Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

Md Mehedi Hasan; Julius Schoop

doi:10.1016/j.procir.2023.03.025

Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

Md Mehedi Hasan, Julius Schoop

Mechanical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.

Original language	English
Pages (from-to)	139-144
Number of pages	6
Journal	Procedia CIRP
Volume	117
DOIs	https://doi.org/10.1016/j.procir.2023.03.025
State	Published - 2023
Event	19th CIRP Conference on Modeling of Machining Operations, CMMO 2023 - Karlsruhe, Germany Duration: May 31 2023 → Jun 2 2023

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation, grant number 2143806, project title “CAREER: Thermo-mechanical Response and Fatigue Performance of Surface Layers Engineered by Finish Machining: In-situ Characterization and Digital Process Twin”.

Publisher Copyright:
© 2023 Elsevier B.V.. All rights reserved.

Keywords

Calibration
Residual Stress
Surface Integrity
Uncertainty Quantification

ASJC Scopus subject areas

Control and Systems Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1016/j.procir.2023.03.025

Cite this

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title = "Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress",

abstract = "Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.",

keywords = "Calibration, Residual Stress, Surface Integrity, Uncertainty Quantification",

author = "Hasan, {Md Mehedi} and Julius Schoop",

note = "Funding Information: This work was supported by the National Science Foundation, grant number 2143806, project title “CAREER: Thermo-mechanical Response and Fatigue Performance of Surface Layers Engineered by Finish Machining: In-situ Characterization and Digital Process Twin”. Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.. All rights reserved.; 19th CIRP Conference on Modeling of Machining Operations, CMMO 2023 ; Conference date: 31-05-2023 Through 02-06-2023",

year = "2023",

doi = "10.1016/j.procir.2023.03.025",

language = "English",

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pages = "139--144",

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AU - Hasan, Md Mehedi

AU - Schoop, Julius

N1 - Funding Information: This work was supported by the National Science Foundation, grant number 2143806, project title “CAREER: Thermo-mechanical Response and Fatigue Performance of Surface Layers Engineered by Finish Machining: In-situ Characterization and Digital Process Twin”. Publisher Copyright: © 2023 Elsevier B.V.. All rights reserved.

PY - 2023

Y1 - 2023

N2 - Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.

AB - Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.

KW - Calibration

KW - Residual Stress

KW - Surface Integrity

KW - Uncertainty Quantification

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U2 - 10.1016/j.procir.2023.03.025

DO - 10.1016/j.procir.2023.03.025

M3 - Conference article

AN - SCOPUS:85164536589

SN - 2212-8271

VL - 117

SP - 139

EP - 144

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 19th CIRP Conference on Modeling of Machining Operations, CMMO 2023

Y2 - 31 May 2023 through 2 June 2023

ER -

Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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