Using Generative AI for Design and Engineering

In the ever-evolving landscape of technology, pivotal moments redefine entire industries. If Google’s search innovation made information universally accessible, ChatGPT is now setting the stage to redefine our interactions, touching upon diverse sectors with its vast capabilities.

ChatGPT has a nearly comprehensive understanding of data from the internet and is built on a solid foundation of pretrained transformers. This vastness has applications in every field, including business, science, marketing, and general understanding. Its influence is evident, whether it is in programming, consulting, or curating training materials (some of which ChatGPT generates on its own).

From our experience the introduction of Large Language Models (LLMs) like ChatGPT in design and engineering is a revolutionary development. It is an exciting and effective method to use text as the main medium for design, development, and simulation. The iterative prompting technique enables designers to zoom into the details with unmatched ease, whether they are envisioning a new machine component, selecting embedded system components, or even creating a product design.

Another amazing feature of ChatGPT is its ability to organize and improve information. Imagine yourself in a situation where you must extract crucial information from a technical datasheet, an academic paper, or any other dense document. GPT-4 plugins are skilled in extracting key information from PDFs, summarizing, and performing formula-based calculations. Numerous internet materials are available for anyone willing to use these skills.

The advantages of integrating ChatGPT with other design tools are only limited by one's creativity. ChatGPT appears as a coding ally with many design tools operating in batch mode and favoring well-known scripting languages like Python, TCL/TK, C, or occasionally Fortran. A well designed prompt can instruct ChatGPT to generate scripts tailored for systems such Altair Hyperworks, Ansys, LSDyna, ProE, Blender, Unreal Engine, and others. By using text prompts to generate variants helps speeds up the construction of designs and deftly configures simulations.

The excitement surrounding NVIDIA's text-to-3D model demonstrates the growing potential of generative AI in 3D creation. ChatGPT addresses the potential for combining creativity and efficiency for both radical idea development and repetitive tasks.

However, there is a learning curve with any approach, and some strategies to support this learning curve include

• Iterative Prompting: If the first prompt is inadequate, don't give up. Iteratively nudging ChatGPT frequently produces superior outcomes. To make informed decisions, it's critical that we have prior understanding of how these software products operate in batch mode (through scripts).
• The Key Is Vigilance: It's crucial to distinguish between truthful and false responses from GPT. You won't be misled by inaccurate results if you have a solid understanding of the issue.
• Divide and Conquer: Breaking down problems into smaller steps rather than trying to find a whole solution all at once frequently results in more precise and thorough responses.

Described below is a simple example using Blender to help illustrate the concept in more detail . We construct two cylinders using text prompts, stack one on top of the other, and combine them using a Boolean operator. Additionally, we have arranged this merged solid in a 4x4 arrangement.

It would be fantastic to hear about your experiences from designers and engineers in the community as we continue to research and learn more about applying Generative AI in engineering design and evaluation. As we continue to explore ChatGPT's and other LLM applications' disruptive potential in the field of engineering, your observations and suggestions will be of the utmost value. More information on the technical and design applications we've worked on into will be shared, soon.

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