Surrogate metamodels from digital image correlation for testing high-performance composite vessels.

In this work, a workflow has been developed for the generation of surrogate metamodels to predict and evaluate failure with a confidence above 95 % in initial service conditions of high-performance cylindrical vessels manufactured in composites by Roll Wrapping technology. Currently, there is no specific testing standardization for this type of vessel and to fill this gap probabilistic numerical models were developed, performed by the Finite Element Method, fed with the material characteristics obtained experimentally by 2D digital image correlation from flat specimens. From the initial numerical model, a surrogate metamodel was generated by stochastic approximations. Once the metamodels were obtained by robust engineering, an experimental ring-ring tensile test was developed under service conditions and deformations were measured by high-precision 3D digital image correlation. Parametric and robust tests showed that the results of the metamodel did not show statistically significant differences, with errors in the rupture part of less than 2 % with respect to the results obtained in the test, being proposed as a basis for new test procedures.

Structural Timber Connections with Dowel-Type Fasteners and Nut-Washer Fixings: Mechanical Characterization and Contribution to the Rope Effect.

Dowel-type fasteners are one of the most used type of connections in timber joints. Its design follows the equations included in the Eurocode 5. The problem with these equations is that they do not adequately contemplate the resistive capacity increase of these joints, when using configurations which provoke the so-called rope effect. This effect appears when using threaded surface dowels instead of flat surface dowels, expansion kits or nut-washer fixings at the end of the dowel. The standards consider this increase through a constant value, which is a poor approximation, because it is clearly variable, depending on the joint displacement and because is much bigger, especially when using nut-washer fixings. It is also very important because of the rope effect trigger interesting mechanisms that avoids fragile failures without warning of the joints. For these reasons, it is essential to know how these configurations work, how they help the joint to resist the external loads and how much is the increase resistance capacity in relationship with the joint displacement. The methods used to address these issues consisted of a campaign of experimental tests using actual size specimens with flat surface dowels, threaded surface dowels and dowels with washer-nut fixings at their ends. The resistance capacity results obtained in all the cases has been compared with the values that will come using the equations in the standards. After the tests the specimens were cut to analyze the timber crushings, their widths, the positions and level of plasticizations suffer in the steel dowels and in the washer-nut fixings and the angle formed in the dowel plastic hinges. With all this information the failure mode suffered by the joints has been identified and compared with the ones that the standards predict. The results for the size materials and types of joints studied shows that the crush width average values go from 20 mm with flat surface dowels, to 24 mm in threaded to 32 mm in threaded with washer-nut fixings. The rope effect force/displacement goes from 100 N/m in threaded surface dowels to 500 N/m in threaded with washer-nut fixings. Finally, the load capacities are on average 290% higher those indicated in the standard. The main conclusion is that the rope effect force should be considered in the standards in more detail as a function of multiple variables, especially the displacement of the joint.

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods.

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.