What is generative design? | Definition from TechTarget


What is generative design?

Generative design is a computer-aided design technique and category of software that uses AI to optimize the design process. Engineers can use generative design software to quickly iterate through many design alternatives and pick the best one according to a set of criteria.

The technique — sometimes referred to as a design exploration process — is used in many fields, including architecture, aerospace and construction. It helps engineers in these fields generate more ideas, generate ideas faster and bring products to market more quickly.

Although the term is most commonly applied to engineering and computer-aided design (CAD), generative design can also apply to artwork. Tools such as Dall-E, Midjourney and Stable Diffusion design realistic art from simple text prompts. Generative design is just one variant of generative AI.

How does generative design work?

Traditional design begins with a model based on the engineer’s prior knowledge. Generative design does not require prior knowledge. The designer or engineer simply needs to define the problem in terms of parameters, and the software will generate solutions. Generative design occurs in three main steps:

  1. Briefing. Engineers or designers input parameters such as spatial requirements, material requirements, cost constraints and performance requirements. They also input overall design goals.
  2. Ideation. The software generates several solutions to the problem, aiming to reach the designer’s goals according to the specified parameters.
  3. Evaluation. The designer or engineer evaluates the generated solutions and chooses the best fit for the problem.

Generative design can mimic the way nature designs systems and structures. This concept is known as biomimicry.

Generative design software uses machine learning in conjunction with user input to improve upon previous iterations. It also draws on data provided by cloud computing to inform its designs.

What is the difference between topology optimization and generative design?

The term generative design is often used interchangeably with topology optimization. However, the two concepts are different.

Topology optimization is an older technique that uses a human-designed CAD model to generate a single optimized model for the engineer. The engineer provides specific loads and constraints, and the software generates a model by optimizing material layout according to the loads and constraints.

Generative design does not need a human-generated design to start. The engineer needs only to feed the software a set of constraints, and the generative design software will come up with many possible designs. It automates the ideation component of design.

Generative design use cases and applications

Engineers can use generative design software to do the following:

  • Create new part designs.
  • Create more efficient parts.
  • Create parts more quickly.
  • Create parts at a lower cost.

Generative design has a variety of use cases across industries. Some examples include the following:

  • Manufacturing. In manufacturing, generative design can be used to find new ways to reduce component weights. It can also be used alongside 3-D printing and additive manufacturing — i.e., the industrial name for 3-D printing.
  • Automotive. In the automotive industry, generative design can be used to explore new geometries and forms for auto parts.
  • Aerospace. Generative design can be used to create new aircraft designs with the goals of improved environmental impact, safety and weight reduction.
  • Architecture. Generative design can be used to create more sustainable and resilient buildings, as well as civil feature designs.
  • Consumer goods. Everyday consumer products, such as sports equipment, can be designed and manufactured to improve performance and to lower cost.

Here are some examples of generative design software applications:

  • Creo by PTC.
  • Fusion 360 by Autodesk.
  • MSC Apex Generative Design by Hexagon Software.
  • NTopology.
  • NX by Siemens.

What are the benefits of generative design?

The benefits of generative design include the following:

  • Simultaneous exploration. Generative design tools can explore many design iterations — sometimes hundreds of thousands — at once.
  • Mass customization. Generative design tools — in conjunction with industry 4.0 techniques such as additive manufacturing — enable efficient mass customization of products and parts through increased efficiency. It’s easier to try a new design or modify specifications.
  • Faster design timeline. Generative design automates some of the rote, time-consuming activities involved with ideation and design. Engineers and designers can focus more on continuous improvement and defining problems more specifically.
  • More design options. Generative design tools create several model options for engineers to choose from.
  • Shallow learning curve. Generative design gives entry-level designers an advantage by enabling them to create something that works from the start. With some previous knowledge of CAD, generative design is relatively easy to learn.

What are the limitations of generative design?

The limitations of generative design include the following:

  • Bias. AI algorithms may do a better job of obscuring bias and making it harder to detect.
  • Quality. Generative design software can create a large quantity of design options. This can make it more difficult to determine which generated designs are quality and which are not. The software doesn’t inherently know the difference.
  • Paradox of choice. If the problem isn’t well defined, the paradox of choice can make deciding on the right design more difficult. More choices can distract from what is most important to solving a problem. In generative design, many of the designs meet the original criteria laid out by the engineer.
  • Guiding the software. The designer still must provide the correct parameters and constraints to the software for its generations to be valuable.

Examples of generative design

Some theoretical example problems that generative design could be applied to include the following:

  • Designing a stadium so that it can fit many people and so that everyone has a good view of the event.
  • Designing a countertop to minimize the amount of waste in production.
  • Designing a more sustainable and resilient building.
  • Reducing the weight of an aircraft engine bracket.
  • Making an electric wheelchair more portable.

Some real-world example projects that used generative design include the following:

  • Moon lander. NASA used Autodesk’s generative design software to create a next-generation moon lander. The lander is more lightweight than traditional landers.
  • Resource-efficient chair. Philippe Starck designed a production chair using generative design. His goal was to create a chair that used as few materials as possible; the result was the first chair designed by AI.
  • Walking car. Hyundai Motor Group designed the Hyundai Elevate, a concept for a car that can walk as well as drive, using generative design.
  • Resource-efficient shoe. New Balance used generative design to create an internal lattice of a shoe’s outsole with a more organic design.
  • New building design. Zaha Hadid Architects used generative design to create the Heydar Aliyev Centre. The building resembles a fluid form that emerges from the natural topography of the landscape.

What is the future of generative design?

Generative design vendors will continue to strive to provide software that aids engineers in their pursuit of new and improved product and component designs. Currently, generative design software is one tool in an engineer’s arsenal. Over time the technology will continue to improve, creating new opportunities and uncovering new obstacles. Generative design will continue to be used to create objects that are more efficient, cost-effective and aesthetically pleasing.

Generative design is just one application of artificial intelligence in manufacturing. Learn the other ways AI can streamline manufacturing processes and increase efficiency.

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