Repercussions of powder contamination on the fatigue life of additive manufactured maraging steel

A wide range of materials is suitable for processing by powder bed fusion (PBF) techniques. Among the latest formulations, maraging steel 18Ni-300, which is a martensite-hardenable alloy, is often used when both high fracture toughness and high strength are required, or if dimensional changes need to be minimised. In direct tooling, 18Ni-300 can be successfully employed in numerous applications, for example in the production of dies for injection moulding and for casting of aluminium alloys; moreover, it is particularly valuable for high-performance engineering parts. Even though bibliographic data are available on the effects that parameters, employed in PBF processes, have on the obtained density, roughness, hardness and microstructure of 18Ni-300, there is still a lack of knowledge on the fatigue life of PBF manufactured parts. This paper describes the fatigue behaviour of 18Ni-300 steel manufactured by PBF, as compared by forging. Relevant negative effects of the cross-contamination of the raw material are originally identified in this paper, which emphasizes the inadequacy of current acceptability protocols for PBF powders. In the absence of contamination, endurance achieved by PBF is found equal to that by forging and consistent with tooling requirements as set out by industrial partners, based on injection moulding process modelling

Reinforcement effectiveness on mechanical performances of composites obtained by powder bed fusion

New material formulations to be used in Additive Manufacturing machines are one of the major interests in this fast growing field. The possibility to tune functional and mechanical properties, by the addition of reinforcements to a polymeric matrix, is hindered by the low provisional capability of the additive manufactured composite. The inherent anisotropy of layer manufacturing combines with mechanisms of filler dispersion and of filler/matrix adhesion in a complex scenario. The paper entails a critical evaluation of mechanical properties measured for several polymeric composites produced by Powder Bed Fusion, in the perspective of provisional models commonly accepted for composite materials. The models are reviewed versus experimental and literature data. The provisional effectiveness is generally good, except for the case of nanometric or surface treated fillers, or of specific anisotropic microstructures obtained by layer manufacturing

Surrogate-based bayesian model updating of a historical masonry tower

This paper presents the surrogate-based Bayesian model updating of a historical masonry bell tower. The finite element model of the structure is updated on the basis of the modal properties experimentally identified thanks to a vibration test. In a general context, model updating results are highly affected by several uncertainties, regarding both the experimental measures and the model. Stochastic approaches to model updating, as the one based on Bayes' theorem, enable to quantify the uncertainties associated to the updated parameters and, consequently, to increase the reliability of the identification. The major drawback of Bayesian model updating is the high computational effort requested to compute the posterior distribution of parameters. For this reason, the paper proposes to integrate the classical procedure with a surrogate model. A Gaussian surrogate is employed for the approximation of the posterior distribution of parameters and the performances of the proposed method are compared to those of an Bayesian numerical method proposed in literature

A Study on the Use of XCT and FEA to Predict the Elastic Behavior of Additively Manufactured Parts of Cylindrical Geometry

Defining general criteria for the acceptability of defects within industrial components is often complicated, since the specific load conditions and the criticality of the given application should be considered individually. In order to minimize the risk of failure, high safety factors are commonly adopted during quality control. However this practice is likely to cause the rejection of components whose defects would be instead acceptable if a more sound knowledge of the component behaviour were achieved. Parts produced by additive manufacturing (AM) may suffer from various defects, including micro- or macro-holes, delamination and microstructural discontinuities. Such processes, which are specially suitable for one-off components, require robust and reliable inspection before a part is accepted or rejected, since the refusal of even a single part at the end of the production process represents a significant loss. For this reason, it would be very useful to simulate in a reliable way whether a certain defect is truly detrimental to the proper working of the part during operation or whether the component can still be used, despite the presence of a defect. To this purpose, the paper highlights the benefits of a synergistic interaction between Industrial X-ray computed tomography (XCT) and finite element analysis (FEA). Internal defects of additively manufactured parts can be identified in a non-destructive way by means of XCT. Then FEA can be performed on the XCT-based virtual model of the real component, rather than on the ideal CAD geometry. A proof of concept of this approach is proposed here for a reference construct produced in an Aluminium alloy by AM. Numerical results of the proposed combined XCT–FEA procedure are contrasted with experimental data from tensile tests. The findings sustain the reliability of the method and allow to assess its full provisional accuracy for parts of cylindrical geometry designed to operate in the elastic field. The paper moves a step beyond the present application limits of tomography as it is currently employed for AM parts and it evidences instead the possibility of extending the usage of tomography to acceptance testing and prediction of operative behaviour

Parameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach

The paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters

A statistical approach for modeling individual vertical walking forces

This paper proposes a statistical approach for modeling vertical walking forces induced by single pedestrians. To account for the random nature of human walking, the individual vertical walking force is modeled as a series of steps and the gait parameters are assumed to vary at each step. Walking parameters are statistically calibrated with respect to the results of experimental tests performed with a force plate system. Results showed that the walking parameters change during walking and are correlated with each other. The force model proposed in this paper is a step-by-step model based on the description of the multivariate distribution of the walking features through a Gaussian Mixture model. The performance of the proposed model is compared to that of a simplified load model and of two force models proposed in the literature in a numerical case study. Results demonstrate the importance of an accurate modeling of both the single step force and the variability of the individual walking force

Design for additive manufacturing and for machining in the automotive field

High cost, unpredictable defects and out-of-tolerance rejections in final parts are preventing the complete deployment of Laser-based Powder Bed Fusion (LPBF) on an industrial scale. Repeatability, speed and right-first-time manufacturing require synergistic design approaches. In addition, post-build finishing operations of LPBF parts are the object of increasing attention to avoid the risk of bottlenecks in the machining step. An aluminum component for automotive application was redesigned through topology optimization and Design for Additive Manufacturing. Simulation of the build process allowed to choose the orientation and the support location for potential lowest deformation and residual stresses. Design for Finishing was adopted in order to facilitate the machining operations after additive construction. The optical dimensional check proved a good correspondence with the tolerances predicted by process simulation and confirmed part acceptability. A cost and time comparison versus CNC alone attested to the convenience of LPBF unless single parts had to be produced