CDFAM Amsterdam, 2025
Program Archive
July 9-10, 2025
The CDFAM Amsterdam 2025 program brought together leading experts in computational design from industry, academia and software development across all scales of application, from micro to mega.
With keynotes will be delivered by Tiffany Cheng of Cornell University, and Mathew Vola, Arup Fellow of Computational Design, and a virtual fireside chat with Federico Casalegno, Executive Vice President of Design at Samsung Electronics
The event was a single track of presentations to encourage the sharing of ideas across disciplines.
Keynote Presentations
Keynote Presentation – Computational Design, Evolutions
Presentation Abstract
Speaker Bio
Mathew Vola is an Arup Fellow and director specialising in computational design. He brings over 20 years of experience in the design and engineering of sustainable developments across Europe, Australia, and Asia.
Harnessing digital tools to deliver sustainable solutions, Mathew is working to develop the next-generation of buildings. Examples include the advanced parametric design modelling for the Smakkelaarspark development, advanced timber design for award-winning HAUT and Mediavaert, or delivering energy positive buildings for Echo and Elements.
Mathew leads the structural engineering practice for Arup in the Netherlands and oversees the development of Arup’s parametric design platform, inForm.
Mathew was recognised for his achievements by the Dutch Institute of Structural Engineers and awarded Structural Engineer of the Year 2021.
Previous Presentation
Organization:
Cornell University
Presenter:
Tiffany Cheng
Bioinspired and biobased 4D-printing for adaptive building facades
Presentation Abstract
What if our buildings and products could be manufactured and operated the way biological systems grow and adapt? As an alternative to conventional construction and manufacturing, I will present a bioinspired approach to making through material programming and 4D-printing. By integrating material, structure, and function, plants change shape over varying spatial-temporal scales in response to external stimuli. I will introduce how computational fabrication enable the bioinspired interplay of cellulosic materials, mesostructures, and adaptive motions to create hygromorphic systems powered by the environment. The developed methods are transferable across scales and applications – from hobbyist 3D-printers to industrial robot platforms and self-adjusting wearables for the body to weather-responsive shading in buildings. Through integrative technologies and interdisciplinary solutions, we can leverage biobased materials and bioinspired design principles to create a built environment that is transformative and resilient.
Interview: Bioinspired and Biobased 4D-Printing for Adaptive Building Facades – Tiffany Cheng
Speaker Bio
Tiffany Cheng is a Taiwanese American designer and builder whose work examines the performance potential of natural and biobased materials for smarter and more sustainable forms of making. As Assistant Professor at Cornell University’s Department of Design Tech, Tiffany directs the MULTIMESO Lab to develop computational fabrication processes for creating bioinspired systems across scales, from self-forming furniture to adaptive building components.
Previously, Tiffany was Research Group Leader at the Institute for Computational Design and Construction (ICD) at the University of Stuttgart, where she led the Material Programming research group and earned her Doctorate in Engineering. Tiffany holds a Master in Design Studies (Technology) from Harvard University and a Bachelor of Architecture from the University of Southern California.
Fireside Discussion with Federico Casalegno and Duann Scott
Presentation Abstract
Speaker Bio
Federico Casalegno, Executive Vice President of Design, Samsung Electronics
Federico is Executive Vice President of Design at Samsung Electronics. He heads the Samsung Design Innovation Center (SDIC) in San Francisco, CA, Next-Generation Experience Planning Team in Seoul, and Experience and Insights teams within Samsung Research.
Federico leads global multidisciplinary teams in the USA, Asia and Europe to design new generation of experiences and envision future products. As a designer, innovator, and social scientist, he focuses on the impact of networked digital technologies on human behavior and society, and designs products, services and meaningful experiences to improves people’s lives.
Before joining Samsung, Federico was an Associate Professor of the Practice at the Massachusetts Institute of Technology, teaching at MIT and MIT Media Lab. He also founded and directed the MIT Design Lab and the MIT Mobile Experience Lab. He previously worked at Motorola, Inc. and Philips Design, envisioning and creating innovative product experiences. He has been awarded honorary professorships at the Glasgow School of Art, University of Glasgow and the Jiangnan University School of Design in Wuxi, China.
He has published several scientific papers in peer-reviewed journals, along with books and articles, and he has won several awards for his design and innovation work. Federico earned the PhD degree in Sociology of Culture and Communication from the Sorbonne University, Paris V, with a focus on mediated communication and social interaction in networked communities and smart cities.
Program
Organization:
CDFAM
Presenter:
Duann Scott
Welcome to CDFAM Amsterdam
Presentation Abstract
Welcome Address – CDFAM Amsterdam 2025
Duann Scott will open CDFAM Amsterdam 2025 with an introduction to the event’s themes, structure, and what attendees can expect over the next two days. With a single-track format, the symposium is designed to foster cross-disciplinary engagement, bringing together experts from industry, academia, and software development to explore the impact of computational design across scales. He will highlight key topics, and set the stage for discussions on how computational methods are transforming design, engineering, and manufacturing.
Speaker Bio
Duann Scott is the founder of CDFAM – Computational Design Symposium, bringing together experts across design, engineering, and software development to explore the future of computational design. He is also the Executive Director of the 3MF Consortium, driving the development of an open data standard for additive manufacturing. With a background spanning industry, academia, and technology, Duann focuses on advancing digital design and manufacturing through collaboration and innovation.
Organization:
New Balance Athletics
Presenter:
Math Whittaker
Beyond Surfaces: Applying Intrinsic Geometry Processing in Art and Design
Presentation Abstract
While computational tools have revolutionized design, many approaches focus on explicit modeling of form. This presentation delves deeper, exploring the creative application of intrinsic geometry processing properties – characteristics inherent to a surface regardless of its embedding in space. These techniques are typically confined to mathematics or specialized engineering domains, however looking this presentation will look at their practical use in art and design.
Key concepts such as the generation and application of smooth vector fields on complex meshes (illustrated through a personal jewelry design project), the use of the cotan Laplacian for simulating surface phenomena or achieving specific smoothing effects, and the deployment of reaction-diffusion systems to generate intricate, organic patterns directly on geometry will be examined.
