CDFAM NYC, 2024
Archive
Recording of Presentations from CDFAM NYC, October 2-3, 2024
Keynote Presentations
From Text to Spaceship: Advancing AI in Aerospace
Presentation Abstract
This presentation introduces the innovative ‘Text-to-Spaceship’ concept at NASA Goddard, focusing on the pivotal role of AI in transforming text-based science objectives into mission designs. We discuss how leveraging current and near-term AI technologies can accelerate the entire mission development process, from initial concept through to hardware realization. Specific attention is given to AI-driven computational design of systems, illustrating how these technologies can improve design agility and performance. By deploying AI to accelerate the translation of scientific goals into tangible mission outputs, we aim to revolutionize spacecraft design and development, unlocking new capabilities and propelling the aerospace industry into a new era of technical innovation and expanded mission capacities.
Speaker Bio
From a young age, Ryan McClelland has been captivated by futurism and technology, aspiring to contribute to a brighter future. As a Research Engineer in NASA GSFC’s Instrument Systems and Technology Division, he pursues developing and implementing digital engineering technologies for space-flight missions. Ryan is particularly excited about the potential of Artificial Intelligence, Virtual Reality, Generative Design, and Digital Manufacturing to accelerate space systems development.
Ryan’s work using AI to develop spaceflight structures has been featured by NBC News, The New York Times, Dezeen, Popular Science, and Aviation Week. He was recently named to the Fast Company 20 in AI list. In addition to his research, Ryan has played a significant role in various flight missions, including designs currently on orbit aboard the Hubble Space Telescope and International Space Station.
2023 Presentation Archive
From Lamps to Lungs
Presentation Abstract
Join Nervous System for an exploration of their collaborations with scientists in the realm of 3D-printed organs. They will show how science inspires their art and design work which then feeds back into their scientific practice. The cycle continues as their work on organs contributes to their recent large scale public artworks and vise-versa.
Inspired by natural patterns, Nervous System creates computational systems that can create a myriad of unique designs. They translate these digital designs into physical products using a mix of digital and traditional manufacturing methods including 3d-printing, casting, and laser cutting. At the heart of these systems are mathematical models of pattern formation.
In this talk, we’ll dive into the fascinating intersection of art, science, and technology, and how Nervous System’s innovative practices are shaping the future of design and biological research.
Speaker Bio
Nervous System is a generative design studio that works at the intersection of science, art, and technology
Wednesday October 2nd
Morning Session – Computational Design
Organization:
CDFAM
Presenter:
Duann Scott
Welcome to CDFAM
Presentation Abstract
Duann Scott, founder of Bits to Atoms and organizer of CDFAM, will kick off the symposium with a welcome address, setting the stage for two days of deep dives into the latest in computational design, additive manufacturing, and emerging technologies. In his opening remarks, Duann will highlight the event’s focus on interdisciplinary collaboration, innovation at all scales, and the critical role of computational frameworks in advancing manufacturing processes across industries. This welcome will frame the discussions to follow, emphasizing the importance of pushing boundaries and fostering connections between industry leaders, academics, and developers.
Speaker Bio
Duann Scott is the founder of CDFAM, the leading symposium series that brings together engineers, designers, academics, and software developers to explore the future of computational design and advanced manufacturing at all scales. Through CDFAM, Duann fosters interdisciplinary knowledge sharing, collaboration and innovation, advancing the boundaries of design and engineering.
In addition to CDFAM, Duann serves as Executive Director of the 3MF Consortium, where he leads efforts to drive the adoption of an open-source file format standard for 3D printing, widely used across the industry.
With a background in design, engineering and business strategy, Duann’s work focuses on advancing new design paradigms through computational tools and processes. He is committed to educating and raising awareness about the transformative potential of these technologies.
Duann also teaches part-time in MIT’s online AMx course, helping shape the next generation of innovators in additive manufacturing.
Form – Matter- Fabrication – Performance: From Control to Uncertainty and Back
Presentation Abstract
The talk is about the on-going research efforts at ForMat (Form and Matter) Lab at Penn State University, directed by Benay Gürsoy. Benay and her team explore the relationship between matter and form, fabrication and performance mediated through the use of digital technologies. Current research in the lab includes work on adaptive robotic fabrication, and the design and sustainable production of mycelium-based building parts and structures.
Speaker Bio
Benay Gürsoy is an Assistant Professor of Architecture and the founder and director of ForMat (Form and Matter) Lab at Penn State. Her research and teaching focus is on computational making, digital fabrication, biofabrication, and shape studies. Benay completed her PhD studies in Architectural Design Computing Program at Istanbul Technical University in 2016 and was awarded the Best Ph.D. Dissertation Award by The Graduate School of Science, Engineering, and Technology. She has published and presented her research internationally and received awards, including the 2010 Young CAADRIA Award, and 2015 CAAD Futures Best Paper Award, 2021 and 2022 AIA UpJohn Research Initiatives and 2022 SOM Foundation Research Prize.
Emerging Technology within the Design Process, Examples from Across Industries
Presentation Abstract
In a rapidly evolving technological landscape, integrating artificial intelligence (AI) and emerging technologies into design processes is revolutionizing client experiences across various industries. This presentation explores how IDEO leverages its human-centered design approach to incorporate AI and other cutting-edge technologies into client-facing projects, enhancing innovation and driving impactful results. We’ll share stories about designing for inclusivity in XR, defining new kinds of AI-native products, bringing personality and emotion to the IoT, imagining the future of healthcare and more. We’ll also highlight our practice of building and sharing provocative, low-fidelity prototypes to explore our edges, build skills, and start conversations with the industry.
