Research

21 pages, 4753 KiB

Open AccessArticle

Effect of Loading Frequency on the Fatigue Response of Adhesive Joints up to the VHCF Range

by Davide Pederbelli, Luca Goglio, Davide Paolino, Massimo Rossetto and Andrea Tridello

Appl. Sci. 2023, 13(23), 12967; https://doi.org/10.3390/app132312967 - 04 Dec 2023

Viewed by 644

Modern structures are designed to withstand in-service loads over a broad frequency spectrum. Nonetheless, mechanical properties in numerical codes are assumed to be frequency-independent to simplify calculations or due to a lack of experimental data, and this approach could lead to overdesign or [...] Read more.

Modern structures are designed to withstand in-service loads over a broad frequency spectrum. Nonetheless, mechanical properties in numerical codes are assumed to be frequency-independent to simplify calculations or due to a lack of experimental data, and this approach could lead to overdesign or failures. This study aims to quantify the frequency effects in the fatigue applications of a bi-material adhesive joint through analytical, numerical, and experimental procedures. Analytical and finite element models allowed the specimen design, whereas the frequency effects were investigated through a conventional servo-hydraulic apparatus at 5, 25, and 50 Hz and with an ultrasonic fatigue testing machine at 20 kHz. Experimentally, the fatigue life increases with the applied test frequency. Run-out stress data at 10⁹ cycles follow the same trend: at 25 Hz and 50 Hz, the run-out data were found at 10 MPa, increasing to 15 MPa at 20 kHz. The P–S–N curves showed that frequency effects have a minor impact on the experimental variability and that standard deviation values lie in the range of 0.3038–0.7691 between 5 Hz and 20 kHz. Finally, the trend of fatigue strengths at 2·10⁶ cycles with the applied loading frequency for selected probability levels was estimated. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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17 pages, 5463 KiB

Open AccessArticle

Experimental Study on the Effect of Bonding Area Dimensions on the Mechanical Behavior of Composite Single-Lap Joint with Epoxy and Polyurethane Adhesives

by Mohammad Abbasi, Raffaele Ciardiello and Luca Goglio

Appl. Sci. 2023, 13(13), 7683; https://doi.org/10.3390/app13137683 - 29 Jun 2023

Cited by 2 | Viewed by 1068

Abstract

The effects of joint geometry parameters, such as adherend thickness (1.76, 3.52 mm), joint width (10, 20, 30 mm), and overlap length (10, 20 mm), on the behavior of single-lap joints (SLJs) under tensile loading are investigated in this study. Peak force, joint [...] Read more.

The effects of joint geometry parameters, such as adherend thickness (1.76, 3.52 mm), joint width (10, 20, 30 mm), and overlap length (10, 20 mm), on the behavior of single-lap joints (SLJs) under tensile loading are investigated in this study. Peak force, joint stiffness, shear stress, and normal stress are the investigated properties. SLJs are manufactured with carbon fiber composite adherends and two different types of adhesives, polyurethane and epoxy, which present a flexible and rigid mechanical response. The results showed that increasing all 3 geometric parameters (L, W, T) leads to a significant increase in the load capacity of polyurethane joints (on average, 88.4, 101.5, and 16.9%, respectively). For epoxy joints, these increases were 47.7, 100, and 46%, respectively. According to these results, W is the parameter with the most influence on the load capacity of the joints. However, it was observed that an increase in joint width has no significant effect on adhesive shear and a substrate’s normal stresses. Epoxy SLJs behave approximately elastically until failure, while polyurethane SLJ load-displacement curves include an initial linear elastic part followed by a more ductile behavior before the failure. Joint stiffness is affected by all the parameters for both adhesive types, except for overlap length, which led to a negligible effect on epoxy joints. Moreover, the damage surfaces for both types of joints are analyzed and the internal stresses (shear and peel) are assessed by using the analytical model of Bigwood and Crocombe. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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25 pages, 5413 KiB

Open AccessArticle

Analysis of the Mechanical Performance and Durability of Adhesively Bonded Joints Used in the Milling Tool Industry

by Rafael J. F. de Sousa, Pedro N. Gomes, Daniel S. Correia, Ricardo J. C. Carbas, Eduardo A. S. Marques, Paulo J. C. das Neves, Willian P. Afonso and Lucas F. M. da Silva

Appl. Sci. 2023, 13(8), 4937; https://doi.org/10.3390/app13084937 - 14 Apr 2023

Cited by 1 | Viewed by 1131

Abstract

Epoxy adhesives, widely used in multiple structural applications, are used in the milling tool industry to replace brazing and mechanical fastening when joining the cutting bits to the tool body; though their durability is still a concern. This work aims to evaluate and [...] Read more.

