Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

Abstract

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.

Process Mining Organization (PMO) Modeling and Healthcare Processes

“…The example discussed in Appendix A (AI data process providing PMO outputs) is a data clustering analysis performed by the fuzzy c-means algorithm [28]. Data clustering is a technique suitable for the deploy of data-driven processes [29] and for showing a possible interpretation of the AI outputs to improve organizational solutions. The Konstanz Information Miner (KNIME) open-source tool [30] is executed to test the PMO approach: discussed in Appendix A are more details about the fuzzy c-means algorithm implementa-tion concerning a case study of an Italian hospitalization unit managing the fall risks of patients.…”