Speaker Bios
Jenna Fizel is inspired by translating abstract information into tangible experiences. Jenna helps clients explore design questions through building with emerging tools like AI, XR, and digital fabrication. They believe these tools can provide new perspectives during the design process. Jenna leads an 80+ person internal group focused on making technical skills more accessible to IDEO’s diverse community. Previously, they were a partner at an agency designing physical/digital spaces for clients. Jenna also co-founded a fashion tech startup and served as CTO of an intimate apparel firm. With an academic background in computational geometry from MIT, they see software as a mode of thinking that solves problems through exploration.
Ziyuan ‘Zoey’ Zhu is a creative technologist working at the intersection of design and emerging technology. At IDEO, a global design company committed to fostering social impact with human-centric design, she helps teams tangibly explore the future of product experience with emerging technologies, including generative AI, data visualization, and XR. She was invited to give speeches at SXSW, SF Design Week, MIT Museum, International Design Conference(IDC), Design Museum Week, NY Climate Week, etc. Ziyuan is also an affiliate researcher at MIT, leading research on integrating technology into climate education. Ziyuan holds a dual master’s degree in design study and computer science from MIT.
Design at all Scales Through Computational Craftsmanship
Presentation Abstract
Slicelab merges advanced computational design with innovative fabrication techniques to create groundbreaking design solutions. Our foundation in architectural craftsmanship—from hand drawing and model making to woodshop, 3D modeling, and 3D printing—has cultivated a deep understanding of materiality and form. This expertise enables us to optimize problem-solving through a balanced approach to physical and digital design.
As designers, we test and master a diverse array of software tools, integrating the best features to meet the unique needs of each project. This ensures our designs are both functionally superior and aesthetically compelling.
In this presentation, we will showcase how our strategic use of various design tools and techniques has led to innovative results across different scales and applications. Join us to explore how Slicelab is redefining design through the seamless integration of advanced manufacturing and computational craftsmanship.
About Slicelab
Slicelab is a multi-disciplinary experimental design studio specializing in digital design and complex fabrication consulting. Their projects range across various scales and strive to balance simplicity and complexity. Founded in 2012 by Arthur Azoulai and Diego Taccioli, Slicelab has quickly established itself in the design world.
In 2015, the studio took part in the Autodesk residency at Pier 9 in San Francisco, focusing on additive manufacturing R&D. Their team brings diverse experience from international firms like Jakob + Macfarlane, Asymptote, WRNS Studio, Francis Bitonti Studio, KMD Architects, Rockwell Group, Mindesk VR, and OPT Industries.
Arthur and Diego are also involved in academia, teaching integrated product design at JWU, 3D printed wearables at AAU, and advanced 3D visualization at NYIT. They serve as visiting architecture critics at Cornell, UPenn, and RPI further showcasing their expertise in computational design for additive manufacturing.
Computational Systems Design
Anatomy of Computational Building Geometry: Unveiling the Foundational Methods Beneath Complex Architectural Design
Presentation Abstract
KPF’s computational geometry processes, whether generating complex building components, documenting, optimizing, rationalizing, or form-finding, consistently rely on a set of core common methods. Across geometrically and computationally diverse projects, we have identified a repeated pattern and codified 5 distinct methods that form the logical underpinning of almost every bespoke computational task. These methods—data branching, point sorting, plane-based calculation, cross-referencing, and surface rebuilding—form the foundation of our architectural geometric computation process, much like the unseen bulk of an iceberg beneath the water’s surface. The majority of the work in each computational project involves a combination of these five methods; only after establishing these foundational logics can we implement the bespoke computational logic that handles the specific geometric tasks. Despite their apparent simplicity, these core functions can become extraordinarily complex in large projects due to the vast number of conditions and edge cases which require our core methods to be standardize and scalable. As project complexity increases, these elemental functions become much more important than bespoke computational logics to ensure that all geometric conditions are accounted for. In this presentation, we will define the main foundational methods of computational geometry and then use several geometrically and logically diverse megaprojects to illustrate how these foundational methods form the majority of the computational logic in each. Through classification and case studies, we will codify the core geometric computational methods required to develop any large architectural project at scale
Speaker Bio
Madeleine Metawati Eggers is a computational design specialist at KPF who specializes in parametric modeling and computational problem solving on large-scale and complex buildings. As a core member of the KPF computational design team, she has worked with leadership to guide the computational design team’s role in the office as collaborators in the design process, particularly in representing qualitative design problems such that they can be solved computationally.
Process Automation for Design and Analysis of Injection Molded Parts with Synera
Presentation Abstract
Synera is a process automation platform specifically designed for engineers. Thanks to the user-friendly UI and shareable templates anyone from your team can easily use and modify them to level up their work. The Moldflow connector allows integrating Moldflow in complex, multidisciplinary development workflows. In this presentation, examples will be presented solving typical injection molding design challenges.
Speaker Bio
At Synera, Andrew Sartorelli is the Product Manager for Integrations, as well as Software Partnership Lead where helps brings to market solutions for customer pains using internally developed solutions, as well as leveraging partner solutions. He’s spent the past 10 years working for a variety of engineering software companies including Autodesk, nTopology, Hexagon, and now Synera. At these companies, he’s always been focused on helping bringing the voice of the customer in companies through positions ranging from Technical Support Specialist, Application Engineer, Product Owner, and Product Manager. He holds a BSc. Mechanical Engineering from the University of New Hampshire.
