Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "defect detection" wg kryterium: Wszystkie pola

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 43
Tytuł:
Application of Terahertz Radiation in Non-Destructive Testing of Military-Designated Composite Materials
Zastosowanie promieniowania terahercowego w nieniszczących badaniach materiałów kompozytowych o przeznaczeniu militarnym
Autorzy:
Strąg, Martyna
Świderski, Waldemar
Powiązania:
https://bibliotekanauki.pl/articles/27314940.pdf
Data publikacji:
2023
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
non-destructive testing
terahertz radiation
composites
defect detection
materiały kompozytowe
badania nieniszczące
promieniowanie terahercowe
detekcja defektów
Opis:
The non-destructive testing methods (NDT) are gaining significant attention due to their ability to monitor the objects structure without causing their damage. In recent years, studies focused on NDT have been directed towards imaging with the use of the terahertz (THz) waves. The presented study describes terahertz imaging-based NDT method and testing results on selected military-designated materials with intentionally introduced defects. The main aim of the work was to clearly detect various discontinuities in materials interior and thus, to show the possibilities of the newly developed terahertz-based testing method in transmission mode. The results confirmed high applicability of THz waves for monitoring various materials where each implemented flaw was easily distinguished. Therefore, the presented method looks promising for real applications in everyday practice.
W prezentowanej pracy opisano zastosowanie metody obrazowania nieniszczącego przy wykorzystaniu promieniowania terahercowego. Badaniom poddano wybrane grupy materiałów kompozytowych znajdujących zastosowanie w wojsku, które miały celowo wprowadzone defekty. Głównym celem pracy było wyraźne wykrycie różnych nieciągłości we wnętrzu materiałów kompozytowych, a tym samym pokazanie możliwości nowo opracowanej metody testowania opartej na zastosowaniu promieniowania terahercowego w trybie transmisyjnym. Metoda terahercowa w trybie transmisyjnym, gdzie generator promieniowania i detektor znajdowały się po przeciwnej stronie próbki. W wyniku badań wykryte zostały wszystkie defekty celowo wprowadzone do analizowanych materiałów kompozytowych, wśród których wyróżniono: kompozyty wzmocnione włóknem aramidowym, gazogenerator, wkład do kamizelki kuloodpornej. W ramach tej pracy przedstawiono przykłady efektywnego wykorzystania promieniowania terahercowego jako metody badań nieniszczących oraz potencjalne zastosowanie w monitorowaniu materiałów o przeznaczeniu wojskowym. Wyniki dowiodły, że metoda terahercowa jest w stanie wykryć wady ukryte w kompozytach wzmocnionych włóknami aramidowymi, gazogeneratorze i wkładzie do kamizelki kuloodpornej. Wyniki przedstawione w postaci zdjęć cechowała wyższa jakość w odniesieniu do danych literaturowych.
Źródło:
Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa; 2023, 14, 4 (54); 91--102
2081-5891
Pojawia się w:
Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Camera-based PHM method in rotating machinery equipment micro-action scenarios
Autorzy:
Junfeng, An
Liu, Jiqiang
Zhen, Hao
Mengmeng, Lu
Powiązania:
https://bibliotekanauki.pl/articles/24200809.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
deep learning
condition monitoring
Rmcad
anomaly detection
defect early warning
Opis:
The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 10
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Study of a Compton backscattering wall defects detection device using the Monte Carlo method
Autorzy:
Qin, Xuan
Yang, Jianbo
Du, Zhengcong
Xu, Jie
Li, Rui
Li, Hui
Liu, Qi
Powiązania:
https://bibliotekanauki.pl/articles/24202748.pdf
Data publikacji:
2023
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
compton backscattering
Monte Carlo
nondestructive testing
wall defect
Opis:
In view of the shortcomings of traditional wall defect detection methods, such as small detection range, poor accuracy, non-portable device, and so on, a wall defects detection device based on Compton backscattering technology is designed by Monte Carlo method, which is mainly used to detect the size and location information of defects in concrete walls. It mainly consists of two parts, the source container and the detection system: first, through the simulation and analysis of the parameters such as the receiving angle of thebackscattered particles and the rear collimating material of the detector, the influence of the fluorescent X-ray peak of the detector collimating material on the backscattered particle counts is eliminated and the detected error is reduced; second, the ring array detector design, compared with single array detector and surface array detector, can facilitate real-time detection of defect orientation, expanding the single scan range and improving the detection efficiency. After simulation and comparative analysis, the relevant optimal parameters are obtained: the object is detected using a Cs-137 γ-ray source with an activity of 6 mCi, and a ring detector consisting of four 0.5-inch cube-shaped CsI scintillator detectors is placed at 150° to receive the backscattered photons. The simulation analysis using the Monte Carlo FLUKA program showed that the maximum depth of wall defect detection is 8 cm, the maximum error fl uctuation range of defect depth and thickness is ±1 cm, the overall device weight is <20 kg, and the measurement time is <5 min.
