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Additive manufacturing (AM) offers significantly greater freedom of design compared to conventional manufacturing processes since the final parts are built layer by layer. This enables metal AM, also known as metal 3D printing, to be utilized for improving efficiency and functionality, for the production of parts with very complex geometries, and rapid prototyping. However, despite many technological advancements made in recent years, several challenges hinder the mass adoption of metal AM. One of these challenges is mechanical anisotropy which describes the dependency of material properties on the material orientation. Therefore, in this work, stainless steel 316L parts produced by laser-based powder bed fusion are used to isolate and understand the root cause of anisotropy in AM parts. Furthermore, an efficient and accurate multiscale numerical framework is presented for predicting the deformation behavior of actual AM parts on the macroscale undergoing large plastic deformations. Finally, a novel constitutive model for the plastic spin is formulated to capture the influence of the microstructure evolution on the material behavior on the macroscale.
The behavior of amorphous polymers in contact with gas atmospheres is still an area of both fundamental scientific and applied industrial research. Applications range from the use as barrier materials or protective coatings to active layers in sensor applications (‘artificial nose’) and the large field of gas separation membranes. In all these applications, high concentrations of small penetrant molecules may lead to a plasticization of the polymer. This effect is utilized in processing applications, where supercritical carbon dioxide (CO2) can be used as a plasticizer.4 The phenomenon of penetrant induced plasticization of glassy polymers is also observed in gas separation membranes.5 In the process of natural gas sweetening, the CO2 content of the gas mixture is reduced by separation of the CO2 from the fuel gas methane (CH4) to avoid corrosion of pipelines and to enhance the fuel value. Solubility and diffusivity of the respective gas determine the separation performance of the membrane material, i.e., the permselectivity. Both parameters are connected to the internal structure of the polymer and its free volume. To achieve high throughputs, e.g. to enhance costeffectiveness, it is desirable to increase the CO2 solubility and mobility. However, the observed plasticization and the associated relaxations in the polymer matrix change its structure and free volume, and thereby affect the selectivity of the material.6 In addition, other properties of the polymer are influenced, e.g. a reduction of glass transition temperature,7 yield stress8 and creep compliance9 have been observed. The origin and mechanism of these structural relaxations are poorly understood, as are the factors that influence solubility and mobility of the plasticizing penetrant. This lack of knowledge leads to a development of new or optimized materials, which is in part determined by trial and error. A deeper understanding of the phenomena that accompany gas sorption on the molecular level is therefore needed to control material properties and enable a targeted design of functional materials. Therefore, in this work, laboratory experiments are combined with detailed atomistic molecular simulations. Modelling. In detailed atomistic molecular modeling, the interactions of an assembly of atoms, e.g. a polymer molecule, are calculated according to known physical laws. Several established analysis methods allow an indirect determination of certain properties of such assemblies, others can even be directly calculated.10 However, CPU-power limits both the size and the simulation time of such assemblies. The size of the simulated packing models used in this work (_ 5000 atoms) ranges among the larger models found in the literature. Forcefield based Molecular Dynamics (MD) simulations are calculated in femtosecond steps, but reliable results are usually not obtained until a nanosecond of net simulation time has been performed. Millions of interactions need to be calculated, making the time effort for these ‘virtual experiments’ comparable to laboratory experiments. However, increasing speed of single processors and the possibility of parallel processing will further reduce the evaluation times for such simulations in the future. The goal of computer simulations is therefore to establish reliable methods to predict material properties. Properties of new materials could then be assessed by simulations first and only the most promising materials need to be synthesized for further testing, reducing the expense of trial and error. Although some methods already exist to predict polymer/gas properties from simulations, which show well agreeing results in ideal circumstances, they frequently fail when applied to less moderate conditions, e.g., high penetrant concentrations, long time scales, large penetrants etc. The aforementioned gas induced plasticization of polymers presents such a case where the gap of time scales between experiment and available simulation time amounts to several orders of magnitude. The time scale of simulations is limited to a few nanoseconds and therefore it is not possible to directly simulate relaxations of the glassy matrix as they are observed experimentally. Experiments, on the other hand, yield results of the real macroscopic system, and though molecular details cannot be observed individually, the accumulated effects permit the analysis through models on a statistical or phenomenological basis. It is the aim of this work to survey new approaches of a combined analysis of experimental and modelling results and to establish, where possible, a convergence of boundary conditions or, alternatively, an identification and isolation of comparable aspects of these seemingly incompatible methods of research. To this effect, phenomenological models are utilized as a means of interpretation of experimental data as well as to construe modelling results.
Before the development of computational science, heat conduction problems were mainly solved by analytical techniques. Analytical solutions are exact solutions of differential equations; the investigated physical phenomena, for instance the temperature, are solved locally for one single point independently of the rest of the investigated structure resulting in extremely short computational times. These analytical solutions are however only valid for some simple geometries and boundary conditions making their applications for complex industrial geometries directly not possible. Numerical techniques, such as the Finite Element Method, enable overcoming this problem. However, the numerical simulation of the structural heat effect of welding for complex and large assemblies requires high computational effort and time. Therefore, the wide application of welding simulation in industry is not established, yet. The aim of this study is to combine the advantages of analytical and numerical simulation methods to accelerate the calibration of the thermal model of structure welding simulation. This is done firstly by calibrating automatically the simulation model with a fast analytical temperature field solution and secondly by solving the welding simulation problem numerically with the analytically calibrated input parameters. In order to achieve this goal, the analytical solution of the heat conduction problem for a point source moving in an infinite solid was extended and validated against reference models until a solution for a volumetric heat source moving on a thin small sheet with several arbitrary curved welding paths was found. The potential of this analytical solution by means of computational time was subsequently demonstrated on a semi-industrial geometry with large dimensions and several curved welds. The combined method was then transferred to an industrial assembly welded with four parallel welds. For this joint geometry, it was possible to apply the extended analytical solution. The calibration of the simulation model was done automatically against experimental data by combining the extended fast analytical solution with a global optimisation algorithm. For this calibration, more than 3000 direct simulations were required which run in less computational time than one corresponding single numerical simulation. The results of the numerical simulation executed with the analytically calibrated input parameters matched the experimental data within a scatter band of ± 10 %. The limit of the combined method is shown for an industrial assembly welded with eight overlap welds. For this joint geometry, a conventional numerical approach was applied, since no analytical solution was actually available. The final simulation results matched the experimental data within a scatter band of ± 10 %. The results of this work provide a comprehensive method to accelerate the calibration of the thermal model of the structure welding simulation of complex and large welded assemblies, even though within limitation. In the future, the implementation of this method in a welding simulation tool accessible to a typical industrial user still has to be done.