This talk aims to illustrate these powerful methods and demonstrate how leveraging the inherent mathematical structure of shapes can unlock novel aesthetic possibilities, sophisticated surface treatments, and approaches for design expression beyond conventional digital craft. Attendees will gain insight into applying these advanced computational techniques to enhance their own creative explorations.
Speaker Bio
Math Whittaker is a Computational Design Programmer II at New Balance Athletics, leveraging a unique background that intersects footwear design, art, and technology. With four years in the footwear industry, and prior experience in artworks and wearable tech, he brings diverse expertise. Math specializes in architecting and implementing custom computational design tools using a wide range of frameworks and languages (Grasshopper, C++, C#, Python, JavaScript, Java). He holds a Master’s degree from Harvard University and an undergraduate degree from Manchester School of Art.
Molding the Future: Computational Design in Footwear Tooling
Presentation Abstract
Blending generations of tradition with technology innovation. Exploring how computational tools and additive manufacturing transforms the footwear industry empowering design and production. From the acceleration of ideation with AI, design through to the optimization of production tooling and mold making with generative design and additive manufacturing.
Speaker Bio
René Medel is an Industrial Designer from Chile. He has a passion for footwear and 3D technologies, being a Rhinoceros user, reseller, and authorized trainer since 2001. In 2015, he did the Master of Footwear Innovation at SLEM in the Netherlands and China. After that, he was asked to push the limits of 3D Footwear Design and Additive Manufacturing for IP ideas production GmbH, a division of Birkenstock Group. He is currently Senior Digital Creation Engineer in the Innovation team seating in Germany at framas Group, manufacturer of high-performance plastic components for the entire footwear industry since 1948.
LightSpray™: a new High Performance Upper technology crafted by a Revolutionary single step Manufacturing Process
Presentation Abstract
LightSpray™ redefines footwear manufacturing with an innovative sprayed, not built, process—cutting CO₂ emissions and paving the way for circularity and faster, localized production. On has developed a bespoke, fully automated method to create ultralight, one-piece LightSpray™ uppers in a single step, producing a shoe in just three minutes.
For decades, running shoes have been manufactured using the same multi-step process, involving yarn extrusion, weaving or knitting, sewing, and assembly. The On Innovation Team challenged this norm, replacing traditional methods with a robotic arm that sprays the ultra-light, one-piece upper in a seamless, automated step. This breakthrough minimizes waste and reduces carbon emissions by 75% compared to On’s other racing shoes.
Beyond transforming the product itself, LightSpray™ revolutionizes the entire design and development process. Computational design and robotic programming replace traditional pattern making, enabling every part of the structure to be precisely engineered for performance. Rapid prototyping accelerates innovation, turning ideas into physical products within hours. The result? A precision-sprayed, ultra-thin upper that seamlessly adapts to the foot, eliminating laces, and delivering an exceptionally lightweight shoe at just 170g.
Speaker Bio
Maia Zheliazkova is a computational designer and architect with a PhD in Architectural Technology from Politecnico di Milano. With experience spanning academia and industry, she has explored the intersection of design, technology, and innovation, seeking disruptive solutions across disciplines. Passionate about pushing the boundaries of computational design, Maia blends creativity with cutting-edge technology to redefine performance-driven solutions.
Currently, Maia is a Senior Specialist in Computational Design at On AG’s Innovation Team, where she focuses on developing highly performance-driven design solutions for athletes and pioneering advancements in technology and manufacturing. She has been instrumental in the development and scale-up of LightSpray™ technology, shaping its evolution from the early stages.
Coffee Break
Organization:
Politecnico di Torino
Presenter:
Dario Carbonaro
Computational design and optimization of self-expandable cardiovascular devices
Presentation Abstract
Self-expandable cardiovascular devices, such as vascular stents, stent-grafts, and transcatheter aortic valves (TAVs), are medical devices implanted into diseased anatomies through minimally invasive procedures. Specifically, these devices are crimped into small catheters, where they are subjected to high strains, allowing them to pass through and be placed within the anatomy. Additionally, self-expandable cardiovascular devices are commonly fabricated from nickel-titanium (NiTi) and are capable of elastically recovering their initial shape when extracted from the catheter, even after being subjected to high strains. This capability is related to the super-elastic property of NiTi, which refers to the material’s ability to elastically sustain high strains.
The effectiveness of the treatment depends on the interaction between the devices and the anatomy in which they are implanted. Furthermore, the forces exerted by the devices on the anatomy depend on their design, including geometric and material characteristics. In this context, the computational design and optimization of vascular stents represents an efficient tool for improving their mechanical characteristics and consequently enhancing outcomes and safety of the treatments. In this presentation, an overview is provided on the design optimization of self-expandable cardiovascular devices, with a focus on both geometric features and material properties.
Firstly, a computational framework is presented for the multi-objective shape and cross-sectional size optimization of self-expandable TAV frames, based on finite element simulations of the implantation procedure in different diseased anatomies. Secondly, a computational framework for the design of innovative self-expandable femoral stents is introduced, in which inverse homogenization topology optimization is adopted to generate 2D unit cells with prescribed mechanical characteristics of clinical relevance, incorporating geometric constraints to ensure manufacturability. Finally, a study is presented that combines experimental tests on NiTi samples with finite element analysis of stent-graft mechanical testing, highlighting the potential for designing and optimizing the mechanical properties of self-expandable cardiovascular devices by finely tuning the temperature and processing time of the material heat treatment.
Speaker Bio
Dario Carbonaro is an Assistant Professor in Bioengineering, with a degree in Mechanical Engineering and professional experience as a consultant in computational structural mechanics. His research primarily focuses on the computational design and optimization of medical devices
Open Source CDFAM
Presentation Abstract
Aaron will speak about his journey and experience from industrial designer to computational designer, from using open source tools to developing them, and how these tools have created a versatile workflow and portfolio.
Read the CDFAM Interview with Aaron Porterfield.
Speaker Bio
Aaron Porterfield is an Industrial Designer focused on computational design and digital fabrication. He received a BFA in Industrial Design from the Academy of Art University in San Francisco, CA. He has worked in additive manufacturing for over 10 years, with Autodesk, FATHOM, and his own consultancy F=F.
