Machine learning approach for predicting the peak temperature of dissimilar AA7050-AA2014A friction stir welding butt joint using various regression models

Anandan, B.; Manikandan, M

doi:10.1016/j.matlet.2022.132879

Cited by 20 publications

(2 citation statements)

References 10 publications

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“…At present, the quality evaluation of RFSSW joints mainly adopts two technical paths based on welding process parameters or non-destructive testing. The evaluation of joint quality based on welding process parameters mainly employs the response surface method and machine learning technology to predict the joint quality through various welding process parameters [6][7][8]. RFSSW involves welding process parameters such as the insertion depth of the sleeve, stirring speed, stirring time, and refilling time.…”

Section: Introductionmentioning

confidence: 99%

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode

Zhong,

Xu,

Dong

et al. 2024

Materials

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Surface features are crucial for assessing welding quality because they serve as an intuitive depiction of the quality of the joint and have a major influence on welding strength. According to the characteristics of the refill friction stir spot welding (RFSSW) process and an analysis of the surface-state and internal morphology of RFSSW joints, a method of predicting the mechanical properties of RFSSW joints based on surface-state characteristics was proposed. In this paper, a laser-ranging sensor was used to characterize the surface state of RFSSW joints, and parametric characterization methods of the surface-state features of RFSSW joints were proposed. On this basis, a support vector machine was used to predict and analyze the fracture mode of RFSSW joints. The accuracy of the analysis of the test samples reached 95.8%. This paper provides a more efficient and convenient new method for the quality evaluation of RFSSW joints.

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Section: Introductionmentioning

confidence: 99%

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode

Zhong,

Xu,

Dong

et al. 2024

Materials

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“…The XGBoost model showed the highest accuracy of 95.24% compared with DT and RF models, equal to 90.48%. In another study [181], ML algorithms (LR, PR, SVR, DTR, and RFR) were applied to predict the dissimilar butt-welded joint of AA7050-AA2014A from sets of input variables (RS: 1000-1600 rpm, TS: 30-70 mm/min, tilt angle: 0.5-2.5 • ). There were 108 datasets, with 90% training and 10% testing data.…”

mentioning

confidence: 99%

A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

Prabhakar,

Korgal,

Shettigar

et al. 2023

JMMP

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This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.

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A Physics-Informed Machine Learning Model of Dissimilar Friction Stir Welding to Tailor Residual Stress Using Coupled Eulerian and Lagrangian Approach

Das,

Bag,

Pal

2024

J. of Materi Eng and Perform

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Machine learning approach for predicting the peak temperature of dissimilar AA7050-AA2014A friction stir welding butt joint using various regression models

Cited by 20 publications

References 10 publications

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode

A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

A Physics-Informed Machine Learning Model of Dissimilar Friction Stir Welding to Tailor Residual Stress Using Coupled Eulerian and Lagrangian Approach

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