The generative design market enables engineers and designers to automate the design process using artificial intelligence and machine learning techniques. Generative design software helps generate thousands of design options by varying attributes like geometry, topology, material properties and other factors to optimize for key metrics like strength, weight, manufacturability and cost. This allows for parts and products to be conceived faster and more efficiently.
The Global generative design market is estimated to be valued at US$ 3.25 Bn in 2024 and is expected to exhibit a CAGR of 20% over the forecast period 2024 to 2031.
Key Takeaways
Key players operating in the generative design market are Altair Engineering Inc., Dassault Systèmes SE, Bentley Systems Inc., Caracol AM, Autodesk Inc., MSC Software Corporation (Hexagon AB), Diabatix, ANSYS Inc., Paramatters, Desktop Metal Inc. and nTopology Inc. The increasing adoption of AI and machine learning techniques across industries is expected to drive new opportunities for generative design software vendors. Technological advancements like generative design combined with additive manufacturing now enable the creation of perfect, lightweight structures that couldn’t be made before.
Market drivers
The growing demand to shorten product development cycles and reduce costs are a key growth driver for the generative design market. Generative design use of optimization algorithms help evaluate thousands of design options in hours compared to weeks or months with traditional trial and error methods. This has made generative design increasingly popular for developing components in industries such as automotive, aerospace, industrial equipment and healthcare where lightweight and high strength parts are critical. The ability to explore design spaces more comprehensively and discover innovative new solutions is fueling adoption of generative design worldwide.
Current challenges in Generative Design Market
Some of the key current challenges faced by the Generative Design Market Growth include lack of awareness, design complexity issues, integration challenges, data security concerns and software cost. As generative design is an emerging technology, there remains a lack of awareness among potential customers about its capabilities and benefits. This acts as a roadblock for greater adoption. Design complexity also poses challenges as generative design software needs to effectively handle complex product designs with multiple constraints and trade-offs. Integration of generative design solutions with existing CAD/CAM tools is another area of concern for end users.data security is important as generative design deals with large amounts of proprietary customer and product data. Initial software licensing and maintenance costs can be higher for generative design tools which impacts their adoption.
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