Źródło:
Nukleonika; 2023, 68, 2; 57--63
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vision-based Online Defect Detection of Polymeric Film via Structural Quality Metrics
Autorzy:
Rawashdeh, Nathir
Hazaveh, Paniz
Altarazi, Safwan
Powiązania:
https://bibliotekanauki.pl/articles/2201189.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
industrial inspection
quality control
defect detection
polymer film
vision
Opis:
Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be imple mented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy.
Źródło:
Management and Production Engineering Review; 2023, 14, 1; 61--71
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel data mining approach for defect detection in the printed circuit board manufacturing process
Autorzy:
Bártová, Blanka
Bína, Vladislav
Powiązania:
https://bibliotekanauki.pl/articles/2105321.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
quality management
defect detection
AOI
PCA
PCB
SVM
zarządzanie jakością
wykrywanie defektów
Opis:
This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.
Źródło:
Engineering Management in Production and Services; 2022, 14, 2; 13--25
2543-6597
2543-912X
Pojawia się w:
Engineering Management in Production and Services
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Industrial Application of Deep Neural Network for Aluminum Casting Defect Detection in Case of Unbalanced Dataset
Autorzy:
Awtoniuk, Michał
Majerek, Dariusz
Myziak, Artur
Gajda, Cyprian
Powiązania:
https://bibliotekanauki.pl/articles/2204946.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
machine learning
deep neural network
classification
casting defect
casting defect detection
Opis:
We have developed a deep neural network for casting defect detection. The approach is original because it assumes the use of data related to the casting manufacturing process, i.e. measurement signals from the casting machine, rather than data describing the finished casting, e.g. images. The defects are related to the production of car engine heads made of silumin. In the current research we focused on the detection of defects related to the leakage of the casting. The data came from production plant in Poland. The dataset was unbalanced. It included nearly 38,500 observations, of which only 4% described a leak event. The work resulted in a deep network consisting of 22 layers. We assessed the classification accuracy using a ROC curve, an AUC index and a confusion matrix. The AUC value was 0.97 and 0.949 for the learning and testing dataset, respectively. The model allowed for an ex-post analysis of the casting process. The analysis was based on Shapley values. This makes it possible not only to detect the occurrence of a defect but also to give potential reasons for the appearance of a casting leak.
Źródło:
Advances in Science and Technology. Research Journal; 2022, 16, 5; 120--128
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary
Autorzy:
Wu, Ying
Wang, Ren
Lou, Lin
Wang, Lali
Wang, Jun
Powiązania:
https://bibliotekanauki.pl/articles/2172000.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
fabric texture representation
sparse representation
weave repeat
defect detection
dictionary learning
Opis:
To achieve enhanced accuracy of fabric representation and defect detection, an innovative approach using a sparse dictionary with small patches was used for fabric texture characterisation. The effectiveness of the algorithm proposed was tested through comprehensive characterisation by studying eight weave patterns: plain, twill, weft satin, warp satin, basket, honeycomb, compound twill, and diamond twill and detecting fabric defects. Firstly, the main parameters such as dictionary size, patch size, and cardinality T were optimised, and then 40 defect-free fabric samples were characterised by the algorithm proposed. Subsequently, the Impact of the weave pattern was investigated based on the representation result and texture structure. Finally, defective fabrics were detected. The algorithm proposed is an alternative simple and scalable method to characterise fabric texture and detect textile defects in a single step without extracting features or prior information.
