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Additive manufacturing (AM) has emerged as a commonly utilized technique in the manufacturing process of a wide range of materials. Recent advances in AM technology provide precise control over processing parameters, enabling the creation of complex geometries and enhancing the quality of the final product. Moreover, Machine Learning (ML) has become widely used to make systems work better by using materials and processes more intelligently and controlling their resulting properties. In industrial settings, implementing ML not only reduces the lead time of manufacturing processes but also enhances the quality and properties of produced parts through optimization of process parameters. Also, ML techniques have facilitated the advancement of cyber manufacturing in AM systems, thereby revolutionizing Industry 4.0. The current review explores the application of ML techniques across different aspects of AM including material and technology selection, optimization and control of process parameters, defect detection, and evaluation of properties results in the printed objects, as well as integration with Industry 4.0 paradigms. The progressive phases of utilizing ML in the context of AM, including data gathering, data preparation, feature engineering, model selection, training, and validation, have been discussed. Finally, certain challenges associated with the use of ML in the AM and some of the best-practice solutions have been presented. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of hygrothermal aging on PA6 filaments used as raw material in fused filament fabrication (FFF). The filaments were subjected to various aging conditions to evaluate their physicochemical and thermomechanical properties. The results show that water was uniformly absorbed at all aging temperatures, although the kinetics of absorption varied. The introduction of water caused an increase in chain mobility, resulting in a decrease in the glass transition temperature, crystallinity, Young's modulus, tensile strength, and yield strength. These changes in properties followed a consistent trend, with an initial rapid decrease followed by stabilization at a minimum value common to all temperatures. Notably, elongation at break increased sevenfold at the highest temperature. Compared to samples printed with unaged filaments, those produced with aged filaments exhibited a decrease in mechanical properties. [ABSTRACT FROM AUTHOR]

Manufacturing processes significantly influence microstructural variations in short fiber reinforced composites, which affect damage characteristics and fatigue life. Accurate fatigue life prediction is critical, especially when considering the impact of these manufacturing induced microstructural nuances. In this study, we investigate how manufacturing processes shape microstructures and their impact on fatigue life prediction. We present an advanced micromechanical model for predicting fatigue life in tangible structures, considering the microstructure distribution influenced by manufacturing dynamics. Our model links damage from monotonic loading to fatigue life, resulting in a multi-scale fatigue prediction model. This approach builds a database revealing the interaction between Tsai-Wu failure criterion parameters, manufacturing-induced microstructural variations, and target fatigue life. Using these insights, we fine-tune material properties in finite element simulations for precise design optimization. We illustrate our method using an automotive tailgate made from a sheet molding compound. This research highlights the critical role of manufacturing processes in microstructure variation and fatigue life prediction. It offers the potential for significant vehicle weight reduction, energy savings, and reduced emissions in automotive design and promises to be a valuable tool for optimizing manufacturing process parameters. [ABSTRACT FROM AUTHOR]

In the recent years, additive manufacturing (AM) has been widely expanded for manufacturing the polymer and polymer-based composite parts. The main drawback of the material extrusion additive manufactured parts is the weaker mechanical properties in comparison to the manufactured parts by the conventional methods. The stated weak mechanical properties are due to the weak adhesion between the deposited layers. The poor adhesion between the deposited layers in material extrusion additive manufacturing process is due to the fact that the previous deposited filament (n-1) is already cooled and solidified. To ensure the appropriate adhesion between the two adjacent filaments, the temperature of the first deposited layer (n-1) has to be high enough to obtain a suitable adhesion to the subsequent layer (n) but in an optimum range to avoid the lack of the dimensional accuracy. Therefore, a precise and local measurement of the temperature on the scale of the diameter of the filaments is necessary. In this study, four important process parameters (liquefier temperature, layer height, print speed, and bed platform temperature) were selected to study their effects on the rheological behavior and temperature evolution of the PA6 and CF-PA6 materials during material extrusion process. Then, the impact of the short/chopped reinforcement on the thermal and mechanical properties of the material extrusion additive manufacturing processed polymer-based composites were studied by comparing the obtained results from PA6 and CF-PA6 parts. In one experiment, it was observed that increasing the liquefier temperature from 220 to 240 °C increased the tensile strength and crystallinity percentage of the manufactured PA6 and CF-PA6 specimens. It was determined that the crystallinity percentages of PA6 and CF-PA6 specimens increased from 12.51 to 14.40% and from 19.97 to 20.51%, respectively. One of the existence effects of carbon fibers is highlighted in the higher crystallinity values of the CF-PA6 specimens comparing PA6 specimens. Finally, a time–temperature-transformation diagram was plotted to determine the processability condition of the utilized materials. It can be helpful for the designers and researchers to find out the optimal material extrusion additive manufacturing process parameters condition for the utilized raw materials. [ABSTRACT FROM AUTHOR]

Stereolithography (SLA) is becoming an important fabrication method among the different additive manufacturing techniques. This study investigates the effect of high strain rate on mechanical behavior, considering the fact that materials can be shown different mechanical properties under rapid straining compared to quasi-static loading. In addition, the role of polymerization as a determining factor in the final mechanical properties of the SLA parts is indicated. Regarding that, samples based on urethane dimethacrylate resin material were printed in different directions (θ = [0–90]) and post-treatment was performed with respect to the UV and UV with temperature. The effect of high strain rate was analyzed through a servo-hydraulic machine, monotonic, and interrupted tensile tests ranging from 0.3 to 117.4 s−1. Scanning electron microscopy was applied to analyze the failure surface characteristics. The results show the important effect of strain rate on mechanical properties such that by increasing the strain rate, yield stress and ultimate stress were increased. Furthermore, investigation of the strain rate sensitivity during the different steps of the failure indicates more sensitivity of the non-linear zone of the stress–strain curve with strain rate. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young's modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value. [ABSTRACT FROM AUTHOR]

This article describes a new interactive design approach integrating the constraints associated with production include manufacturing and assembling. The proposed method, in the form of an algorithm, allows optimisation of product design by minimizing production costs at each iteration, without compromising its functionality. The novelty of this algorithm in terms of modeling and optimisation of production costs in the design phase is its ability to dynamically evaluate the cumulative costs of production as a function of design and procedural choices. The availability of this information first allows the identification of design points and/or procedural points that generate significant production costs, and second, suggests improvements and recommendations that aim to optimize production costs. These experiments were conducted at a smart factory installed in our organisation. The proposed algorithm involves four steps. To optimise production costs, the designer must input all of the required data into the simulation and thereby identify the most significant cost elements to optimise. Then, the designer uses the suggested recommendation list to modify the relevant design and/or manufacturing parameters, thus obtaining the new, optimised production costs. If the first result is unsatisfactory, other iterations can be performed. [ABSTRACT FROM AUTHOR]