FEM Analysis as a Tool to Study the Behavior of Methacrylate Adhesive in a Full-Scale Steel-Steel Shear Joint

The use of adhesive to joint structural elements, despite many advantages of this technology, is not a method commonly used in engineering practice, especially in construction. This is mainly due to the poor recognition of the behavior, both in terms of testing and analysis, of joints made on a scale similar to the actual elements of building structures. Therefore, this paper presents the results of model tests and then numerical analyses of adhesively bonded joints made of high-strength steel elements in a full-scale (double-lap joint). In order to properly model the adhesive connection, material tests of the methacrylate adhesive were performed in the field of tensile, shear (in two versions: single lap joint test and thick adherent shear test) and bond properties. Comparison of the results of the model and numerical tests showed very good agreement in terms of the measurable values, which makes it possible to consider the results obtained in the adhesive layer as reliable (not directly measurable in model tests). In particular, the distribution of stresses inside the adhesive layer, the range of plastic zones and areas of loss of adhesion are presented and discussed. The results indicate the possibility of a reliable representation of the behavior of adhesively bonded joints of high-strength steel, thus providing a tool for the analysis of semirigid adhesive in large-size joints.

Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method.

DISIMILLAR LAP JOINT FRICTION STIR WELDING (FSW) USING VARIED LENGTH OF PIN

The lap joint will be used on aluminum 6061 and 10 mm thick brass with the Friction Stir Welding method. The probe used is EMS 45 steel with variations in pin lengths of 11 mm, 11.5 mm and 12 mm. The results of this study are in length 11.5 mm with the highest Vickers hardness value of 104.26 VHn compared to 11 mm and 12 mm pin length is 98.93 VHn and 70.43 VHn. The results of shear stress are 67.32 MPa at 12 mm pin length, higher than the 11 mm and 11.5 mm pin lengths of 40.2 MPa and 42.14 MPa.

Effect of Coating and Welding Wire Composition on AHSS GMA Welds

Advanced high-strength steels (AHSS) such as complexphase (CP) and high-formability (HF) steel offer weightsaving advantages for automotive applications such as chassis and frame applications. To prevent material oxidation, materials are often galvanized to protect the substrate from corrosion. However, the weldability of coated AHSS becomes challenging due to the trapping of zinc in the weld molten pool, which could lead to weld defects such as porosity and liquid metal embrittlement cracks. This work focused on the weldability of AHSS (CP800 and 980HF) using the gas metal arc welding process. The roles of both galvanized iron coating and filler material on weld strength were investigated. The welds were performed using two different filler materials: a low-strength filler (ER70S-6) and a high-strength filler (ER100S-6) material. In addition, two different joint configurations were studied: lap joints and butt joints. The results showed that the butt joint had a higher strength compared to the lap joints. Furthermore, the strength of the butt joint overmatched the base material strength in all of the tested materials (both in galvanized and uncoated). In general, lap joint strength undermatched the base material strength, which was attributed to the rotation during tensile testing that induced unaccounted bending stress on the lap joint, while using a higherstrength welding wire improved the tensile strength material in the lap joint configuration. The hardness profiles in the 980HF steel also showed a significant hardness mismatch due to the formation of a fully martensitic microstructure in the heat-affected zone, which led to suppressing the deformation across the lap joint.

Material Model Effect for Simulating a Single-Lap Joint with a Blind Rivet

This paper concerns the influence of the material modeling method on the results of strength analyses. The research object was a single lap joint with a blind rivet (ISO 12996). The results of numerical strength analysis for various configurations of material models with material and contact nonlinearity were compared not only with the experimental results of such a connection but also with the values estimated using classical analytical tools (pressure stress and Hertz stress). The research aimed to determine how the results of numerical analyses (FEMs) were influenced by the method of modeling the material model and how it relates to the experimental results. As part of the analyses, a discrete riveted model and material models were constructed. The analyses took into account various load cases (from 10 to 90% of the connection capacity) to better illustrate the relationship between the numerical and experimental results. As a result of the conducted analyses, it was determined that the linear-elastic model was an acceptable and suggested solution (with a load of up to 90% of the load capacity of the joint connection) for further tests. The work was summarized with general and specific conclusions relating to all cases of numerical modeling. In addition, the summary includes suggestions for future works.

Experimental Study of the Impact of Glass Beads on Adhesive Joint Strength and Its Failure Mechanism

The use of modern structural adhesives provides a lightweight, practical, and high strength joining methodology, which is increasingly being adopted in the automotive and aeronautical sectors, among many others. However, the strict mechanical performance standards that must be met in these applications require a constant search for ways of improving the adhesives’ behavior, which has led to the growing use of reinforcements as a way of improving the capabilities of bonded joints. The aim of this work was, thus, to analyze how the addition of inorganic fillers to the adhesive layer affects a joint’s strength and its failure mechanism. To this end, single lap joint specimens with mild steel and high strength steel substrates were tested, at quasi-static speeds, and with different amounts of glass microspheres reinforcing two different structural adhesives. The experimental results indicated that the addition of glass particles reduced the joint performance for both substrates under study. Furthermore, the failure pattern was found to evolve from adhesive failure to a cohesive type of failure as the amount of glass particles present in the adhesive was increased.