Prediction of Asymmetric Yield Strengths of Polymeric Materials at Tension and Compression Using Spherical Indentation

Engineering polymers generally exhibit asymmetric yield strength in tension and compression due to different arrangements of molecular structures in response to external loadings. For the polymeric materials whose plastic behavior follows the DruckerPrager yield criterion, the present study proposes a new method to predict both tensile and compressive yield strength utilizing instrumented spherical indentation. Our method is decomposed into two parts based on the depth of indentation, shallow indentation, and deep indentation. The shallow indentation is targeted to study elastic deformation of materials, and is used to estimate Young's modulus and yield strength in compression; the deep indentation is used to achieve full plastic deformation of materials and extract the parameters in Drucker-Prager yield criterion associated with both tensile and compressive yield strength. Extensive numerical computations via finite element method (FEM) are performed to build a dimensionless function that can be employed to describe the quantitative relationship between indentation force-depth curves and material parameters of relevance to yield criterion. A reverse algorithm is developed to determine the material properties and its robustness is verified by performing both numerical and experimental analysis

Adhesion Strength of Al/Epoxy Resin Interface over a Wide Range of Loading Rates

This study evaluated the interfacial adhesive strength between aluminium alloy and epoxy resin (Al/epoxy resin) over a wide range of strain rates (loading rates). We conducted three types of tests with different loading rate, i.e., a quasi-static tensile test for the range of lower loading rate, a Split Hopkinson Bar (SHB) for the range of middle loading rate, and Laser Shock Adhesion Test (LaSAT) for the range of higher loading rate. LaSAT is a unique test of adhesion evaluation, since laser induced shock wave is employed to lead interfacial fracture. In parallel, finite element method (FEM) is conducted in order to calculate stress distribution at the interface during LaSAT. As a result, it was found that the interface between the aluminium alloy and the epoxy resin interface shows significant loading rate dependency of the adhesion strength and this tendency is very similar to that of bulk epoxy materials

Repeated Laser Shock-Wave Adhesion Test for Metallic Coatings: Adhesion Durability and Its Mechanism Studied by Molecular Dynamics Simulation

We evaluated the adhesion of polycrystalline metallic coatings using the laser shock-wave adhesion test (LaSAT). This study used Cu plating on stainless steel as a coating model. The adhesion strength and toughness were successfully estimated using LaSAT and finite element method with cohesive zone model. Next, repeated LaSAT was conducted to apply cyclic loading to evaluate adhesion fatigue life, i.e., the number of loading cycles required for delamination. Furthermore, this study performed molecular dynamics (MD) simulations to elucidate the adhesion mechanism for the Cu/Fe interface. To verify our model, the interfacial fracture toughness was computed using the MD simulation and compared with the results of LaSAT. Next, cyclic loading was applied to the MD model to investigate crack initiation around the interface. We found that dislocations are generated from the internal grain and are accumulated at grain boundaries. This accumulation results in fatigue crack initiation due to stress concentration

Machine learning–based estimation method for the mechanical response of composite cellular structures

Cellular materials, including porous materials, are widely utilized in engineered and natural systems because their mechanical performance undergoes changes such as compressive deformation and energy absorption under impact loading. The mechanical response of these materials is notably influenced by their inherent cellular structure, specifically the geometric arrangement pattern. Nonuniform arrangements can result in significant variations in mechanical performance, posing challenges for material selection and the geometrical design of cellular structures. In this study, we established a machine learning (ML)–based approach to design the geometric arrangement (architecture) of cellular materials, aiming to achieve improved mechanical performance under uniaxial compression. In particular, we investigated the peak force at the plateau region and the work of energy absorption until structural densification occurs. Various patterns of internal geometry were modeled using the finite element method, and uniaxial deformation behavior was simulated to generate the training data for the ML approach. A neural network was employed as the ML method, correlating the cellular geometric patterns with the mechanical performance, including force–displacement curves and the relationship between peak force and work during energy absorption. The results indicate that the proposed method can accurately predict the mechanical response of any given geometric pattern within the defined scope. Thus, this approach is valuable for discovering cellular structures that can achieve desired mechanical responses