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Surface roughness of adherends is a crucial factor in determining the performance and failure mechanism of adhesive joints. However, the research dedicated to the examination of fracture mechanism for adhesive joints influenced by surface roughness at microscale is limited. This work conducts systematic experimental and numerical investigations into the effect of microstructural roughness on the performance and fracture mechanism of multi-type adhesive single lap joints (SLJ). The adherend materials are aluminium alloy (Al) and polyphthalamide (PPA), ground into three roughness grades for the fabrication of SLJs using an epoxy adhesive. The mechanical properties of the adhesive, adherends and SLJs derived from experimental studies are utilized to calibrate the microparameters in Discrete Element Method (DEM) models for a numerical analysis. The newly developed DEM models demonstrate efficacy in predicting the performance and capturing the failure modes of multi-type SLJs realistically with distinct microroughness profiles. Finally, the influencing mechanisms of microstructural roughness on the performance of multi-type SLJs are investigated, including the microscale interfacial bonds and mechanical interlockings. The effects of microstructural roughness on the microscale failure mechanisms of multi-type SLJs are also explored and discussed, including the crack initiation, coalescence, and propagation within the adhesive and interface. • Investigate the properties and performance of SLJs through advanced experiments. • Develop DEM model to predict performance of SLJs with distinct roughness. • Develop DEM model to capture the failure mode of SLJs with distinct roughness. • Discuss the influencing mechanism of roughness on the performance of SLJs. • Discuss the effects of roughness on the microscale failure mechanism of SLJs. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel discrete element model was developed for hybrid adhesive joints. • The model can capture the transitional micromechanical response due to different adhesive thicknesses by only one-time calibration. • The adaptive transition of failure modes by different adherends and joint configurations can be simulated. • The correlation of fracture energies to adhesive thickness is well produced and presents promising results. • Robust prediction on joint behaviors with various designs can be achieved. This work developed a discrete element model to adaptively capture the transitional micromechanical response and failure mode of adhesive joints with dissimilar adherend materials and different configurations. Especially, the development of the model only requires one-time calibration. Loctite EA 9497 epoxy adhesive, aluminium (AL) and polyphthalamide (PPA) were selected to make different types of hybrid adhesive joints for the lab tests. The modelling applicability in simulating Mode I, Mode II cohesive failure and adhesive, mixed failure modes was subsequently validated with the experimental data. The validation shows that the proposed model can accurately capture the observed failure modes and joint performances. It is followed by investigating the ability to adaptively obtain the variation of fracture energies with different adhesive thicknesses. The results agree well with the current reports on the growth trend of fracture energies which see a rise till the thickness reaching 0.8 mm and subsequently decline to a plateau. Finally, key factors including the adhesive thickness, lap length were selected to perform a parametric study to investigate their influences on the failure mechanism and micromechanical response of hybrid joints. It is found that a thickness range of 0.1–0.3 mm is adequate to obtain satisfactory joint strength whilst thicker adhesive over 0.6 mm will decrease the joint strength. This is due to that a thinner adhesive layer can facilitate its cohesive fractures and thus fully use the resistance of adhesive. A range of lap length from 6 mm to 12.5 mm was found to have a higher efficiency of improving the joint strength when AL adherend was used. [ABSTRACT FROM AUTHOR]