Previous Presentations
Leveraging Physics-Based Modeling for Part and Process Design Optimization
Presentation Abstract
Sandia National Labs is a systems integrator and design agency with additional production responsibility for critical components. As such, advanced and additive manufacturing offer significant potential value to our mission responsibilities. Novel functionality and efficiencies can be achieved through complex part geometries, design of new or functionally graded composites or nano-structured materials, and the leveraging of data via a “network of things” and machine-learned models for integrated AI controls and process optimization. Taken together, if fully realized, these developments hold out promise for a new era of digitally integrated product realization that is precise, responsive, and “smart”. However, shortcomings in establishing the technical basis for determining reliable performance margins persist due to the complex, coupled physical processes that create the final material as the part itself is being built. Developing sufficient scientific understanding of these processes to achieve the levels of control required for rapid realization and qualification of processes or parts is itself a challenge. A true design for AM methodology must further invert this scientific understanding to achieve targeted performance margins. This presentation details a number of ongoing efforts to develop a physics-based modeling framework for advanced and additive manufacturing that is predictive of process outcomes based on settings and can be used to provide optimized design workflows. Examples are shown for DIW stress pads and cushions and metal laser powder bed fusion.
This work was supported by the LDRD program at SNL, managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
Speaker Bio
Jeremy Lechman is manager of the Energetics, Multiphase and Soft Matter Sciences Department, Engineering Sciences Center, Sandia National Laboratories
Modernising Engineering Design Processes with Computational Tools
Presentation Abstract
This presentation explores our innovative approach to converting traditional design tools and workflows into comprehensive computational systems that enable automation, optimisation, and efficient data handling. Focusing on a complex but outdated tool for designing refrigeration systems, we established robust standards and methodologies for this transformation. By analysing existing tools, spreadsheets, and workflows in collaboration with discipline experts, we derived logical frameworks and mapped every operation and data variable into a graph database. This process ensures modular function reuse and comprehensive tracking of variable usage throughout the tools. The presentation will highlight our methodologies, the resulting standards, and the significant advancements in design automation and optimisation.
Speaker Bio
Sean Turner, P.E., serves as the Director of Innovation at Henderson Engineers. In this role, Sean spearheads innovation initiatives, representing cutting-edge advancements to clients and partners while overseeing internal innovation projects. He devises novel approaches to building systems design aimed at delivering high-performance, data-driven, optimised projects. His efforts are focused on enhancing efficiency and engagement for project teams, ensuring a streamlined and effective design process.
Dauphin Flores is a Lead Computational Engineer within the Innovation Department at Henderson Engineers and the director of this project. Dauphin has played a pivotal role in the development of various computational tools at Henderson. His expertise in computational engineering drives the advancement of design automation and optimisation, significantly contributing to the firm’s innovative capabilities and technical excellence.
Afternoon Session – Scaling Bespoke for Human Needs
A Journey to Digital Prosthetics
Presentation Abstract
LifEnabled makes custom-fit prosthetic devices for the developing world that are simple, durable and new. Partner with LifEnabled to impact the world.
Speaker Bio
Brent Wright, CP, BOCO, is a Partner and Designer at Advanced 3D, and a clinician for patients on the ground at Eastpoint Prosthetics and Orthotics in Raleigh, NC. While he has a background with Fused Deposition Modeling (FDM), Brent has been utilizing new methods using MultiJet Fusion and Selective Laser Sintering lately, looking to create prostheses that are not only functional but light and flexible.
Rethinking DfAM: Across the Production Floor
Presentation Abstract
DfAM (Design for Additive Manufacturing) has often focused on product characteristics (performance and aesthetics) and printing optimization (support and nesting strategies).
However, this drastically overlooks all the steps between the printing process and final use of the product. Nearly all additive my manufactured parts/products undergo a series of steps once printed: post-processing, assembly, finishing, and QC. DfAM philosophies can leverage additive as a technology and design efficiencies at each step of this process to improve lead times and streamline workflows.
Speaker Bio
Ankush Venkatesh is the Intrapreneur, Additive Manufacturing at Glidewell Dental Laboratories. In addition to writing for publications such as Harvard Business Review, Forbes, Ankush has also been speaker at the largest 3D printing events in the world including Formnext, Rapid+ TCT, and Additive Manufacturing Strategies (AMS).
At Glidewell, Ankush is heavily involved in new product development, streamline additive manufacturing workflows, business strategy, and commercialization of digital manufacturing technologies.
Embedding data and clinical decision-making within the digital prosthetic socket fitting process
Presentation Abstract
Prosthetic sockets for people with amputation are traditionally provided using a manual, plaster-based process where the clinician captures the shape of the limb using a plaster cast, makes modifications to this surface to load and off-load particular biomechanical regions, before this modified shape is used as the base to fabricate the final device.
While digital processes have been around for several decades, which use 3D scanning and surface-based sculpting tools to replicate the traditional processes, using this digital record to learn from and support the socket design processes has been limited. With increasing adoption of 3D printing in the industry, the need for data to support in device design and fitting is increasing.
Radii Devices are a UK-based startup using machine learning techniques to learn from historical records and support fitting of these devices within a clinical setting. By providing clinicians access to this technology at the point-of-care, their aim is to provide an improved fitting process for both clinicians and prosthetic users.
Speaker Bio
Nathan Shirley
Lead Computational & Industrial Designer
HP Computational Design
Nathan helps lead a tactical Design, Engineering, UX and SW team that implements mass customization design engines for HP’s 3D printing customers. We operate as a highly specialized consultancy to help them navigate the complex design and integration path to customizable production at scale in Multi-Jet Fusion, HP’s industrial 3D printing Technology. We focus mainly on Medical & Consumer Body Fitment use cases like orthotics, prosthetics, eyewear, footwear and performance sporting gear, as well as on engineered lattice meta-material behaviors.
Interview with Nathan Shirley from CDFAM NYC 2023
Joshua Steer, PhD is the Founder and CEO of Radii Devices Ltd. He completed his PhD in the Bioengineering department of the University of Southampton, UK in 2019, where he continues as a Visiting Research Follow. His prior academic research and subsequent work at Radii focuses on using software and data, such as 3D scans, to support the fitting of Prosthetics and Orthotics, and has contributed to 17 peer-reviewed journal articles. He collaborates on projects with multiple partners including the Department of Veteran Affairs Office of Advanced Manufacturing and National Health Service in the UK.
