Generative design combines algorithms with specifications to provide a new level of automation for product design. As a result, designers can account for multiple rules, goals, and constraints during the initial stages of a project.
Generative design was first used by architects to study functional and aesthetic options for “signature” projects. Soon, manufacturing designers realized generative design offers a more streamlined approach, enabling the use of design and simulation solutions at the same time. The designer and the software can quickly review hundreds of options in search of the best way forward.
Today, newer technologies and design goals, including artificial intelligence (A.I.), additive manufacturing (AM), multi-objective optimization, and sustainability are influencing product design. The massive increase in processing power makes simulation more affordable and more prevalent, increasing its use in early design. All five areas can benefit from the use of generative design—and vice versa—turning overwhelming competing demands into an opportunity to take product innovation to another level.
Examining the intersection of generative design with these topics, there are several emerging trends. For example, A.I. applied to generative design allows for large-scale data analysis. Product performance data and previously stated design parameters become available. The A.I. tool can also search for relevant past successes and failures, using that knowledge to generate better designs. And information from previous iterations can be used to suggest new concepts, within an acceptable threshold, that still meet specifications.
AM can benefit from novel, complex geometries that are difficult or impossible to produce using traditional manufacturing methods. Some of these same complexities make lighter products possible.
Generative design can ensure the needs of AM are fully accounted for in the design process and, sometimes, suggests organic, complex shapes. AM is well suited to manufacture these designs, allowing the engineer to offer the most optimal design without needing to degrade it for the sake of manufacturability.
Multi-objective optimization is a complex aspect of product design that involves balancing competing design objectives. It is often represented by a set of solutions known as the Pareto front, which identifies tradeoffs between different objectives. Generative design can be used to explore many design options early in the process, and optimizing results against diverse goals.
Design for sustainability is an increasingly important aspect of product engineering. Using generative design for sustainability studies allows for material efficiency, lifecycle assessment, the use of renewable and recyclable materials, and energy efficiency to all become initial design parameters.
Simulation and generative design are digital soulmates. They both aim to solve the “what if” factors in design. When enabled by A.I., or matched with the specifications of AM or sustainability, simulation as part of a generative design process makes sure all the elements of physics important to the product are accounted for from the start.
Generative design is a game-changing technology transforming the product design industry. The ability to account for multiple rules, goals, and constraints during the initial stages of a project, along with the integration of newer technologies and design goals, enables designers to quickly find the best possible balance between competing objectives. Generative design, when paired with A.I., AM, multi-objective optimization, and sustainability, is opening the door to greater product innovation.
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