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

Md Mehedi Hasan, Julius Schoop

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish
Pages (from-to)139-144
Number of pages6
JournalProcedia CIRP
StatePublished - 2023
Event19th CIRP Conference on Modeling of Machining Operations, CMMO 2023 - Karlsruhe, Germany
Duration: May 31 2023Jun 2 2023

Bibliographical note

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


  • Calibration
  • Residual Stress
  • Surface Integrity
  • Uncertainty Quantification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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