Interview with Aaron Porterfield ahead of CDFAM Amsterdam
Liquid Fashion: Design and Manufacturing of the Ariel Swipe Bag with Rapid Liquid Printing
Presentation Abstract
This presentation delves into our collaboration with Coperni to develop and release the Ariel Swipe Bag for Paris Fashion Week 2024, a project exemplifying the principles of Design for Advanced Manufacturing. By leveraging our novel production methods, we navigated design creativity and manufacturing constraints to realize an innovative outcome. Key topics include the design development process, technical advancements, and the synergy between aesthetics and functionality enabled by our unique capabilities. The presentation further highlights our portfolio of past projects, including BMW and Hyundai concept seats, Black Imagination lamps, and luxury handbags, showcasing the breadth and versatility of our approach to advanced manufacturing in design.
Read the CDFAM interview with RLP
Speaker Bio
Hamilton Forsythe is a Senior Computational Geometry Engineer leading the development of RLPs proprietary software ecosystem : VEER.
Hamilton enjoys applying innovative computational techniques to new fabrication methods with a focus on building systems to turn novel methods of creation into accessible modes of manufacturing.
Before joining RLP, Hamilton earned his BSAD from MIT where he was an undergraduate researcher at the MIT Self Assembly Lab and Media Lab, Tangible Media Group.
Kimball Kaiser is a designer working in additive manufacturing, digital fabrication, and architecture. He currently leads the production team at Rapid Liquid Print, collaborating with designers and engineers in fields ranging from automotive, O&P, fashion, and footwear to develop end-use parts.
He has a background in architecture and has practiced with firms in the US and Japan. He holds a Master of Science from MIT and a Master of Architecture from the University of Michigan.
Organization:
Datameister
Presenter:
Ruben Verhack
Constrained creativity in AI-accelerated automotive design
Presentation Abstract
The presentation explores the transformative potential of AI in automotive design, emphasizing the need for tools that embrace constrained creativity within the industry’s unique challenges. Current generic 2D and 3D generative tools often fall short in addressing the intricate constraints of automotive design, such as manufacturability, stakeholder needs, and engineering requirements. By collaborating with a world-renowned design studio, we have mapped out a novel inside-out design process that integrates custom AI tooling directly into designers’ workflows. Rather than replacing designers, this approach empowers them by eliminating non-creative, time-consuming tasks and reducing design iterations between stakeholders. The result is a faster, more effective path to optimal designs that balance creativity, feasibility, and client requirements. During the talk, we will share insights from this collaboration and showcase in-house results that highlight how AI can redefine workflows in automotive design.
Interview: Datameister – Constrained creativity in AI-accelerated automotive design
Speaker Bio
Ruben is a seasoned entrepreneur and AI expert with a double PhD in computer science from Ghent University and TU Berlin, recognized with prestigious awards like the Google Faculty Award and multiple Best Paper Awards. He has extensive experience in AI-driven innovation. As the founder of Datameister, a fast-growing 3D-AI venture studio, Ruben leads the development of cutting-edge solutions in computer vision, deep learning, and generative AI for industries including design/manufacturing, media/entertainment, and medical imaging. His work bridges the gap between advanced research and production-grade AI technologies, making him an influential voice in computational design.
Lunch Break
From structure to sound: unlocking the potential of vibroacoustic design
Presentation Abstract
Noise pollution is one of the leading global environmental pollutants, resulting in the loss of one million life years annually. This has prompted the introduction of stringent regulations on the acoustic performance of structures, without compromising their structural integrity. Topology optimization offers a promising approach to developing innovative structures that meet these conflicting requirements. However, in many applications, considering vibroacoustic coupling from the early design stages is essential, as it directly impacts the accuracy of the acoustic performance and structural stability, ensuring that both functional and regulatory requirements are met. This added complexity to the optimization process largely influences the resulting structures and is crucial for achieving optimal performance. This presentation will provide a comprehensive overview of an intricate vibroacoustic topology optimization framework and focusses on its potential applications. It will showcase optimization results across various scales, from unit cell and metamaterial design to supercell and finite component levels. Novel, intricately engineered structures that balance lightweight design, structural stiffness, and acoustic performance will be presented, demonstrating the potential of vibroacoustic design in meeting modern performance standards.
Interview: Vanessa Cool – From Structure To Sound: Unlocking The Potential Of Vibroacoustic Design
Speaker Bio
Vanessa Cool graduated in 2020 with a master in Mechanical Engineering at KU Leuven in Belgium. She obtained her PhD in April 2024 within the Mecha(tro)nics System Dynamics research group at KU Leuven. Her doctoral research focused on developing design optimization frameworks for vibro-acoustic sandwich meta-structures, with a particular emphasis on their characterization, reduced order modeling, and topology optimization. She undertook a 6 months research stay within the TopOpt group at DTU, Denmark. From April 2024 onwards, she is pursuing an FWO junior postdoctoral fellowship at KU Leuven in the field of mass-manufacturable, high performant topology optimization.
Flexible Geometric Modeling and Atypical Simulation Solvers to Streamline Design Optimization
Presentation Abstract
Simulation-driven design serves two important purposes: wider exploration of the design space and goal-seeking optimization. Regardless of the regime, the workflow spanning geometry creation, simulation setup, results interrogation and geometry redesign needs to be as seamless as possible to make this approach viable. However, there is often a significant overhead associated with manual, non-value-adding tasks. Examples include converting all geometry into a common representation prior to simulation, meshing for simulation, (re)applying simulation boundary conditions and, finally, making meaningful geometry updates based on simulation results. In this talk, we will showcase some of the approaches and methods we use in Altair Inspire to:
- Concurrently model with up to four different geometry representations
- Prepare simulation boundary conditions that remain fixed, irrespective of geometry changes
- Run simulations on components and assemblies without having to harmonise all geometry into a single representation
- Prepare a design exploration or optimization to close the loop between design and simulation
- Automatically update geometry based on the simulation findings
These workflows take place entirely within Altair Inspire, which also reduces the need for lossy conversions or file transfers between different software products.