Epoxy adhesives, widely used in multiple structural applications, are used in the milling tool industry to replace brazing and mechanical fastening when joining the cutting bits to the tool body; though their durability is still a concern. This work aims to evaluate and characterise the effect of environmental factors associated with a tool’s life cycle on the performance of these bonded joints. A gravimetric analysis was conducted on bulk adhesive plates for water, cutting emulsion and dielectric fluid to obtain diffusion and relaxation rates. Novel real joint shear specimens were developed to enable strength testing on joints which are comparable with the final application. These specimens were immersed in fluids and subjected to thermal cycles or a corrosive finishing surface treatment to simulate the tool’s life cycle. The joint’s resistance was then benchmarked. Lastly, a dimensional variation test was carried out on tool prototypes before and after ageing, showing no significant dimensional variation which could compromise the cutting performance of the tool. Overall, though it was possible to identify a decrease in strength of around 20% in most tests, joint strength was still within the values necessary for safe operation, with a large safety factor still being retained. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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15 pages, 4162 KiB

Open AccessArticle

Strain Prediction Using Deep Learning during Solidification Crack Initiation and Growth in Laser Beam Welding of Thin Metal Sheets

by Wenjie Huo, Nasim Bakir, Andrey Gumenyuk, Michael Rethmeier and Katinka Wolter

Appl. Sci. 2023, 13(5), 2930; https://doi.org/10.3390/app13052930 - 24 Feb 2023

Viewed by 1167

Abstract

The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage [...] Read more.

The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision. To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about

0.04

, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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19 pages, 2675 KiB

Open AccessArticle

Identifying Weak Adhesion in Single-Lap Joints Using Lamb Wave Data and Artificial Intelligence Algorithms

by Gabriel M. F. Ramalho, António M. Lopes, Ricardo J. C. Carbas and Lucas F. M. Da Silva

Appl. Sci. 2023, 13(4), 2642; https://doi.org/10.3390/app13042642 - 18 Feb 2023

Cited by 2 | Viewed by 1132

Abstract

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive [...] Read more.

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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16 pages, 5720 KiB

Open AccessArticle

Artificial Intelligence (AI)-Based Evaluation of Bolt Loosening Using Vibro-Acoustic Modulation (VAM) Features from a Combination of Simulation and Experiments

by Jianbin Li, Yi He, Qian Li and Zhen Zhang

Appl. Sci. 2022, 12(24), 12920; https://doi.org/10.3390/app122412920 - 15 Dec 2022

Cited by 1 | Viewed by 1963

Abstract

The detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave–surface interactions at the joint interfaces. Such coefficients show a [...] Read more.

The detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave–surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences of structural and material nonlinearities. A reliable inspection method of residual bolt torque is proposed in this study using support vector regression (SVR) with acoustic features from VAM. By considering the material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), the responses of aluminum–aluminum and composite–composite bolted joints during the VAM test were accurately simulated. The SVRs were subsequently established based on the database built by combining simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses had higher accuracy compared to the conventional nonlinear coefficients. Requiring limited experimental data, the proposed method can achieve a reliable inspection of bolt torque by including the simulated data in the machine training. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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18 pages, 2423 KiB

Open AccessArticle

A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data

by Joy Pal, Shirsendu Sikdar, Sauvik Banerjee and Pradipta Banerji

Appl. Sci. 2022, 12(21), 11107; https://doi.org/10.3390/app122111107 - 02 Nov 2022

Cited by 3 | Viewed by 1439

Abstract

This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine [...] Read more.

This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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13 pages, 8593 KiB

Open AccessArticle

Solid-State Rotary Friction-Welded Tungsten and Mild Steel Joints

by Beata Skowrońska, Mariusz Bober, Paweł Kołodziejczak, Michał Baranowski, Mirosław Kozłowski and Tomasz Chmielewski

Appl. Sci. 2022, 12(18), 9034; https://doi.org/10.3390/app12189034 - 08 Sep 2022

Cited by 8 | Viewed by 1443

Abstract

This paper is a study of the microstructure and other selected properties of solid-state, high-speed, rotary friction-welded tungsten and mild steel (S355) joints. Due to the high affinity of tungsten for oxygen, the welding process was carried out in a chamber with an [...] Read more.

This paper is a study of the microstructure and other selected properties of solid-state, high-speed, rotary friction-welded tungsten and mild steel (S355) joints. Due to the high affinity of tungsten for oxygen, the welding process was carried out in a chamber with an argon protective atmosphere. Joints of suitable quality were obtained without any macroscopic defects and discontinuities. Scanning electron microscopy (SEM) was used to investigate the phase transformations taking place during the friction welding process. Chemical compositions in the interfaces of the welded joints were determined by using energy dispersive spectroscopy (EDS). The microstructure of friction welds consisted of a few zones, fine equiaxed grains (formed due to dynamic recrystallization) and ultrafine grains in the region on the steel side. A plastic deformation in the direction of the flash was visible mainly on the steel side. EDS-SEM scan line analyses across the interface did not confirm the diffusion of tungsten to iron. The nature of the friction welding dissimilar joint is non-equilibrium based on deep plastic deformation without visible diffusive processes in the interface zone. The absence of intermetallic phases was found in the weld interface during SEM observations. Mechanical properties of the friction-welded joint were defined using the Vickers hardness test and the instrumented indentation test (IIT). The results are presented in the form of a distribution in the longitudinal plane of the welded joint. The fracture during strength tests occurred mainly through the cleavage planes at the interface of the tungsten grain close to the friction surface. Full article

(This article belongs to the Special Issue Advanced Diagnosis/Monitoring of Jointed Structures)

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