The integration of generative AI (GenAI) into tools such as cloud-based SaaS (software-as-a-service) products are driving dynamic transformation in the industrial automation space. The emergence of ChatGPT and a host of other GenAI breakthrough platforms opened the flood gates of potential applications for large language models (LLMs) trained on publicly available information and the standard prompt-response interaction pattern within industrial technologies. Since then, a number of partnerships have been and are being formed between industrial automation providers and GenAI platforms to develop tools designed to assist automation system developers.
These collaborations have developed prototype tools focused on creating an evaluation framework to assess and improve the responses generated by AI in the context of industrial automation. Such tailored AI solutions aim to improve productivity in control system design, expedite code creation, answer product questions, assist with debugging and generate innovative ideas.
Using GenAI for generative design Generative design, a longstanding approach in various tools, is experiencing a significant evolution with the integration of GenAI. While generative design itself is not new and has long used traditional AI to enhance system and product design, the introduction of GenAI brings a new dimension by incorporating human-in-the-loop capabilities. This evolution is transforming how engineers and manufacturers conceptualize, create and optimize automation technologies.
It's important to differentiate between existing generative design capabilities that use traditional AI and the emerging trend of integrating GenAI. Traditional generative design relies on pre-defined algorithms and constraints to generate design options, while GenAI introduces a more dynamic and interactive process. With GenAI, human expertise is combined with AI's generative capabilities, allowing for more intuitive and context-aware design iterations.
At its core, the integration of GenAI into generative design represents a fundamental shift in the creative process. Unlike traditional generative design methods that rely on AI algorithms alone, the addition of GenAI introduces a more interactive and iterative approach, where engineers can provide feedback and guide the AI system towards more optimal solutions. This allows them to explore vast design spaces and generate numerous potential designs based on specified parameters, constraints and performance goals.
This approach is particularly well-suited to the complex world of automation systems, where multiple variables and competing objectives often need to be balanced, especially in product development and optimization.
The generative design process
The generative design process begins with engineers defining the key parameters and constraints of the automation system they wish to design. These could include factors such as spatial limitations, load- bearing requirements, energy efficiency targets, material preferences and cost constraints.
Additionally, performance goals are set, which might encompass metrics like cycle time, precision, reliability and adaptability to different production scenarios.
Once these inputs are established, the AI-driven generative design system goes to work. It rapidly iterates through countless design possibilities, evaluating each against the specified criteria. The power of this approach lies in its ability to consider design options that a human engineer might never conceive, free from the limitations of preconceived notions or traditional design conventions.
For example, consider the process of creating a new physical product, such as a latch for a space shuttle door. Traditionally, engineers would model the product using tools like Autodesk or AutoCAD, create a prototype, run tests and analyze the results. The challenge then becomes how to efficiently incorporate these test results into design improvements. After initial modeling and testing, the AI could be prompted to generate multiple design alternatives based on the test results. For instance, if a performance parameter is suboptimal, AI might suggest five different ways to enhance the design, complete with explanations for each option.