Speaker Bio
Wesley Essink is Director of Software Engineering for Implicit Modeling at Altair. His role focuses on developing Altair’s non-traditional modeling capabilities enabling users to create complex, simulation-driven design workflows that are both intuitive and efficient for engineers and designers. He was also a co-founder and CTO of Gen3D which was acquired by Altair in 2022 and has over a decade of experience of academic and industrial R&D in design and manufacturing.
Organization
SimScale
Presenter:
David Heiny
CEO & Co-Founder
SimScale – Physics & AI engineering simulation in the cloud
Presentation Abstract
Speaker Bio
Coffee Break
accelerating design optimization using implicit geometry
Presentation Abstract
Speaker Bio
Design Optimization for Advanced Manufacturing through Forward Looking Performance Simulation
Presentation Abstract
Of course we want to make our designs more efficient, more reliable, more cost effective, and more manufacturable, but how can we do all of that? Forward looking physics based simulation driven design optimization is the way. Recently, we have seen many examples of brackets that are topology optimized based on structural loading, or heat exchangers that are optimized based on lattice fill and heat transfer.
These are perfect examples of optimizations that can be done through the use of physics based simulations looking ahead at future states of the components, and driving information from those simulations back into the design of the geometry before it is ever manufactured. This leads to overall efficiency in the products that are being created, if we couple that design with an advanced manufacturing method that can produce the topology and structure that is optimal. However, there is even more opportunity still available if we include even more advanced methods of simulation, such as noise, vibration, acoustics, RF response, fluid flow, and other conditions that components could be optimized for.
This presentation will demonstrate the value and opportunity of some advanced simulation methods that can drive optimized designs for Advanced Manufacturing methods.
Speaker Bio
Chris Robinson is the Senior Product Manager of Additive Manufacturing (AM) and Sheet Metal Stamping at Ansys. He started into manufacturing as a 16 year old in an aerospace machine shop and began his research related to AM while designing satellite components in 2004. He has been involved in AM research for 21 years at Sandia National Laboratories, Utah State University, NAVAIR, Boeing, 3DSIM and Ansys. Chris has worked on AM product, process, material, equipment, and software development projects at every stage of development from fundamental ideation to commercial flight qualification.
AI Judges in Design: Statistical Perspectives on Achieving Human Expert Equivalence With Vision-Language Models
Presentation Abstract
The subjective evaluation of early stage engineering designs, such as conceptual sketches, traditionally relies on human experts. However, expert evaluations are time-consuming, expensive, and sometimes inconsistent. Recent advances in vision-language models (VLMs) offer the potential to automate design assessments, but it is crucial to ensure that these AI “judges” perform on par with human experts. However, no existing framework assesses expert equivalence.
This research introduces a rigorous statistical framework to determine whether an AI judge’s ratings match those of human experts. We propose statistical metrics that broadly cover these assessment areas: interrater reliability, agreement, error metrics, correlation and relative rank assessment, distribution-similarity analysis, and equivalence tests. We apply this framework in a case study evaluating four VLM-based judges on key design metrics (uniqueness, creativity, usefulness, and drawing quality). These AI judges employ various in-context learning (ICL) techniques, including uni- vs. multimodal prompts and inference-time reasoning. The same statistical framework is used to assess three trained novices for expert-equivalence. Results show that the top-performing AI judge, using text- and image-based ICL with reasoning, achieves expert-level agreement for uniqueness and drawing quality and outperforms or matches trained novices across all metrics.
This has implications for scaling design evaluation in education and practice, and provides a general statistical framework for validating AI judges in other domains requiring subjective content evaluation.
Speaker Bio
Kristen M. Edwards is a PhD candidate in Mechanical Engineering at the Massachusetts Institute of Technology’s DeCoDE Lab. Kristen is a National Science Foundation Graduate Research Fellow, Ida M. Green Fellow, and GEM Fellow. At MIT Kristen researches artificial intelligence applied to engineering design with special focus on sustainable design and global development. Kristen has spoken internationally on artificial intelligence and multimodal machine learning for engineering design and her work is published in journals including Science Advances and the Journal of Mechanical Design.
Networking Event, 5-7 PM
Thursday, July 10th
9AM – 5PM Presentations and Networking
Organization:
3MF Consortium
Presenter:
Duann Scott
The Industry and ISO Standard File Format for Communicating More Than Geometry for Additive Manufacturing
Presentation Abstract
As additive manufacturing matures, the need for a standardized data format that captures the full complexity of AM workflows has become critical. The 3MF (3D Manufacturing Format) specification was developed to overcome the limitations of legacy file formats like STL and OBJ by enabling reliable exchange of manufacturing-ready data, including mesh geometry, material assignments, build configurations, and advanced representations such as support structures and voxel fields.
Now published as ISO/IEC 25422:2025 – Information technology — 3D Manufacturing Format (3MF) specification suite, 3MF formalizes a global standard for communicating AM data. This presentation will explore its adoption across more than 100 software and hardware platforms, its role in enabling traceable and interoperable design-to-print workflows, and recent developments such as the volumetric extension for voxel- and field-based representations, supporting advanced simulation, multi-material printing, and AI-driven design.
Speaker Bio
Duann Scott is the founder of CDFAM – Computational Design Symposium, bringing together experts across design, engineering, and software development to explore the future of computational design. He is also the Executive Director of the 3MF Consortium, driving the development of an open data standard for additive manufacturing. With a background spanning industry, academia, and technology, Duann focuses on advancing digital design and manufacturing through collaboration and innovation.
Organization:
Toolkit3D
Presenter:
Sarah Clevinger
From 2D to Mass Production: Computational Design at Scale with Toolkit3D
Presentation Abstract
Toolkit3D will showcase how brands have reduced product design timelines from days to minutes by integrating anatomical scan data, performance parameters, and lattice optimization into a repeatable design engine. The result is a scalable pipeline for mass customization that accelerates production without sacrificing precision.
This session will walk through how the platform ingests variable input data (such as unique body shapes or pressure maps), generates manufacturable geometry including conformal lattice structures, and automates preparation for production, regardless of manufacturing process.