Previous Presentation
How CCM leveraged Toolkit3D and the Carbon Platform to Build an Automated Pipeline for Mass Customization of High-Performance Hockey Products
Presentation Abstract
This presentation explores how brands like CCM are partnering with software platforms like Toolkit3D, and using additive manufacturing tools from Carbon to offer customization to consumers with groundbreaking products like the AXIS XF Goalie Mask and the SUPER TACKS X Total Custom helmet.
While designers at CCM used Carbon’s Design Engine Pro to create superior lattice pads that increase breathability in the AXIS XF Goalie Mask and the SUPER TACKS X Total Custom helmet, developers at Toolkit3D integrated the Scan-to-Fit Design Engine with Carbon Custom Production Software to build an automated workflow for custom parts, including computational design techniques and automated production preparation.
In summary, CCM with Toolkit3D built an automated pipeline for products with Carbon’s dual-cure materials and advanced additive manufacturing processes that yield custom fit products automatically and at scale for all athletes– elite to amateur.
Speaker Bio
Puneet Jhaveri is a Senior Applications Engineer at Carbon 3D where he leverages his expertise and training in mechanical engineering and physiology to develop custom products. Originally on a medical track, Puneet’s
interests shifted after being exposed to architecture and industrial design, sparking a passion for blending technical skills, aesthetics, and creative problem-solving. Puneet now focuses on pushing the boundaries of product design through innovative, cross-disciplinary approaches.
Previous Presentations
Mid-Afternoon Session – Data at the Heart of Design3:00 PM
Releasing the .3MF Volumetric + Implicit Extension
Presentation Abstract
The 3MF volumetric extension allows for the communication of additive manufacturing data at the voxel, and/or pure implicit level unlocking complex, multi-parameter, and multi-material applications in a lightweight, open source data format.
The presentation will give an overview of the newly released specification along with example case studies for software and hardware developers, along with the value proposition for designers and engineers.
Speaker Bio
Dr.-Ing. Jan Orend is a mechanical engineer with a focus on mechatronics. He received his Ph.D. in the field of metallurgy at TU Clausthal. Since 2015, he has been working at EOS GmbH, developing software for print preparation and process control for metal and polymer 3D printing. In his free time, he also enjoys using FDM 3D printing for hobby projects.
Procedural BIM: Large Scale Metadata Workflows from Design to Manufacture
Presentation Abstract
Traditional 3D modeling often captures only the outcomes of design processes, and not the underlying decisions and logic. Procedural BIM is an approach that addresses this by representing an architectural project as a network of interconnected, metadata-enhanced models. This network not only stores the outcomes but also embodies the design thinking, enabling scripts to dynamically establish relationships, generate new objects, and propagate information across the network while adhering to the principles of the design.
The presentation will detail the application of this innovative framework in the design, fabrication, and installation of over 23,000 unique curtain wall panels for a large-scale project, featuring 8.5 million individual fabricated components. The panels feature extreme cold-bent glass, with complex three-dimensional frames that are prefabricated to unlock rapid installation. Metadata also enabled the development of a machine-learning model derived from 3,500 material simulations to reverse engineer the flat shape of twisted panels.
By leveraging a data-centric approach, the system acts as both a record and a map of the design’s relationships, dependencies, and logic. It allows for simultaneous representations at various levels of detail, and a continuous flow of information from the building’s overall massing down to the holes on a fabricated component.
Speaker Bio
Keyan combines his dual education in architecture and structural engineering with a deep expertise in computational design. He spent 10 years at Front, Inc. developing facade systems for high profile projects around the world and working with many of the premier global design firms. He then went on to lead the Computational Design team for Grimshaw’s New York Studio, and now runs an independent consultancy focused on automation and digital processes for designers and makers. His career has been especially focused on parametric design, complex simulations, DfMA, and anything related to the process of transforming data into real, physical constructions.
Simulation-Driven Continuous Engineering: Enabling Innovation Throughout the Product Lifecycle
Presentation Abstract
A digital model undergoes multiple transformations throughout the product lifecycle and relies on various mathematical models and computer representations. These include CAD or implicit representations during design, slices, and G-code during process planning, and CT scans during manufacturing inspection. The current simulation tools’ inability to directly work with these native representations, instead insisting on conversion to meshes, makes performance prediction cycles extremely slow, manual, and fragile. This severely limits the parameter space at each stage and hinders the computational design and engineering of innovative, high-performance products. Furthermore, it fragments the already siloed product lifecycle management (PLM) as different data formats cannot be easily integrated for holistic decision-making.
Our solution to these challenges lies in continuous engineering through simulation on native representations. By employing immersed methods of moments and mesh-free simulation techniques, we ensure continuity across heterogeneous geometry and material models, throughout the design-to-manufacture process, and across arbitrary resolutions, solvers, and platforms. We have successfully demonstrated components of the continuous engineering platform by enabling, for example, performance simulation of as-planned and as-manufactured models, path-level additive process simulation, and virtual additive manufacturing (AM) part qualification. Additionally, we are developing *Generative.AM*, software technology for the generative design of pre-qualified AM components through a DARPA grant. Our commercial solutions today allow rapid exploration of large design spaces with extremely complex designs. By integrating with leading platforms like Synera, nTop, and Rhino Grasshopper as well as custom workflows such as those in Houdini, we empower users to seamlessly incorporate advanced simulations into their computational design processes, driving unprecedented innovation and efficiency.
