Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint
In this paper, a data-driven approach that is based on the k-mean clustering and local outlier factor (LOF) algorithm has been proposed and deployed for the management of non-destructive evaluation (NDE) in a welded joint. The k-mean clustering and LOF model algorithm, which was implemented for the...
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| Veröffentlicht in: | Processes 2022-10, Vol.10 (10), p.1923 |
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| creator | Oleka, Chijioke Jerry Aikhuele, Daniel Osezua Omorogiuwa, Eseosa |
| description | In this paper, a data-driven approach that is based on the k-mean clustering and local outlier factor (LOF) algorithm has been proposed and deployed for the management of non-destructive evaluation (NDE) in a welded joint. The k-mean clustering and LOF model algorithm, which was implemented for the classification, identification, and determination of data clusters and defect location in the welded joint datasets, were trained and validated such that three (3) different clusters and noise points were obtained. The noise points, which are regarded as the welded joint defects/flaws, allow for the determination of the cluster size, heterogeneity, and silhouette score of the welded joint data. Similarly, the LOF model algorithm was implemented for the detection, visualization, and management of flaws due to internal cracks, porosity, fusion, and penetration in the welded joint. It is believed that the management of welded joint flaws would aid the actualization of the Industry 4.0 concept in the development of lightweight products for manufacturing. |
| doi_str_mv | 10.3390/pr10101923 |
| format | Article |
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The k-mean clustering and LOF model algorithm, which was implemented for the classification, identification, and determination of data clusters and defect location in the welded joint datasets, were trained and validated such that three (3) different clusters and noise points were obtained. The noise points, which are regarded as the welded joint defects/flaws, allow for the determination of the cluster size, heterogeneity, and silhouette score of the welded joint data. Similarly, the LOF model algorithm was implemented for the detection, visualization, and management of flaws due to internal cracks, porosity, fusion, and penetration in the welded joint. It is believed that the management of welded joint flaws would aid the actualization of the Industry 4.0 concept in the development of lightweight products for manufacturing.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr10101923</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accident prevention ; Algorithms ; Automation ; Classification ; Clustering ; Deep learning ; Defects ; Electronic data processing ; Experimental methods ; Flaw detection ; Heterogeneity ; Identification and classification ; Image coding ; Industrial Revolution ; Innovations ; Machine learning ; Manufacturing ; Mechanical properties ; Neural networks ; Nondestructive testing ; Outliers (statistics) ; Porosity ; Preventive maintenance ; Process controls ; Research methodology ; Simulation ; Ultrasonic imaging ; Welded joints</subject><ispartof>Processes, 2022-10, Vol.10 (10), p.1923</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Accident prevention Algorithms Automation Classification Clustering Deep learning Defects Electronic data processing Experimental methods Flaw detection Heterogeneity Identification and classification Image coding Industrial Revolution Innovations Machine learning Manufacturing Mechanical properties Neural networks Nondestructive testing Outliers (statistics) Porosity Preventive maintenance Process controls Research methodology Simulation Ultrasonic imaging Welded joints |
| title | Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint |
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