This isn’t conceptual, it’s computational design powering real-world, on-demand manufacturing at industrial scale.
Whether you’re working in orthotics, wearables, protective gear, or consumer products, you’ll see how our design engines and modular workflows can radically compress timelines, reduce complexity, and democratize access to custom-fit manufacturing.
Toolkit3D is creating a new standard: design once, fit anything, manufacture anywhere.
Speaker Bio
Sarah Clevinger is the Chief Product Officer at Toolkit3D, a platform enabling automated, scan-to-print pipelines for consumer and medical applications. Her mission is to bring computational design out of the lab and into the hands of real-world users, enabling mass production of custom-fit products at scale.
Organization:
ShapeDiver + Orthosolid
Presenter:
Edwin Hernandez + Age Van Boxem
Democratising Computational Design via Cloud Based Applications
Presentation Abstract
While computational designers have always created powerful and sophisticated software, the reach of their work often couldn’t exceed that of their modeling tool of choice. We’ll show a novel way to build and deploy web applications, including complex user interfaces and interactions with 3d geometry and data visualization with nothing but Grasshopper.
As part of ShapeDiver’s presentation, Age Van Boxem will present Orthosolid, a platform powered by ShapeDiver that enables clinicians to design and order custom 3D-printed orthoses through an intuitive digital interface. He will explore how computational design and UX come together to simplify complex and outdated workflows, making advanced customization accessible within clinical practice and without engineering expertise.
Read the CDFAM ShapeDiver interview
Speaker Bio
Edwin Hernandez is a Senior Computational Designer and Project Manager at ShapeDiver. With over a decade of experience, he has contributed to projects across 15+ industries, from AEC and product design to jewelry and 3D printing, collaborating with companies across Europe, the USA, Canada, Australia, and New Zealand.
With a background in Architecture from Andes University in Bogotá, Colombia, and Computer Animation from the SAE Institute in Melbourne, Australia, Edwin began his career by providing drafting and 3D modeling services in Australia and New Zealand. His passion for technology-driven design solutions led him to master Grasshopper and web-based applications, culminating in his role at ShapeDiver in Vienna, Austria, in 2018.
At ShapeDiver, Edwin specializes in developing highly optimized parametric models, integrating external databases, leveraging C# programming, and custom Grasshopper plugins to create cloud-based applications that streamline and automate design and manufacturing processes. He also shares his expertise through teaching, webinars, online tutorials, and industry collaborations.
Age Van Boxem is a Product Design Engineer at Orthosolid, a platform for customised 3D-printed orthoses. With a background in Industrial Design, he focuses on the intersection of computational design, healthcare, and user experience. At Orthosolid, Age develops intuitive digital tools and parametric workflows that make it easy for clinicians to design personalized orthotic solutions
Organization:
Synera
Presenter:
Andrew Sartorelli
From Days to Hours – Accelerating the RFQ process through scalable FEA automations
Presentation Abstract
Engineering organizations tackling process automation face a persistent challenge: how to effectively share and distribute automation solutions across teams. Critical knowledge often remains siloed, limiting its impact and accessibility, while non-automation experts struggle to utilize tools created by domain specialists. This slows processes and places additional strain on already overburdened expert departments.
This presentation examines a real-life example where automating an FEA simulation enables CAD designers to independently evaluate their designs, receiving results within hours rather than waiting days for the FEA department. This shift allowed for more frequent evaluations, faster feedback, reduced dependencies, and, most importantly, a significantly faster RFQ process.
We’ll explore practical approaches to implementing similar solutions, highlighting strategies for scaling expert knowledge and unlocking organizational potential.
Speaker Bio
Product Management & Partnership Lead | Mechanical Engineering Software Solutions | Driving Innovation Through Strategic Partnerships
Coffee Break
Real-Time Computer-Aided Optimization (CAO): How GPU-Native Simulation Changes the Industry
Presentation Abstract
Computer-aided engineering (CAE) has been a foundational tool in aerospace and photonics design, but slow workflows, high costs, and constrained design exploration limit its potential. Traditional methods rely heavily on intuition and a few simulations to validate designs, leaving vast opportunities untapped. However, a paradigm shift is underway: integrating mathematical optimization techniques like adjoint optimization and inverse design into CAE is redefining what’s possible in engineering.
This modern approach – Computer-Aided Optimization (CAO) – directly leverages advanced mathematical optimization to automate and enhance the design process. CAO replaces intuition-driven, validation-focused methods with a data-driven, goal-oriented workflow by specifying design goals and using algorithms to refine configurations iteratively. Techniques like inverse design, which uses objective functions and gradient-based optimization, and adjoint methods, which enable efficient sensitivity analysis, are central to this transformation.
GPU-native simulations amplify the impact of these methodologies, making it feasible to address industry-scale problems in a fraction of the time previously required. High-performance GPU computing accelerates the iterative optimization process, enabling rapid exploration of vast design spaces with unprecedented fidelity. Applications range from optimizing aerodynamic performance in aerospace to creating innovative photonic devices like metalenses and quantum computing components.
This synergy of mathematical optimization and GPU acceleration positions CAO as the future of engineering design. By reducing costs, accelerating development cycles, and enabling robust design exploration, CAO allows engineers to confidently tackle complex challenges. Whether designing aircraft or photonic circuits, these advancements fundamentally reshape how industries approach innovation, driving breakthroughs across disciplines and unlocking new possibilities for high-performance, efficient design.
Read the interview Real-Time Computer-Aided Optimization (CAO): How GPU-Native Simulation Changes the Industry
Speaker Bio
Qiqi Wang: Associate Professor at MIT specializing in computational sciences, chaotic systems, and optimization for aerospace. His pioneering work bridges theoretical research with practical applications in unsteady aerodynamics and exascale computation.
Momchil Minkov: Momchil Minkov is the Director of the Tidy3D solver at Flexcompute Inc., leading the development of the company’s high-performance, hardware-accelerated FDTD solver. During his PhD studies at EPFL, Switzerland, and postdoctoral fellowship at Stanford University, he co-authored over 50 publications on computational electromagnetism and optimization of photonic devices.
