Prediction model for bead reinforcement area in automatic gas metal arc welding

Shim, Ji-Yeon; Zhang, Jan-Wei; Yoon, Han-Yong; Kang, Bong-Yong; Kim, Ill-Soo

doi:10.1177/1687814018781492

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Along with the maturity of related theories such as machine learning and neural networks, the task of welding quality prediction is increasingly implemented by scholars using related techniques. Artificial neural networks (ANN): In the automatic gas metal arc welding processes, the response surface methodology and ANN models are adopted by Shim et al 26 predict the best welding parameters for a given weld bead geometry. Lei et al 27 used a genetic algorithm to optimize the initialization weights and biases of the neural network.…”

Section: Related Workmentioning

confidence: 99%

“…Artificial neural networks (ANN): In the automatic gas metal arc welding processes, the response surface methodology and ANN models are adopted by Shim et al 26 predict the best welding parameters for a given weld bead geometry. Lei et al 27 used a genetic algorithm to optimize the initialization weights and biases of the neural network.…”

Section: Related Workmentioning

confidence: 99%

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Prediction and optimization method for welding quality of components in ship construction

Liu,

Cheng,

Jing

et al. 2024

Sci Rep

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Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prediction and optimization method for welding quality of components in ship construction

Liu,

Cheng,

Jing

et al. 2024

Sci Rep

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“…An ensemble of variable neighborhood search-based gene expression programming and black-box metamodels is presented by Wu et al [23] to ensure the reflection from welding PP to welding quality and energy consumption. Additionally, some machine learning (ML) methods like radial basis function, artificial neural network, and CatBoost [24,25] are applied to construct models between PP and responses based on the experiment data. The experiment-driven, data-driven modeling method exhibits more applicability in comparison to numerical approaches.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

Yan,

Zhang,

2023

Sustainability

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The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability.

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“…Nagesh et al [ 13 ] built a back propagation neural network (BPNN) model with the inputs (electrode feed rate, arc-power, arc-voltage, arc-current, arc-length, and arc-travel rate), outputs (bead height, bead width, depth of penetration, and area of penetration) and yielded accurate results in shielded metal-arc welding. Shim et al [ 14 ] used BPNN and response surface methodology to predict bead reinforcement area by welding voltage, arc current, welding speed, contact tube weld distance, and welding angle, and obtained good quality predictions in automatic gas metal arc welding. Kshirsagar et al [ 15 ] used a two-stage algorithm that consisted of support vector machine (SVM) and an ANN to improve the prediction performance in automated tungsten inert gas (TIG) welding.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Gao

2021

Materials

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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.

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Prediction model for bead reinforcement area in automatic gas metal arc welding

Cited by 12 publications

References 25 publications

Prediction and optimization method for welding quality of components in ship construction

Prediction and optimization method for welding quality of components in ship construction

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

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

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