Generative Design Algorithms for Product Development

Blog • 3D Printing

Product development often involves complex optimization challenges. Design teams tackle these tasks iteratively, refining an initial concept through engineering analysis and adjustments. Yet, each iteration requires time and resources, limiting the number of design refinements possible within the project timeline. This constraint means teams rarely have the flexibility to explore alternative solutions beyond their initial assumptions, leading to a final design that may not be fully optimized.

Discover the value of algorithmic generative design and how it can change this process!

Digital simulation and analysis operate so rapidly that designs can be evaluated in seconds. Algorithms automatically adjust part geometry between simulations, eliminating the need for manual refinements. Leveraging artificial intelligence, advanced generative design systems can explore an expansive range of potential solutions, running thousands of simulations to pinpoint a design with the most desirable characteristics.

For certain engineering challenges, generative algorithms now surpass human teams in performance. They can also uncover unconventional solutions that traditional methods might never reveal.

Generative design algorithms are most commonly used for structural optimization, creating parts that deliver the necessary strength, stiffness, and fatigue resistance with minimal material. These applications are essential in weight-sensitive designs, such as internal structures for handheld tools (enhancing ergonomics), sports equipment (boosting performance), and vehicles or aircraft (reducing fuel consumption or increasing payload). For products where material costs drive expenses, improving structural efficiency also offers significant savings in both cost and sustainability.

In automotive, aerospace, and sporting goods industries, generative algorithms have achieved impressive reductions—cutting part costs by 6 to 20 percent, part weights by 10 to 50 percent, and development times by 30 to 50 percent. For instance, a power-tool manufacturer used these algorithms to reduce a die-cast support bracket’s weight by 26 percent and its cost by 8 percent, all while maintaining compatibility with the larger assembly. In another example, generative optimization helped cut the weight of a major die-forged component by about 40 percent, removing a full kilogram from the final product.

Generative design and additive manufacturing (AM) are often considered ideal partners, as AM machines handle the complex, organic shapes these algorithms frequently produce. However, AM isn’t essential for implementing generative design. Today’s advanced generative systems can adapt to the constraints of various manufacturing processes. This flexibility broadens the types of parts generative design can optimize, enabling design teams to explore a wider range of manufacturing techniques easily.

Generative algorithms go beyond structural design. They’re already used in various engineering fields, including electrical and thermal design, fluid-flow optimization, optics, and acoustics. Architects and urban planners also adopt generative techniques to optimize building layouts and urban spaces. Additionally, similar algorithms tackle complex optimization challenges outside of traditional product design.

Strong design discipline remains essential: while generative design can produce innovative, non-intuitive solutions, engineers still need to validate these outputs through testing or analysis and confirm that each design aligns with the intended manufacturing process. As algorithms become more advanced and engineers fully harness these tools across applications, the collaboration between human expertise and machine intelligence will continue to progress.

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

One of the first hurdles is stakeholder acceptance of the resulting designs. Generative algorithms often produce parts that look vastly different from traditional, human-crafted designs—sometimes even appearing “alien” or unsettling to some observers. This reaction can limit internal acceptance, even when the designs offer technical advantages. Using generative design for customer-facing parts presents similar challenges, though some companies successfully leverage it to create products with a distinct, highly differentiated look.

Another significant challenge lies in company culture. Embracing generative design on a large scale could reshape a company’s talent needs, expertise, and resource allocation in product development. For instance, generative solutions may require less time from seasoned engineers and designers, leading to shorter development cycles. This shift prompts established companies to rethink organizational design and resource distribution while potentially lowering entry barriers for new competitors.

A third challenge involves process integration. Companies must assess how generative design fits within their engineering workflows, data platforms, and toolchains. With generative design technologies advancing rapidly, flexibility becomes essential. Companies will likely need adaptable systems that allow them to use diverse tools from various vendors, ensuring they can update and exchange design tools as technology progresses. Achieving this requires open, flexible systems and high agility across product development and IT functions. In the future, agility in algorithmic generative design will be key.

In the coming years, generative design algorithms will continue to advance, becoming more powerful, versatile, and user-friendly. With greater computing power, generative design will expand beyond individual parts, enabling the optimization of entire assemblies and, eventually, complete products.

Several forward-thinking companies are already moving beyond pilot programs, applying algorithmic generative design across their organizations. This shift requires investments in tools, training, and cultural adaptation. For those ready to commit, the potential gains in time-to-market, cost reduction, and product performance are substantial.

Links to Other Pages

• Best Generative Design Software: Discover leading generative design software options, revolutionizing design processes with innovative algorithms and capabilities.
• Creo Parametric: Check out 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: See the transformative impact of artificial intelligence (AI) in architecture, reshaping design and construction practices with innovative technologies.

TriStar PLM Solutions welcomes questions. Feel free to CONTACT US if you can’t find what you’re looking for, or call us at 800-800-1714