• Demand for Higher Quality Parts and Products to Drive Generative Design in Industrial Applications to US$44.5 billion by 2030
• ABI Research’s forecasts that, Generative Design in Industrial Applications will grow to US$10.8 billion by 2022 and US$44.5 billion by 2030 with a CAGR of 24%.
Generative design enables the design of lighter, cheaper components while maintaining strength and solidity, ultimately creating a higher quality product. For that reason, the adoption of generative design software is taking off in aerospace & defense, automotive, footwear & clothing, furniture, industrial machines, and oil and gas industries and leading to an uptick in engineering throughput in each of them, finds ABI Research, a market-foresight advisory firm providing strategic guidance on the most compelling transformative technologies.
“The rise of the sharing economy and additive manufacturing will drive both demand for and the ability to produce higher-quality goods,” explained Pierce Owen, Principal Analyst at ABI Research. “Generative design expands design possibilities by creating shapes different from those that humans would create. It idealizes the design by creating something that best fits the constraints to optimize the products for various requirements.”
For several years now, industrial companies have used geometric topology optimization – eroding the geometric shape of a product given a set of constraints to improve performance – and are using it more as the use cases for additive manufacturing have increased. Generative design takes that another step by creating, or generating, the geometric shapes from an engineer’s requirements rather than changing existing shapes. Also, unlike topology optimization, generative design creates many iterations, variations and/or alternatives for engineers to compare, rather than simply removing unnecessary pieces or particles. Additive will drive both generative design and topology optimization as it provides greater build freedom to fulfill a wider variety of designs.
Traditional Computer-Aided Design (CAD) vendors such as Dassault Systèmes, Siemens and recently, PTC, already have or will have embedded generative design capabilities within their CAD environments as plug-ins or kernels. PTC acquired Frustum in November 2018 to do exactly that within Creo. Several of Siemens products, including NX, NX Nastran, HEEDS, Capital, and Simcenter 3D generate and validate designs in the context of constraints with a combination of Artificial Intelligence (AI), rules-based algorithms and GPU processing.
Dassault Systèmes brings generative design together with traditional CAD, convergent modeling, topology optimization, physics-based design and simulation within its CATIA product. Additional Dassault Systèmes SolidWorks has an exclusive partnership with DM Labs Live Parts, generative design software from Desktop Metal that “grows” parts based on the way embryos and cells grow. Some design engineering consultancies, such as GRM Consulting, also offer their own generative design software.
The pricing for generative design tools, modules, and products varies wildly depending on the business model, the computing hours, and the brand. For newer, standalone products, subscriptions can cost as little as US$1,000/year for 10 hours of computing power per month. For larger established brands with a full CAD product, that product with all its modules can cost as much as US$65,000 per year. As a result, the generative design software market has brought in about US$3.5 billion in 2018, but according to ABI Research’s forecasts that will grow to US$10.8 billion by 2022 and US$44.5 billion by 2030 with a CAGR of 24%.
But, to meet and exceed the complex demands of many different industries and scale adoption, generative design vendors need to understand the challenges of the engineers that use their software, set expectations for how the software can help, and provide holistic solutions. “Generative design can bring spectacular advantages to complex designs or organizations that need more throughput from engineering departments. The downside is a steep learning curve, and some engineers find it a difficult change to start using generative tools. Vendors should try to avoid disrupting the way engineers work and simply look to provide a tool to make them more productive and reach better final designs faster,” concluded Owen.
These findings are from ABI Research’s Generative Design in Industrial Applications report. This report is part of the company’s Smart Manufacturing service, which includes research, data, and Executive Foresights.
