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Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 19 Feb 2022 Spain Publisher:MDPI AGJournal:Applied Sciences, volume 12, page 2,191 (eissn: 2076-3417,

Authors: Shikha Suman; Ashutosh Karna; Karina Gibert;

doi: 10.3390/app12042191

handle: 2117/363818

Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

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Abstract

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.

Country

Spain

Related Organizations

Keywords

Technology, :Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC], QH301-705.5, QC1-999, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic, Hierarchical clustering, Calinski–Harabasz index, Machine learning, Aprenentatge automàtic, Biology (General), QD1-999, CURE, Three-dimensional printing, Cluster validity indices, T, Physics, 3D printing, Engineering (General). Civil engineering (General), Industry 4.0, 004, CURE; hierarchical clustering; cluster validity indices; Calinski–Harabasz index; bootstrapping; Industry 4.0; 3D printing, Chemistry, cluster validity indices, bootstrapping, Bootstrapping, Intelligent sensors, TA1-2040, hierarchical clustering, Impressió 3D

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	4
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average