Physics-Driven Generative Design for Laser Powder Bed Fusion in Aerospace
Presentation Abstract
Laser Powder Bed Fusion (L-PBF) has shown transformative potential for the aerospace industry, with substantial investments being directed globally to leverage its benefits. However, broader industrial adoption of L-PBF faces barriers primarily due to limitations in the performance of components manufactured with the technique, productivity of the technique, and scalability of the technology. These limitations currently hinder L-PBF’s competitiveness with traditional manufacturing methods for aerospace, affecting both cost-efficiency and sustainability.
In this talk we will present a physics-driven generative design framework tailored for L-PBF, leveraging advanced multi-physics simulations to tackle the complex thermo-fluid-structural design challenges that arise in aerospace applications. The framework integrates computational fluid dynamics, heat transfer, and structural mechanics simulations. By coupling these simulation-driven insights with generative design techniques, our approach offers a robust pathway to create high-performance aerospace components. Results from case studies demonstrate the ability of our framework to reduce costs and design times while achieving superior mechanical properties under aerospace-relevant loading conditions.
Read the CDFAM Interview with ToffeeX
Speaker Bio
Thomas leads technical R&D at ToffeeX. He is an aerospace engineer specialized in aerodynamics, heat transfer, and optimization.
Manufacturing Driven Design
Presentation Abstract
Cognitive Design Systems integrates Manufacturing-Driven Design (MDD) and Simulation-Driven Design into its proprietary Cognitive Design software, transforming the way products are conceived. This innovative approach enables engineers to convert concepts into manufacturable, high-performance designs within seconds.
With automated Design for Manufacturing (DfM) checks and real-time modifications, the software ensures compatibility with processes like Additive Manufacturing, Machining, Die Casting, Injection Molding, and Forging. Proven through collaborations with Safran, Thales, Valeo, and Mitsubishi Electric, the platform reduces design cycle times by up to 90%.
Speaker Bio
Rhushik is a designer who firmly believes that design shapes the future we envision. As a trained mechanical engineer with over 11 years of experience in the design and manufacturing industry, Rhushik has helped automotive, medical, and aerospace clients turn ideas into production-ready solutions. In 2021, Rhushik co-founded Cognitive Design Systems to bridge the gap between what is conceivable and what is manufacturable. Through Cognitive Design, we enable companies to push boundaries and bring innovative concepts to life with an automated, manufacturing-driven design approach.
Geodesic slicing: A generalised framework for multi-axis 3D printing
Presentation Abstract
Multi-axis 3D printing has become a widely adopted technology across a range of industries. In recent years, computational designers and engineers have increasingly explored non-planar slicing strategies for large-format and robotic 3D printing in order to produce support-free parts with better strength and surface quality. While many solutions remain tailored to specific applications, the development of more generalised and automated toolpath generation workflows is essential to broaden the accessibility and scalability of these technologies.
Geodesic fields offer a highly adaptable approach to 3D volumetric slicing, enabling optimised toolpath generation for complex geometries. This presentation introduces the geodesic slicing framework implemented within Aibuild, showcasing its integration with the platform’s comprehensive suite of design and fabrication tools. The method supports a general-purpose workflow that minimises the need for complex input parameters, making it suitable for a wide range of use cases.
We will demonstrate the current implementation and available controls, and present a variety of outputs generated using this method, including freeform surfaces, multi-directional features, and branching structures, highlighting the potential of geodesic slicing to streamline fabrication in both experimental and production environments.
Speaker Bio
Alessandro Zomparelli is a computational designer with a background in engineering and architecture, specialising in the exploration of 3D printing technologies across scales. Over the years, his research has ranged from complex architectural structures to wearable products (MHOX design). He also contributed to academic research as a fellow at the CREATE group at the University of Southern Denmark (2020–2023), focusing on architectural geometry and digital fabrication. Alessandro is also known for developing open-source Blender add-ons like Tissue, which simplify computational design and fabrication workflows.
Currently, Alessandro is a Senior Geometry Engineer at Aibuild, a leading 3D-printing software company advancing large-format Additive Manufacturing technologies, including Polymer Extrusion, Metal DED, WAAM, Cold Spray, Concrete, and Paste extrusion on robotic and gantry systems.
At Aibuild, he collaborates with the Geometry Team to develop advanced computational algorithms that automate the fabrication of highly complex geometries, driving efficiency, precision, and scalability for industries such as aerospace, automotive, and construction.
Lunch, Networking
Rhino, Grasshopper 2, and TRfem: Computing Heat Flow Inside
Solids
Presentation Abstract
While environmental analysis tools in Rhino typically
focus on surfaces and 2D domains, optimization workflows in design for
additive manufacturing often require analyzing physical properties
inside solids. In this talk, we introduce some of the key novelties of
Grasshopper 2 for Rhino, we show why its concept of field manipulation
is an ideal platform for such tasks, and present TRmesh and TRfem – a
pair of finite element plugins for Grasshopper 1 and 2 that compute
heat conduction within volumetric/tetrahedral domains, natively in
Rhino. We focus on typical applications such as the design of heat
exchangers or, conversely, insulation. Covering both geometric
modeling and accurate simulation, GH2, TRmesh and TRfem together
enable a slick physics-informed topology workflow.
Interview: TRfem: Thermal Simulation in Grasshopper II with Mathias Fuchs
Speaker Bio
Mathias, Mathematician by training, has worked in applied statistics and statistical methods, as researcher at ZH architects, and as a general Rhino freelancer for years. He is now a developer at McNeel for Grasshopper 2 and an independent plugin developer and consultant focussing on numerical methods and simulationfor physics-informed engineering in design.
How topology optimization and additive manufacturing can create a new generation of green steel construction
Presentation Abstract
The digitalization of the construction sector could potentially produce more efficient structures, reduce material waste and increase work safety. Current strategies for the realization of automated steel constructions see the application of metal 3D printing processes as an opportunity to build a new generation of efficient steel structures with reduced material use. This, though, requires advanced multidisciplinary knowledge in manufacturing, metallurgy, structural engineering and computational design. Recent effort has been made in order to combine computational design with current digital fabrication procedures to realize efficient steel structures for the future. The present work aims at providing insights to current explorations on the combined application of computational design and metal 3D printing process in construction towards a new generation of optimized and resource-efficient structures
Speaker Bio
Dr. Eng. Vittoria Laghi is an Assistant Professor at University of Bologna in Italy, former lecturer at Massachusetts Institute of Technology and visiting researcher at TU Braunschweig in Germany.
