{"id":17085,"date":"2022-11-30T17:29:41","date_gmt":"2022-11-30T22:29:41","guid":{"rendered":"https:\/\/d3.harvard.edu\/platform-digit\/?post_type=hck-submission&p=17085"},"modified":"2022-11-30T17:30:16","modified_gmt":"2022-11-30T22:30:16","slug":"ndesign-generative-design-the-future-of-product-development","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/ndesign-generative-design-the-future-of-product-development\/","title":{"rendered":"nTopology- Generative Design & The Future of Product Development"},"content":{"rendered":"\n\n\n

Value Proposition<\/strong>:
While product design and development have been improved on since the day of hand-drawn models and analysis, it is still far from an optimized process. There is a vast array of different computer-aided design (CAD) software used to model products. Those all export to different finite element analysis (FEA) systems used to analyze the properties and limits of said products. Modeling, and especially iterating on the results of FEA, takes a lot of person-hours and (from personal experience as a recovering engineer) can be a very frustrating process.<\/p>\n\n\n

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Source: K. Sweeney<\/figcaption><\/figure>\n<\/div>\n\n\n

Enter, nTopology. <\/strong>nTopology leverages generative AI to optimize the CAD process. Designers define the boundary conditions and product requirements, enter their initial design, and the system takes iteration into its own hands.

nTopology is a goal-seeking system. The user provides an initial input and boundary conditions, and the system iterates automatically until it reaches an optimized solution. This is the same process a designer would use to manually craft a 3D model, but far faster and more efficient.<\/p>\n\n\n

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Goal-driven feedback loops asymptote towards a set solution (Gharajedaghi, 2011)<\/figcaption><\/figure>\n<\/div>\n\n\n

Challenges<\/strong>
Technical:<\/em> nTopology has opted against a standard “black box” model. Instead they have discretized the process into individual, user-controlled “boxes” to allow their clients to have more granular control. Similar to Professor Avi Goldfarb’s point about decoupling prediction and judgement, nTopology leaves the entirety of the latter to the designer. While this is an appealing feature for engineers, who value control over their work, it increases the technical complexity of the product, as now multiple predictive and individually-actuated models are required.

Consumer-Facing:<\/em> As previously mentioned, engineers deeply value control over their work. While generative AI can optimize the process and make their roles more efficient, there is a psychological resistance towards allowing a program to take the reins. Additionally, convincing users of safety and reliability of the models is a core challenge. FEA lives by the adage “garbage in = garbage out.” Convincing users that the AI is sufficiently able to apply constraints and equations in the way an experienced engineer would is a major hurdle.

Competition: <\/em>While generative design is still a nascent field, there are some sharks beginning to circle. Namely ANSYS, a well-renowned software company who makes FEA software used across multiple high-reliability industries (healthcare, automotive, aerospace, etc…) recently debuted “Design Discovery.” This allows users to immediately optimize their designs as they perform FEA. This should absolutely be on nTopology’s radar, as ANSYS is a well-established company with deep pockets. Winning bids against them will require both outpricing them and making the design process so seamless that users want to forgo their existing systems. <\/p>\n\n\n\n

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ANSYS’ Design Discovery feature mass optimizing a bracket. The user inputs the model on the left and the software iterates until it reaches the model on the right (Source: ANSYS)<\/figcaption><\/figure>\n\n\n\n

Opportunities<\/strong>
Efficiency:<\/em> In my experience, the worst part of being a mechanical engineer is the tedium of CAD. Systems are usually complex and hard to use. It’s easy to make errors that take hours to resolve, and having to iterate ad infinitum to earn the extra performance of detail-orientation is often a mind-numbing procedure. The idea of allowing a generative AI to take optimization into its control while still granting the engineer overall design judgement is a highly-appealing product.
Additionally, designing complex geometry often has high-payoff for lightweighting while maintaining strength properties, but physically designing these complex surfaces is incredibly time consuming. An opportunity for nTopology is to focus on promoting the AI as a “last mile” designer. The engineer does the bulk of the work in getting the initial design built, then the AI controls the more-frustrating final tweaks.

Scientific Advancement:<\/em> All of FEA is built on a few core equations behind the phsyics of materials and structures. While we know a lot about the behaviors of classically-manufactured structures, the advent of new processes (like 3D-printing and laser sintering) has brought new challenges to our understanding of how products behave. Generative AI like nTopology poses an opportunity for scientists and engineers to run studies and optimization exploration far faster than traditional build-iterate-test methodology. nTopology even offers a feature that optimizes the internal print structure of additively-manufactured products, an area previously left as a “black box” to designers.<\/p>\n\n\n\n

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Some of the applications of lattice design featured on nTopology’s website. <\/figcaption><\/figure>\n\n\n\n


Cost-Savings:<\/em> A benefit of being able to rapidly iterate on your design and manufacturing plan is that you reduce the need for excess materials and person-hours in designing. This poses a significant cost-savings opportunity for mechanical product firms. <\/p>\n\n\n\n

What Should nTopology Do?<\/strong><\/h2>\n\n\n\n

Focus on the Product as a Last-Mile Solution: <\/strong>When you’re selling to users you are also potentially disintermediating, positioning yourself as a tool to make their lives easier rather than a disruptor is a much more effective marketing tool.

Run Studies and Write Whitepapers Focused on Reliability and Effectiveness for New Manufacturing Techniques: <\/strong>This is a big jump in design technology and methodology. Establishing effectiveness and trust in the industry makes selling far easier. nTopology should focus on building academic and industry partners who are willing to run design studies and physical tests on generatively-designed products to prove the AI can do what a human can faster without sacrificing quality.

Develop an End-To-End Design Solution: <\/strong>This is something nTopology has already started to do brilliantly. Unlike ANSYS, which is a standalone FEA product that allows you to import CAD from other systems, nTopology integrates directly with CAD software. Putting the whole pipeline in a single suite is a big differentiator for them as a solution. <\/p>\n","protected":false},"excerpt":{"rendered":"

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