Process engineering, Bayesian optimization, and the messy reality of precision manufacturing. Written by the team that builds Reaxiomatic.
A single month of unstructured parameter experimentation on one precision machining line can consume 15-20% of that line's capacity. We broke down where the time actually goes.
Traditional DOE requires you to design the experiment before you run it. Bayesian search updates as data arrives. For high-mix shops, that difference matters.
SPC charts flag when your process has already drifted. Parameter optimization is about finding the settings that prevent drift in the first place.
Change feed rate and you've also changed surface finish, tool temperature, and cutting force. The optimization problem is joint across all of these, not sequential.
AS9100 and ITAR compliance require traceability, not just conformance. Your parameter records need to be as traceable as your material certs.
Most precision shops have explored maybe 8-12% of their feasible parameter space. The high-yield zones are probably in the unexplored 90%. Here is how to tell.
Closed-loop CNC control adjusts in real time. But the parameters it adjusts around still come from human engineers. AI process search works on a different timescale.
A failed FAI sends you back to parameter iteration. The second attempt is often just as unstructured as the first. There is a better way to use the inspection data you already have.
We started thinking this was a machine learning problem. It turns out it is mostly a data structure problem. Two years of building taught us where the real difficulty lives.
Published cutting parameter tables treat surface finish as a downstream result. In practice, meeting a Ra spec often requires backing off feed rates that would otherwise be fine for dimensional tolerance.
Cpk measures whether your current settings can hold the spec. It says nothing about whether better settings exist. Capability studies and parameter optimization answer different questions.
Precision manufacturers run the same parameter experiments repeatedly because the data from prior runs sits in disconnected spreadsheets. We built Reaxiomatic to connect those data points.
