Applications of Artificial Intelligence in Mechanical Engineering

Team OG Oil & Gas Domain

At first glance, the topics looked familiar, but the depth surprised me. Coming from an aerospace systems engineering background with recent automotive platform work, the session helped connect design thinking to things like requirements trade studies and HMI decisions, not just sticky notes. The walkthrough of problem framing and user empathy filled a gap I’ve had when moving from technical specs to early concept discussions, especially on cross‑functional programs. One challenge was compressing the exercises into a one‑hour format. Translating empathy maps into something usable for a regulated aerospace or automotive environment isn’t trivial, and it took a bit of mental effort to see how this fits alongside DFMEA and certification constraints. Still, the examples made it workable. A practical takeaway was the emphasis on writing clearer problem statements before jumping into solutions. That’s already been applied on a current automotive subsystem project to reset a design review that was stuck on premature architecture choices. Difficulty-wise, it felt accessible but not watered down, which helped keep it relevant for someone already in industry. The content felt aligned with practical engineering demands.

edward pappoe Deputy Director engineering

At first glance, the topics looked familiar, but the depth surprised me. Design thinking often gets treated as a soft skill, yet this session connected it to real engineering work. Coming from projects in aerospace systems integration and automotive ADAS development, the framing around problem definition hit home. Too often, requirements flow-down or DFMEA starts before the actual user problem is clear. One challenge was compressing the full design thinking cycle into a one-hour format. Some exercises felt rushed, especially when trying to map empathy insights to technically constrained environments like avionics certification or EV thermal management. Still, the examples helped bridge that gap. A useful takeaway was the emphasis on reframing problem statements before locking architectures. That’s something already applied on an automotive HMI update, where a quick stakeholder mapping exercise exposed a missed serviceability issue. The course also filled a knowledge gap around how design thinking can coexist with regulated aerospace processes instead of fighting them. The content stayed practical and didn’t overpromise career transformation, which was refreshing. Overall, it sharpened how early decisions can reduce downstream rework. It definitely strengthened my technical clarity.

edward pappoe Deputy Director engineering

Initially, I wasn’t sure what to expect from this course, especially given it was only an hour and I already use ChatGPT casually at work. Coming from an automotive background with some exposure to aerospace systems, I was curious whether it would actually add value beyond basic prompts. What worked well was the explanation of how large language models reason and where they break down. That helped me rethink how to use ChatGPT for tasks like drafting requirements for an automotive ECU update and summarizing aerospace-style verification documents. One challenge was translating the generic examples into engineering-specific workflows; the course doesn’t fully walk you through domain-heavy use cases like thermal analysis notes or failure mode discussions. Still, it highlighted the importance of structured prompts and iteration, which was a gap in my understanding. A practical takeaway was learning how to constrain outputs so they’re more usable for real projects—like asking for assumptions, limitations, or step-by-step logic instead of a polished answer. I’ve already applied this while reviewing design trade-offs and preparing internal technical summaries. The course isn’t deep, but it’s grounded enough to be useful for working engineers. It definitely strengthened my technical clarity.

jyoti sahu

While poking at our obs gaps during a prod review, this course popped up and fit a beginner lane I needed. It links the math to day‑to‑day decisions without assuming you’re already running fancy infra, which helped me map models to what actually breaks in prod. The bit that stuck was Chapter 3’s soft sensors for distillation columns, especially the example where a PCA model drifted after a feed change and they walked through how to catch it with simple logging. i spun a tiny repo to mirror that, opened a PR to add drift checks in CI, and sanity‑checked inference RPS; the exercise felt close to how I’d test this before touching k8s. I wasn’t sold on the light treatment of pharma constraints and wished there was a page on validation docs, but that’s a scope call. It’s adjusted how I size the work and what to tackle first when AI ideas collide with ops reality.