Machine Learning Approach for Defects Identification in Dissimilar Friction Stir Welded Aluminium Alloys AA 7075-AA 1100 Joints

Mishra, Akshansh

doi:10.3844/jastsp.2020.88.95

Cited by 3 publications

(1 citation statement)

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“…This system obtains better results in both offline and online monitoring processes. Extension rate and normal fracture strength of given mechanical segment and image processing calculations can be easily applied for defect identification in the mechanical segments [127].…”

Section: Resultsmentioning

confidence: 99%

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

Chadha

Selvaraj

Gunreddy

et al. 2022

Material Design & Processing Communications

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Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.

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Section: Resultsmentioning

confidence: 99%