Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Awasthi, Utsav; Wang, Zhigang; Mannan, Nasir; Pattipati, Krishna R.; Bollas, George M.

doi:10.1016/j.jmapro.2022.06.027

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Journal Article · Fri Jul 08 00:00:00 EDT 2022 · Journal of Manufacturing Processes

DOI:https://doi.org/10.1016/j.jmapro.2022.06.027· OSTI ID:1977318

Awasthi, Utsav ^[1]; Wang, Zhigang ^[2]; Mannan, Nasir ^[3]; Pattipati, Krishna R. ^[4]; Bollas, George M. ^[5]

UTC Institute for Advanced Systems Engineering, Storrs, CT (United Staes); OSTI
Raytheon Technologies Research Center, East Hartford, CT (United States)
Connecticut Center for Advanced Technology Inc., East Hartford, CT (United States)
University of Connecticut, Storrs, CT (United States)
UTC Institute for Advanced Systems Engineering, Storrs, CT (United Staes)

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

View Accepted Manuscript (DOE)

Research Organization:: University of California, Los Angeles, CA (United States); University of Connecticut, Storrs, CT (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007613

OSTI ID:: 1977318

Journal Information:: Journal of Manufacturing Processes, Journal Name: Journal of Manufacturing Processes Journal Issue: C Vol. 81; ISSN 1526-6125

Publisher:: Society of Manufacturing Engineers; ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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