UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIREY06139


Registration ID:
312776

Page Number

744-749

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Title

Automatic Time-Lapse Neural Network Modelling Based On Low-Level Data

Authors

Abstract

Automatic process modelling (APM) is an enabling technology for the development of smart fabrication systems (IMSs). The analysis of obtained models enables the prompt identification of error-prone measures and the design of effective mitigation strategies, from parameter optimization to the production of tailored staff training, in all aspects of the manufacturing process. In this work as propose a Time Delay Neural Network (TDNN) applied to low-level data for the automatic recognition of various process phases in collaborative industrial tasks. As selected TDNN because, while retaining computational performance, they are suitable for modelling time dependent processes over long sequences. As acquired two novel datasets reproducing a standard IMS environment to experimentally evaluate the recognition efficiency and the generalization capability of the proposed process. Datasets (including manually annotated ground-truth labels) are publicly accessible to allow other methods to be evaluated on them, replicating a standard environment for Industry 4.0. The first dataset replicates a collaborative robotic system in which a human operator communicates with a robotic manipulator while performing a pick and place function. The second package is a human tele-operated, aided robotic manipulation for assembly applications. The results obtained are superior to other literature methods, and indicate an improved computational efficiency.

Key Words

Intelligent manufacturing, Industry 4.0, Time delay neural network, TDNN, Collaborative Robot.

Cite This Article

"Automatic Time-Lapse Neural Network Modelling Based On Low-Level Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.744-749, June-2019, Available :http://www.jetir.org/papers/JETIREY06139.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Automatic Time-Lapse Neural Network Modelling Based On Low-Level Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp744-749, June-2019, Available at : http://www.jetir.org/papers/JETIREY06139.pdf

Publication Details

Published Paper ID: JETIREY06139
Registration ID: 312776
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 744-749
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162


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