Virtual and Physical Prototyping

AI and Machine Learning Driven Design and Quality Control in 3D Bioprinting and Soft Material Fabrication

Recent advances in 3D bioprinting and soft material fabrication have enabled increasingly complex structures and functions to be realized across biomedical, biological, and manufacturing applications. At the same time, the fabrication processes underlying these systems remain highly sensitive to interactions among materials, processing conditions, and environmental factors. As a result, achieving reproducible outcomes, consistent quality, and reliable performance prediction continues to be a central challenge. In many cases, process development still relies heavily on empirical trial-and-error approaches and expert intuition, which can limit scalability, transferability across platforms, and systematic understanding. Against this backdrop, there is growing interest in structured design methodologies that combine experimental prototyping with quantitative analysis and modeling to better characterize, predict, and control fabrication behavior in bioprinting and soft material systems.

Improving the predictability and robustness of bioprinting and soft material fabrication processes is essential for both fundamental research and practical implementation. Variability in print fidelity, material performance, and biological outcomes not only hampers reproducibility but also poses barriers to standardization, scale-up, and broader adoption in applied settings. Systematic approaches to process optimization and quality control are therefore increasingly needed to bridge the gap between laboratory-scale demonstrations and reliable manufacturing workflows. Data-informed and computational tools, including statistical analysis and machine learning techniques, can serve as valuable complements to experimental studies by supporting parameter exploration, performance evaluation, and process monitoring. When used in conjunction with physical prototyping and domain knowledge, such approaches can contribute to deeper insight, more efficient development cycles, and improved confidence in fabrication outcomes, without replacing experimental validation.

This Article Collection welcomes contributions that advance systematic design, optimization, and quality assessment strategies in 3D bioprinting and soft material fabrication, in alignment with the scope of Virtual and Physical Prototyping. Topics of interest include experimental and virtual prototyping of bioprinting processes; quantitative evaluation of printability, structural fidelity, and biological performance; process monitoring and in situ quality assessment; feedback and control strategies; hybrid modeling frameworks that combine physical insight with empirical or data-driven methods; and bioink or soft material development with demonstrated relevance to manufacturability and process control. Machine learning and other computational techniques are encouraged where they are used as supportive tools within broader experimental or modeling frameworks. The Collection welcomes original research articles, review papers, and short communications that contribute to improved reproducibility, reliability, and understanding of fabrication processes.

Keywords: 3D bioprinting; Process optimization; Soft material fabrication; Experimental prototyping; Data-driven modeling

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 "AI and Machine Learning Driven Design and Quality Control in 3D Bioprinting and Soft Material Fabrication" from the drop-down menu in the submission system.

Shinji Sakai is a Professor at the Graduate School of Engineering Science, The University of Osaka, Japan. His research centers on the design and processing of biomaterials and soft materials for 3D bioprinting, tissue engineering, and regenerative medicine, with a strong emphasis on experimental prototyping and process optimization. As part of these efforts, he has incorporated data-driven and machine learning–based approaches to support the analysis, prediction, and improvement of printing performance and cell viability. He has published widely on extrusion-based bioprinting and bioink development, contributing to both fundamental understanding and practical manufacturing strategies.

Kelum Chamara Elvitigala is a specially appointed Assistant Professor at the Global Center for Medical Engineering and Informatics, The University of Osaka, Japan. His research centers on tissue engineering and regenerative medicine, with a particular focus on engineering controllable cellular microenvironments to regulate cell behavior. As part of these efforts, he integrates AI-based modeling approaches to analyze and predict cellular responses in complex 3D bioprinted culture systems. He has published on engineered cellular microenvironments and AI-based modeling and control of printing processes, contributing to both fundamental understanding of cell–material interactions and practical bioprinting strategies.