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ABSTRACTThree-dimensional concrete printing (3DCP) is progressing from lab pilots to large-scale manufacturing, encountering limitations with conventional printable material – cement mortar. Coarse aggregate concrete (CAC) emerges as a promising alternative due to its superior material properties, cost-effectiveness, and sustainability, attracting considerable interest in academia and industry. This paper explores the integration of CAC into 3DCP, focusing on three critical aspects: mixture design of 3D printable concrete, innovative methods of 3D printing process, and structural behaviours of 3D printed concrete specimens, structural members and systems. It elucidates the relationships among mixture composition, processing parameters, early-age material properties, and printability requirements. Furthermore, particle-bed 3D printing technology for CAC is discussed, highlighting advantages and challenges compared to extrusion methods. Ultimately, this review provides valuable insights into the technical challenges and prospects of 3D printing coarse aggregate concrete (3DPCAC) technology, aiming to foster advancements in research and construction practices.

Thermoplastic polymers are widely used in industry to generate parts with reasonable production costs, lightweight, chemical stability, sustainability, and recyclability compared to other materials such as metals, metalloids, or even thermoset polymers. The innovative additive manufacturing (AM) techniques, e.g., fused deposition modelling (FDM), can be used to fabricate thermoplastic products with complex geometries and specific properties. However, the mechanical integrity of those FDM-printed plastic parts can be greatly impacted by a phenomenon named material anisotropy. In this study, an experimental study on a popular 3D printing polymer material—acrylonitrile butadiene styrene (ABS)—is performed to determine how FDM process parameters affect the mechanical properties of the printed ABS parts. This study uniquely concentrates on investigating mechanical anisotropy in FDM-printed ABS, delving into a combination of key printing parameters for a comprehensive exploration. Meanwhile, a finite-element-based numerical analysis is also utilised to numerically evaluate the influences of infill percentage and build orientations on the mechanical properties of the 3D-printed ABS materials for comparison. It generates a better understanding of material anisotropy and helps to find the optimal FDM process parameters to print high-quality ABS parts and may attract industrial interests in transitioning from traditional ABS part production methods such as injection moulding or hot pressing to additive manufacturing.

Additive manufacturing (AM) has inherent mechanical strength inconsistencies when the build orientation changes. To address this issue, theoretical models, including analytical and numerical models, can be developed to predict the material properties of additively manufactured materials. This study develops a systematic finite element (FE)-based multiscale numerical model and simulation process for the polymer acrylonitrile butadiene styrene (ABS). ABS samples are fabricated using fused deposition modelling (FDM) to determine the material properties and mechanical behaviours. For macroscale analysis, good agreement between the numerical and experimental tensile strength of transverse samples proved that the FE model is applicable for applying a reverse engineering method in simulating the uniaxial tension of samples. The FE modelling method shows its capability to consider infill density effects. For mesoscale analysis, two methods are developed. The first method is a representative volume element (RVE)-based numerical model for all longitudinal samples. The second method is analytical and based on the rule of mixtures (ROM). Modified rule of mixtures (MROM) models are also developed, which demonstrate an improvement compared to the original ROM models. The research outcomes of this study can facilitate the AM process of parts in various engineering fields.

Additive manufacturing (AM) is a sustainable and innovative manufacturing technology to fabricate products with specific properties and complex shapes for additive manufacturable materials including polymers, steels, titanium, copper, ceramics, composites, etc. This technology can well facilitate consumer needs on products with complex geometry and shape, high strength and lightweight. It is sustainable with having a layer-by-layer manufacturing process contrary to the traditional material removal technology—subtractive manufacturing. However, there are still challenges on the AM technologies, which created barriers for their further applications in engineering fields. For example, materials properties including mechanical, electrical, and thermal properties of the additively manufactured products are greatly affected by using different ways of AM methods and it was found as the material anisotropy phenomenon. In this study, a detailed literature review is conducted to investigate research work conducted on the material anisotropy phenomenon of additively manufactured materials. Based on research findings on material anisotropy phenomenon reported in the literature, this review paper aims to understand the nature of this phenomenon, address main factors and parameters influencing its severity on thermal, electrical and mechanical properties of 3D printed parts, and also, explore potential methods to minimise or mitigate this unwanted anisotropy. The outcomes of this study would be able to shed a light on improving additive manufacturing technologies and material properties of additively manufactured materials.

It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations.

Stainless steels (SUS) and dual-phase (DP) steels have tension-compression asymmetry (TCA) in mechanical responses to full loading cycles. This phenomenon can significantly influence sheet metal forming of such metals, however, it is difficult to describe this behaviour analytically. In this research, a novel analytical method for asymmetric elastic-plastic pure bending using the Cazacu–Barlat 2004 asymmetric yield function is proposed. It only uses material parameters in tension along with an asymmetry coefficient related to the yield function. Bending operations of SUS304 and DP980 are investigated as two case studies. In the pure bending for both SUS304 and DP980, moment–curvature diagrams are analytically obtained. Furthermore, linear and nonlinear springback behaviours of SUS304 are analytically investigated. Moreover, using the analytical model as a user-defined material, a numerical model is developed for both steels under pure bending. In the V-bending case of SUS304 with and without TCA effects, the springback behaviours of the material are investigated numerically. In addition, considering friction effects, the analytical method is further modified for predicting springback behaviours in the V-bending of 16 types of SUS304 with various strengths are determined. All the analytical and numerical results have good agreement with those experimental results from literature for validation.