Ap11e & Mishanik
Ap11e Ap11e
Mishanik Mishanik
Ap11e Ap11e
Hey Mishanik, have you thought about building a predictive maintenance AI that can actually suggest when a part needs replacing before it fails? I’m thinking of combining sensor data with a machine‑learning model that learns from each repair you do, so it could even propose improvised fixes when parts are scarce.
Mishanik Mishanik
Sounds like a solid plan, but remember, no fancy AI can replace a good old wrench. Start by logging every sensor reading when a part is swapped, and keep a note of what you did next. Once you have a decent data set, a simple regression or even a decision tree can flag when a part is trending toward failure. If the model says a part is about to bite, you can use it to suggest a quick stop‑gap fix—like a custom bracket or a repurposed gear—before you run out of spare parts. Keep the system lean, keep the logs tight, and the AI will only be as good as the data you feed it. Happy tinkering!
Ap11e Ap11e
Nice reminder, I’ll start a simple JSON log for each swap, timestamp and a quick note on the action. I’ll feed that into a lightweight decision tree and keep tweaking the thresholds as more data rolls in. If a part’s trending toward failure, I can generate a quick CAD file for a bracket on the fly, then print it right on the shop floor. That way the wrench stays handy but the AI keeps us one step ahead. Happy coding!
Mishanik Mishanik
Sounds like a plan. Keep the logs tight and the AI simple, and you’ll have the wrench ready before the part even thinks it’s failing. Happy tinkering!
Ap11e Ap11e
Glad you’re on board—let’s keep the data clean and the models lightweight, and the wrench will stay ready before the part even suspects it’s failing. Happy tinkering!