The parts produced by additive manufacturing are inherently subjected to discretization effects due to their layer-based addition. The stair-stepping effect on the surface quality is inevitable for most of the techniques and it becomes more dominant for the regions having small surface inclinations. The stair-stepping influences the mechanical properties as well as the aesthetic perception. Many researchers have been presented several approaches to overcome or minimize the stair-stepping effects and improve the surface quality of additively manufactured parts. The attempts have been made generally for the FDM-printed objects, however, there is no or fewer efforts have been made for parts of large-scale additive manufacturing (LSAM). Due to higher deposition rates (up to 50 kg/hrs.) and larger nozzle diameters (i.e. bead size) the discretization effect is more in large-scale additive manufacturing. In this paper, the presented methods to mitigate the stair-stepping effect and improving the surface quality of additive manufacturing are reviewed and practicing in large-scale 3D printing is discussed. A preliminary experimental study of 3D printing with a non-planar toolpath was carried out and the results were presented.

Reverse engineering (RE) aims at the reproduction of products following a detailed examination of their construction or composition. Nowadays, industrial applications of RE were boosted by combining it with additive manufacturing. Printing of reverse-engineered elements has become an option particularly when spare parts are needed. In this paper, a case study was presented that explains how such an approach can be implemented in the case of products with asymmetric mechanical properties and using replacement materials. In this case study, a reverse engineering application was conducted on a textile machine spare part. To this end, the nearest material was selected to the actual material selection and some mechanical tests were made to validate it. Next, a replacement part was designed by following the asymmetric push-in pull-out characteristic. Finally, the finite element analysis with Additive Manufacturing was combined and validated experimentally.

Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don’t always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes.

In this study, thermal deformation in non-planar, large-scale additive manufacturing (LSAM) was experimentally and numerically investigated. A Bézier-based non-planar build surface was fabricated by CNC machining, and a single layer of ABS was deposited using a hybrid LSAM system. Toolpaths with raster angles of 0° and 45° were generated for surface-conformal printing. Infrared thermography was employed to monitor the thermal history during deposition. A three-dimensional finite element model was developed to simulate transient heat transfer and thermally induced deformation. Experimental deformation was quantified by 3D scanning and compared with simulation results. The results show that the slope geometry strongly influences deformation direction: negative slopes promote contraction, whereas positive slopes lead to upward deflection. Maintaining the material temperature above the glass transition temperature significantly reduces skew deformation. The finite element method predictions demonstrate strong agreement with experimental measurements, with normalized root mean square errors (NRMSEs) of approximately 11% for thermal deformation and 10% for temperature history. The proposed framework enables prediction and mitigation of thermal warping in non-planar polymer additive manufacturing.

The development of additive manufacturing (AM) techniques has sparked interest in porous structures that can be customized in terms of size, shape, and arrangement of pores. Porous lattice structure (LS, called also lattice struct) offer superior specific stiffness and strength, making them ideal components for lightweight products with energy absorption and heat transfer capabilities. They find applications in industries such as aerospace, aeronautics, automotive, and bone ingrowth applications. One of the main advantages of additive manufacturing is the freedom of design, control over geometry and architecture, cost and time savings, waste reduction, and product customization. However, the designation of appropriate struct/pore geometry to achieve the desired properties and structure remains a challenge. In this part of the study, five lattice structs with various pore sizes, with two volume fractions for each, and shapes (ellipsoidal, helical, X-shape, trapezoidal, and triangular) were designed and manufactured using selective laser sintering (SLS) additive manufacturing technology. Mechanical properties were tested through uniaxial compression, and the apparent stress-strain curves were analyzed. The results showed that the compression tests revealed both monotonic and non-monotonic stress-strain curves, indicating different compression behaviors among the structures. The helical structure exhibited the highest resistance to compression, while other structures showed similarities in their mechanical properties. In Part II of this study provides a comprehensive analysis of these findings, emphasizing the potential of purpose-designed porous structures for various engineering applications.