Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities

Z Hu, S Mahadevan - The International Journal of Advanced …, 2017 - Springer
One of the major barriers that hinder the realization of significant potential of metal-based
additive manufacturing (AM) techniques is the variation in the quality of the manufactured …

Review of statistical model calibration and validation—from the perspective of uncertainty structures

G Lee, W Kim, H Oh, BD Youn, NH Kim - Structural and Multidisciplinary …, 2019 - Springer
Computer-aided engineering (CAE) is now an essential instrument that aids in engineering
decision-making. Statistical model calibration and validation has recently drawn great …

Digital twins: state-of-the-art and future directions for modeling and simulation in engineering dynamics applications

DJ Wagg, K Worden… - … -ASME Journal of …, 2020 - asmedigitalcollection.asme.org
This paper presents a review of the state of the art for digital twins in the application domain
of engineering dynamics. The focus on applications in dynamics is because:(i) they offer …

Uncertainty quantification in prediction of material properties during additive manufacturing

Z Hu, S Mahadevan - Scripta materialia, 2017 - Elsevier
Based on our experience gained from uncertainty quantification (UQ) of traditional
manufacturing, this paper discusses UQ for additive manufacturing with a focus on the …

Unified framework and survey for model verification, validation and uncertainty quantification

S Riedmaier, B Danquah, B Schick… - Archives of Computational …, 2021 - Springer
Simulation is becoming increasingly important in the development, testing and approval
process in many areas of engineering, ranging from finite element models to highly complex …

Uncertainty quantification for additive manufacturing process improvement: recent advances

S Mahadevan, P Nath, Z Hu - … -ASME Journal of …, 2022 - asmedigitalcollection.asme.org
This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to
additive manufacturing (AM). Physics-based as well as data-driven models are increasingly …

Statistical model calibration and design optimization under aleatory and epistemic uncertainty

Y Jung, H Jo, J Choo, I Lee - Reliability Engineering & System Safety, 2022 - Elsevier
Statistical model calibration is a framework for inference on unknown model parameters and
modeling discrepancy between simulation and experiment through an inverse method in the …

Data augmentation-based prediction of system level performance under model and parameter uncertainties: role of designable generative adversarial networks …

Y Yoo, UJ Jung, YH Han, J Lee - Reliability Engineering & System Safety, 2021 - Elsevier
Owing to uncertainty factors present in the system, computer-aided engineering (CAE)
models suffer from limitations in terms of accuracy of test model representation. This paper …

Model validation and scenario selection for virtual-based homologation of automated vehicles

S Riedmaier, D Schneider, D Watzenig, F Diermeyer… - Applied Sciences, 2020 - mdpi.com
Due to the rapid progress in the development of automated vehicles over the last decade,
their market entry is getting closer. One of the remaining challenges is the safety assessment …

Optimal design of experiments for optimization-based model calibration using Fisher information matrix

Y Jung, I Lee - Reliability Engineering & System Safety, 2021 - Elsevier
Statistical model calibration to infer unknown model parameters and model bias has been
widely developed through comparison between simulation response and experimental data …