Alberto Ciampaglia

The fatigue response of Additive Manufacturing (AM) components is driven by manufacturing defects - whose size mainly depends on process parameters - and by the resulting microstructure - mainly affected by heat treatments and process parameters. In the paper, Machine Learning (ML) algorithms are applied to estimate the fatigue response from AM process parameters and heat treatment properties. Feed-forward neural networks (FFNN) and physics-informed neural network (PINN) algorithms are designed and validated on literature datasets of AM AlSi10Mg alloy, proving the effectiveness of physics-based ML approaches in predicting the fatigue response of AM parts. Leveraging PINN interpretability, the authors analyse the relationship between process parameters and fatigue response. © 2023 Elsevier Ltd

Multiscale analysis of composite laminates allows for predicting the mechanical response of these materials avoiding cumbersome experimental campaigns. The matrix and fibre material properties and the size of the Representative Volume Element (RVE) are the main parameters affecting the accuracy of multiscale models. This paper proposes a statistical inverse method to calibrate micromechanical material parameters from macroscale experiments and 3D reconstruction. First, glass fiber reinforced epoxy laminates have been analysed with Computer Tomography (CT), then, the material 3D microstructure has been reconstructed and fibre, matrix, and voids were segmented. Tensile tests have been performed on the composite specimen, measuring the surface strains with a Digital Image Correlation (DIC) system. The reconstructed volume, converted to a voxel mesh, has been used to compute the homogenized response of composite by Fast Fourier Transform (FFT) analysis. By comparing the marginal distribution of homogenized material stiffness extracted from DIC data of tensile tests, with the conditioned distribution computed by varying the FFT model parameter, a Stochastic Volume Element (SVE) is finally calibrated. A probabilistic multiscale model based on the SVE that propagates the uncertainty from the microscale to the structure level is presented. © 2023 Elsevier Ltd

In this work, an investigation of the crush response of a simplified CFRP origami crash box subjected to axial impact is proposed. Crash boxes are thin-walled structural components of the vehicles designed to absorb energy during impact events at low-medium velocity. In particular, the crash boxes must guarantee a progressive and controlled energy absorption, avoiding peak of force (and thus acceleration) that can lead to passenger injury. In recent years, crash boxes made of carbon fibre reinforced polymer (CFRP) have found application in the automotive sector. However, their brittle failure mode leads to an irregular crushing trend characterized by peak of force. Thereafter, the crushing behaviour of the composite material structures can be improved by modifying their geometrical parameters. Among the most promising solutions, the origami structure is increasingly considered for crash boxes. The origami crash box here considered consists of four axially stacked basic structures. Each basic structure is composed of four trapezoidal faces and four triangular faces. The upper cross section is squared, whereas the lower cross section has an octagonal shape. The structural behaviour of the origami component was investigated according to different sizes of the triangular faces. The numerical models were simulated with the finite element commercial code LS-Dyna in its explicit formulation. The optimal shape of the origami structure in terms of maximum energy absorption and limited force peak was defined in LS-OPT environment. The objective function of the shape optimization algorithm was set to maximize the energy absorption, while limiting the peak of force. The optimal shape defined presented larger sizes in the top basic structures than in the bottom parts, resulting in more inclined faces. The result suggested that more inclined faces in the top part can guarantee a fracture-triggering effect in the crash box, which ensured a smaller peak force. © 2020 Elsevier Ltd