As the growth of AI continues to impact daily life, many engineers and technical professionals are seeking ways to implement AI into workflows. For mechanical engineers, potential benefits abound in numerous areas, ranging from design to manufacturing and facility maintenance. But even with potential benefits on the radar, some are hesitant to dive in, wondering how to get started with AI. Others wonder how to maintain control of projects and workflows as an engineer — guiding input and verifying output while addressing data security and data ownership issues.
Note: This is the final segment in a three-part series of articles on artificial intelligence for mechanical engineers. In the first article, we explored how AI can be a partner, rather than a threat, for mechanical engineers seeking to reap the benefits of AI. In the second article, we explored practical ways to implement AI in mechanical engineering workflows. In this article, we explore how to become more forward-thinking in AI..
Image source: fadi/stock.adobe.com.
For mechanical engineers seeking to become more AI-savvy, many standard engineering and scientific principles still apply. Productive AI implementation includes common methodical steps such as: defining a problem, gathering data on the problem, forming and testing hypotheses, collecting and analyzing output data, and reporting results.
However, AI also requires some new ways of thinking, such as considering big-picture scenarios in a top-down manner, and letting AI do tedious analytical work that has historically consumed much of engineers’ time. To develop these new ways of thinking, engineers can take proactive steps, such as enhancing their AI literacy, identifying AI-appropriate tasks, and cultivating an AI-forward mindset in their organization.
As with any new technology, AI requires training and education. Engineers don’t have to be AI experts, but they should understand the basics of AI — how the various tools work, as well as how to develop meaningful prompts, manage data, and avoid common pitfalls of AI.
“If you understand the basics of how AI works — what it can do, what it cannot do — you are better off,” said Dr. Shrikant Savant, Data Analysis & Science Director, with SOLIDWORKS. “You can then avoid having unrealistic expectations from AI tools.”
To learn how AI works, some have made headway by simply diving in and using publicly available tools such as OpenAI’s ChatGPT or Google’s Gemini. By crafting prompts for these tools, you can use AI to explore hypothetical questions, generate text, create images, and accomplish a wide variety of other tasks.
AI-based tools such as the SOLIDWORKS Design Assistant can also help you learn how AI works. The Design Assistant includes a collection of tools with built-in machine learning algorithms that learn from you as you design, offering suggestions based on your workflows.
The SOLIDWORKS Design Assistant uses AI technology to suggest results based on individual workflows. Image source: SOLIDWORKS. Click image to enlarge.
For those seeking more structured training, a variety of courses are available. Coursera, an online platform founded by AI gurus Daphne Koller and Andrew Ng, offers a variety of AI courses and certifications. edX, a consortium of universities and private companies, provides online learning opportunities in AI and other technical fields. Amazon, Google, Microsoft, and other tech firms also offer AI courses, as do training-focused firms such as Udemy and Udacity.
A growing number of universities and technical schools are including AI coursework, but the dynamic nature of AI makes it difficult for large educational institutions to keep up with the latest technology. “This is such a fast-evolving field that things become obsolete very quickly,” noted Savant.
Another key to AI success is identifying tasks that benefit from AI. Because AI can help automate mundane tasks and improve an organization’s overall efficiency, engineers can focus on guiding the process — identifying potential areas that could benefit from AI, outlining the basic workflow, then letting AI do the heavy lifting. In these scenarios, AI is not replacing engineers — it is augmenting them and freeing them up to do more critical thinking.
“It requires a different mindset,” said Savant. “The engineering role needs to change from bottom-up to more of a top-down approach, where you focus on the high-level task and goals of a project, then the lower-level tasks can be done quickly by AI agents. Engineers should learn to leverage these tools.”
As an example, Savant cited a programming project that would require significant human time commitment using a traditional approach. With the help of AI, the team could define basic architectural units, and an AI agent could generate code for each unit. The agent also could help troubleshoot issues and develop fixes, with humans overseeing the process. “Humans can focus on the higher-level concepts and applications. This is where humans still do better than AI agents,” noted Savant.
Human verification of agentic work is imperative to keep the project on target and successful. Image source: Emanuel/stock.adobe.com.
With engineers maintaining control over projects, coding and other programming tasks can often be performed by AI tools such as Windsurf and Cursor. These and other AI-based tools are designed to allow humans to define a problem and key parameters, then let the AI tool generate the code, while keeping humans involved in the progression of program development.
Another area where AI offers promise is in design of products or processes where multiple decisions need to be made. The implications of these decisions often require detailed analysis, which can be time-consuming. AI can help handle some of the detailed analysis, freeing up engineers to consider multiple design alternatives or more creative concepts.
“When you are designing something as a mechanical engineer, any design changes or design decisions that you make have implications on various aspects of that product,” said Savant. “How you manufacture the product, how you package it, how you deliver and deploy it. Then, what's the life cycle of the part, how do you discard it, and what's the sustainability? All these things are inter-dependent.” With this and other complex problems, AI, coupled with human oversight, can help solve problems more efficiently.
Human involvement is also key in managing data used for AI modeling. While guiding AI processes, engineers should also be involved in verifying that input data is drawn from appropriate sources — whether internal data based on an organization’s previous projects or external data tapping into various third-party and public sources. And after generating results with AI, engineers play a key role in reviewing and validating output data for accuracy and suitability.
Along with training and identification of AI-appropriate projects, engineers can help cultivate a more forward-thinking environment in several ways. Again, many of these concepts are similar to those already practiced by successful engineers, though with a slightly different twist.
Benefits of AI are more likely to be achieved by those who are curious, willing to experiment, and open-minded. Most engineers are good at asking questions and experimenting, though they also favor proven methods over uncertainties. AI may require more “outside the box” thinking, where engineers consider new ways of doing business, which may create discomfort.
Much of the discomfort can be calmed by working with other team members to share ideas and support each other while exploring AI. With multiple sets of eyes on input and output, teams are more likely to produce useful results and identify potentially invalid results generated by AI. Also, team members will likely have different levels of experience with AI, and a supportive environment can help team members grow individually and as a group.
A team approach to applying AI can help ease anxiety and produce better results. Image source: Maryna/stock.adobe.com
An optimistic view about AI can also help cultivate a forward-thinking environment. AI is one of the most significant technological developments in recent history, and it presents exciting opportunities for engineers to be at the forefront of applying the technology. With AI viewed as a partner, rather than a threat, mechanical engineers can reap the benefits of AI, improving their organization’s capabilities and expanding their individual career opportunities.
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This article was sponsored by SOLIDWORKS.
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