Speaker Bio
Dr. Neel Goldy Kumar is a Product Manager and Engineer at Intact Solutions, Inc., where he leads the development of cutting-edge commercial simulation technologies. With a Ph.D. in Mechanical Engineering, he has extensive expertise in modeling and simulation, particularly in high-complexity areas such as additive manufacturing and composites. Over his seven years at Intact, Dr. Kumar has helped pioneer the Immersed Method of Moments technology and has been a key contributor to multiple SBIR-funded projects. He has also served as Principal Investigator for a NIST SBIR on the virtual validation of complex heterogeneous components made using metal additive manufacturing. Currently, Dr. Kumar is spearheading the launch of Intact.Simulation software across various computational design platforms.
Computational Design for Large Gas Turbine Engines
Presentation Abstract
Speaker Bio
Previous Presentations
Networking Event
3D Scanning by NYCAP3D
2024 Supporting Sponsors
Thursday October 3rd
Morning Session – Architected Materials
Where It Works and Where It Doesn’t: A Critical Overview of Machine Intelligence in Computational Design
Presentation Abstract
Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel lattice structures. Until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially when the design space is being explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive lattice, TPMS, foam and metamaterial datasets can be used to accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge.
In this talk, I will present a comprehensive overview of how state-of-the-art machine intelligence techniques can be used for the computational design, discovery and development of lattices and other cellular solids. I will show individual approaches categorizing them based on methodology and application and will further discuss machine intelligence trends for a wide range of computational design problems. Most importantly, I will critique AI and ML and will look into where it does work and where it does not, hopefully providing a better understanding of possible AI applications in the computational design context
Speaker Bio
I am a researcher in the field of computational design and mechanical metamaterials working at the University of Edinburgh. My research area includes artificially engineered metamaterials (to enhance a specific mechanical characteristic such as stiffness and strength), lattices, self-assembling 4D printed materials and bio-inspired materials and structures. My background in mechanical engineering allows me to map the characteristics of these innovations to applications in industry including transport, space and energy sectors. For instance, one of my latest projects included weight shaving from wind turbines using large-scale lattice structures in order to tackle the fatigue loading problems, using state-of-the-art computational design approaches.
Most recently, I have been developing new design and optimization frameworks based on AI and ML models, focussing on the benefits that these approaches can bring to old-fashioned optimization.
Spherene Metamaterial in Simulation-Based DFAM
Presentation Abstract
Despite the enormous potential of leveraging the rich information embedded in biological form, and the rising interest in bio-inspired design, there is no generalized, accessible computational design tool that enables it. In this presentation I will identify what I believe are the reasons for this gap and propose a framework to address it in the context of nine distinct types of architected materials, introduced here as “Bio-Motifs”. This framework consists of three pillars: (i) knowledge graphs, (ii) mathematical models, and (iii) data and information. I will elucidate aspects of this framework with examples from ongoing work spanning diverse organisms and structural elements such as sea sponge networks, honeybee hair, honeycomb, scales and branching structures. I will also demonstrate how we use computational design, simulation and additive manufacturing to both understand the functional basis for biological form, and leverage that understanding to engineer novel application solutions.
Speaker Bio
Christian Waldvogel is the founder of spherene, a Zurich-based company developing autonomous design software. He holds a Master’s degree in architecture from ETHZ, made his first 3D print in 1999, and spent most part of the 21st century as a conceptual artist. His work, which aims to reflect humanity as a species, on a planet and in the universe, was published and exhibited worldwide, and has directly led to the discovery of the spherene geometry in 2012.
Previous Presentation at CDFAM Berlin, 2024
Additive Manufacturing of Ceramics: How Far Can You Go Using Computational Design?
Presentation Abstract
This presentation is a review on the developments of complex ceramic structures at the SUPSI’s Hybrid Materials Laboratory. From the first attempts to use CAD to explain the thermo-mechanical behaviour of ceramic foams by finite element modelling (FEM), this practice is now a fundamental step fully integrated in the ceramic additive manufacturing (AM). Demanding end users’ requirements can be satisfied thanks to the combination of CD, simulation, and AM to solve multi-physics tasks. This presentation will show several examples of ceramic components working in high temperature, harsh conditions such as: re-entry thermal protection, porous burners, volumetric solar receivers, high temperature waste heat recovery systems, power to X components and periodic open cellular structures for catalysis.
Speaker Bio
Graduated in naval and mechanical engineering at the University of Naples in 1989, in 1992 specialized in composite materials at the Center for Composite Materials at the University of Delaware in USA. After ten years in the industry he is now professor at SUPSI, responsible of the Hybrid Materials laboratory at SUPSI and faculty member at the Doctoral School of Industrial Engineering of the University of Padova (I) . His research fields are: process engineering of polymer and ceramic matrix composites, oxide and carbide ceramics, design and additive manufacturing of complex ceramics. He has been project manager in national (Innosuisse, SNF), European (FP5, FP6, FP7 and H2020 ) projects. Nowadays his group is focusing on net shape processing of complex ceramics (oxides, carbides and composites) by additive manufacturing. Prof. Ortona has published about 100 peer-reviewed papers and 6 patents. He is member of the evaluation body of the “Practice to Science” founding scheme at SNF and editor of The journal of American Ceramic Society, Materials and Hybrid Advances
Accelerating Time to Market for Purpose-Built AM Software
Presentation Abstract
For the additive manufacturing industry to grow, unlocking production applications is critical. Building web applications targeting the AM industry is needed to make 3D printing easier to use in production settings. However, developing cloud-based applications that deal with complex geometries and CAD-like capabilities typically requires specialized expertise and a lengthy development process. This talk describes how two AM startups worked together to bring a web application for additive manufacturing to market in a compressed timeframe. We will present General Lattice’s Frontier web application for digital materials and describe their use of the Metafold implicit geometry kernel API. This collaboration allowed General Lattice to focus on their differentiated IP: offering validated materials and geometries from partner vendors to facilitate expedited path to commercialization.
