welding defects
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2022 ◽  
Author(s):  
Jianzhong Ju ◽  
Zhili Long ◽  
Shuyuan Ye ◽  
Yongzhi Liu ◽  
Heng Zhao

Abstract Ultrasonic vibration used in friction stir welding (FSW) has shown advantages in reducing welding defects and improving welding quality. How to design an ultrasonic tool holder is a challenge because the holder is rotating in a confined space. In this study, we design a 20 kHz integrated ultrasonic tool holder in FSW. This novel configuration can be applied in general machining equipment. The elastic modulus is measured by non-destructive acoustic testing to attain the precise frequency. Three FSW transducers with alloy steel are designed by the modal analysis and the transducer prototypes are fabricated. The effect of pre-tightening force on transducer frequency is investigated, where the prestress of the piezoelectric stack instead of the torque is tested to achieve an optimal working frequency. The vibration of the transducers is measured by a Doppler Vibrometer System. It proved that the resonant frequencies are well consistent between simulation model and the experiment by the elastic modulus testing and the pre-tightening optimization. Moreover, the experiment demonstrates that the vibration amplitude is significantly different, even in a slight difference of steel material properties are adopted. The dynamic performance of the designed transducers is acceptable by the vibration measurement.


Author(s):  
Bernadett Spisák ◽  
Zoltán Bézi ◽  
Szabolcs Szávai

Welding is accompanied by the presence of weld residual stresses, which in case of dissimilar metal welds even with post weld heat treatment cannot be removed completely therefore they should be considered when assessing possible welding defects. The measurement of residual stress in metal weld is a very complex procedure and also in the investigated case could not be carried out as it is the part of a working plant. However, by modelling these processes, the residual stresses and deformation of the components caused by this manufacturing method can be determined. It is important to calculate these values as accurately as possible to determine the maximum load capacity of the structure. The structure under examination was the dissimilar metal weld of a VVER-440 steam generator. 2D simulations were performed, where temperature and phase-dependent material properties were implemented. Different loading scenarios were considered in the numerical analysis. The results can be useful to determine the real loading conditions of a given component and can be used to predict stress corrosion crack initiation locations, as well as to evaluate the lifetime and failure mode prediction of welded joints.


Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 99
Author(s):  
Luis D. Cedeño-Viveros ◽  
Ciro A. Rodriguez ◽  
Victor Segura-Ibarra ◽  
Elisa Vázquez ◽  
Erika García-López

A novel manufacturing approach was used to fabricate metallic scaffolds. A calibration of the laser cutting process was performed using the kerf width compensation in the calculations of the tool trajectory. Welding defects were studied through X-ray microtomography. Penetration depth and width resulted in relative errors of 9.4%, 1.0%, respectively. Microhardness was also measured, and the microstructure was studied in the base material. The microhardness values obtained were 400 HV, 237 HV, and 215 HV for the base material, HAZ, and fusion zone, respectively. No significant difference was found between the microhardness measurement along with different height positions of the scaffold. The scaffolds’ dimensions and porosity were measured, their internal architecture was observed with micro-computed tomography. The results indicated that geometries with dimensions under 500 µm with different shapes resulted in relative errors of ~2.7%. The fabricated scaffolds presented an average compressive modulus ~13.15 GPa, which is close to cortical bone properties. The proposed methodology showed a promising future in bone tissue engineering applications.


2021 ◽  
Vol 12 (1) ◽  
pp. 123
Author(s):  
Gwang-ho Yun ◽  
Sang-jin Oh ◽  
Sung-chul Shin

Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints, and in welding defect inspection, among nondestructive inspections, radiographic inspection is widely applied during the production process. To perform nondestructive inspection, the completed weldment must be transported to the nondestructive inspection station, which is expensive; consequently, automation of welding defect detection is required. Recently, at several processing sites of companies, continuous attempts are being made to combine deep learning to detect defects more accurately. Preprocessing for welding defects in radiographic inspection images should be prioritized to automatically detect welding defects using deep learning during radiographic nondestructive inspection. In this study, by analyzing the pixel values, we developed an image preprocessing method that can integrate the defect features. After maximizing the contrast between the defect and background in radiographic through CLAHE (contrast-limited adaptive histogram equalization), denoising (noise removal), thresholding (threshold processing), and concatenation were sequentially performed. The improvement in detection performance due to preprocessing was verified by comparing the results of the application of the algorithm on raw images, typical preprocessed images, and preprocessed images. The mAP for the training data and test data was 84.9% and 51.2% for the preprocessed image learning model, whereas 82.0% and 43.5% for the typical preprocessed image learning model and 78.0%, 40.8% for the raw image learning model. Object detection algorithm technology is developed every year, and the mAP is improving by approximately 3% to 10%. This study achieved a comparable performance improvement by only preprocessing with data.


