Generative design takes significantly less time to generate multiple solutions while reducing human bias, says Vandhna Babu.

Generative design revolutionises products in the way that they are made. It is a computer-aided design technique and category of software in which 3D models are created and optimised by cloud computing and AI.

In the overall process, an engineer first sets up various requirements for the model, which can include the various manufacturing processes, loads, and constraints, and the software offers multiple designs that meet those requirements. It uses the power of AI to develop new high-performance design iterations that help solve complex challenges, reduce component weights and manufacturing costs, scale customisation, and optimise performance.

It differs from the traditional design, where the process begins with a model based on an engineer’s knowledge (e.g., CAD, CAM). Generative design begins with design parameters and uses AI to generate the model. The better the engineer defines these different criteria, the better the system can discover possible solutions. By modifying the design parameters in an increasingly refined feedback loop, engineers can find highly optimised and customised design solutions.

Applications

Applications are in various industries such as aerospace, automotive, architecture, manufacturing, consumer goods, among others. For example, in the automotive industry, engineers utilise generative design to reduce component weights, improve weak design areas, decrease production costs through component consolidation, and reduce the time to market for new products.

WHILL, a Japanese electric vehicle manufacturer, wanted to make a more portable motorised wheelchair by reducing the weight of its heaviest part – the battery case. Using Autodesk’s Fusion 360 generative design software, the company created new design that reduced the battery case weight by 40%

New Balance, a footwear manufacturing company, has extensively used generative design and 3D printing and developed a midsole that would help a runner achieve better results.

What problems does generative design solve?

Product differentiation – Generative design helps in creating high-performing new parts and products that exceed requirements and not easily duplicated by the competition.

Expert results – There is no need for special training and skilling. Even entry-level mechanical engineers can create using generative design without extensive knowledge. And while selecting from a myriad of suitable options and solutions, artificial intelligence, included with generative design technology, can quickly help filter the different solutions and design options.

Reduced product costs – Generative design can save money by rooting out over-designed parts. These are designs that, while reliable, may use more materials or complex manufacturing methods than necessary.

Optimised reliability – Carrying out stress analyses on the generative design results help validate the quality and durability of the designs. This ensures the reliability and durability of the part.

Faster time to market – Generative design takes significantly less time to generate multiple solutions while reducing human bias.

Some popular software programs used by engineers around the world include Fusion 360 from Autodesk, Creo Generative Design from PTC, nTop Platform from nTopology, NX from Siemens, MSC Apex Generative Design from MSC Software, among others.

The accompanying images are examples of how some parts may look like using generative design.

Article Courtesy: NASSCOM Community – an open knowledge sharing platform for the Indian technology industry: https://community.nasscom.in/index.php/communities/engineering-research-design/what-generative-design-engineering

Images courtesy of Formlabs.

Sources:

1. https://www.ptc.com/en/blogs/cad/beginner-guide-generative-design

2. https://formlabs.com/asia/blog/generative-design/

3. https://www.autodesk.com/design-make/emerging-tech/generative-design

4. https://www.ptc.com/en/technologies/cad/generative-design

Vandhna Babu is Principal Analyst – Research at NASSCOM. Her areas of interest include ER&D, Engineering, Digital, Automotive, Aerospace and Defence, 5G, Cloud, GCC, etc. 

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