Registered users can unlock up to five pieces of premium content each month.
Beyond Topology Optimization |
NEWS |
Industrial companies have used geometric topology optimization for several years now and are using it more because they have increased the use cases for Additive Manufacturing (AM). Topology optimization involves changing the geometric shape of a product given a set of constraints to improve its performance. Generative design takes this up a step by creating, or generating, the geometric shapes from nothing rather than changing the existing shapes.
Generative engineering takes this to another level. It not only creates the geometric shape but can also create entire systems architectures and fit the electrical systems. Let’s take a car as an example. Given the right constraints and rules, generative engineering can design the architectural configurations for the chassis, the power train, the mechanical structures and components, the wiring harnesses, and the electrical system. Of course, like any form of Artificial Intelligence (AI), it works best when guided by a domain expert or, in this case, a real-life human engineer.
Expanding Design Possibilities |
IMPACT |
Generative design and generative engineering expand design possibilities by using AI to create shapes different from those that humans would create. It idealizes the design by creating something that best fits the constraints to optimize the products for various requirements. Still, this does not matter if the company cannot build the product in the real world. The generative engineering software must also provide or integrate with software that provides production simulation to ensure buildability.
In general, AI-designed products require AM to capture the complex geometries. The designs often require further refining for traditional manufacturing. This means that generative tools provide the most value for products that use AM but can still provide incredible value to those that do not use AM.
Also, some vendors provide tools to consider all the designs and processes available for cost purposes. This empowers manufacturers to figure out the business case for a new design. If the cost exceeds the value of the design, the manufacturer can tweak the constraints, such as the materials, to get to a favorable cost/value balance.
Some of the best vendors will also allow for integration with Internet of Things (IoT) product data. This opens a feedback loop whereby the manufacturer can use data from its products in the real world to train the generative networks for future designs. Many companies have started IoT initiatives for this very reason.
Recommendations for Generative Engineering Vendors |
RECOMMENDATIONS |
To flexibly adapt to industrial clients’ needs and to help them achieve scale, generative engineering vendors must:
Following this strategic guidance should help generative engineering vendors and their clients implement scalable solutions.
For more insights and perspectives on manufacturing and the industrial Internet, please check out ABI Research’s Smart Manufacturing service.