The Increasing Role of Artificial Intelligence in Engineering: Part 1

To some, artificial intelligence (AI) might seem like the latest high-tech buzzword being tossed around. But AI is more than the latest tech trend. AI is enabling new technologies and improvements that simply weren’t possible until recently, evolving the definition of what it means to be a product designer or engineer.

AI’s capabilities and potential are ushering in improvements that, from a business perspective, can help drive broader corporate goals due to increases in efficiencies and other time savings. That value alone proves the business case for investing in AI technologies. AI can help designers to reduce cycle times, operational costs, and environmental impacts while making improvements in light weighting and general innovation. This helps engineers deliver high-quality work in less time—while reducing potential frustrations and stressors over meeting business objectives.

As an engineer, adopting these tools and methodologies could be beneficial not only to your design process but also to your value as a member of your organization. AI is not a competitive force to the work engineers do, but rather a new tool to leverage and adopt into existing product development and design processes.

Artificial Intelligence and Generative Design

As engineers work to continually improve their designs, they gradually raise the bar on themselves in terms of expectations for the quality of their work. Improvements are dependent on the design tools available, deadlines, and human bias, limiting the scope of what can be accomplished in a reasonable timeframe. Engineers don’t have unlimited time to design. If they did, they could always make their designs better. With traditional methods, they need to deliver a robust solution so they can move forward and meet deadlines. After all, missed deadlines can have negative impacts on the business, including cutting back on engineering staff.

AI, specifically in the form of generative design, can add a lot of value to engineers’ work while taking significantly less time and reducing human bias. Generative design automatically creates models that meet given specifications and optimizes them according to stated goals. It produces multiple solutions simultaneously, all satisfying the given requirements, using the power of cloud computing.

This allows engineers to produce designs they wouldn’t have been able to create without the help of AI. Additionally, AI is unconstrained by human bias and limitations, like assumptions based on previous designs or personal preferences. Designers give the generative design tool the criteria they have, and it provides a unique answer that a human most likely wouldn’t have come up with.

AI techniques operate under guiding principles that closely mirror the human design process:

• Identify the problem to solve

• Define what makes one solution better than another

• Model the general form of the problem

• Gather, or synthesize, specific solutions to the model

• Determine how well each solution performs

• Adjust the model and produce new solutions

• Continue this cycle until satisfied

AI essentially elevates the breadth, depth, and precision of each step. In the case of product design, software such as Creo’s AI-driven generative design enables users to define their task through geometry, constraints, and loads—just as one would with FEA-simulation tools. The ideal outcome is defined through explicit goals and functional constraints. Most of the work is done within the generative design space, with set boundaries the optimized part cannot extend beyond.

AI simultaneously evaluates all simulation conditions at every point within the design space, weighing the benefit and cost of placing material at that location. By considering all points at once, the solution becomes a continuous field, and from this field a shape takes form. As the shape evolves, the system continues to improve the design until no other form can outperform its function.

Generative design allows engineers to continue to produce increasingly excellent work, meeting—or surpassing—heightened expectations in less time. Getting that time back provides engineers the freedom to better understand how to improve designs and solve use cases not by focusing on parts, but by understanding the entire system and being able to redefine the requirements of specific parts to optimize the result of the whole.

Stay tuned for Part Two of this discussion on the increasing role of AI in engineering, where we'll expand on how generative design helps accomplish the benefits described throughout this blog with a real-world example.