Evidence map›Paper›PMID 36295256›Full record

ReviewMaterials (Basel, Switzerland)2022

On Smart Geometric Non-Destructive Evaluation: Inspection Methods, Overview, and Challenges.

Ali Jaber, Sasan Sattarpanah Karganroudi, Mohammad Saleh Meiabadi, Ahmad Aminzadeh, Hussein Ibrahim, Mehdi Adda, Hossein Taheri

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ali JaberInstitut Technologique de Maintenance Industrielle, 175, Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada.
Sasan Sattarpanah KarganroudiInstitut Technologique de Maintenance Industrielle, 175, Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada.ORCID 0000-0001-7226-5671
Mohammad Saleh MeiabadiDepartment of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre Dame West, Montreal, QC H3C-1K3, Canada.ORCID 0000-0002-9171-4812
Ahmad AminzadehInstitut Technologique de Maintenance Industrielle, 175, Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada.ORCID 0000-0002-8312-8303
Hussein IbrahimInstitut Technologique de Maintenance Industrielle, 175, Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada.ORCID 0000-0002-9177-2967
Mehdi AddaDépartement de Mathématique, Informatique et Génie, Université du Québec à Rimouski, Rimouski, QC G56 3A1, Canada.ORCID 0000-0002-5327-1758
Hossein TaheriDepartment of Manufacturing Engineering, Georgia Southern University, 332 Southern Drive, Statesboro, GA 30458, USA.ORCID 0000-0003-1704-8775

Funding

Fonds de Recherche du Québec - Nature et Technologies 2020-CO-275264
6 · The paper itself

Abstract

Inspection methods, also known as non-destructive evaluation (NDE), is a process for inspecting materials, products, and facilities to identify flaws, imperfections, and malfunctions without destruction or changing the integrity of materials, structures, and mechanisms. However, detecting those defects requires test conducting and results inferring, which is highly demanding in terms of analysis, performance, and time. New technologies are therefore needed to increase the efficiency, probability of detection, and interpretability of NDE methods to establish smart inspection. In this context, Artificial intelligence (AI), as a fundamental component of the Industry 4.0, is a well-suited tool to address downsides associated with the current NDE methods for analysis and interpretation of inspection results, where methods integrating AI into their inspection process become automated and are known as smart inspection methods. This article sheds a light on the conventional methods and the smart techniques used in defects detection. Subsequently, a comparison between the two notions is presented. Furthermore, it investigates opportunities for the integration of non-destructive evaluation (NDE) methods and Industry 4.0 technologies. In addition, the challenges hindering the progress of the domain are mentioned as the potential solutions. To this end, along with Industry 4.0 technologies, a virtual inspection system has been proposed to deploy smart inspection.

Indexed as

artificial intelligence (AI)Industry 4.0machine learning (ML)non-destructive evaluation (NDE)smart inspection

Identifiers

PMID36295256
PMCPMC9608848

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.