Źródło:
Fibres & Textiles in Eastern Europe; 2022, 3 (151); 33--40
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A nested autoencoder approach to automated defect inspection on textured surfaces
Autorzy:
Oz, Muhammed Ali Nur
Kaymakci, Ozgur Turay
Mercimek, Muharrem
Powiązania:
https://bibliotekanauki.pl/articles/2055170.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
autoencoder
defect detection
automatic visual inspection
deep learning
autoenkoder
wykrywanie defektów
inspekcja wizyjna
inspekcja automatyczna
uczenie głębokie
Opis:
In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 515--523
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Defect Detection Using Deep Learning-Based YOLOv3 in Cross-Sectional Image of Additive Manufacturing
Autorzy:
Choi, Byungjoo
Choi, Yongjun
Lee, Moon-Gu
Kim, Jung-Sub
Lee, Sang-Won
Jeon, Yongho
Powiązania:
https://bibliotekanauki.pl/articles/2048889.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
additive manufacturing
deposition defect
data augmentation
YOLOv3
object detection
Opis:
Deposition defects like porosity, crack and lack of fusion in additive manufacturing process is a major obstacle to commercialization of the process. Thus, metallurgical microscopy analysis has been mainly conducted to optimize process conditions by detecting and investigating the defects. However, these defect detection methods indicate a deviation from the operator’s experience. In this study, artificial intelligence based YOLOv3 of object detection algorithm was applied to avoid the human dependency. The algorithm aims to automatically find and label the defects. To enable the aim, 80 training images and 20 verification images were prepared, and they were amplified into 640 training images and 160 verification images using augmentation algorithm of rotation, movement and scale down, randomly. To evaluate the performance of the algorithm, total loss was derived as the sum of localization loss, confidence loss, and classification loss. In the training process, the total loss was 8.672 for the initial 100 sample images. However, the total loss was reduced to 5.841 after training with additional 800 images. For the verification of the proposed method, new defect images were input and then the mean Average Precision (mAP) in terms of precision and recall was 0.3795. Therefore, the detection performance with high accuracy can be applied to industry for avoiding human errors.
Źródło:
Archives of Metallurgy and Materials; 2021, 66, 4; 1037-1041
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of thinning of homogeneous material using active thermography and classification trees
Autorzy:
Dudzik, Sebastian
Dudek, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/1848987.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
active thermography
classification tree
defect detection and characterization
material thinning detection
Opis:
Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.
Źródło:
Metrology and Measurement Systems; 2021, 28, 1; 89-105
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic defect detection and characterization by thermographic images based on damage classifiers evaluation
Autorzy:
Dinardo, Giuseppe
Fabbiano, Laura
Tamborrino, Rosanna
Vacca, Gaetano
Powiązania:
https://bibliotekanauki.pl/articles/221850.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
thermography
non-destructive testing
thermal barrier coatings
image segmentation
uncertainty analysis
Opis:
In the framework of non-destructive evaluation (NDE), an accurate and precise characterization of defects is fundamental. This paper proposes a novel method for characterization of partial detachment of thermal barrier coatings from metallic surfaces, using the long pulsed thermography (LPT). There exist many applications, in which the LPT technique provides clear and intelligible thermograms. The introduced method comprises a series of post-processing operations of the thermal images. The purpose is to improve the linear fit of the cooling stage of the surface under investigation in the logarithmic scale. To this end, additional fit parameters are introduced. Such parameters, defined as damage classifiers, are represented as image maps, allowing for a straightforward localization of the defects. The defect size information provided by each classifier is, then, obtained by means of an automatic segmentation of the images. The main advantages of the proposed technique are the automaticity (due to the image segmentation procedures) and relatively limited uncertainties in the estimation of the defect size.
Źródło:
Metrology and Measurement Systems; 2020, 27, 2; 219-242
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deblurring approach for motion camera combining FFT with α-confidence goal optimization
Autorzy:
Huang, Lve
Wu, Lushen
Xiao, Wenyan
Peng, Qingjin
Powiązania:
https://bibliotekanauki.pl/articles/174045.pdf
Data publikacji:
2020
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
image deblurring
fast motion camera
confidence goal optimization
fast Fourier transform
high-railway defect detection
Opis:
Sharp images ensure success in the object detection and recognition from state-of-art deep learning methods. When there is a fast relative motion between the camera and the object being imaged during exposure, it will necessarily result in blurred images. To deblur the images acquired under the camera motion for high-quality images, a deblurring approach with relatively simple calculation is proposed. An accurate estimation method of point spread function is firstly developed by performing the Fourier transform twice. Artifacts caused by image direct deconvolution are then reduced by predicting the image boundary region, and the deconvolution model is optimized by an α-confidence statistics algorithm based on the greyscale consistency of the image adjacent columns. The proposed deblurring approach is finally carried out on both the synthetic-blurred images and the real-scene images. The experiment results demonstrate that the proposed image deblurring approach outperforms the existing methods for the images that are seriously blurred in direction motion.
