Abstract. Nowadays the demands of high reliability, high mechanical properties under fatigue loading and ultra-light-weight design are topics of pressing needs in different industry applications. Additive manufacturing technique has great potential to meet such demands and Selective Laser Sintering (SLS) is the dominantly used one due to its versatility in material design, construction and the much higher achievable mechanical strength compared to its competing technologies like fused filament fabrication. In this study, it is aimed to give an insight to the fracture behaviour of SLS produced lattice structure with macro-porosity under compression load. The stress type and distribution on the lattice structure under compression load are anisotropic because of the unit cell topologies and the anisotropic processing condition. Therefore, different kinds of fracture types are evident depending on the region of the fractured struts.

Laser metal deposition (LMD) is an additive manufacturing process used in manufacturing freeform geometries, repair applications, coating and surface modification, fabrication of functionally graded materials. It has a broad range of applications in various industries, including aviation, space, defence, automotive, tooling, etc. In this work, a multi-physics model of the LMD process was developed to rapidly predict the geometrical characteristics of the single clad track using the commercial software package Flow-3D. The volume of fluid (VOF) method was integrated to differentiate the interface between the metallic and gaseous cells. To validate the numerical model single bead tracks were deposited, and cross-sections of the beads were analysed. Mathematical formulae to predict different aspects of the single clad track (height, width, and depth) were derived using regression analysis. The influence of the process parameters on the geometrical characteristics of the single clad track was analysed in detail using analysis of variance (ANOVA). Both multi-physics model and mathematical regression model results were compared to the experimental measurements. The results were in good agreement with the experimental results. Graphical abstract

Directed energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melting. Thermal effects can cause distortions and defects on the parts during the DED process, therefore they should be evaluated and taken into account during the manufacturing of products. Melting pool control and DED bead geometries should be defined properly as well. In this work, an Artificial Neural Network model has been applied considering the DED process parameters in order to predict the geometrical patterns and create a local reinforced product as a hybrid manufacturing technology. Although lots of studies are available on topology optimization for manufacturing methods such as casting, extrusion, and powder bed fusion, topology optimization for the DED process is not widely taken into consideration to predict the design geometrical patterns. DOE RSM and ANN approaches were applied in this study to predict convenient dimensions, topology based geometrical patterns of local stiffeners and heat source power optimizing the energy, total mass, and peak force results of the hybrid part. A single bead track deposition is simulated in terms of validation of the numerical heat source model, and cross-sections of the beads are analysed. A cross-member structure is manufactured using the DED device and the structure is correlated under the three point bending physical conditions on test bench. It has been investigated that locally reinforced cross beam has much more energy absorption and peak force values than plain model. The results showed that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies.