Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm – A Machine learning approach

Balachandar, K.; Jegadeeshwaran, R.

doi:10.1016/j.matpr.2021.02.061

Cited by 22 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Output: a probability distribution of the weld being good, broken, or having an air bubble [103,104] AlexNet, a CNN containing 5 layers of filter sizes 5, 3, 3, 3, and 3 with the number of filters being 96, 256, 384, 384, and 256, which took in images from the weld pool and classified them into two classes; ok and not ok…”

Section: Resultsmentioning

confidence: 99%

“…The correlation occurs between the tensile strength and the surface appearance and relates to various input and process parameters [67,78,125]. It proves over 95 percent accurate in detecting the good and bad welds, which helps greater in online monitoring and feedback systems [58,103,[131][132][133][134][135][136]. A modified LSVM is also being used to obtain temperature signals of different frequency bands another step towards obtaining more data to check what predicts better weld quality [137][138][139][140][141][142][143][144].…”

Section: Discussionmentioning

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

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Therefore, estimation models for tool wear and surface roughness should be developed. Recently, various estimation approaches have been proposed, e.g., artificial neural networks [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], regression [ 9 , 11 , 12 , 13 , 14 , 15 , 16 ], support vector machines [ 17 , 18 , 19 , 20 ], response surface methodology [ 21 , 22 ], random forest [ 18 , 23 , 24 ], and adaptive network-based fuzzy inference systems [ 25 , 26 , 27 ]. In general, the chosen model and data directly affect estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Huang

Lee

2021

Sensors

View full text Add to dashboard Cite

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

show abstract

“…The results showed that the Artificial Neural Network model resulted in a good accuracy score of 0.94. Balachandar et al [11] monitored the Friction Stir Welding tool condition by using the Random Forest algorithm which gave better results. There is a limited number of research studies available on the application of Supervised Machine Learning Classification models in the Friction Stir Welding Process.…”

Section: Introductionmentioning

confidence: 99%

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

Mishra¹

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm – A Machine learning approach

Cited by 22 publications

References 10 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

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

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

Contact Info

Product

Resources

About