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The technological development of the 3D printing industry has been tremendous starting from stereolithography in the 1980 s. Low throughput and surface quality had been the major obstacles to this technology, which offers very limited constraints to the geometrical shapes. In this work, we designed a low-cost volumetric 3D printer that, unlike traditional 3D printers, polymerizes the whole geometry within the revolving resin container. Optimized projections needed for the polymerization of target objects were developed in MATLAB R2020b using the concepts of tomographic reconstruction. The rotation of the resin container and the projections rate were matched to develop the target geometries after the oxygen depletion. Commercially available low viscous Anycubic plant-based resins were used as the printing material. 3D objects of centimeter scale could be manufactured in 30 seconds with extremely low surface roughness in the sub-micron range. The dimensional deviation of final components from the CAD model was found to be within 5%. The overprinting on existing solid structures was successfully experimented with and thus opens the way for future developments. Commercialization of volumetric 3D printers would result in mass production of complex customer-specific functional components which in turn boosts the manufacturing sector significantly. KEYWORDS 3D printing, photopolymerization, tomography, volumetric additive manufacturing, 1 | INTRODUCTION The popularity of additive manufacturing technologies is attaining more reach due to their capability to fabricate complex customer-specific components. Ever since its inception in the 1980 s [...]

The requirement for post-processing methods in 3D printing has increased due to its major limitations such as poor mechanical and surface properties. Thermal annealing, a heat-treatment procedure, has proven to be an excellent approach for increasing the mechanical strength of additive manufactured thermoplastics. The optimized thermal annealing parameters for maximum tensile strength are identified. The Adaptive Neuro-fuzzy Inference System (ANFIS) methodology is employed in this study to forecast the tensile strength of thermal annealed fused filament fabricated polylactic acid (PLA), carbon fiber filled polylactic acid (PLA-CF), and polycarbonate acrylonitrile butadiene styrene (PC-ABS). The root mean square error (RMSE) of the predicted tensile strength of PLA, PLA-CF, and PC-ABS are obtained as 1.012, 0.5, and 0.835 respectively. The coefficient of determination for the predicted model of thermoplastics PLA, PLA-CF, and PC-ABS is determined as 0.98, 0.99, and 0.89 respectively. The ANFIS model developed is successful in determining the tensile strength of annealed 3D printed thermoplastics at various annealing conditions of temperature and duration. Further, the study investigates the effect of heat treatment on the compressive, impact, and flexural properties of PLA prints. [ABSTRACT FROM AUTHOR]

To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a regression relationship between tool wear ratio and the process inputs has been developed.

Inconel 718 is a corrosion-resistant and high strength nickel-based alloy with wide range of applications, including components for cryogenic tankage, liquid fueled rockets and casings for aircraft engines. The material is characterized by high hardness, high temperature strength, low thermal conductivity and high strength causing it extremely difficult to machine. Micro-Electrical Discharge Machining (Micro-EDM) is a non-conventional method that has a potential to overcome these restrictions for machining of Inconel 718. Response Surface Method (RSM) was used for modelling the tool Electrode Wear Rate (EWR) and Material Removal Rate (MRR) with the input factors, such as voltage (V), peak current (Ip) and pulse on-time (Ton). The RSM analysis of variance results show that the main input factors' pulse off-time and voltage are significant in controlling the tool electrode wear rate at 95% confidence level. An increase in voltage from 30 to 45 V and pulse on-time from 1 to 3 µs causes a linear decrease in EWR by 35%. Using response surface modeling, a minimum EWR of 12.3184 µm³ /min and a maximum MRR of 37.2151 µm³ /min is obtained at a current of 1.07 A, pulse ontime of 4.44 µs, pulse off-time of 4.06 µs and voltage of 60 V. [ABSTRACT FROM AUTHOR]