Źródło:
Optica Applicata; 2020, 50, 2; 185-198
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm
Wykrywanie defektów tkaniny za pomocą czułego algorytmu segmentacji roślin (SPSA)
Autorzy:
Nisha, M. Fathu
Vasuki, P.
Roomi, S. Mohamed Mansoor
Powiązania:
https://bibliotekanauki.pl/articles/234459.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
external stimulation
fabric pattern
sensitive behaviour
texture
zewnętrzna stymulacja
wzór tkaniny
wrażliwe zachowanie
tekstura
Opis:
Fabric quality control and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using a sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the sensitive plant biologically named “mimosa pudica”. This method consists of two stages: The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with the aid of the SPSA. The SPSA proposed was developed for defective pixel identification in non-uniform patterns of fabrics. In this paper, the SPSA was built through checking with devised conditions, correlation and error probability. Every pixel was checked with the algorithm developed to be marked either a defective or non-defective pixel. The SPSA proposed was tested on different types of fabric defect databases, showing a much improved performance over existing methods.
Wraz z rozwojem zapotrzebowania klientów na rynku mody kontrola jakości tkanin i wykrywanie defektów odgrywa kluczową rolę w przemyśle tekstylnym. W pracy przedstawiono wykrywanie defektów tkanin przy użyciu czułego algorytmu segmentacji roślin (SPSA), który został opracowany na podstawie rośliny o nazwie biologicznej „mimosa pudica”. Ta metoda składa się z dwóch etapów. Pierwszy etap to wzmocnienie kontrastu wadliwego obrazu tkaniny, a drugi etap segmentował defekty tkaniny za pomocą SPSA. Proponowana metoda z użyciem SPSA została opracowana do identyfikacji wadliwych pikseli w niejednorodnych wzorach tkanin. W artykule przedstawiono wyniki SPSA, a także dokonano ich weryfikacji pod kątem korelacji i prawdopodobieństwa błędu. Każdy piksel został sprawdzony za pomocą opracowanego algorytmu, tak aby został zaznaczony piksel wadliwy lub nieuszkodzony. Proponowany algorytm SPSA został przetestowany na różnych typach baz danych defektów tkanin i wykazał niezwykłą wydajność w stosunku do istniejących metod.
Źródło:
Fibres & Textiles in Eastern Europe; 2020, 3 (141); 84-87
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A New Approach to Experimental Testing of Sheet Metal Formability for Automotive Industry
Autorzy:
Jasiński, C.
Kocańda, A.
Morawiński, Ł.
Świłło, S.
Powiązania:
https://bibliotekanauki.pl/articles/350906.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
vision system
defect detection
Erichsen cupping test
laser speckle
Opis:
Advanced vision method of analysis of the Erichsen cupping test based on laser speckle is presented in this work. This method proved to be useful for expanding the range of information on material formability for two commonly used grades of steel sheets: DC04 and DC01. The authors present a complex methodology and experimental procedure that allows not only to determine the standard Erichsen index but also to follow the material deformation stages immediately preceding the occurrence of the crack. Accurate determination of these characteristics in the sheet metal forming would be an important application, especially for automotive industry. However, the sheet metal forming is a very complex manufacturing process and its success depends on many factors. Therefore, attention is focused in this study on better understanding of the Erichsen index in combination with the material deformation history.
Źródło:
Archives of Metallurgy and Materials; 2019, 64, 4; 1231-1238
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Two Advanced Vision Methods Based on Structural and Surface Analyses to Detect Defects in the Erichsen Cupping Test
Autorzy:
Jasiński, C.
Świłło, S.
Kocańda, A.
Powiązania:
https://bibliotekanauki.pl/articles/353647.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
defect detection
Erichsen cupping test
laser speckle
vision system
Opis:
Due to the wide range of various sheet metal grades and the need to verify the material properties, there are numerous methods to determine the material formability. One of them, that allows quick determination of sheet metal formability, is the Erichsen cupping test described in the ISO 20482: 2003 standard. In the presented work, the results of formability assessment for DC04 deepdrawing sheet metal were obtained by means of the traditionally carried out Erichsen cupping test and compared with the resultsobtained by using two advanced methods based on vision analysis. Application of these methods allows extending the traditional scope of analysis during Erichsen cupping test by determination of the necking and strain localization before fracture. The proposed methods were compared in order to dedicate appropriate solution for the industrial application and laboratory tests respectively, where the simplicity and reliability are the mean aspects need to be considered when applied to the Erichsen cupping test.
Źródło:
Archives of Metallurgy and Materials; 2019, 64, 3; 1041-1049
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 43

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies