Virtual and Physical Prototyping

Integrated Generative Design and Printing in Additive Manufacturing for Extremely Complex Applications

The field of additive manufacturing (AM) has grown exponentially, evolving from a prototyping tool to a transformative manufacturing method for highly intricate applications. Integrated generative design and printing represent the next frontier, offering a paradigm shift in addressing the challenges of designing and manufacturing complex geometries, materials, and processes. This approach cluster combines advanced computational methods with AM to unlock unprecedented possibilities for creating functional, high-performance, and customized components in industries such as aerospace, healthcare, and energy. We invite researchers to contribute to this Collection to explore innovative techniques and methodologies that push the boundaries of generative design and printing in AM.

The importance of this topic lies in its potential to overcome current limitations in AM. Designing complex materials and geometries by traditional software tools or by hand is increasingly infeasible as complexity rises. Traditional approaches to setting up AM processes struggle with highly intricate scenarios, hindering the full realization of AM's potential. Integrated generative design and printing offer solutions by automating and optimizing material, geometry, and processing scenario generation. These advances not only enable the efficient creation of complex components but also extend AM's applications to new industries, supporting breakthroughs in both functionality and scalability.

We welcome original research and review articles that fit the scope of this journal, focusing on the integration of generative design and printing in AM. Potential topics include but are not limited to:

• Generative design methods for AM materials and processes.
• Design of complex geometries for AM, such as lattices and freeform shapes.
• Development of topology-optimization-based generative design methods for AM.
• Efficient AM simulation tools for complex and large-scale AM components.
• Generation and optimization of AM process scenarios, including post-processing strategies.
• Generative design and optimization methods for multi-material and 4D printing of smart materials.
• Data and AI-driven methods for AM generative design and printing process chain optimization.
• Advanced metrology and quality control methods for complex and large-scale AM components.
• Case studies of extremely complex and large-scale AM applications in industrial or medical contexts.
• Complex meta-material or functional structure design & printing.

We encourage contributions in the form of research papers and review articles that provide innovative insights and methodologies for advancing integrated generative design and printing in AM. Join us in shaping the future of additive manufacturing.

Keywords: Generative design & printing; Complex Geometries; computation-driven; Multi-material & 4D Printing; Data-driven and AI-driven optimization and simulation

­­­­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 [20 November 2025].

Please contact Zhan Yu at zhan.yu@taylorandfrancis.com with any queries and discount codes regarding this Article Collection.

Please be sure to select "Integrated Generative Design and Printing in Additive Manufacturing for Extremely Complex Applications" from the drop-down menu in the submission system.

Yicha Zhang is a full professor in the Department of Mechanical Engineering at INSA Hauts-de-France, Université Polytechnique Hauts-de-France. His research expertise spans generative design, bio-inspired design, integrated design and planning for AM, hybrid AM, and the development of AM-based product-service systems. He also focuses on applying additive manufacturing to industrial and medical domains for extremely complex applications. In 2021, he was awarded the CIRP Taylor Medal for his work on generative design and printing of bio-inspired metallic support structures.

Yunlong Tang is assistant director of Monash Centre for Additive Manufacturing and lecturer at Faculty of Engineering, Monash University. His major research interests are in the general field of design and manufacturing, including smart manufacturing, additive manufacturing, and its related design methods, sustainable design and manufacturing, AI-aided design, CAD/CAM, design and fabrication of lattice or cellular materials.

Javier Munguia is Associate Professor in Engineering Design at Warwick University, UK. Dr Munguia has over a decade of experience working with Additive Manufacturing /3D Printing technologies, CAD/CAM Design, Industrial and manufacturing engineering. His research encompasses: 3D CAD/CAE design, analysis and design optimization: parametric/generative design, topology optimization; Medical devices design & manufacture; Materials characterization, fatigue/long cycle materials tests; Machinery / test-bench design / purpose built 3D printing systems; Design for Additive Manufacturing / 3D Printing; Design theory and methodologies.

Yuchun SUN is the Director of Center of Digital Dentistry, Peking University School and Hospital of Stomatology, Professor/Chief Physician/Renowned Expert of Prosthodontics. Focus on the independent innovation research and transformation of dental precision intelligent digital bionic diagnosis and treatment technology: intelligent manufacturing, artificial intelligence in medicine, biomimetic materials, intelligent robot, digital biological reconstruction.