Deep Learning Techniques for Flaw Characterization in Weld Pieces from Ultrasonic Signals

Sudheera, K.; Nandhitha, N. M.; Sai, VPaineni Bhavagna Venkat; Kumar, Nallamothu Vijay

doi:10.1134/s1061830920100083

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Conversely, Fernández et al [ 11 ] presented in their work an ANN–LSTM architecture for the detection and classification of defects in the welding process based on video sequences. Additionally, Sudheera et al [ 12 ] used LSTM to interpret ultrasonic signals to characterise welding defects. The large variation in the length of the processed input sequences affected the accuracy of the data.…”

Section: Introductionmentioning

confidence: 99%

Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network

2021

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The motivation of the presented paper is the desire to create a universal tool to analyse the process of austenite decomposition during the cooling process of various steel grades. The presented analysis concerns the application of Recurrent Artificial Neural Networks (RANN) of the Long Short-Term Memory (LSTM) type for the analysis of the transition path of the cooling curve. This type of network was selected due to its ability to predict events in time sequences. The proposed generalisation allows for the determination of the austenite transformation during the continuous cooling process for various cooling curves. As training data for the neural network, values determined from the macroscopic model based on the analysis of Continuous Cooling Transformation (CCT) diagrams were used. All relations and analyses used to build training/testing or validation sets are presented in the paper. The modelling with the use of LSTM network gives the possibility to determine the incremental changes of phase transformation (in a given time step) with the assumed changes of temperature resulting from the considered cooling rate.

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Section: Introductionmentioning

confidence: 99%

Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network

2021

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“…Image Processing algorithms along with the acquired thermographs enable us to accurately predict the hidden information in the structures, condition monitoring of electrical equipment's etc. [4][5] [6].In this paper, feasibility of image processing techniques for the segmentation of anomalies in thermographs is studied. Section 2 deals with research database.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Infrared Thermography for Health Monitoring of Archeological Structures

Nandhitha

Roslin

Chakravarthi

et al. 2021

Advances in Parallel Computing

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Archeological assets of the nation are to be preserved and rejuvenated. Ageing of these sites poses a major challenge in assessing the health of these structures. Hence it necessitates a technique that is non contact non invasive and non hazardous. Passive InfraRed Thermography is one such technique that uses an IR camera to capture the temperature variations. Thermal variations are mapped as thermographs. Interpretation of thermographs provides information about the health of the archeological structures. As the paradigm has shifted to computer aided interpretation, segmentation techniques and line profiling are used for describing the hotspot. Of the various segmentation techniques, morphological image processing provides accurate segmentation of cold spot.

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Convolutional Neural Networks for Weld Defect Detection from Ultrasonic Signals

Sudheera

Nandhitha

Roslin

et al. 2022

Lecture Notes in Mechanical Engineering

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Deep Learning Techniques for Flaw Characterization in Weld Pieces from Ultrasonic Signals

Cited by 6 publications

References 6 publications

Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network

Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network

Feasibility of Infrared Thermography for Health Monitoring of Archeological Structures

Convolutional Neural Networks for Weld Defect Detection from Ultrasonic Signals

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