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Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM's appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model's performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model's performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data. [ABSTRACT FROM AUTHOR]

Purpose: This study aims at compensating for sintering deformation of components manufactured by metal binder jetting (MBJ) technology. Design/methodology/approach: In the present research, numerical simulations are used to predict sintering deformation. Subsequently, an algorithm is developed to counteract the deformations, and the compensated deformations are morphed into a CAD model for printing. Several test cases are designed, compensated and manufactured to evaluate the accuracy of the compensation calculations. A consistent accuracy measurement method is developed for both green and sintered parts. The final sintered parts are compared with the desired final shape, and the accuracy of the model is discussed. Furthermore, the effect of initial assumptions in the calculations, including green part densities, and green part dimensions on the final dimensional accuracy are studied. Findings: The proposed computational framework can compensate for the sintering deformations with acceptable accuracy, especially in the directions, for which the used material model has been calibrated. The precise assumption of green part density values is important for the accuracy of compensation calculations. For achieving tighter dimensional accuracy, green part dimensions should be incorporated into the computational framework. Originality/value: Several studies have already predicted sintering deformations using numerical methods for MBJ parts. However, very little research has been dedicated to the compensation of sintering deformations with numerical simulations, and to the best of the best of the authors' knowledge, no previous work has studied the effect of green part properties on dimensional accuracy of compensation calculations. This paper introduces a method to omit or minimize the trial-and-error experiments and leads to the manufacturing of dimensionally accurate geometries. [ABSTRACT FROM AUTHOR]

Tangential surface tension forces on a gasâ€"liquid interface due to surface tension gradients have been implemented in the computational fluid dynamics (CFD) solver icoReactingMultiphaseInterFoam provided by the open-source software environment of OpenFOAM OpenCFD Ltd (ESI Group) OpenFOAM (online) https://www.openfoam.com/ (accessed 21 May 2021), so that the Marangoni effect can be taken into account, which is a main driver of heat transfer in additive manufacturing processes that comprise a melt pool. The solver surpasses the capabilities of similar open-source projects by considering a wide range of physical effects, e.g. multiple phases, melting, solidification, evaporation, and laser beam heat sources with an arbitrary intensity distribution and thus makes it an appealing framework, especially for the simulation of the laser powder bed fusion (LPBF) process. Herein, all relevant details and derivation considering the Marangoni effect are provided and validated by means of a benchmark problem by comparing the obtained results with the available analytical solution, with the results obtained from a commercial CFD tool and with the results of other authors. The modified solver is additionally validated by comparing the results from LPBF simulations with experimental data. Furthermore, the influence of the surface tension modeling on the mushy region is investigated. The optimized implementation shows improvements of the simulation results in both the dimensions and shape of the melt pool and the resulting surface with regard to the experimental data. [ABSTRACT FROM AUTHOR]

A physically based model is developed to predict deposit profiles during wire and arc additive manufacturing (WAAM). The model takes material properties and process parameters as input, determines the amount of melt and its penetration component through a thermal analysis, and calculates the layer and bead geometries through a capillary-gravity analysis. While none of the model parameters and constants is adjusted by fitting to the experiments, it can well predict the layer height and width of walls manufactured from a grade of mild steel at various conditions. The model is claimed to be valid for a wide range of materials and conditions, even with very heavy deposited beads. The outputs are calculated from basic geometrical features such as bead curvature and contact and penetration angles. Hence, the presented model can help in understanding the effects of process parameters on the geometry of the layers. The inter-layer temperature, the travel speed, the wire feed speed, and the heat input are analyzed, accordingly. [ABSTRACT FROM AUTHOR]

Carbon nanotube-reinforced polymers belong to a class of composite materials, which have been largely investigated due to their special electrical, thermal and mechanical properties. In the case of electrical conductivity of carbon nanotube-reinforced polymer, a critical amount of carbon nanotubes as a filler material enables a sharp increase of electrical conductivity. At this certain volume fraction of carbon nanotubes, the material turns into a conductor because of the percolation effect. This effect occurs due to the formation of a closed pathway of filler material through the system. Mesoscopic simulation models of these materials were carried out to predict their electrical conductivity. In this paper, the effects of carbon nanotube curvature and their alignment parallel or perpendicular to the electrical flow direction inside a representative volume element were analysed and an optimized carbon nanotube distribution is presented. A network with a longest average path length per (rigid) carbon nanotube but also cross-linking (only nearly isotropic alignment) shows the best conductivity in the preferred direction. [ABSTRACT FROM AUTHOR]

