Artificial Intelligence Applications for Friction Stir Welding: A Review

Eren, Berkay; Güvenç, Mehmet Ali; Mıstıkoğlu, Selçuk

doi:10.1007/s12540-020-00854-y

Cited by 61 publications

(51 citation statements)

References 192 publications

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“…The literature review revealed that the combined method had an efficiency of 96% for predictions [63]. The single-step method gave a 6-32% difference in range in terms of tensile strength, but the combined method demonstrated a 1-6%, difference in tensile strength, which was lower than the single-step method, as shown in Table 2.…”

Section: Literature Reviewmentioning

confidence: 98%

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

et al. 2021

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In this study, we present a new algorithm for finding the optimal friction stir welding parameters to maximize the tensile strength of a butt joint made of the semisolid material (SSM) ADC12 aluminum. The welding parameters were rotational speed, welding speed, tool tilt, tool pin profile, and rotational direction. The method presented is a variable neighborhood strategy adaptive search (VaNSAS) approach. The process of finding the optimal friction stir welding parameters comprises five steps: (1) identifying the type and range of friction stir parameters using a literature survey; (2) performing experiments according to (1); (3) constructing a regression model using the response surface method optimizer (RSM optimizer); (4) using VaNSAS to find the optimal parameters for the model obtained from (3); and (5) confirming the results from (4) using the parameter levels obtained from (4) to perform real experiments. The computational results revealed that the tensile strength generated from VaNSAS was 3.67% higher than the tensile strength obtained from the RSM optimizer parameters. The optimal parameters obtained from VaNSAS were a rotation speed of 2200 rpm, a welding speed of 108.34 mm/min, a tool tilt of 1.23 Deg, a tool pin profile of a hexagon, and a rotational direction of clockwise.

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Section: Literature Reviewmentioning

confidence: 98%

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

et al. 2021

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“…|| || (7) Table 4 shows the classification report of SVM algorithm. From the results it is observed that it resulted in the accuracy score of 0.89.…”

Section: Support Vector Machine (Svm) Algorithmmentioning

confidence: 99%

Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints

Mishra¹

2021

Preprint

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Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K-Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms.

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“…They are successfully used to analyse technological processes and phenomena occurring within them. Eren et al [ 40 ] used artificial neural networks to describe the friction stir welding process. Yan and Chen [ 41 ] used adaptive control for the optimization of the free forging process, where neural networks were used to modify proportional–integral–derivative controller settings and increase machining accuracy.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

2021

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This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R2 = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R2 = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

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Artificial Intelligence Applications for Friction Stir Welding: A Review

Cited by 61 publications

References 192 publications

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints

Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

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