Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Mishra, Akshansh; Dutta, Saloni Bhatia

doi:10.18485/aeletters.2020.5.1.3

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…Apart from this, image pyramids and image reconstructions were used to analyze the defects on various weld samples. Convolution neural networks are proved to be another best model for detecting defective vs. nondefective welds by processing their images on the production line [72]. This system obtains better results in both offline and online monitoring processes.…”

Section: Resultsmentioning

confidence: 99%

“…Such neural networks are known as fully connected neural networks (FCNNs). A model is able to detect the defects and faults in the FSW, which usually undergoes three processes, including the mathematical process of convolution, feature extraction to detect the defects at any corner, max pooling to reduce the dimension, the addition of fully connected layers, and then the softmax function to determine the probability of defects and faults [72]. The model determining the correlation between rotational tool speeds, sample extracted position, and thermal data can be trained to obtain.…”

Section: Convolution Filtersmentioning

confidence: 99%

“…The decision is a widely used supervised learning algorithm for classification problems. It is commonly used to detect fracture and predict UTS for FSW [59,72]. It is based on the principle that it uses parent nodes to make decisions and leaf nodes for their output.…”

Section: Influence Of Regression and Classification Model On Fswmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Convolution Filtersmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…These issues are highlighted in their paper's conclusion and future scope section. The algorithms (image pyramid and image reconstruction) were applied to determine the FSW-processed AA 6060 T5 plates [175]. Four welded specimens were prepared with different sets of RS: 1500-2000 rpm, TS: 200-400 mm/min, and AF: 1.5-2.5 kN.…”

Section: Machine Learning (Ml)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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“…The relevance of this work in industries is to make aware of the interdisciplinary application between fields like computer science and mechanical/manufacturing engineering and to produce defects-free products [7] by analysing the various objects with different issues like machining and welding of materials, checking the geometry of the components, detection of defects etc. The input images are converted into different output images including Otsu's binary threshold image, and the images with the edge/geometry of the joint and clips identified by Sobel, Scharr, and Prewitt operators, Python coding validation and the verification in which image segmentation and edge detection.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection and Defects Checking of Binder Clip and Welded Joint using a Python-Based Algorithm: Applications in Quality Inspection

Senthil Murugan,

Sathiya,

Hariharan

et al. 2023

AWET

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Machine vision is a computer vision system that enables a computer to work on image-based inspection and analysis for different applications. In this computer vision, a camera and sensor were used to view an image for its analysis with the help of some sort of algorithms, then processed to infer the image-based data. Machine vision systems along with Python programs can be used for many interdisciplinary applications like weld inspection, online monitoring in manufacturing auto components etc. In this study, the “Edge detection python algorithm” was developed and run through “Google Colab” notebook to inspect the edges and the boundaries of samples like faying surface-modified friction welded dissimilar joints and a binder clip (paper clamp) to check any defects or cracks and straightness etc. With the help of this Python algorithm, the edge detection was done by Sobel, Scharr, and Prewit operators. An input image of the weld joint and the binder clip were converted into Otsu’s binary threshold image. The matrix vision camera and the CMOS sensor were used in the machine vision set-up to take the images. This written algorithm is helpful to trace the edges of any kind of solids components. The edges of the binder clips and the weld joint/zone were detected. The binder clips were inspected under two different cases namely the clip in folding condition (Case I) and the binder clip in unfolding condition (Case II). The results showed a defect that was identified in the weld zone and no bending was in the binder clips. This kind of study is useful in manufacturing industries for quality inspection purposes with a new machine vision set up for online inspection of fabricated components like nuts and bolts etc.

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Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Cited by 4 publications

References 11 publications

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

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

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

Edge Detection and Defects Checking of Binder Clip and Welded Joint using a Python-Based Algorithm: Applications in Quality Inspection

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