Estimation method of yield strength of ferromagnetic materials based on pulsed eddy current testing

Li, Kaiyu; Qiu, Ping; Wang, Pin; Zixiang, Lu; Zhang, Zhengda

doi:10.1016/j.jmmm.2020.167647

Cited by 17 publications

(2 citation statements)

References 27 publications

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“…Based on this observation, an electrical conductivity estimation technique was proposed, and its performance was evaluated using copper, brass, and aluminum specimens. In addition, the yield strength of steels was estimated using an eddy-current technique with a pulse (square) input [6]. The pulse input provides a deeper inspection depth and more features for yield strength estimation than the conventional singlefrequency sinusoidal input.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive yield strength estimation for 3D-printed Ti-6Al-4V plates using eddy-current measurement

Lim

Lee

Sohn

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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This study provides the development of a nondestructive yield strength estimation technique for metal 3D printed Ti-6Al-4V components using eddy-current measurement. Based on the relationship between the electrical conductivity and the grain size of the material, and the Hall-Petch relationship, the yield strength of metallic materials can be correlated by eddy-current phase value. First, a theoretical expression for the yield strength using the eddy-current phase value is explicitly derived. Then, for the experimental validation, the specimens with various yield strengths were produced by adjusting the cooling rate during the printing of each specimen. Then, the coefficients in the theoretical expression were estimated using the actual yield strength of specimens obtained via the conventional destructive tensile tests and the eddycurrent phase values. Finally, the yield strength estimation performance was examined using the eddy-current signals obtained from test specimens with unknown yield strengths. The results indicate that the proposed technique can precisely estimate 3D printed Ti-6Al-4V components. The novelty of this study lies in (1) the derivation of an explicit relationship between the eddy-current phase value and the yield strength, (2) nondestructive yield strength estimation technique based on eddy-current testing, and (3) application to 3D printed Ti-6Al-4V plate specimens with various yield strengths.

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Section: Introductionmentioning

confidence: 99%

Nondestructive yield strength estimation for 3D-printed Ti-6Al-4V plates using eddy-current measurement

Lim

Lee

Sohn

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

View full text Add to dashboard Cite

show abstract

“…To assess the quality and correctness of the production process, both at the production stage and in the operational control phase, preference is given to non-invasive diagnostic methods -the so-called non-destructive testing. Such methods include, among others tomography (electric, ultrasound, magnetic) Rymarczyk, Kozłowski, Kłosowski, and Niderla, 2019;Kłosowski et al, 2020), electromagnetic testing or eddy current method (Li et al, 2021), visual research method (Deng et al, 2018), penetration method (Kryukov et al, 2016) or magnetic method (Tönshoff, Karpuschewski, and Regent, 1999). The main directions of the use of the eddy current method are the detection of material defects -defectoscopy, testing of material properties -structroscopy and the determination of dimensions (mainly thickness measurements).…”

Section: Introductionmentioning

confidence: 99%

The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management

Kłosowski¹,

Kulisz²,

Lipski³

et al. 2021

ERSJ

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The aim of the article is to develop an algorithm for classifying cracks in the analyzed images using modern methods of deep machine learning and transfer learning based on pretrained convolutional neural network -Inception-ResNet-v2. Design/Methodology/Approach: Transfer learning based on the pretrained convolutional neural network was used to categorize the images. The fully conected layer of the Inception-ResNet-v2 network has been modified. The last layer was trained using a two-class (binary) linear SVM (Support Vector Machine). In total, 20,000 training cases (images) were used to train the fully connected layer within transfer learning process. The research analyzed the possibility of using the deep neural networks for quick and fully automatic identification of cracks / defects on the surface of analyzed parts. Findings:The results indicate that pretrained convolutional neural network using SVM to train a fully connected layer is a very effective solution for visual crack / fault detection. In the analyzed model, a positive classification was obtained at the level of 99.89%. Practical Implications: The model presented in the article can be used in quality control carried out by process monitoring. An effective model for identifying defective parts can be used in both logistics and production processes. Originality/Value: A novelty is the use of a freely available, deep neural network trained to classify 1000 categories of various images for binary categorization of faults (cracks). The algorithm was adjusted by replacing the primary, 1000-output fully connected layer in the Inception-ResNet-v2 network with a binary layer (2 categories). The fully connected layer has been trained using the classification version of the popular SVM learner, but thanks to the combination of this layer with the sophisticated fearure extraction ability of the pre-trained Inception-ResNet-v2 deep network, the resulting predictive model enables the classification of defects with a very high level of accuracy.

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The Image Classification Method for Eddy Current Inspection of Titanium Alloy Plate Based on Parallel Sparse Filtering and Deep Forest

Yidan,

Zou,

et al. 2024

J Nondestruct Eval

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Estimation method of yield strength of ferromagnetic materials based on pulsed eddy current testing

Cited by 17 publications

References 27 publications

Nondestructive yield strength estimation for 3D-printed Ti-6Al-4V plates using eddy-current measurement

Nondestructive yield strength estimation for 3D-printed Ti-6Al-4V plates using eddy-current measurement

The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management

The Image Classification Method for Eddy Current Inspection of Titanium Alloy Plate Based on Parallel Sparse Filtering and Deep Forest

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