Speaker Bio
Daniel is an entrepreneur and mathematician with extensive experience in geometric computing for the Additive Manufacturing industry. Before co-founding Metafold, Daniel held numerous roles in engineering firms working on complex geometric challenges in large-scale commercial construction. He is passionate about solving problems arising in industry with geometry and computation.
Marek Moffett, co-founder General Lattice, leading General Lattice’s computational design and additive manufacturing team, focusing on the generation of lattice architectures and DFAM techniques.
Leveraging years of research across several aspects of latticing, my goal is to deliver advanced material solutions to the industry, ultimately driving a wide spread adoption of superior performing products
Previous Presentations
Mid-Morning Session – Multi-Objective Optimization
Realizing Differentiable Physics in Digital Engineering
Presentation Abstract
In the context of traditional and advanced industrial settings, the adoption of Scientific Machine Learning (SciML) requires operating in digital-physical environments governed by large-scale, three-dimensional, multi-modal data streams that are confounded with noise, sparsity, irregularities and other complexities that are common with machines and sensors interacting with the real, physical world. Digital engineering domains—that is CAD, CAM, CFD, and so on—and advanced manufacturing settings provide exemplary environments to separate tried & tested SciML from unreliable “AI” and game engines. This talk elaborates on such digital-physical environments and the non-trivial needs from computational design and engineering tools. Special attention is given to differentiable programming in multi-physics settings, which done well is the catalyst for bringing autonomous, data-driven, machine-learnable techniques to advanced manufacturing and digital engineering worlds.
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
Beyond One-Size-Fits-All: Adaptive Comfort Strategies in Building Design
Presentation Abstract
This presentation explores how we use computational tools to optimize occupant comfort and well-being in indoor spaces. We emphasize the importance of occupant-centric design, prioritizing occupants’ needs and well-being in the building design process. Through case studies in residential dwellings and hospital environments, we demonstrate the application of machine learning algorithms and advanced simulation tools to predict and optimize comfort across various indoor environmental conditions, ultimately designing spaces tailored to unique user needs
Speaker Bio
Noresh is a mechanical engineer with computational design expertise at Stantec. As part of the Digital Practice team, he plays a core role in identifying opportunities for design process automations and providing custom solutions for workflow efficiency. He works across multidisciplinary teams guiding the integration of computational design in building performance simulations and parametric analysis. He is also pursuing a PhD in Engineering at Drexel University at the Building Science and Engineering group, where his research centers on Indoor Environmental Quality (IEQ), occupant comfort, and occupant-centric control
Agustín Salas is a Senior Associate and Lead Senior Architectural Designer at Stantec, with over 22 years of experience. He specializes in bioclimatic architecture and computational design, holding a postgraduate degree from the Polytechnic University of Cataluña (UPC) and a master’s degree from the Institute for Advanced Architecture of Catalonia (IAAC). Agustín is dedicated to creating architecture that enhances human well-being and minimizes ecological impact.
Leading multidisciplinary teams across Stantec’s offices in the USA, Canada, and Europe, Agustín has delivered innovative solutions for complex architectural challenges. His projects include public and private spaces, mixed-use developments, high-end residences, and institutional buildings, with a focus on sustainability and design excellence.
Agustín’s international portfolio spans regions such as the U.S., Israel, Russia, Mexico, and the Caribbean. His work integrates advanced computational tools, artificial intelligence, and bioclimatic strategies, all aimed at crafting functional, elegant solutions that inspire and uplift users. Passionate about transforming the future of architecture, Agustín combines creativity and technology to drive innovation in the field.
New advancements in physics-driven design: physics models for net-zero and human-centric software tools
Presentation Abstract
We showcase the latest advancements in physics-driven engineering design software, especially new multi-physics modelling tools for net-zero applications such as carbon capture, as well as the integration of new design tools which remove the ‘black-box’ feeling engineers often experience while using design tools such as topology optimization. We demonstrate how these new features are incorporated into ToffeeX, allowing fast iterations and integration into whole new workflows.
Speaker Bio
As a mathematician and aerospace engineer with a PhD in fluid dynamics from Imperial College, Marco is deeply passionate about modeling natural phenomena. His core value lies in enabling progress and movement, both intellectually and practically. Having founded ToffeeX, he leads the company in his role as CEO, leveraging his expertise to drive innovation and positive change.
Afternoon Session – Data Preparation and Manipulation
Optimising Hardware Development with Software Practices
Presentation Abstract
Kiera will discuss Ocado Technology’s additive-first approach to robotics hardware development. This design process, with additive at its centre, unlocks the benefits of agile software development for the world of hardware, allowing the team to create the world’s lightest and most efficient grocery fulfilment bot.
Agile practices allowed the team to experiment, learn, and iterate at speed with many of the product concepts being designed in just a few weeks. Achieving these results with traditional manufacturing approaches would have taken many times longer due to the design, tooling and procurement lead times associated with testing multiple concepts.
Speaker Bio
Kiera is a Software Engineer on the Industrial DevOps team at automation and robotics platform company Ocado Technology. She joined Ocado Technology in 2021 in the Supply Chain Simulation team in London, working on a site and network digital twin for Ocado’s automated warehouses. In 2023 she moved to Ocado’s Stockholm development centre to join the team developing the next generation of Ocado’s fulfilment robot, the 600 series. Her role in Industrial DevOps is to develop tools that can be used to apply the software concepts of DevOps to the world of hardware development. When she is not writing or reviewing code, she can be found planning her next TTRPG session.
How Much More FEA Could You Complete if You Didn’t Have to Mesh?
Presentation Abstract
Engineering creativity and innovation are too often stifled by the tedious process of building simulation models. The process adds no value, can take weeks or months to complete, and far too often results in a model that has gotten out of sync with the design model.