• The sacrificial water-soluble support fixtures were made with additive manufacturing technique. • The mechanical performance of the joints with the modified features increased significantly. • The DE model could simulate the initiation and propagation of micro-cracks inside the adhesive and along the interface. • The modified bonded joints demonstrate a clear reduction in peak stress along the bond line. The research presented in this paper used an innovative method to modify the configuration of adhesively bonded joints for improved mechanical performance. Additive manufacturing was employed to produce sacrificial support structures with a water-soluble filament (Polyvinyl Alcohol). The design freedom offered by additive manufacturing makes it easy to tailor fixtures to any geometry, which can be used to accurately make the desired fillet shape at the end of the adhesive bond line. In addition to the experimental tests, the finite element method (FEM) was used to study the stress distribution along the bond line for four different modified bonded joints, whilst the discrete element method (DEM) was used to estimate the joint failure load and crack path in the adhesive bond line due to its strength in describing the initiation and progression of micro-cracks. The results show that the novel manufacturing method can produce an accurate fillet at the end of the bond line, regardless of the adhesive type. The mechanical performance of the joints with the modified features increased significantly. Furthermore, the failure load and crack path obtained from the DEM model is in close agreement with experimental and finite element (FE) results. Hence, the failure mechanism of the hybrid joints is then summarised. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel discrete element model considering micro-roughness was developed for adhesive joints. • The microscale crack initiation, coalescence and propagation of adhesive and adhesion interface can be captured. • Micro-mechanisms due to surface roughness on the micro/macroscale behaviour were found. • Key statistical features such as root mean square roughness, correlation length, skewness were discussed. Micro-roughness at adhesion surface yields significant influences on the structural behaviour of adhesive joints. Investigations into the micromechanical mechanism are extremely limited. This works developed a novel particle-based model of joints with stochastic microstructural features of roughness, which can capture refined multi-scale responses as first of this kind. Aluminium adherends with mechanical surface treatments were firstly scanned using 3D laser scanning microscope. The statistical features and reconstruction method of micro-roughness profiles were determined. Single lap shear tests on joints made of epoxy adhesive (Loctite EA 9497) and treated aluminium adherends were performed to provide testing data and observations on failure modes. The refined numerical models were subsequently developed to examine the influences of the actual micro-roughness on the micromechanical behaviors and failure mechanism. The mechanical interlocking, mitigation on crack propagation due to the irregular roughness were investigated. It is followed by introducing the reconstructed roughness of various magnitudes and further numerically examining the micromechanical responses by key stochastic parameters such as root mean square roughness and correlation length. The results indicate that the mechanical interlocking contribute more to enhancing the joint strength than the increase of adhesion area by micro-roughness. A rougher surface in either horizontal or vertical directions does not exhibit a consistent improvement of joint strength, which also depends on the threshold of roughness and the surface skewness triggering the transition of failure modes. [ABSTRACT FROM AUTHOR]

Discrete element (DE) model has a great feasibility in modelling the microstructural behaviours of adhesive composite joints. However, it demands a sophisticated calibration process to determine microscale bond parameters, which involves massive efforts in both experimental and numerical investigations. This work developed a novel calibration strategy based on DE models and genetic expression programming (GEP) approach for predicting the behaviours of hybrid composite joints encompassing the material nonlinearity, large ductile deformation and multiple fracture modes. In the developed strategy, both the bulk and interlaminar-like properties of ductile adhesives were concerned to suit various joint configurations. GEP modelling was performed based on the datasets from virtual DE experiments. Symbolic regression models were subsequently developed to facilitate the parameters determination. A series lab tests were conducted to validate the numerical results. It shows that the calibrated DE model can adaptively simulate the featured behaviours of both the ductile adhesive and composite joints with different configurations well in most examined occasions. Therefore, it could be suggested to generalize the developed strategy in the development of other DE models for saving the massive efforts in the calibration process of microstructural parameters. [Display omitted] • A novel calibration strategy for discrete element models of ductile adhesive joints was proposed. • Machine learning method was used to facilitate the complex calibration. • The proposed strategy can save massive efforts in the calibration process of microstructural parameters. • Modelling reliability was thoroughly examined with lab tests using Teroson MS 9399 adhesive. • Robust prediction on joint behaviours with material nonlinearity and multiple fracture modes can be achieved. [ABSTRACT FROM AUTHOR]

Particle-based model has strength and flexibility in modelling the microstructures of adhesives and interface in adhesive joints. In this work, a procedure with genetic expression programming (GEP) technique to calibrate the microscale parameters of discrete element (DE) model was proposed for brittle adhesives. Two categories of adhesive properties, the bulk property of thick adhesive and interlaminar-like property of thin adhesive, were discussed. For the bulk property, three target properties of adhesives, i.e. tensile strength, peak strain, secant modulus, were set as the reproduced features. 300 sets of adjustable microscale parameters were produced to run the numerical tests and generate datasets. GEP was then employed to find regression formulas for predicting the target properties as a function of the microscale parameters. For the interlaminar-like property, fracture energies of the cohesive failure of thin adhesives were approximated. A similar procedure of combined DE modelling and GEP was performed to find the regression models to estimate the fracture energy. The developed regression formulas can cover a general range of brittle adhesives. Loctite EA 9497 adhesive was selected to perform a series of lab tests, of which the results were subsequently used to examine the applicability of the DE model with calibrated parameters. The numerical results exhibit good agreements with testing data and observation. • Machine learning based approach was developed to calibrate the microscale parameters of DE models. • The bulk and interlaminar-like properties of brittle adhesives in various joint designs were calibrated. • Symbolic regression models of concerned properties were developed. • The developed DE models were validated with lab tests using brittle epoxy Loctite EA 9497 as example. [ABSTRACT FROM AUTHOR]