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The Future Of Making

Solve Budget Constraints
and Design Challenges with
Generative Design

Explore how cost-conscious generative design can now help engineers keep within budgetary constraints while producing innovative designs

Generative design uses biomimicry and other algorithms to create designs that are innovative, but wildly different from traditional geometries. Biomimicry approaches imitate the models, systems, and elements found in nature to produce and evaluate different alternatives; and iterating through these designs to identify the best solution. The results often incorporate organic and natural-looking shapes, some of which are outside the bounds of human imagination and which may look just plain weird. But these designs work, incorporating unquestionable innovation by exploring many valid geometric options within specified material, operating and manufacturing requirements. However, to date, generative design has failed to take one key constraint into account: cost.

In this post, we examine the balancing act between cost and innovation that most engineers must manage, explain how cost-conscious, next-generation generative design can help engineers keep within budgetary constraints, and reveal the benefits a cost-centric approach can bring to the design process.

Pie-in-the-Sky Designs

Engineers are often perfectionists, working around the clock to improve their designs to the nth degree. This is no bad thing, after all, problem-solving is second nature to many engineers. They want to find the best solution to the problem they’re working on, within the requirements they’re given. The problem, of course, is that they have to work with competing constraints, for example, having to minimize both manufacturing costs and weight. This balancing act is a highly resource-intensive process, which takes time and often causes project costs to skyrocket.

Generative design produces innovative design concepts. Courtesy of General Motors

Generative design produces innovative design concepts, regardless of their feasibility. Luckily, additive manufacturing can make the production of whole new classes of designs practical from a small-scale manufacturing standpoint. However, additive techniques are not always available or viable for large-scale production, and engineers must find an alternative way to use traditional manufacturing methods to produce these novel geometries. This is where most generative design capabilities are inadequate, producing designs that are only good for additive manufacturing. But generative design capabilities cannot work within the constraints of subtractive manufacturing required to enable large-scale production.

Cost-Conscious Generative Design

Cost-conscious generative design incorporates costing into the generative design process from the outset. Engineers can balance the cost of different manufacturing processes, materials and geometries, to apply the right trade-off and make a design practical for real-world manufacturing environments. The costing capabilities of generative design evaluate common manufacturing processes, including additive and subtractive methods. They combine this information with regional economic data, including labor rates, overhead costs, and material prices, to provide a robust cost analysis. When evaluating a specific manufacturing method, engineers can input the production volume, material details, and geometry to obtain an estimated cost.

Next generation of generative design provides a robust cost analysis. Courtesy of Claudius Peters

The next generation of generative design leverages this costing capability as a controllable outcome. The software produces a design and then assesses the cost, which is tracked and measured as the solution generates a large number of potential designs. Companies can then explore the resulting outcomes in terms of the median and range of manufacturing costs, filtering by different objective ranges and sorting by the median estimated cost.

Generative design is now allowing innovation and cost
to live in perfect harmony

Automation is creating a new class of digital assistant, enhancing the capabilities of the world’s knowledge workers. Today’s generative design solutions serve as an idealistic sidekick for the engineering community, fueling innovation but also failing to constrain costs. Generative design must be reined in, otherwise, engineers are prone to get carried away, creating innovative and novel designs without regard for the financial and practical implications of manufacturing the final product.

Generative design allows comparing costs and manufacturing methods. Courtesy of MJK Performance

This is why cost constraints are now included in groundbreaking, next-generation generative solutions, changing the dynamic and allowing engineers to take a pragmatic approach to their designs. Instead of creating brilliant but infeasible designs, they can design smart, on-target products and parts. As a result, generative design ticks two important boxes: providing an automated source of innovation and allowing companies to take into consideration the cost of these innovative ideas, to make the right design choices. Next-generation generative design is now a highly valuable simulation tool, allowing innovation and cost to live in perfect harmony in the digital domain, so that organizations can create the best products for the real world.


Generative design, to date, is innovative, but creates wildly different designs without taking cost into account. That's a problem, because most designs are only suitable for small-scale additive manufacturing techniques and not the large-scale subtractive techniques required for practical production.

Generative design fuels the idealistic side of engineers, allowing them to create increasingly complex, but sometimes unfeasible, geometries. When engineers employ generative algorithms to take cost into account, it often hampers innovation and adds time to the design process.

Next-generation generative design allows engineers to assess cost, treating it as a variable that’s tracked across the design process. This cost-analysis strategy allows engineers to evaluate costs across common manufacturing techniques and using regional economic information. They can input the production volume, material details, and geometry to obtain an estimated cost and explore the resulting outcomes.

Generative design has long been an idealistic virtual assistant but it is now getting reined in, allowing engineers to create smart, on-target designs. As a result, generative design is now a source of innovation as well as pragmatic efficiency for the engineering community.

Generative Design Resource Center

Explore a variety of generative design manufacturing customer stories, articles, ebooks, whitepapers, and infographics.

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