Predicting weld bead width and depth of penetration from infrared thermal image of weld pool using artificial neural network

2012 ◽  
Vol 54 (5) ◽  
pp. 272-277 ◽  
Author(s):  
S Chokkalingham ◽  
M Vasudevan ◽  
S Sudarsan ◽  
N Chandrasekhar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weld Pool ◽  
Thermal Image ◽  
Weld Bead ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Artificial Neural

scholarly journals Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1494
Author(s):  
Ran Li ◽  
Manshu Dong ◽  
Hongming Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Welding Speed ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Training Dataset ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Bead Width ◽  
Artificial Neural

Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

scholarly journals Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

2017 ◽  
Vol 7 (3) ◽  
pp. 593
Author(s):  
Novizon Novizon ◽  
Zulkurnain Abdul-Malek ◽  
Aulia Aulia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Histogram Method ◽  
Good Tool ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Long Time ◽  
Artificial Neural

<p>Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.</p>

scholarly journals Controlling the Width of Weld Bead from the Weld Pool Using Machine Vision and Artificial Neural Network

2019 ◽  
Vol 269 ◽  
pp. 04003
Author(s):  
Ario Sunar Baskoro ◽  
Duvall Anggraita Purwanto ◽  
Agus Widyianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Welding Speed ◽  
Vision System ◽  
Welding Current ◽  
Weld Bead ◽  
Network Systems ◽  
Manual Measurement ◽  
Artificial Neural

In this study, the development of artificial neural network systems was proposed to keep the width of weld bead constant by controlling the welding speed. During Gas Tungsten Arc Welding, the weld bead was observed directly using machine vision system that utilized CCD camera. Matlab software was used for image processing algorithm and training the data. In training the data, two methods were used which are training with normalization and without normalization. ANN input parameters were arc current, welding speed, number of pixel and location of weld bead. Double hidden layer was used where each one of them consists of 25 nodes, and the output parameter is new controlled welding speed. The testing data was performed using 100, 105 and 110 A with initial welding speed of 1.35, 1.40 and 1.45 mm/s. The measurement of weld bead was taken using two different methods, machine vision and manual measurement. The result showed that the width of weld bead on welding current of 105 A is close to the target of 7 mm with the average error of 0.49 mm. The best result for the machine vision and manual measurement can be achieved when the welding current is 110 A with a normalization.

Correlation between Third Harmonic Leakage Current and Thermography Image of Zinc Oxide Surge Arrester for Fault Monitoring Using Artificial Neural Network

2014 ◽  
Vol 554 ◽  
pp. 598-602 ◽  
Author(s):  
Yusuf Novizon ◽  
Abdul Malek Zulkurnain ◽  
Abdul Malek Zulkurnain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Critical Parameters ◽  
Thermal Images ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The ageing level of ZnO materials in gapless surge arresters can be determined by using either the traditional leakage current measurements or recently introduced thermal images of the arrester. However, a direct correlation between arrester thermal images and its leakage current (and hence the ageing level) is yet to be established. This paper attempts to find such a correlation using an artificial neural network (ANN). Experimental work was carried out to capture both the thermal images and leakage current of 120kV rated polymeric housed gapless arresters. Critical parameters were then extracted from both the thermal image and the leakage current, before being exported into the artificial neural network tool. Using the leakage current level, the conditions of the arrester are classified as normal, caution, and faulty. The ANN correctly classifies the ageing level using only the thermal image information with an accuracy of 97%, which is highly encouraging.

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