Dr. Laghi received her PhD degree in Structural Design at University of Bologna in 2021 with a thesis on the application of steel 3D printing in construction.
She received her MEng degree at University of Bologna (Italy) in 2016, and her thesis has been partially developed at University of California Berkeley, where she also attended one semester as an exchange student.
Her research mainly focuses on the structural applications of steel 3D printing technologies for a new generation of efficient structures. Part of her doctoral activity has been developed in Amsterdam (with an internship at MX3D) and at TU Delft, Netherlands. She then joined TU Braunschweig as visiting post-doctoral researcher to study large-scale 3D printing solutions for construction.
Her previous background includes among others: earthquake-resistant design, insulating concrete form solutions, structural optimization applications, retrofitting solutions for masonry structures and energy dissipating systems for frame structures.
She authored 50 peer-reviewed publications and conference proceedings. Recently, she co-deposited three patents for innovative mobile 3D printing solutions and innovative lattice structural elements to reduce the environmental impact of metal structures.
Stress-based Design of Lightweight Horizontal Structures for 3D Concrete Printing
Presentation Abstract
Concrete is one of the most widely used materials in construction, but it’s also a major contributor to CO₂ emissions. In mid-rise buildings, slabs and beams alone account for over 40% of the concrete used. This raises an important question: how can we build these elements more efficiently while reducing their environmental impact?
In this talk, I’ll share how robotic 3D Concrete Printing (3DCP) and structural optimisation can work together to create lighter, more material-efficient beams and slabs. By integrating computational design, Finite Element Analysis (FEA), and stress-based material placement, we developed a workflow that reduces waste while maintaining strength.
I’ll introduce 3DLightBeam and 3DLightBeam+, beams with double the strength-to-weight ratio of conventional 3DCP beams, and 3DLightSlab, a ribbed slab designed for efficiency. Structural testing and Life-Cycle Analysis (LCA) confirmed that this approach can lead to more sustainable concrete structures.
This presentation will explore the practical potential of 3DCP in structural applications and what it means for the future of concrete construction.
Speaker Bio
Luca Breseghello is a Postdoctoral Researcher at the Technical University of Denmark (DTU) in Copenhagen. His research focuses on leveraging 3D Concrete Printing (3DCP) to develop environmentally and socially sustainable construction methods through computational design. Previously, he was a doctoral researcher at the University of Southern Denmark (SDU) as part of the CREATE Group, where he explored design-to-fabrication workflows that challenge conventional concrete construction through 3DCP.
He holds a Master’s in Architecture from Politecnico di Milano and has teaching and research experience at the same university. He has instructed in international workshops and worked at GXN, the innovation branch of 3XN/GXN in Copenhagen, contributing to experimental projects such as Blade Runner in collaboration with Odico, as well as large-scale architectural developments like the Sydney Fish Market (2017).
Organization:
Urban Futures Lab
Presenter:
Ben Dru + Julia Barashkov
strategic urban foresight
Presentation Abstract
Urban environments face unprecedented challenges, and traditional planning methods are ill-equipped to address them. Urban Futures Lab presents a novel methodology for urban trend analysis and strategic foresight. We integrate computational analysis with cultural insights and stakeholder engagement.
Our approach extends beyond conventional trend analysis methods borrowed from fashion and commerce. We introduce a systematic framework specifically designed for urban contexts. We combine quantitative data analysis with cultural datasets, including social media sentiment, community values, and local knowledge. This creates a comprehensive understanding of emerging urban trends and their implications. Rather than attempting to predict a single future, we aim to expand the range of possible scenarios and prepare cities for multiple potential outcomes.
Speaker Bio
Julia Barashkov(a) and Ben Drusinsky are the co-founders of Urban Futures Lab. Julia and Ben have combined experience in urban planning, data science, governance research, and design. They led 30 successful projects in the MENA region and North Europe.
They have affiliations with institutions like Bezalel Academy of Art, University of Tokyo, HCU and TU Delft.
Coffee Break
Building Beyond Imagination: Engineering the Unthinkable with AI
Presentation Abstract
Three decades ago, engineering took a quantum leap, moving from physical test and trial to virtual physics simulation—a shift that dramatically boosted efficiency and cut waste. Now, we’re entering a new era: the rise of AI-driven Large Physics Models (LPMs) that are reshaping engineering and unlocking solutions to urgent challenges like climate change and energy efficiency.
From vehicle design and aerospace to renewable energy, AI is able to accelerate, invent and optimize designs beyond human imagination, discovering new shapes and structures which are unlocking previously inaccessible performance.
Join us for an inspiring showcase of the art of the possible, featuring real-life applications that drive meaningful impact in the world around us.
Interview: Building Beyond Imagination: Engineering the Unthinkable with AI – Nico Haag, PhysicsX
Speaker Bio
Nico is the Co-Founder and Director of Engineering at PhysicsX, a pioneering deeptech company at the forefront of AI and engineering, dedicated to driving breakthrough innovations. Collaborating across Delivery, R&D, and Platform teams, he leads the development of advanced computer-aided engineering (CAE) and AI-accelerated design optimization methodologies. With extensive expertise in both engineering and deep learning, Nico works at the intersection of these fields to meet the important challenges of our time and build beyond human imagination. Prior to co-founding PhysicsX in late 2019, Nico built his career in the automotive and motorsport industries working for companies like Bentley Motors, Audi Motorsport and Mercedes-Benz.