Coreform’s new Flex product accelerates engineering design by reducing the meshing burden through easier and more powerful FEA. Coreform Flex leverages cutting-edge techniques from FEA and computational geometry to streamline the process of setting up FEA models. At the same time, Flex provides simulation results that are more accurate and robust than traditional low-order FEA across linear, nonlinear, static and dynamic simulation regimes. In most cases, users report that simulation models that usually take several weeks to build can now be built in less than a day.
In this presentation, Dr. Michael A. Scott will give a concrete and accessible overview of the technical underpinnings of the Flex approach to FEA. He will also outline a few of the ways the Flex approach can accelerate engineering design and unleash the creativity of engineers.
Speaker Bio
Mr. Matt Sederberg is a pioneer in introducing new technologies to the CAD/CAE industry and has successfully started and sold a company in this space. Mr. Sederberg introduced T-Splines to the CAD market as its CEO in 2005, bootstrapping that company on SBIR funding to create plugins and integrated components used by over 2000 customers, sold through a channel with over 50 resellers. He created the premier brand in the industry, leading to a successful acquisition by market leader Autodesk in 2011, then led Autodesk’s \$40M automotive design product line. In 2016 Mr. Sederberg left Autodesk to join Coreform, where he now serves as Chief Strategy Officer.
Automating CAM with AI: lessons from applying deep learning to geometry
Presentation Abstract
Computer-Aided Manufacturing (CAM) has revolutionized the manufacturing industry over the past century by enabling the use of software tools to generate machine programs. However, a significant limitation remains: these tools still require substantial input from highly skilled human operators. As production technologies have advanced — from multi-degree-of-freedom (multi-DOF) robots to 3D printers and complex milling machines — the complexity of programming these machines has also increased. This growing complexity has made CAM a bottleneck in the adoption of advanced production techniques, particularly as batch sizes shrink and CAM-associated labor costs per part rise.
At two companies I am involved with: ArcNC, where we focus on CAM for robotic welding, and Oqcam, which specializes in dental CAM; we have explored various deep learning techniques to automate different aspects of the CAM process. In this talk, I will provide a high-level overview of our approaches, share key learnings from our journey, and discuss potential future directions for integrating modern deep learning approaches into CAM and design.
Speaker Bio
Ben Schrauwen is an investor and entrepreneur, currently the Co-Founder of ArcNC, Oqcam, and Raidyn. He previously co-founded and served as CEO of Oqton, which was acquired by 3D Systems. Before that, he was a Senior Director in Autodesk’s manufacturing division. Ben also served as a Professor at Ghent University, where he founded a pioneering machine learning research group. He holds a PhD in Computer Engineering from Ghent University and was a visiting researcher at Harvard.
Innovate with design-driven cost reduction strategies by leveraging Magics SDK: from idea-to-production
Presentation Abstract
In today’s competitive landscape, organizations are increasingly commercializing meaningful additive manufacturing (AM) applications while striving to optimize costs and improve their bottom line. We will address the pressing need to eliminate repetitive activities by simplifying AM workflows through DfAM partnerships and developing design automation processes.
Gain insights into how to automate design workflows for mass customization, streamline repetitive data and build preparation tasks, and debug build processing workflows using visualization tools powered by Magics SDKs.
We will discuss how the industry must embrace collaboration and openness to enable innovation at scale with AM. By the end of this talk, you will have a clearer understanding of how to enhance your AM capabilities through partnership integrations, automation, and innovative solutions.
Speaker Bio
Afternoon Session – Future States
Knowledge, Data, Trust. Bridging the Gap Between High Value Manufacturing and Rapid Advances in AI Capabilities
Presentation Abstract
If we can increase the efficiency and success rate of engineering by improving the process of designing physical products, reducing the cost and timelines of doing so, and making our engineering workforce more productive, it will create huge benefits to society. Currently, skilled labour shortages, cost overruns and long development timelines frequently result in the failure of once promising technologies, products, and businesses. These difficulties destroy invested capital and hamper our efforts to address climate change.
We must produce new solutions faster that will have more complex use cases and dependencies. This is happening elsewhere, yet the rapid advances in AI capabilities that are currently supercharging other industries, are limited within engineering and high-value manufacturing, why is this? There is a gap to overcome, but what is this gap, and can we all work together to bridge it?
We must produce new solutions faster that will have more complex use cases and dependencies. This is happening elsewhere, yet the rapid advances in AI capabilities that are currently supercharging other industries are limited within engineering and high-value manufacturing. Can we enable true AI and ML impact beyond surrogate simulations? There is a gap to overcome, but what is this gap, and can we all work together to bridge it?
Speaker Bio
Joe Griston is a Software Engineer turned Chief People Officer turned startup Founder. Alongside others, Joe built the world’s largest labour marketplace https://www.freelancer.com/ enabling 70 million people to work together, then built the once-promising https://arrival.com/ to its $13.6 billion public listing, and is now leading EQT-backed Generative Engineering to market.
Laurence Cook holds a PhD from Cambridge and was a Postdoc Computational Engineer at Stanford, Cambridge, and within MIT’s ACDL (Aerospace Computational Design Lab). Built applications including hypersonic space planes, passenger jet aircraft, and Formula 1 tech and is now a Co-Founder of Generative Engineering.
Design Optimization for Multi-Material Laser Powder Bed Fusion (MM-LPBF)
Presentation Abstract
Additive manufacturing (AM) technology has afforded greater degree of geometrical design freedoms not otherwise available through traditional manufacturing. Multi-material laser powder bed fusion (MM-LPBF) combines the great geometric and surface roughness resolution associated with LPBF with selective powder deposition (SPD), allowing for the special tailoring of material based on functional design requirements. For instance, advanced heat exchanger design can now include copper fins for efficient heat dissipation combined with nickel alloys for structural strength, and stainless steel for corrosion resistance. The ability to selectively engineer the design and material assignment of multiple metals in true 3D voxel approach into a single component can produce extreme design advantages for both part consolidation and unnecessary material reduction. In most engineering applications (e.g. aerospace, automotive, space) weight is considered a critical design factor. Part and assembly consolidation, as well as light weighting associated with new AM technology, can now be extended beyond traditional single material design and on many length scales. To facilitate this aspiration, we have developed a framework utilizing topology optimization capable of simultaneous multi-material design, inspired by the newfound design freedoms enabled by MM-LPBF. Our motivation exists to investigate and develop new design methods which incorporate manufacturing process considerations (e.g. build orientation, minimum feature size) to produce multi-material metallic designs which meet clear objective functions, such as maximizing stiffness or thermally fluidic heat dissipation.
