Generative Design Algorithms for Product Development

Blog • 3D Printing

Most product development tasks are complex optimization problems. Design teams approach them iteratively, refining an initial best guess through rounds of engineering analysis, interpretation, and refinement. However, each iteration takes time and money, and teams may achieve only a handful of iterations within the development timeline. Because teams rarely have the opportunity to explore alternative solutions that depart significantly from their base-case assumptions, too often, the final design is suboptimal.

See why algorithmic generative design is important!

Digital simulation and analysis are so fast that designs can be evaluated in seconds or less. Algorithms can automatically adjust the geometry of a part between simulations, with no manual refinement required. Using artificial intelligence techniques, these new generative design systems can explore a much larger universe of possible solutions, comparing the results of thousands of simulations to close in on a design that delivers the most favorable attributes.

For some types of engineering problems, generative algorithms already outperform human engineering teams. Furthermore, they can produce non-intuitive solutions that may never have been found using traditional processes.

Today’s most common use for generative design algorithms is structural optimization, creating parts that provide sufficient strength, stiffness, and fatigue resistance with minimum material. Such applications are common wherever weight is a primary consideration, such as in the design of internal structural parts for handheld tools (to improve ergonomics), sports equipment (to enhance performance), vehicles and aircraft (to reduce fuel consumption or increase payload), or any product where shipping weight is a significant cost driver. When the material is a primary cost driver, greater structural efficiency can lead to substantial savings from a cost and sustainability perspective.

Across industries ranging from automotive to aerospace to sporting goods, generative algorithms have reduced part cost by 6 to 20 percent, part weight by 10 to 50 percent, and development time by 30 to 50 percent. (Exhibit) A power-tool manufacturer, for example, reduced a die-cast support bracket’s part weight by 26 percent and its cost by 8 percent without affecting the interface between the part and the larger assembly. For a significant, die-forged component, generative optimization yielded a weight reduction of around 40 percent—subtracting a whole kilo from the finished product.

Generative design and additive manufacturing (AM) technologies are often seen as natural partners since AM machines cope well with the complex, organic shapes that often emerge from such algorithms. Yet AM is hardly a requirement in implementing generative design: the latest generative systems can be configured to account for limitations in manufacturing processes. That flexibility expands the range of parts that generative design can target while making it easier for design teams to evaluate alternative manufacturing techniques.

Moreover, generative algorithms are not limited to structural design tasks. The approach is already being applied to other engineering domains, such as electrical and thermal design, fluid-flow optimization, optics, and acoustics. Architects and urban planners are even adopting generative techniques to optimize the layouts of buildings and city spaces. Similar algorithms are applied to complex optimization problems arising outside product design.

For today’s product-development leaders, generative design technologies present significant cultural, organizational, and competitive challenges.

One of the first barriers is likely to be stakeholder acceptance of the resulting parts and products: generative design algorithms produce designs that may radically differ from their human-designed predecessors. Some observers even find them “alien” or disturbing. That can hinder internal stakeholders’ acceptance of generative solutions, even when the proposed designs are technically superior. Using generative design for customer-facing parts creates similar acceptance challenges, although some companies are already capitalizing on the approach to create products with a unique and highly differentiated appearance.

A second major challenge is company culture. The large-scale adoption of generative approaches could change a company’s talent, know-how, and resource requirements in the product development function. For example, generative solutions may involve less time from experienced engineers and designers, enabling shorter product-development cycles. That raises questions about organizational design and resource allocation for established players and potentially lowers the barriers to entry for new competitors.

The third set of issues concern process integration. Companies must consider how generative approaches mesh into existing engineering processes, data platforms, and toolchains. The rapid development pace of generative design technologies means companies will likely need more flexibility to use different tools from different vendors, with the ability to exchange and upgrade their design tools as technology evolves. That requires open, adaptable systems and high-level agility in product development and IT functions. Algorithmic generative design agility will be the future.

In the coming years, generative design algorithms will continue to evolve, becoming more powerful, more widely applicable, and easier to use as algorithmic generative design improves. As additional computing power becomes available, it will be possible to extend the approach beyond the part level to permit the optimization of assemblies and, ultimately, complete products.

Several leading companies are already taking algorithmic generative design beyond the pilot phase and applying it across their organizations. That requires investment in tools, education, and cultural change. However, for those willing to commit, the positive effects on time to market, cost, and product performance are likely to be significant.

Links to Other Pages

• Best Generative Design Software: Discover leading generative design software options, revolutionizing design processes with innovative algorithms and capabilities.
• Creo Parametric: Explore Creo Parametric, a powerful 3D CAD modeling software known for its robust capabilities and comprehensive design tools.
• ANSYS Design Optimization for Engineers: Learn about ANSYS Design Optimization, empowering engineers to enhance product performance and efficiency through advanced simulation-driven design.
• 3D Model Optimization With Creo 7: Optimize 3D models effectively using Creo 7, leveraging advanced tools and features for streamlined design processes and improved product outcomes.
• AI in Architecture: Design and Construction: Explore the transformative impact of artificial intelligence (AI) in architecture, reshaping design and construction practices with innovative technologies.

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