Nico Haag – Co-Founder and Director of Engineering
Building Surrogate Models for Physics Simulation using a No-Code Approach
Presentation Abstract
This project demonstrates a no-code methodology for building surrogate models for engineering simulation. Using such methods, physics simulation analysts can tap seamlessly into the potential of surrogate models, transforming traditional simulation workflows to be more efficient and flexible. In this abstract, we present a workflow of how to use simulation result data to build a 3D surrogate model that any analyst can utilize without requiring programming skills—enhancing the usability of AI-driven simulation tools for broader adoption.
Finite Element Method (FEM) simulations are often computationally intensive and challenging to scale, especially for complex structural applications. Our methodology minimizes these resource-heavy processes with a graph-based surrogate model optimized for computational efficiency. To achieve this, we utilized automated extract, transform, and load (ETL) workflows to process the raw simulation data into a shape and format suitable for AI ingestion. We show how, through no-code data processing automation, analysts can focus on deriving insights rather than getting lost in technical details.
The dataset used comprised linear static analysis results of a Press Bench model, performed using SOLIDWORKS Simulation. Parametric variables included back height, feet width, and plate length, and the results predicted were displacement and stress. Using data processing and management tools, we first extracted and converted the surface field and volumetric field data, from the original raw format into an open-source “AI-ready” format (. csv,.vtk). This allowed us to gather all simulation data in one place to better understand the data distributions, patterns, and correlations between variables. In the next step, we cleaned the collected data while maintaining different data versions and keeping track of changes. As a final step, using the cleaned and processed dataset, we trained a Graph Neural Network. The model was trained to predict accurate stress and displacement fields within seconds (>90% accuracy), using the 3D volume mesh data as inputs. The whole process from raw data to a trained model took approximately one workday to develop. The same approach will be tested on large deformation nonlinear structural analysis.
This project demonstrates how structural simulation data can be used to build surrogate models that accelerate the design process. Advances in AI modeling tools now make these models widely accessible, enabling engineers to leverage physics simulation data without coding or deep machine learning expertise—expanding the possibilities in product design optimization.
Speaker Bio
Asparuh Stoyanov is the Co-Founder and CTO of Key Ward GmbH, a Berlin-based company specializing in AI-driven engineering solutions. With a strong background in mechanical engineering, computer vision, AI, and software development, he has played a key role in developing AI-powered solutions for energy-efficient engineering design.
Prior to founding Key Ward, he worked on cutting-edge applications in automotive AI and intelligent systems, focusing on real-time computer vision, sensor fusion, and machine learning. His expertise extends across product development, AI-driven optimization, and industrial automation.
Before transitioning into AI-driven technologies, he worked as a Design & Test Engineer for various companies, gaining firsthand experience in the tedious and iterative process of designing new products. This hands-on exposure to traditional engineering challenges fueled his passion for leveraging AI and automation to streamline design cycles and improve efficiency.
Having strong academic and industrial experience in mechanical engineering, combined with over 10 years of expertise in Software Development and AI, Asparuh bridges traditional engineering with state-of-the-art AI technology. His ability to integrate engineering principles with advanced AI-driven solutions makes him a key innovator, driving forward next-generation industrial and automotive advancements.
The Unreasonable Effectiveness of Simulation Intelligence
Presentation Abstract
Speaker Bio
Alexander Lavin is a leading expert in AI-for-science and probabilistic computing. He’s Founder & CEO of Pasteur Labs (and non-profit “sister” Institute for Simulation Intelligence), reshaping R&D with a new class of AI-native simulators, commercializing in energy security, aerospace, materials & manufacturing sectors (https://simulation.science). For the last dozen years, Lavin has focused on artificial general intelligence (AGI) research with top startups in neuroscience and robotics (Vicarious, Numenta), and sold his prior ML-simulation startup Latent Sciences to undisclosed pharmaco in neurodegeneration R&D. Lavin also serves as AI Advisor for NASA, overseeing physics-ML efforts for the NASA-ESA “Digital Twin Earth” projects. Previously, Lavin was a spacecraft engineer with NASA and Blue Origin, and won several international awards for work in rocket science and space robotics (including Google Lunar XPrize during graduate studies at Carnegie Mellon). Lavin was named Forbes 30 Under 30 in Science, and a Patrick J. McGovern Tech for Humanity Changemaker
Organization:
CustoMED.ai
Presenter:
Or Benifla
TEMP: Enabling Surgeons with Patient-Specific Guides: A Computational Design Journey
Presentation Abstract
In the fast-evolving landscape of healthcare, the demand for patient-specific surgical solutions has grown a lot. Yet, manual methods remain prohibitively time-consuming, costly, and reliant on highly specialized skills, making them inaccessible for everyday surgeries. At CustoMED, we are bridging this gap by harnessing computational design and AI to automate the creation of personalized surgical guides, empowering surgeons to design and implement patient-specific solutions independently and efficiently.
in the presentation we will go through our developmental philosophy behind our product, highlighting our modular, iterative approach built on computational design tools like Grasshopper. Through real-world case studies and insights into our reverse-engineering methodology, we’ll showcase how parallel development and deep integration of user feedback have enabled us to refine and scale our solutions for real-world impact.
CDFAM interview with Or Benifla
Speaker Bio
Or Benifla is the Chief Product Officer and co-founder of Customed.ai, a company dedicated to advancing personalized surgical solutions through AI and automation. With decade of experience in 3D design and manufacturing, including teaching at the prestigious Bezalel Academy of Arts and Design, Or has developed a deep understanding of the intersection between creativity and technology.
Throughout his career, Or has worked on a wide variety of projects involving advanced manufacturing, honing his expertise in creating practical and innovative solutions. At Customed.ai, he focuses on making precision surgical tools scalable and accessible, enabling healthcare providers to offer better care with ease and efficiency.
5PM event Ends
The venue for CDFAM Amsterdam is 3DMZ, located at Oudeweg 91-95, 2031 CC in Haarlem, part of the Amsterdam Metropolitan Area.
Housed in a former industrial facility, 3DMZ has been transformed into a creative and technological hub featuring various labs equipped with industrial 3D printers, robot arms, and 3D scanners, fostering innovation at the intersection of design and manufacturing.
If you are interested in participating as a speaker at future events, we invite you to submit an abstract and share your expertise with the CDFAM community, nominate a speaker, or nominate a computational design project you think we should hi-light.
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