Abstract

Additive manufacturing (AM) processes have the ability to build complex geometries from a wide variety of materials. A popular approach for metal-based AM processes involves the deposition of material particles on a substrate followed by fusion of those particles together using a high intensity heat source, e.g. a laser or an electron beam, in order to fabricate a solid part. These methods are of high priority in engineering research, especially in applications for the energy, health, and defense sectors. The primary reasons behind the rapid growth in interest for AM include: (1) the ability to create complex geometries which are otherwise cost-prohibitive or difficult to manufacture, (2) increased freedom of material composition design through the adjustment of the ratios of the composing powders, (3) a reduction in wasted materials, and (4) the fast, low-volume, production of prototype and functional parts without the additional tooling and die requirements of conventional manufacturing methods. However, the highly localized and intense nature of these processes elicits many experimental and computational challenges. These challenges motivate a strong need for computational investigation, as does the need to more accurately characterize the response of parts built using AM. The present work will discuss these challenges and methods for creating multiscale material models that account for the complex phenomena observed in the AM production environment. The linkage between process, structure, and property of AM components, e.g., anisotropic plastic behavior combined anisotropic microstructural descriptors afforded through enhanced data compression techniques, will also be discussed. 

Abstract

AM processes are characterized by complex thermal cycles that have a deep influence on the microstructural transformations of the deposited alloy. In this work, a general model for the prediction of microstructure evolution during solid state transformations of Ti6Al4V is presented. Several formulations have been developed and employed for modeling phase transformations in other manufacturing processes and, particularly, in casting. The proposed model is mainly based on the combination and modification of some of these existing formulations, leading to a new overall model specifically dedicated to AM. The accuracy and suitability of the integrated model is enhanced, introducing new dedicated features. In fact the model is designed to deal with fast cooling and re-heating cycles typical of AM processes because: (a) it is able to consider incomplete transformations and varying initial content of phases and (b) it can take into account simultaneous transformations.The model is implemented in COMET, an in-house Finite Element (FE)-based framework for the solution of thermo-mechanical engineering problems. The validation of the microstructural model is performed by comparing the simulation results with the data available in the literature. The sensitivity of the model to the variation of material parameters is also discussed.

Abstract

The RAMSSES project aims to show the benefits of advanced materials in maritime applications by implementing 13 market driven demo cases. The entire process chain and a wide range of applications (structural components, equipment, ship integration, repair) are covered. Installation and assessment of demonstrators on shore or on board will reveal the high technology readiness. The test program, based on risk assessment and supervised by rule making bodies, targets on ensuring relevance for commercial approval beyond the project. While demonstrators will support commercialisation of specific products, RAMSSES is also engaged strategically in enabling more rapid and agile material innovation in the European Maritime industry. The first key element is a knowledge repository for test data and best practice procedures, allowing reuse of such information for similar future cases. Secondly, standardised risk scenarios will help easing approval processes in the future, and finally a new materials innovation platform will enhance continuous technology transfer within the maritime sector and beyond.

Abstract

The usability of machine learning approaches for the development of in-situ process monitoring, automated anomaly detection and quality assurance for the selective laser melting (SLM) process receives currently increasing attention. For a given set of real machine data we compare two established methods, principal component analysis (PCA) and -variational autoencoder (ß-VAE), for their applicability in exploratory data analysis and anomaly detection. We introduce a PCA-based unsupervised feature extraction algorithm, which allows for root cause analysis of process anomalies. The ß-VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems.

Abstract

Metamaterials research and, moreover, their obtaining by additive manufacturing is trendy because of their potential industrial applications, since lattice structures can save weight when integrated in larger parts. For this reason, it is important to draw these new material properties in order to compare them with traditional structures obtained by classic manufacturing methods. In this work we present a numerical procedure to determine the mechanical properties of a metamaterial based on periodic cells. To date, for these materials there are no standard procedures to test and validate their structural properties, and specifically to determine their mechanical bending behaviour. We apply a virtual testing procedure using finite elements to determine the homogeneous elastic modulus, Young's modulus and shear modulus, using a set of modified bending virtual test, based on the three point bending and the four point bending test [1]. Theses physical testing procedures are common in sandwich composite materials [2], and can be apply to metamaterial testing taking into account some geometrical and material aspects to determine the proper parameters for testing. The results obtained are macroscopically independent on the number of elemental cells used in the metamaterial, and permit us to consider the intrinsic defects that may appear in the 3D additive manufacturing materials.

