STABILITY BOUNDARY AND OPTIMAL STABLE PARAMETER IDENTIFICATION IN MACHINING

    公开(公告)号:US20220161381A1

    公开(公告)日:2022-05-26

    申请号:US17529558

    申请日:2021-11-18

    申请人: UT-Battelle, LLC

    摘要: 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.