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An online inference method for condition identification of workpieces with complex residual stress distributions
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
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2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 192-204Article in journal (Refereed) Published
Abstract [en]

The residual stress field of structural components significantly influences their comprehensive performance and service life. Due to the lack of effective representation means and inference methods, existing methods are confined to inspecting local residual stress rather than the entire residual stress field, rendering the inference of complex residual stress fields quite difficult. In response to the challenges associated with the requirement for extensive sets of deformation force data from the current workpiece and the inherent difficulty in establishing a stable relationship between deformation forces and residual stress fields, this paper introduces a novel inference method of residual stress field is proposed based on a data-causal knowledge fusion model, where causal knowledge is introduced to eliminate the coupling effect of geometric change on residual stress, which can make up the drawback of pure data driven model. The proposed approach can accurately inference the residual stress within the workpieces, which provides an important basis for deformation control and part property improvement.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 73, p. 192-204
Keywords [en]
Condition identification, Coupling effect, Data-causal knowledge fusion model, Residual stress field, Stable relationship
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-343673DOI: 10.1016/j.jmsy.2024.01.012ISI: 001184741100001Scopus ID: 2-s2.0-85184522579OAI: oai:DiVA.org:kth-343673DiVA, id: diva2:1839865
Note

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-04Bibliographically approved

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Wang, Lihui

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