Stability boundary and optimal stable parameter identification in machining
US12181853B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 18, 2021 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Apr 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/49065
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A Bayesian learning approach for stability boundary and optimal parameter identification in milling without the knowledge of the underlying tool dynamics or material cutting force coefficients. Different axial depth and spindle speed combinations are characterized by a probability of stability which is updated based upon whether the result is stable or unstable. A likelihood function incorporates knowledge of stability behavior. Numerical results show convergence to an analytical stability lobe diagram. An adaptive experimental strategy identifies optimal operating parameters that maximize material removal rate. An efficient and robust learning method to identify the stability lobe diagram and optimal operating parameters with a limited number of tests/data points.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.