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.

In this paper, an integrated method for the design of suspension components with lightweight materials is proposed. Starting from the well-established design flow for chassis components, the paper analyse the manufacturing and design aspect which are mutual dependent in composite structures, providing technical guidance. The proposed design methodology is applied to an innovative suspension archetype patented by Magneti Marelli, F.L.E.C.S. (Flexible Link Elevated Compliance Suspension). First, an analysis of available lightweight material is carried out to define which one best fits the design requirements in terms of density, stiffness, cost and manufacturability. Second, structural simulations, based on finite element analysis, are performed to investigate the influence of the thickness and material on the elasto-kinematic performance of the suspension. According to the stress distribution, an optimization of the shape and the stacking sequence of the component is performed. The results show that 8.5 mm carbon fibre reinforced epoxy is the best solution for the blades, and 5 mm aluminium alloy for the reinforcement tube wall thickness. Third, with respect to the final design, an adhesive interface is analysed and designed, using the Goland-Reissner model.

Origami-shaped composite structures are currently being explored for their ability to absorb energy in a progressive and controlled manner. In vehicle passive safety applications, this prevents the occurrence of peak forces that could potentially cause injuries to vehicle passengers. The work presents the design of a carbon fiber-reinforced polymer (CFRP) crash box for a Formula Student race car, using a numerical model validated by experimental tests. An initial characterization of the material is conducted according to the standards. Following, six origami samples are manufactured and subjected to crash tests to gather accurate experimental data. The numerical model is validated on the tests and used for the design of the race car’s impact attenuator. The designed crash box meets the Formula Student requirements while reducing the total mass by 14% and the maximum deceleration of 21% compared with the previous design. The study confirms the potential use of origami structures to improve crashworthiness while reducing vehicle weight.

The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AlSi10Mg and the Ti6Al4V alloy, proving their effectiveness and predicting capability.