Guest Editors
Dr. Jan Akmal
Jan Akmal is a Postdoctoral Researcher at the Department of Mechanical Engineering, Aalto University and at Metal Materials, Electro Optical Systems (EOS) Finland. His scientific research focuses on implementation of industrial additive manufacturing with emphasis on decision-making, design, embedding, metrology, and machine learning for additive manufacturing. He received his D.Sc. degree in Mechanical Engineering in 2022 and M.Sc. degree in Mechanical Engineering in 2017 with distinction from Aalto University. Dr. Akmal has learning experience from countries including Canada, Pakistan, Germany, and Finland. He has gained over seven years of experience working on additive manufacturing (3D printing) technologies.
Prof. Dr. Mika Salmi
Mika Salmi is a Research Director at Aalto University Digital Design Laboratory and Assistant Professor at the Department of Mechanical Engineering. He did his D.Sc. in 2013, focusing on medical applications of additive manufacturing in surgery and dental care. Prof. Salmi has published over 70 scientific and technical papers on industrial and medical additive manufacturing applications. Currently, he is involved in projects related to 3D printing of spare parts and medical applications, 4D printing, and researching industrial additive manufacturing applications.
Summary
Additive manufacturing (AM), colloquially known as 3D printing, is increasingly developing into a general-purpose technology analogous to computers and electric drives that serve as the building blocks of our industries (Akmal et al., 2022). Today, AM technologies can cure, bond, dispense, deposit, and fuse a wide range of materials composed of metals, polymers, ceramics, elastomers, and hybrid materials (ISO/ASTM 52900, 2021).
The underlying mechanism of layer-by-layer manufacturing uses digital 3D model data to create physical objects with unparalleled geometric freedom — enabling generative design and topology optimization. Within the last decade, the advent and proliferation of the additive process has been increasing exponentially (Wohlers, 2022). To this end, AM is assisting the transition to industry 4.0 comprising of mega trends of internet of things, big data, and artificial intelligence (Akmal, 2022).
The overarching mission of this Special Issue is to collect research articles contributing to the emerging stream of research and advances in our understanding of the interplay between geometry and material properties to evaluate the total performance of the additively manufactured components. To this end, this Special Issue calls for submissions establishing and substantiating novel materials, designs, optimizations, process modelling, and testing of components made by additive manufacturing, particularly using novel assessment approaches.
References:
Akmal, J. S. (2022). Switchover to additive manufacturing: Dynamic decision-making for accurate, personalized and smart end-use parts [Aalto University]. http://urn.fi/URN:ISBN:978-952-64-1013-5
Akmal, J. S., Salmi, M., Björkstrand, R., Partanen, J., & Holmström, J. (2022). Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts. International Journal of Operations and Production Management, 42(13), 358–384. https://doi.org/10.1108/IJOPM-01-2022-0054
ISO/ASTM 52900. (2021). ISO/ASTM 52900:2021(en) Additive Manufacturing - General principles - Terminology (2nd ed.). ISO/ASTM International 2015.
Wohlers. (2022). Wohlers Report. Wohlers Associates Inc.
Keywords
Material selection; Materials processing; Material modelling; Process-structure-property-performance; Design for additive manufacturing; Generative design; Topology optimization; Lattice optimization; Process-structure-performance properties; Defect detection and repair for additive manufacturing; Accuracy of additively manufactured parts; Machine learning for additive manufacturing
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