AI Spectrum – Examining the Benefits of AI Powered Generative Design – Summary
The capabilities of AI are growing every day, and with that growth comes new opportunities for applying it. One opportunity being explored is AI’s role in product design, specifically with how it can help enable generative design and generative engineering. When powered by AI, generative design offers the ability to explore a broader design space and revolutionize the design process. To delve deeper into the transformative power of AI-driven generative design, Tod Parrella, Senior Manager of the NX Industry and Design Product Management Team, joins Spencer Acain on the AI Spectrum Podcast. Together they explore how generative design transforms the role of engineers, the challenge of getting people to introduce AI into the design processes, and the industries already implementing generative practices.
Using AI to enable generative design has immense potential to accelerate the design process. Generative design itself already offers engineers ways to make product design more efficient by generating hundreds or even thousands of iterations based on an engineer’s requirements, including materials, manufacturing, and more. Traditionally, engineers must choose a methodology to approach their design with, which can be difficult to ascertain when taking the different considerations for macro parameters of the larger design and the micro parameters of localized subjects into account. Introducing AI to generative design allows the system itself to examine the different design methodologies available, learn from previous design iterations, and choose the best approach for the engineer. Spencer aptly describes this as “optimizing the problem of optimizing your design.” With the AI selecting the methodology and performing the iterations, generative design offers not just greater efficiency but the ability to examine many more possibilities than before.
This new form of automated design also changes the roles of engineers in the design process, turning them more into orchestrators. Shifting the responsibilities of choosing design methodologies and generating designs onto algorithms grants engineers significantly more free time to consider what they are trying to achieve with their products. They can spend more time defining the correct set of requirements for their product’s development rather than having to design multiple iterations of the same product themselves. Instead of pushing numbers and performing tests, engineers can get back to being engineers.
Of course, getting people to adopt AI into their design process is a challenge on its own due to a lack of confidence in automation. Poor examples of AI and machine learning in the past have not made AI appealing to some people in industry. In Tod’s eyes, however, the key to convincing people otherwise is to base your AI technology on proven real-world successes. This is the strategy he and his team commit to, and by having their AI learn from proven successes, the technology has given better performances and outcomes in products. That being said, the human factor will never be completely removed from the design process. After all, algorithms can still make mistakes, so it will be up to human engineers to validate and sign off on generated designs, a practice Tod does not see going away any time soon. With humans still well involved in the design process, in addition to teaching AI technology with proven successes, new adopters may rest easier when integrating automation into their product design.
Generative practices have already been adopted by several key industries, namely aerospace and energy production. The primary reasons for this, Tod explains, are how these industries have the significant engineering challenge of maximizing performance capabilities while optimizing fuel usage. This is especially critical as companies in these industries look for alternative power sources to account for the growing threat of climate change while retaining their outputs, in which generative design can be a useful tool. All in all, the adoption of generative practices has been a valuable tool for industries where even the smallest gain in efficiency could have a large impact on operating conditions or the cost of doing business. Of course, the benefits of generative design are not exclusive to these industries and can be leveraged by others in meaningful ways.
Generative design is already a revolutionary tool for accelerating product design in key industries and can only improve with the introduction of AI technology. While the wider lack of confidence in automation is still a challenge to overcome, the efficiency and free time that AI-driven generative design grants engineers is worth the undertaking. AI-driven generative design is only a single instance of how AI is transforming the landscape of engineering design and ushering in a new era in designing the products of tomorrow.
To listen to the full discussion, check out the podcast here.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries, Siemens Digital Industries Software is where today meets tomorrow.
