Virtual and Physical Prototyping

Alloy Design, Process-Chain Optimisation and Advanced Characterisation for Metal Additive Manufacturing

This Collection focuses on physically informed and resource-efficient process-chain design for metal additive manufacturing (AM). It includes work that connects alloy and feedstock engineering (including reused, recycled, and upcycled powder streams), processing and post-processing route design, accelerated characterisation (in-situ/operando and large-scale facilities), and mechanistic or data-driven modelling. Contributions should quantify and optimise trade-offs between material performance, energy input, material utilisation, experimental effort, lead time, and qualification cost, using integrated workflows that link chemistry and powder state to build parameters, post-processing, microstructure, and properties.

Despite its potential, metal AM still relies heavily on trial-and-error parameter tuning and conservative post-processing, leading to high energy use, long lead times, and costly qualification. Scrap parts, off-spec builds, and spent powders are often downcycled, even though they are close to usable alloys. Broader industrial adoption requires process-chain strategies that explicitly balance performance, energy, material use, experimental effort, and cost. Case studies that integrate metallurgy with mechanistic models, surrogate optimisation, and targeted experiments are key to reducing qualification effort, increasing resource efficiency, and enabling AM in applications with tight margins and high sustainability demands.

Alloy and feedstock design for metal AM:

• compositional tuning for AM-specific constraints
• particulate modifiers and grain refiners
• scrap-, recycled- or upcycled-based feedstocks
• powder reuse strategies and their impact on microstructure and properties

Post-processing and heat treatments:

• control over multi-stage solutioning, ageing, HIP and thermomechanical routes
• local or graded treatments and shortened or energy-aware schedules
• post-processing routes tailored for fatigue, creep and high-temperature performance

Modelling and data-driven methods:

• integration of CALPHAD, solidification and microstructure models with experiments
• surrogate models, Bayesian optimisation, active learning and related ML frameworks for multi-objective design and Pareto optimization

Accelerated characterisation and qualification:

• multi-scale and multi-modal characterisation used to map AM and validate models
• in-situ and operando measurements during building or post-processing linked to microstructure/property evolution
• feedback/control strategies aimed at reducing defects, scrap or rework and improving energy or time efficiency
• high-throughput or indirect mechanical testing and workflows that reduce experimental effort in process-chain optimisation
• statistical and model-assisted qualification strategies that combine limited testing with predictive models

Keywords: Accelerated material characterisation; Alloy and feedstock design; Heat treatment and post-processing; Multi-objective design and Pareto optimization; Resource-efficient process-chains

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is [30 September 2026].

Please contact [Zhan Yu] at [[email protected]] with any queries and discount codes regarding this Article Collection.

Please be sure to select "Alloy Design, Process-Chain Optimisation and Advanced Characterisation for Metal Additive Manufacturing" from the drop-down menu in the submission system.

Dr. Konrad Gruber is an assistant professor at Wroclaw University of Science and Technology, where he leads the metal powder bed fusion laboratory at the Department of Advanced Manufacturing Technologies, Faculty of Mechanical Engineering. In 2021, he received the Prime Minister of Poland Award for his doctoral dissertation and subsequently gained postdoctoral experience through stays in Switzerland, Germany, and the United States. His research focuses on metal additive manufacturing processes and materials for harsh environments, with an emphasis on alloy and feedstock design, post-processing strategies, and data-driven optimisation of resource-efficient process chains. Gruber is the principal investigator of the RAPTOR project (NCN, OPUS 27) and the FieldPBF project (NCBR, LIDER XV), both funded in Poland.

Dr. Efthymios Polatidis is an Assistant Professor at the University of Patras (Greece), Department of Mechanical Engineering & Aeronautics. He earned his engineering diploma at the University of Thessaly and his PhD at the University of Manchester, focusing on synchrotron X-ray diffraction of residual stresses. He previously held research positions at the Max Planck Institute for Intelligent Systems and the Paul Scherrer Institute, where he also served as a beamline scientist for the POLDI neutron diffractometer. His research centers on microstructure–property relationships in metals, using advanced synchrotron and neutron methods with a strong interest in metal additive manufacturing.

Prof. Chaolin Tan is a Distinguished Professor at Soochow University. He was awarded the Excellent Young Scholars of China (Overseas) and Provincial Distinguished Professor (Jiangsu) in 2024. He was a Senior Scientist and PI at the Singapore Institute of Manufacturing Technology (SIMTech), A*STAR Research Entities from 2020 to 2025. He served as an Honorary Research Fellow at the University of Birmingham and an Associate Professor at Guangdong University of Technology in 2019. He is a Fellow of IAAM and recognized in the World’s Top 2% Scientist Ranking from 2022 to 2025. He is on the Editorial Board of the International Journal of Machine Tools and Manufacture (IF 18.8) and the International Journal of Extreme Manufacturing (IF 23.1). He has contributed more than 50 SCI papers as First and Corresponding Author and 3 books, including 20 papers in flagship journals (e.g., Materials Today, IJMTM (6 papers), and IJEM (5 papers)) with an impact factor higher than 14, and 8 ESI highly cited and hot papers. His Google Scholar citations exceed 6,000 with an H-index of 45. He leads 7 national grants in Singapore and China with a total budget exceeding S$3 million. His research experience in additive manufacturing exceeds 10 years, with research interests in materials innovations, machine learning, and heterostructured materials around AM and energy-field-assisted AM.