org/10.36922/ijamd.3153