Tracing a Weld Line using Artificial Neural Networks

Robotic manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in today’s market so that automation can be achieved at a faster...

Full description

Bibliographic Details
Main Authors: Srinath Hanumantha Rao, V. Kalaichelvi, R. Karthikeyan
Format: Article
Language:English
Published: Atlantis Press 2018-09-01
Series:International Journal of Networked and Distributed Computing (IJNDC)
Subjects:
ANN
Online Access:https://www.atlantis-press.com/article/125905553/view
id doaj-c1b717a91ad24756be378eec92ee7052
record_format Article
spelling doaj-c1b717a91ad24756be378eec92ee70522020-11-24T21:50:23ZengAtlantis PressInternational Journal of Networked and Distributed Computing (IJNDC)2211-79462018-09-016410.2991/ijndc.2018.6.4.4Tracing a Weld Line using Artificial Neural NetworksSrinath Hanumantha RaoV. KalaichelviR. KarthikeyanRobotic manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in today’s market so that automation can be achieved at a faster rate. This paper aims to present a real-time implementation of a method to control a Tal Brabo! Robotic manipulator to move along a given weld line in order to be utilized in factories for increasing production capacity and decreasing production time. The controller used here is provided by Trio, whose ActiveX component is interfaced to MATLAB. Images were captured to identify weld lines in every possible alignment to find points of interest and the neural network was trained in order to follow a given weld line once the work-piece was placed on the work-table.https://www.atlantis-press.com/article/125905553/viewRobotANNweldingimage processing
collection DOAJ
language English
format Article
sources DOAJ
author Srinath Hanumantha Rao
V. Kalaichelvi
R. Karthikeyan
spellingShingle Srinath Hanumantha Rao
V. Kalaichelvi
R. Karthikeyan
Tracing a Weld Line using Artificial Neural Networks
International Journal of Networked and Distributed Computing (IJNDC)
Robot
ANN
welding
image processing
author_facet Srinath Hanumantha Rao
V. Kalaichelvi
R. Karthikeyan
author_sort Srinath Hanumantha Rao
title Tracing a Weld Line using Artificial Neural Networks
title_short Tracing a Weld Line using Artificial Neural Networks
title_full Tracing a Weld Line using Artificial Neural Networks
title_fullStr Tracing a Weld Line using Artificial Neural Networks
title_full_unstemmed Tracing a Weld Line using Artificial Neural Networks
title_sort tracing a weld line using artificial neural networks
publisher Atlantis Press
series International Journal of Networked and Distributed Computing (IJNDC)
issn 2211-7946
publishDate 2018-09-01
description Robotic manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in today’s market so that automation can be achieved at a faster rate. This paper aims to present a real-time implementation of a method to control a Tal Brabo! Robotic manipulator to move along a given weld line in order to be utilized in factories for increasing production capacity and decreasing production time. The controller used here is provided by Trio, whose ActiveX component is interfaced to MATLAB. Images were captured to identify weld lines in every possible alignment to find points of interest and the neural network was trained in order to follow a given weld line once the work-piece was placed on the work-table.
topic Robot
ANN
welding
image processing
url https://www.atlantis-press.com/article/125905553/view
work_keys_str_mv AT srinathhanumantharao tracingaweldlineusingartificialneuralnetworks
AT vkalaichelvi tracingaweldlineusingartificialneuralnetworks
AT rkarthikeyan tracingaweldlineusingartificialneuralnetworks
_version_ 1725884435361955840