Abstract

This paper explores the self-healing abilty of the ionomer called Surlyn® 8940 (manufactured by DuPont), a partially neutralised poly(ethylene-co-methacrylic acid) (EMAA) ionomer copolymer, and the improvement of the mechanical properties after adding a nano-reinforcement of multiwall carbon nanotubes (MWCNTs) to the structure of the polymer. It begins with the manufacture the nanocomposite material, based on Surlyn® 8940 as raw material, and the mixture process with MWCNTs. Afterwards, an extrusion process was optimized to obtain a homogeneous reinforced filament, which was subsequently used to manufacture the final test samples by means of a 3D printing machine. Specimens with different concentrations of MWCNTs (0.0, 0.1, 0.5, 1.0 % wt.) were manufactured, in order to test the mechanical properties and self-healing ability of unreinforced and nano-reinforced samples. The results show that nano-reinforced samples had an increase of their self-healing ability (increase for 0.5 y el 1% wt. of MWCNTs) and mechanical properties (σ_TS 18.35, 25.91, 26.92, 28.02 MPa to 0, 0.1, 0.5 y 1 % wt. de MWCNTs respectively). Specimens of 5% of MWCNTs have also been manufactured with different mixing times in the extrusion chamber to study the conductivity of the polymer. Electrical conductivity values ​​for 8 and 10 minutes of mixing times reached S/m units order have been obtained.

Abstract

The present study investigates the enhanced energy absorption capabilities in the radial direction of structures through the combination of continuous glass fiber reinforced polyamide hollow sections (cGF/PA) and complex cellular cores manufactured by 3D printing cFF (continuous Fibre Fabrication). For this purpose, two types of auxetic structures based on conventional reentrant structure designs (hexagonal and octagonal) have been characterised. Likewise, the energy absorption capacity (SEA) of the filled profiles has been analysed and compared with the capability of the hollow profiles. Falling weight impact tests have been carried out for the dynamic characterization of the profiles and the effect of concentric fibers and the auxetic core on filled profiles has been analysed. On the one hand, it has been identified that the octagonal re-entrant structure (RSH) presents a higher impact performance, reaching a SEA value of 8.5 kJ/kg. On the other hand, it has been found a substantial improvement in the absorption capability of the filled profiles (17.7 kJ/kg) compared to the capability of the hollow profiles (2.5 kJ/kg). The SEA values achieved are promising, as they are 18% higher than the values found in literature.

Abstract

Metal Additive Manufacturing (AM) processes such as Laser Powder Bed Fusion (LPBF) suffer from part distortion due to the localized melting and resolidification of the metal powder, which introduces stresses and strains. Despite becoming more and more important as a manufacturing process, options for simulating the printing process to predict the distortions are limited, especially because existing solutions often require very long computation times. In this work, we present the results of an implementation of the inherent strain method on graphics processing units (GPUs) that exploits the massive parallelism of the many GPU cores to speed up the simulations considerably compared to CPU-based implementations.

Abstract

Carbon fibre matrix composites are consolidated in high-demanding industries such as aeronautics and aerospace. Nowadays, thermoset composites are fully integrated on several new aircrafts in production. However, thermoplastic composites are progressively emerging as a better solution due to their end-of-life recyclability and optimized manufacturing process. One of the processes used to manufacture final parts from thermoplastic composite materials is press-moulding, in which a high-temperature heating system is combined with the application of pressure at high speeds. This article studies the optimization of press-moulding process in combination with an Additive Manufacturing (AM) produced metallic mould core. The aim of this study is the manufacturing of a thermoplastic aeronautical part, that would be submitted to real-world performance test. For the press-moulding process, a high-temperature infrared heating system was designed, supported by thermal oil mould heating, this heating system was combined with a mould-pressing process by integrating the system in a commercial injection machine. Regarding the AM tooling, produced by Wire Arc Additive Manufacturing (WAAM) process, was designed to reduce tooling weight, and optimise thermal cycle. Moreover, manufacturing data from WAAM tooling was recorded and compared with that obtained from a symmetrical conventional manufacturing made mould. The analysis shows savings in terms of material and energy consumption, as well as cycle times reduction. The final part metrology study shows indistinguishable results between the press-moulding process carried out using the WAAM tooling and that manufactured through conventional methods.