Three-dimensional (3D) bioprinting technology has emerged as a powerful biofabrication platform for tissue engineering because of its ability to engineer living cells and biomaterial-based 3D objects. Over the last few decades, droplet-based, extrusion-based, and laser-assisted bioprinters have been developed to fulfill certain requirements in terms of resolution, cell viability, cell density, etc. Simultaneously, various bio-inks based on natural–synthetic biomaterials have been developed and applied for successful tissue regeneration. To engineer more realistic artificial tissues/organs, mixtures of bio-inks with various recipes have also been developed. Taken together, this review describes the fundamental characteristics of the existing bioprinters and bio-inks that have been currently developed, followed by their advantages and disadvantages. Finally, various tissue engineering applications using 3D bioprinting are briefly introduced.

Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%.

Existing studies have successfully identified optimal process parameters to enhance relative density and mechanical properties. However, these optimizations often incur high experimental costs due to the need for repetitive experiments. This research introduces an efficient optimization framework using two design of experiment (DoE) methods and machine learning algorithms, aimed at optimizing the magnetic properties of Fe–4.5Si material. The framework consists of three steps: (1) identification of the process parameter region that ensures high relative density using cubic specimens, (2) development of surrogate models to predict magnetic properties, such as maximum relative permeability and core loss, using toroidal specimens, and (3) multi-objective optimization using the surrogate models and a multi-objective genetic algorithm (MOGA) to enhance magnetic properties. The results demonstrate that this method can efficiently optimize process parameters with a limited number of experiments. Furthermore, the versatility of this framework allows its application to other materials in the early stages of research, even without prior knowledge of specific materials.

This study investigates the challenges of additively manufactured interlocking structures, emphasizing joinability issues due to thermal deformation. These challenges become pronounced when fabricating high-density structures without fully interconnected layers, a trait common in soft magnetic materials. Here, a detailed analysis assessing deformation concerning pin thickness and build orientation in a representative interlocking model is performed. Utilizing stress and thermal simulations of the additive manufacturing process, it is shown that a compensated design considerably enhances the joinability of these structures. These findings offer valuable perspectives for advancing the design of additive manufacturing components, particularly in soft magnetic materials such as electric motor stators, which require both insulation and density.

In this study, design for additive manufacturing (DfAM) of release agent injection manifold for hot forging has been performed to achieve weight reduction and flow path optimization. The weight reduction of 53.5% was achieved, thereby enabling the application of stainless steel 316L, which has high strength and corrosion resistance. Lightweight manifolds using Al-Mg-10Si and SUS316L materials were fabricated by PBF-type metal 3D printer. The feasibility test showed that mold life was improved by 14% by solving residual release agent problem. In addition, the flow path optimization results suggested that the flow standard deviation of each outlet dropped sharply from 264 to 75 ㎤/s. This approach demonstrated that DfAM for release agent manifold could be applied to increase mold life and improve product quality and productivity for hot forging.

Additive manufactured interlocking structures often face challenges in achieving suitable joinability due to thermal deformation. This issue becomes particularly interesting when fabricating structures that require high density but not fully connected layers, such as soft-magnetic materials. This study focuses on systematical investigation of the deformation as a function of finger thickness and building direction in model interlocking structure. In the perpendicular direction to the build plate, the distortions of joint part were significantly reduced due to lower thermal stress. In addition, the effect of laser power and scan speed on interlocking structure are systematically discussed. Additionally, through stress and thermal simulations of additive manufacturing process, it was confirmed that the compensated model can enhance the joinability of interlocking structures. These findings contribute to the development of optimal design for additive manufactured parts of soft magnetic materials such as stators in electric motor components, which needs insulations and density.