2021 ◽  
Vol 15 (2) ◽  
pp. 77
Author(s):  
Agus Probo Sutejo ◽  
Haerul Ahmadi ◽  
Tasih Mulyono

The examination of defects in radiographic films necessitates specialized knowledge, as indicated by an expert radiographer (AR) degree, yet the subjectivity of AR in identifying defects is problematic. To overcome this subjectivity, an automatic welding defect identification is needed. This is executed by using Matlab to create artificial neural networks, which is beneficial for users with the graphical user interface (GUI) feature. One of the breakthroughs in the figure extraction into seven feature vector values is the geometric invariant moment theory. This prevents translation, rotation, and scaling from changing the figure's characteristics. Therefore, a welding defect identification system with a geometric invariant moment was created in the digital radiographic film figure to overcome the reading error by AR. The identification system obtained an accuracy rating of 89.9%.


2021 ◽  
Vol 11 (24) ◽  
pp. 11892
Author(s):  
Vera Barat ◽  
Artem Marchenkov ◽  
Vladimir Bardakov ◽  
Marina Karpova ◽  
Daria Zhgut ◽  
...  

This paper presents a study of acoustic emission (AE) during the deformation of dissimilar welded joints of austenitic steel to pearlitic steel. One of the specific problems in these welded joints is the presence of decarburized and carbide diffusion interlayers, which intensively increase in width during long-term high-temperature operation. The presence of wide interlayers negatively affects the mechanical properties of welded joints. Moreover, welded defects are difficult to diagnose in welded joints containing interlayers: due to the high structural heterogeneity, interlayers create structural noises that can hinder the detection of defects such as cracks, pores, or a lack of penetration. The AE method may become a complex decision for diagnosing dissimilar welded joints due to applicability to the testing of heterogenic materials with a complex microstructure. Specimens cut from dissimilar welded joints of austenitic steel to pearlitic steel were tested by tension to rupture, with parallel AE data registration. According to the research results, the characteristic features of the AE were revealed for specimens containing defects in the form of lack of penetration as well as for specimens with diffusion interlayers. The results obtained show that the AE method can be used to test both typical welding defects and diffusion interlayers in welded joints of steels of different structural classes.


2021 ◽  
pp. 009524432110588
Author(s):  
Mustafa Kemal Bilici

Modern thermoplastic materials are used in an expanding range of engineering applications, such as in the automotive industry, due to their enhanced stress-to-weight ratios, toughness, a very short time of solidification, and a low thermal conductivity. Recently, friction stir welding has started to be used in joining processes in these areas. There are many factors that affect weld performance and weld quality in friction stir welding (FSW). These factors must be compatible with each other. Due to the large number of welding variables in friction stir welding processes, it is very difficult to achieve high strength FSW joints, high welding performance, and control the welding process. Welding variables that form the basis of friction stir welding; machine parameters, tool variables, and material properties are divided into three main groups. Each welding variable has different effects on the weld joint. In this study, friction stir welds were made on high density polyethylene (HDPE) sheets with factors selected from machine parameters and welding tool variables. Although the welding performance, quality, and strength gave good results in some conditions, successful joints could not be realized in some conditions. In particular, welding defects occurring in the combination of HDPE material with FSW were investigated. Welding quality, defects, and performances were examined with macrostructure. In addition, the tensile strength values of some the joints were determined. The main purpose of this study is to determine the welding defects that occur at the joints. The causes of welding defects, prevention methods, and which weld variables caused were investigated. Welding parameters and welding defects caused by welding tools were examined in detail. In addition, the factors causing welding defects were changed in a wide range and the changes in the defects were observed.


Author(s):  
Xinhua Shi ◽  
Lin Li ◽  
Suiran Yu ◽  
Lingxiang Yun

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.


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