Development of mathematical model with a genetic algorithm for automatic GMA welding process

Thao, D. T.; Kim, I. S.; Na, H. H.; Jung, S. M.; Shim, Ji-Yeon

doi:10.1007/s00170-014-5842-3

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…In addition, a certain degree of increase in the dosage of additional paint is due to touch-up operations caused by painting defects. Therefore, the established painting dosage objective function is shown in Equation (16).…”

Section: Establishment Of Multi-objective Evaluation Function For Pai...mentioning

confidence: 99%

“…The process planning scheme with the lowest production cost is obtained by searching a limited space of feasible solutions with the intelligent water drop algorithm [15]. Thao et al developed a predictive model of welding process parameters by using the genetic algorithm to improve the robot welding performance [16]. Jing et al used the genetic algorithm to obtain the global optimal machining process route for parts [17].…”

Section: Introductionmentioning

confidence: 99%

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Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC).

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Section: Establishment Of Multi-objective Evaluation Function For Pai...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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show abstract

“…They successfully applied an artificial intelligence algorithm to obtain the welding parameters with the optimal bead height based on an objective function. Thao et al 63) develop new algorithms based on a full factorial design with two replications to investigate the effects of welding parameters on top-bead width as a function of key output parameters in the robotic GMA welding process. Son et al 64) explored the back-propagation and Levenberg-Marquardt network to associate the welding parameters with the features of the bead width, and concluded that Levenberg-Marquardt networks has better predicting ability than the backpropagation network as shown in Fig.…”

Section: Neural Network Modelsmentioning

confidence: 99%

A Review on Optimizations of Welding Parameters in GMA Welding Process

Kim

Park

2018

Journal of Welding and Joining

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With the increase of automatic welding system employed in the manufacturing industries, the selection of optimal welding parameters must be more specific to ensure that quality is obtained. Furthermore, it is necessary to have a suitable model that establishes the interrelationship between welding parameters and bead geometry to get the desired weld ability as quality since it is a complicated process, which involves interactions of thermal, mechanical, electrical and metallurgical phenomenon. Many researchers have reported theoretical, numerical, empirical and AI models to give the optimal welding conditions for GMA(Gas Metal Arc) welding process. In addition, controlling the welding parameters plays an important role in ensuring the quality of the weld. However, there is a need to comprehensively review the GMA welding process in terms of different independent and dependent welding parameters for the purpose of modeling and optimization of GMA process. In this paper, several experimental design and optimization methodologies are reviewed and discussed with current literature on experimental design, multiple regressions analysis, the neural network, fuzzy logic, and genetic algorithm to improve the quality of weldments. This review underlines the need of development of appropriate nature inspired algorithm for the optimization of such advanced manufacturing process.

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“…As compared with the approximate equations, experimental methods have been reported to achieve better accuracy. Regression analysis [14][15][16][17][18][19][20][21][22] and artificial neural networks (ANNs) 17,20,[22][23][24][25][26][27] have been used widely to model the relationship between bead geometry and welding parameters based on experimental data. According to Xiong et al, 17 Kim et al 20 and Lee and Um, 22 ANNs show superior performance to regression analysis in predicting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal pass planning for robotic welding of large-dimension joints with nonuniform grooves

Yan

Fang

Ong

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Large-dimension welded joints are widely used in shipyards and oilfields. These joints typically have thick and nonuniform welding grooves, thus requiring multi-pass welding processes and tens of hours to complete. Although the development of robotic welding systems can shorten the welding time, it is important to have a detailed plan of the entire process before welding starts. Welding pass planning is crucial for the subsequent robot trajectory generation and provides the welding sequence in a deep groove and the welding parameters of every pass. A knowledge database is used in this research to obtain the relationship between the bead geometry and the welding parameters. A layer-by-layer welding pass planning scheme is proposed to address the nonuniform groove geometry, and an optimization method is proposed with the objective to maximize the section area of the weld bead, so that the number of passes can be minimized. The planning results show that the optimization method is able to provide feasible passes and welding parameters for every pass.

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Development of mathematical model with a genetic algorithm for automatic GMA welding process

Cited by 7 publications

References 11 publications

Ship Painting Process Design Based on IDBSACN-RF

Ship Painting Process Design Based on IDBSACN-RF

A Review on Optimizations of Welding Parameters in GMA Welding Process

Optimal pass planning for robotic welding of large-dimension joints with nonuniform grooves

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