Machine learning based hierarchy of causative variables for tool failure in friction stir welding

Du, Youwei; Mukherjee, T.; Mitra, Prasenjit; DebRoy, T.

doi:10.1016/j.actamat.2020.03.047

Cited by 44 publications

(18 citation statements)

References 40 publications

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“…In all sectors, automating processes and working with data help to improve process efficiency. Determining the process factors and causative variables for tool failure of FSW was put to an end by neural network models with high accuracy of over 96% [67]. An RL algorithm implementation using a 4 × 4 Q table.…”

Section: Resultsmentioning

confidence: 99%

“…The decision tree does not have an understanding of the system behind friction stir welding; rather, it looks at the information gain and the correlation which might not imply causality. Hence, small changes in the training dataset may drastically change the architecture of the tree, thus changing the most important parameter Input: welding and rotational speeds, tilt angle, axial pressure, shoulder, and pin Radius, plate thickness, and the work plate material properties of thermal diffusivity Output: the probability of the pin breaking [67] 17 Material Design & Processing Communications Table 4 shows the application of various models to the friction stir welding process. The optimum parameter with the required value of UTS avoids any defects.…”

Section: Resultsmentioning

confidence: 99%

“…Various parameters prone to effect include flow stress, temperature, torque, and strain rate. Peak temperature and traverse force have less influence on tool failure and can be considered input to the decision tree [67,69]. The fracture location can also be easily determined with a decision tree with the lowest possible score of 0.5 [59].…”

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: Resultsmentioning

confidence: 99%

Section: Influence Of Regression and Classification Model On Fswmentioning

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

“…Gobert et al [ 21 ] described an in-situ defect detection and process monitoring strategy for PBF using a method of combining ML with high-resolution imaging. ML model has also been used in the research of the other AM technologies [ 22 , 23 , 24 , 25 , 26 , 27 ] and friction stir welding [ 28 , 29 ] in recent years, which validates the potential of ML for better manufacturing process.…”

Section: Introductionmentioning

confidence: 88%

Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method

Chen

Wang

et al. 2020

Materials

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Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.

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“…Many Machine Learning approaches are frequently used for the Friction Stir Welding process [10][11][12][13]. Friction Stir Welding is a robust joining procedure used to unite materials that are hard to connect using typical welding methods [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures

Mishra¹,

Suman

2021

Preprint

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Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an objective (loss function). CNN is finding various applications in image recognition, semantic segmentation, object detection, and localization. The present work deals with the prediction of the welding efficiency of the Friction Stir Welded joints on the basis of microstructure images by carrying out training on 3000 microstructure images and further testing on 300 microstructure images. The obtained results showed an accuracy of 80 % on the validation dataset.

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Machine learning based hierarchy of causative variables for tool failure in friction stir welding

Cited by 44 publications

References 40 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

Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method

Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures

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