Using generative AI in Collaborative Design Work to Boost Inspiration and Communication

At this point, it is important to review the difference between generative design and other technologies such as topology optimisation, grid optimisation or the like, which are often listed under one name. Simply put, generative design means humans and computers working together to create objects beyond human imagination. This application comes with a vast array of tools for design, exploration and analysis which will be explored in greater depth in future blog posts. Even after the initial designs have been produced by SiteSolve, architects and developers can provide the human element and customise them to a specific brief or site, respecting the cultural context, aesthetic appeal and the local community. We’ve seen customers that have specific targets for their project, e.g. whether the main target of the scheme is maximising south-facing apartments, maximising net area or efficiency, or a combination of criteria.

This has the potential to minimise the number of components in a product, as well as enhancing component performance. The user creates basic CAD blocks for areas which must be included or excluded in the design, such as boss features. Hypothesising that the bolt structure of the engine block would put too much stress on the engine mount over time, he used generative design to develop a new bolt configuration that would be more structurally sound. He also sees potential in emerging technologies within the additive manufacturing space. “For example, generative design as a technology at Autodesk is only just over a year old now. And understandably, with anything that’s new tech like that, and the geometries that they see it produce, there’s a natural anxiety to assume that this is out of the question for me or there’s no way that I can use it at my company.

Enhance Design Cycle Productivity

Generative design uses artificial intelligence (AI) and machine learning to design parts that can be manufactured to be absolutely optimal for their intended use. For an aircraft or a car, for example, this might be a component that is both lighter and stronger, leading to increased fuel efficiency and lower CO2 emissions. This study is the first known application of Generative Design to a biomedical implantable device. An advanced design technique, used to develop components for aerospace and automotive industries, has been harnessed in the production of bespoke medical devices. Since 2016, GM has launched 14 new vehicle models with a total mass reduction of more than 5,000 pounds., or more than 350 pounds per vehicle. Of those models, more than half of the vehicles shed 300 pounds or more including the all-new 2019 Chevrolet Silverado, which reduced mass by up to 450 pounds.

Topology optimisation returns only one optimised concept for evaluation based on the human-designed model. Lastly, it returns to the user an optimised mesh design result that must be rebuilt in a CAD system that is intended for downstream use. There are different types of AI, including narrow or weak AI, which is designed to perform specific tasks, and strong AI, which has the capability of performing any intellectual task. Some of the most common applications of AI include voice recognition, autonomous vehicles, virtual personal assistants, and image recognition. Crafting effective prompts is crucial for successful communication with generative AI. Well-crafted prompts should be clear, concise, and unambiguous, to ensure that the AI understands the user’s intent accurately.

Faster Design Iteration

The AI system then uses computer-aided design (CAD) software to build the part per the pre-established specifications. Kirsh is part of a conversation between researchers from across The Bartlett – including Robert Aish, Sean Hanna and Abel Maciel – who are collaborating on an inquiry into what they see as the newly-energised promise of generative design. The term “Artificial genrative ai Intelligence” (AI) was coined as long ago as the 1950s, and, while certain disciplines have already put it into practice, recent developments mean it could soon have a significant impact on designers of 3D environments. By striking this balance, we can harness the true potential of future generative AI while building a more equitable and responsible digital landscape for all.

The development time required to create generative design software will impact the cost. More complex and sophisticated software will take longer to develop and test, which will increase the cost. It provides topology optimization and other advanced simulation capabilities that help designers to develop more efficient and productive designs. ANSYS Discovery Live is a simulation software that supports real-time, interactive generative design. It uses physics-based simulations to build and evaluate multiple prototype options in real-time, allowing designers to quickly explore and change their designs. At this point, AI is best seen as a tool that can assist designers, rather than replace them.

New ideas can also be explored by pushing boundaries in a safe, cost-effective, and risk-free way – letting you see if an idea works well before any spades are put into the ground. Artificial intelligence is taking the world by storm, with almost every industry seeing new tools being created seemingly overnight. The aerospace sector is among the most highly regulated industries, with components having significantly smaller tolerances for error due to the extreme applications in which they are typically employed. With a few modifications, commercial-grade AI tools might be capable of creating components for crucial space missions. The hackathon’s goal was to explore the way designers change their approach when working with different tools.

• Mr Iorio says that generative design starts from the basis of design intent (i.e. what a part or design has to achieve) but can then generate many alternatives to come up with the desired outcome.
• Designers can input criteria such as brand guidelines, target audience, or design objectives, and AI algorithms can generate multiple design concepts or compositions to choose from, helping designers explore different possibilities efficiently.
• Executives and engineers from the two companies will participate in a series of onsite engagements to exchange ideas, learnings, and expertise.
• The productivity of the product development department in the design phase can increase immensely, as well as the gain in creativity and innovation if more time can be invested in researching conceptual designs.

With a great professional team and years of experience behind us, we are full of enthusiasm to assist and consult you throughout the entire process of generative design software development and beyond. When carefully planning each step, Magora is ready to predict and prevent a tiny error. Based on these factors, the cost of creating generative design software can range from tens of thousands of dollars to millions of dollars. The cost will depend on the specific needs of the project and the level of sophistication required for the software. It’s important to work with a skilled development team  and carefully evaluate the requirements of the project to accurately estimate the cost of development.

This can lead to more informed and effective design decisions that are made quicker and easier. While SiteSolve has been a great tool for many, we understand generative AI is not for everyone. Looking more generally, I’ve drawn up pros and cons to help you decide on whether to take the AI plunge. By analysing data inputs, such as artistic preferences, previous interactions, and contextual information, the system generates customised art pieces, exhibitions, and recommendations. This level of tailoring ensures that fans feel a sense of ownership and emotional resonance with the art, leading to a more profound and memorable experience.

The new tool has the potential to reduce the number of iterations needed to reconcile design and engineering considerations. For example, by gaining an early insight into how to reduce the drag generated by a new design, aerodynamics can be optimised – a key factor in maximising the driving range potential of battery electric vehicles. These legislations aim to introduce transparency and traceability into fashion supply chains. By definition, when this is completed, the supply chains will generate a massive volume of data – the precise requirement for effective machine learning (ML) and subsequent Generative AI.