Aerospace carbon fibre-reinforced components are cured under high pressure (7 bar) and temperature in an autoclave. As in an industrial environment, the loading of an autoclave usually changes from cycle to cycle causing different thermal masses and airflow pattern which leads to an inhomogeneous temperature distribution inside the carbon fiber-reinforced plastic part. Finally, the overall process can be delayed and the part quality can be compromised. In this paper, the heat transfer in a small laboratory autoclave has been investigated using calorimeter measurements and a fluid dynamic model. A complex turbulent flow pattern with locally varying heat transfer coefficient has been observed. Especially, the pressure and the inlet fluid velocity have been identified as sensitive process parameters. Further finite element simulations with adjusted boundary conditions provide accurate results of the curing process inside of the components for selective process control. The heat transfer coefficient has been found to be almost stationary during the observed constant pressure autoclave process allowing a separated investigation of the heat transfer coefficient and the curing of the components. The presented method promises therefore a detailed observation of the autoclave process with reduced computational effort. [ABSTRACT FROM AUTHOR]

Carbon nanotube reinforced polymers belong to a class of composite materials, which have been largely investigated due to their specific electrical, thermal and mechanical properties. In the case of electrical conductivity of carbon nanotube reinforced polymers, a critical amount of filler material ensures a sharp increase of conductivity. The material forms a percolation pathway and instantly turns the composite into a conductor. Mesoscopic simulations of these materials were carried out to predict electrical conductivity of carbon nanotube reinforced polymers and their critical amounts. This research work deals with percolation thresholds, converging representative volume elements and the effect of the discontinuous behaviour of conductivity considering tunnelling effects found in atomistic approaches on mesoscopic simulation models. [ABSTRACT FROM AUTHOR]

Laser sintering of polymers is a steadily improving additive manufacturing method. Mainly used materials are semicrystalline thermoplastics, and as part of those, polyamide 12 has established most. When semicrystalline polymers cool down from the melt, they exhibit volume shrinkage due to crystallization. This crystallization occurs non-uniformly within the produced parts and thus is responsible for part warpage. Aim of this study was to investigate the crystallization kinetics of an, in laser sintering widely used, polyamide 12-based polymer available by supplier EOS with appellation PA2200. For that purpose, several isothermal and non-isothermal DSC measurements were taken. The isothermal measurements were analyzed according to the theory of Avrami. Furthermore, the parameters of the crystallization model by Nakamura were calibrated, and both conditions were simulated. It was found that the isothermal data are very well describable by the theory of Avrami as well as the model by Nakamura can be used to model both conditions. [ABSTRACT FROM AUTHOR]

The microstructures of additively manufactured Sc- and Zr-modified aluminum alloys are significantly influenced by the nucleation role of solid intermetallic particles in undercooled liquid. To replicate such effects, a precipitation model relying on L12-Al3Sc particles is developed. An initiation criterion is proposed based on the precipitation kinetics of primary particles to address solute trapping under high solidification rates. Avrami's equation is then used to estimate the progress of precipitation. The model is integrated into a cellular automata (CA) analysis to simulate the resulting solidified microstructure, in that the precipitation model is performed implicitly within the CA cells. It is shown that, in accordance with the experimental findings, the proposed simulation approach can predict the distinct fine- (FG) and coarse-grained (CG) zones at the fusion boundary and the meltpool core, respectively. The model can also deliver the reported enhancement of the FG zone under lower scanning speed and higher platform temperatures. These findings are explained in terms of particle number densities at different meltpool regions. Moreover, a semi-2D simulation with a very small cell size is suggested to address the extremely fine grain structure within the FG zone. [ABSTRACT FROM AUTHOR]