Finite element simulation and experimental verification of ultrasonic non-destructive inspection of defects in additively manufactured materials

Taheri, Hossein; Koester, Lucas W.; Bigelow, Timothy A.; Bond, Leonard J.

doi:10.1063/1.5031508

Cited by 12 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rough surface of AM parts can thus exhibit a kind of periodicity and elementary egg-shaped irregularities [142]. Finite element models [143] are likely to be the best solution to both model the bulk macrostructure of such components and their surface roughness-such models have been recently used to model scattering in virtual simulated microstructures of polycrystalline materials either with equiaxed grains [144][145][146] or transverse isotropic as in austenitic welds [147]. The increasing power brought by the spectral finite elements (SEM) method is noticeable [148][149][150] in terms of calculation time and could be very useful for roughness with complex shape irregularities; SEM has recently been used for modeling ultrasonic propagation in granular materials, such as concrete [151].…”

Section: Summary Discussion and Perspectivesmentioning

confidence: 99%

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

Darmon

Dorval

Baqué³

2020

Applied Sciences

View full text Add to dashboard Cite

This paper proposes a brief review of acoustic wave scattering models from rough surfaces. This review is intended to provide an up-to-date survey of the analytical approximate or semi-analytical methods that are encountered in acoustic scattering from random rough surfaces. Thus, this review focuses only on the scattering of acoustic waves and does not deal with the transmission through a rough interface of waves within a solid material. The main used approximations are classified here into two types: the two historical approximations (Kirchhoff approximation and the perturbation theory) and some sound propagation models more suitable for grazing observation angles on rough surfaces, such as the small slope approximation, the integral equation method and the parabolic equation. The use of the existing approximations in the scientific literature and their validity are highlighted. Rough surfaces with Gaussian height distribution are usually considered in the models hypotheses. Rather few comparisons between models and measurements have been found in the literature. Some new criteria have been recently determined for the validity of the Kirchhoff approximation, which is one of the most used models, owing to its implementation simplicity.

show abstract

Section: Summary Discussion and Perspectivesmentioning

confidence: 99%

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

Darmon

Dorval

Baqué³

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Due to the intricacy of manufacturing process in AM techniques, as well as geometrical complexity of the produced parts and surface finish condition [46], new NDE and metrology methods have to be developed for quality and maintainability of AM parts. Modeling and simulation applications in NDE become even more essential in NDE 4.0 since they can provide crucial information on NDE experimental design, integration of NDE in design and manufacturing processes and interpretation of NDE results [47][48][49].…”

Section: Internet Of Things (Iot)-related Applications and Nde 40mentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Taheri

Bocanegra

Taheri

2022

Sensors

View full text Add to dashboard Cite

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part’s integrity or causing damage to the component being tested. This includes monitoring materials’ condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

show abstract

“…Multi-scale simulations. Multiphysics numerical modelling is a promising means of allowing the interplay of the various factors to be studied, pointing to robust experimental design [245]. Thermal modelling of AM processes such as LPBF can determine a thermal history of a build.…”

Section: Physical Propertiesmentioning

confidence: 99%

Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

Hashemi

Parvizi

Baghbanijavid

et al. 2021

International Materials Reviews

View full text Add to dashboard Cite

In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.

show abstract

Finite element simulation and experimental verification of ultrasonic non-destructive inspection of defects in additively manufactured materials

Cited by 12 publications

References 16 publications

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

Contact Info

Product

Resources

About