Speaker Bio
Dr. Guha Manogharan is the Emmert H. Bashore Faculty Development Associate Professor of Mechanical Engineering at The Pennsylvania State University – University Park. He is the Co-Director of CIMP-3D (Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) and also heads the Systems for Hybrid – Additive Processing Engineering – The SHAPE Lab which focuses on additive and hybrid manufacturing with an emphasis on biomedical, defense and aerospace applications. Dr. Manogharan received his Ph.D. (2014) and M.S. (2009) from North Carolina State University. He has received the 2022 ASME Early Career Leadership (ECLIPSE) award, and several young investigator awards (2021 ASTM, 2020 NSF CAREER, 2018 FAME Jr., 2017 SME Outstanding Young Manufacturing Engineer Award and 2016 IISE Outstanding Young Investigator by Manufacturing and Design Division). His current work is supported by NSF, DoE, ONR, AFRL, IACMI, and Manufacturing PA.
Dr Ajit Panesar (AP) is an Associate Professor in Design for Advanced Manufacturing at the Dept. of Aeronautics, Imperial College London (ICL) and has extensive experience in the fields of design-optimisation including leveraging machine learning (ML) in design, modelling, and optimisation considering additive manufacturing (AM) constraints. He leads the Innovative DEsign and adv. manufacturing (IDEA) lab is key research theme leader for the “Computational Tools” theme within the EPSRC funded DfAM network. Additionally, he sits on the scientific committee for the Additive Fabrication of Composites Conference and has contributed towards the ASTM standards for AM design guide. AP has authored a book chapter titled “Simulation driven design and the role of optimisation in DfAM” for the ASM Handbook Vol 24B “Fundamentals of AM Design and Applications” and contributed to over 40 publications and leading conferences, with several papers exceeding 500+ citations. AP has forged numerous successful collaborations and secured >£3M funding from Research Councils/UKSA/Catapults/Industry to deliver his research vision.
State of the Art B-Rep Generation
Presentation Abstract
Boundary representation (B-rep) 3D models are the standard 3D representation used in the manufacturing industry. However, only recently has machine learning research begun to make progress on generative models capable of producing B-rep models. This talk will give a summary of the current state of the art for generating B-rep models. In particular it will cover, BrepGen, our recent work using diffusion models, that have proved extremely successful in the image domain, to the problem of B-rep generation.
Speaker Bio
Karl is a Senior Research Manager at Autodesk Research focused on data-driven design software for manufacturing. He holds a Ph.D. in Computational Design from Carnegie Mellon University and has presented his research internationally at conferences such as ICML, CVPR, ACM SIGGRAPH, ACM UIST, and ACM CHI. His work at Autodesk has won numerous awards including Fast Company Innovation By Design Honoree and Core77 Design Awards Research and Strategy Honoree.
Previous Presentation
Empowering Organizations with Engineering Intelligence to Revolutionize Product Development
Presentation Abstract
In today’s highly competitive landscape, engineering organizations face increasing pressure to deliver complex, high-performing products at an accelerated pace, all while managing tighter margins. To maintain an edge, teams must shorten design cycles and enhance product performance and quality simultaneously.
However, traditional simulation and optimization tools often fall short of meeting the demands of today’s fast-moving production environments. Design teams struggle to fully utilize the insights from these tools and seamlessly integrate them into their workflows. Engineering Intelligence (EI) represents a significant advancement, leveraging modern AI techniques to overcome these challenges. EI enables a paradigm shift by offering multi-physics and multi-component generative design, more flexible automation, and the ability to harness historical data to expedite simulations.
Adopting EI offers a transformative approach, integrating simulation, CAD, and engineering data into an intelligent, cohesive platform that accelerates the product development lifecycle from concept to market. By drawing on enterprise engineering knowledge and existing processes, EI facilitates near-autonomous design, significantly reducing development time.
At the heart of this transformation is a new breed of engineers—the CAE Data-Scientists—who blend simulation expertise with data science skills. Our innovative NC platform drives this impact through three layers: Core, which converts underutilized CAE/CAD data into actionable insights using advanced AI and deep learning; Studio, which overcomes traditional infrastructure constraints by embedding engineering data and optimization into interactive tools; and Platform, which supports the deployment, sharing, and maintenance of EI-powered workflows.
EI has already proven its value in real-world applications. An automotive supplier has seen higher RfQ win rates, faster development times, and significant cost savings. A major OEM in the energy sector rebuilt its design chain for a critical component, achieving 50% faster design cycles and improving annual throughput by thousands of megawatt-hours. This presentation will demonstrate how EI can tackle today’s most pressing engineering challenges and deliver measurable ROI within 18 months.
Speaker Bio
Thomas graduated from Ecole Polytechnique Fédérale de Lausanne (Switzerland), with a master’s degree in mechanical engineering, and a specialization in Neuroprosthetics. After his graduation, he worked as a researcher in the Computer Vision Laboratory of EPFL from which Neural Concept is also a spin-off. He then joined Neural Concept in the very early days of the company, more than six years ago. After being Director of Operations in the company, leading the Application Engineering team for the past 4 years, Thomas is now Managing Director US for the company.
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