I work at the edge of PLM and Digital Manufacturing — not because it was the path of least resistance, but because it's where some of the most complex and underappreciated engineering problems live. And now I'm asking: what changes when you bring AI and robotics into this?
I'm a PLM Technical Consultant who started with mechanical engineering and ended up deep inside enterprise product lifecycle systems — configuring workflows, structuring product data, and making sure engineering teams can actually use the tools built for them.
My core domain is Dassault Systèmes' 3DEXPERIENCE ecosystem — ENOVIA, CATIA, DELMIA — across automotive and aerospace environments. I've done the pre-sales demos, the full implementations, the server configurations, the user training. I understand how these systems behave in theory and, more importantly, how they behave in practice.
But curiosity doesn't sit still. PLM systems are sitting on enormous amounts of engineering data — and most of it is either underused or completely ignored. That's the gap I'm trying to close — using AI and machine learning to make digital manufacturing smarter, more predictive, and more useful.
PLM tells you what is being made and how it changes. Robotics and automation are where it actually gets made. Intelligent manufacturing connects the two — product data flowing into cells, machines, and sensors that can sense, decide, and adapt. That's the direction I'm moving in: studying it at UCD, and learning the hands-on side through self-study across Linux, Python, ROS 2, PLC programming, and data analytics.
When I'm not working on that: building side projects, writing about PLM and Industry 4.0, and pursuing an MEngSc in Robotics and Intelligent Manufacturing at University College Dublin, after completing an MS in AI/ML at Woolf University.