publications

2023

1. Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components

   Alessio Centola, Alberto Ciampaglia, Andrea Tridello, and 1 more author

   2023

   Abs

   The aim of the present paper is to predict the fatigue life of Selectively Laser Melted (SLMed) Ti6Al4V components via the process parameters, the thermal treatments, the surface treatments and the stress amplitude, adopting machine learning techniques to reduce the cost of further fatigue testing, and to deliver better predictive fatigue designs. The studies resulted in reliable algorithms capable of predicting trustful fatigue curves. The methods have been trained with experimental data available in the literature and validated on testing sets to assess the extrapolation limits and to compare the different methods. The behavior of the networks has also been mapped by varying one SLM process parameter at the time, highlighting how each one affects the life. © 2023 The Authors. Fatigue & Fracture of Engineering Materials & Structures published by John Wiley & Sons Ltd.

2. 

   Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts

   A. Ciampaglia, A. Tridello, D.S. Paolino, and 1 more author

   2023

   Abs

   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

3. 

   Physics based data driven method for the crashworthiness design of origami composite tubes

   Alberto Ciampaglia, Dario Fiumarella, Carlo Boursier Niutta, and 2 more authors

   2023

   All Open Access, Hybrid Gold Open Access

   Abs

   A novel method based on a physics informed data driven model is developed to design an origami composite crash tube. The structure consists of two axially stacked basic components, called modules. Each module presents lower and upper square sections with an octagonal section in the middle. The parameters of the octagonal cross-section and the height of each module are optimized to maximize the energy absorption of the tube when subjected to an axial impact. In contrast to standard surrogate modelling techniques, whose accuracy only depends on the amount of available data, a Physics-informed Neural Network (PINN) scheme is adopted to correlate the crushing response of the single modules to that of the whole origami tube, constraining the data driven method to physically consistent predictions. The PINN is first trained on the results obtained with an experimentally validated Finite Element model and then used to optimize the structure. Results show that the PINN can accurately predict the crushing response of the origami tube, while consistently reducing the computational effort required to explore the whole design domain. Also, the comparison with a standard Feed Forward Neural Network (FFNN) shows that the PINN scheme leads to more accurate results. © 2023, The Author(s).

4. 

   Data driven statistical method for the multiscale characterization and modelling of fiber reinforced composites

   A. Ciampaglia

   2023

   Abs

   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

2021

1. 

   Impact response of an origami-shaped composite crash box: Experimental analysis and numerical optimization

   A. Ciampaglia, D. Fiumarella, C. Boursier Niutta, and 2 more authors

   2021

   Abs

   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