Analysis of GMAW process with deep learning and machine learning techniques

Martínez, Rogfel Thompson; Bestard, Guillermo Alvarez; Silva, Alysson M.A.; Alfaro, Sadek Crisóstomo Absi

doi:10.1016/j.jmapro.2020.12.052

Cited by 32 publications

(8 citation statements)

References 33 publications

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“…Huang et al [3] performed quality diagnosis in GMAW by varying the arc signal using empirical mode decomposition and extreme learning machine; finally, the combination has shown reliable results in identifying weld quality. Thompson et al [4] performed image analysis in GMAW using deep learning and machine learning techniques for predicting the bead geometry and verified with experimental results. Kamble et al [5] studied GMAW process parameters by formulating a mathematical model for estimating the values and comparing them with experimental results.…”

Section: Introductionmentioning

confidence: 91%

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Devendran¹,

Varthanan²

2021

Mater. Res. Express

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Welding operation decides the quality of product standards in all metal work products like automobiles, aerospace vehicles, and many more. The quality of the welding process is more reliable by automating the process with robots. In this research work, the GMAW operation is automated with the “Fanuc Robot Arc mate 100iC/12” robot. The material characteristics such as ultimate tensile strength, hardness, and impact strength of weldments are predicted using a fuzzy system using triangular membership function (TrMF) and trapezoidal membership function (TMF). The simulated results are validated by comparing with experimental work, the experiments are designed using orthogonal array L18, and material characteristics are studied using fractography test. The fuzzy system is trained with experimental results using the IF-Then rule base with the help of the L18 orthogonal array. The inference system has predicted the accuracy rate of weldment mechanical properties, showing a lower error rate.

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Section: Introductionmentioning

confidence: 91%

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Devendran¹,

Varthanan²

2021

Mater. Res. Express

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“…This method had the ability of efficiently detecting abnormalities at various levels helping in the process of analysing and understanding scenes from videos. A restricted Boltzmann machine (RBM) is a generative undirected network containing a hidden and a visible layer (Martínez et al, 2021). As a RBM has a bipartite structure, data distribution is learnt accurately compared with that of the layers of deep generative networks.…”

Section: Related Workmentioning

confidence: 99%

Intelligent video surveillance using enhanced deep belief based multilayered convolution neural network classification techniques

Kaliappan

Saravanan

Dakshinamoorthy

et al. 2022

The Photogrammetric Record

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Video surveillance has undergone numerous changes in the past few years and several pieces of research have been carried out in this field. Object tracking is the significant task in such systems, and hence it is essential to review the standard approaches dealing with object detection, classification and tracking. This work proposes a novel classification technique for a detected object of a moving scene from a video dataset. Initially the dataset has been processed and prepared for data training based on neural networks. The data has been classified using the proposed enhanced deep belief based multilayered convolutional neural network (EBMCNN). The major focus is on deep learning applications involved in estimating the count, total persons involved and the activities in a crowd where all criteria are taken into consideration, thereby achieving security through video analysis. Identifying theft and the detection of violence are some security measures where video is converted to frames which are then processed to analyse the individuals along with their activities. The classification is performed through comparative analysis of a real‐time dataset. Experimental results show the accuracy 97%, precision 93.8%, recall 87.7% and F‐1 score 87.5%.

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“…In this system, XGBoost, with the best prediction performance, is selected as the machine learning model. With the adoption of different algorithms, expert systems have been widely studied and applied in various fields of science, engineering, agriculture and medicine, providing valuable experience for the research and development of welding expert systems in this paper [29][30][31][32][33][34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

GMAW welding procedure expert system based on machine learning

Wang¹,

Chen²,

Sun³

et al. 2023

Intell Robot

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In order to simplify the robot preparation before welding and improve the automation of the whole welding process, an intelligent expert system for Gas Metal Arc Welding is designed in this paper. In the system, the user inputs the initial welding information and the output interface displays suitable welding procedure parameter schemes. The user can choose the schemes according to the actual requirements or directly generate the welding procedure specification required by the enterprise format for direct use. In addition, the system also combines the database technology and XGBoost algorithm in the field of machine learning, migrates the model trained on the data set to predict the welding raw data, accumulates more data for daily use to optimize the model, which makes the whole system more systematic and intelligent, and achieves the goal of more accurate use.

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Analysis of GMAW process with deep learning and machine learning techniques

Cited by 32 publications

References 33 publications

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Intelligent video surveillance using enhanced deep belief based multilayered convolution neural network classification techniques

GMAW welding procedure expert system based on machine learning

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