Selective laser melting (SLM) is one of the most important and successfully additive manufacturing processes in 3D metal printing technologies. Critical quality issues such as porosity, surface ...roughness, crack, and delamination continue to present challenges within SLM-manufactured parts. Monitoring and in-process defect diagnosis are the key to improving the final part quality. Currently, it greatly hinders the adaptability and the development within the defect detection system since the setup restricts the vision and photo diode applications in the SLM process monitoring. Additionally, defect detection with traditional classification approaches makes the system rather complex due to introducing a series of steps. To meet these needs, this study proposes a novel method for the defect detection within the SLM parts. The setup was flexibly conducted using a microphone, and the defect detection was obtained by the framework of deep belief network (DBN). It is implemented by a simplified classification structure without signal preprocessing and feature extraction. The experimental results showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signal preprocessing.
3D bioprinting is a pioneering technology that enables fabrication of biomimetic, multiscale, multi-cellular tissues with highly complex tissue microenvironment, intricate cytoarchitecture, ...structure-function hierarchy, and tissue-specific compositional and mechanical heterogeneity. Given the huge demand for organ transplantation, coupled with limited organ donors, bioprinting is a potential technology that could solve this crisis of organ shortage by fabrication of fully-functional whole organs. Though organ bioprinting is a far-fetched goal, there has been a considerable and commendable progress in the field of bioprinting that could be used as transplantable tissues in regenerative medicine. This paper presents a first-time review of 3D bioprinting in regenerative medicine, where the current status and contemporary issues of 3D bioprinting pertaining to the eleven organ systems of the human body including skeletal, muscular, nervous, lymphatic, endocrine, reproductive, integumentary, respiratory, digestive, urinary, and circulatory systems were critically reviewed. The implications of 3D bioprinting in drug discovery, development, and delivery systems are also briefly discussed, in terms of in vitro drug testing models, and personalized medicine. While there is a substantial progress in the field of bioprinting in the recent past, there is still a long way to go to fully realize the translational potential of this technology. Computational studies for study of tissue growth or tissue fusion post-printing, improving the scalability of this technology to fabricate human-scale tissues, development of hybrid systems with integration of different bioprinting modalities, formulation of new bioinks with tuneable mechanical and rheological properties, mechanobiological studies on cell-bioink interaction, 4D bioprinting with smart (stimuli-responsive) hydrogels, and addressing the ethical, social, and regulatory issues concerning bioprinting are potential futuristic focus areas that would aid in successful clinical translation of this technology.
Additive manufacturing (commonly known as 3D printing) is defined as a family of technologies that deposit and consolidate materials to create a 3D object as opposed to subtractive manufacturing ...methodologies. Fused deposition modeling (FDM), one of the most popular additive manufacturing techniques, has demonstrated extensive applications in various industries such as medical prosthetics, automotive, and aeronautics. As a thermal process, FDM may introduce internal voids and pores into the fabricated thermoplastics, giving rise to potential reduction on the mechanical properties. This paper aims to investigate the effects of the microscopic pores on the mechanical properties of material fabricated by the FDM process via experiments and micromechanical modeling. More specifically, the three-dimensional microscopic details of the internal pores, such as size, shape, density, and spatial location were quantitatively characterized by X-ray computed tomography (XCT) and, subsequently, experiments were conducted to characterize the mechanical properties of the material. Based on the microscopic details of the pores characterized by XCT, a micromechanical model was proposed to predict the mechanical properties of the material as a function of the porosity (ratio of total volume of the pores over total volume of the material). The prediction results of the mechanical properties were found to be in agreement with the experimental data as well as the existing works. The proposed micromechanical model allows the future designers to predict the elastic properties of the 3D printed material based on the porosity from XCT results. This provides a possibility of saving the experimental cost on destructive testing.
The metal-based additive manufacturing (MAM) processes have great potential in wide industrial applications, for their capabilities in building dense metal parts with complex geometry and internal ...characteristics. However, various defects in the MAM process greatly affect the precision, mechanical properties and repeatability of final parts. These defects limit its application as a reliable manufacturing process, especially in the aerospace and medical industries where high quality and reliability are essential. MAM process monitoring provides a technical basis for avoiding and eliminating defects to improve the build quality. Based on of the nature of the MAM build defects, this article conducts a thorough investigation of monitoring methods, and proposes a machine learning (ML) framework for process condition monitoring. According to the structure of ML models, they are divided into shallow ML-based and deep learning-based methods. The state-of-the-art ML monitoring approaches, as well as the advantages and disadvantages of their algorithmic implementations, are discussed. Finally, the prospects of ML based process monitoring researches are summarized and advised.
In this article, a method of hybrid convolutional neural networks (CNNs) is proposed for powder-bed fusion (PBF) process monitoring. The proposed method can learn both the spatial and temporal ...representative features from the raw images automatically based on the advantages of the CNN architecture. The results demonstrate the superior performance of the proposed method compared with the traditional methods with handcrafted features. The overall detection accuracy of four process conditions, e.g., overheating, normal, irregularity, and balling, can be up to 0.997. In addition, it is found that the temporal information for PBF process monitoring by the vision detection of the process zone (including melt pool, plume, and spatters) is significant. As the proposed method can save image processing steps, it simplifies the procedure on feature extraction. This makes it more suitable for online monitoring applications.
Numerous bioactive molecules produced in cells are involved in the process of bone formation. We consider that appropriate, simultaneous application of two types of bioactive molecules would ...accelerate the regeneration of tissues and organs. Therefore, we combined aspirin‐loaded liposomes (Asp@Lipo) and bone forming peptide‐1 (BFP‐1) on three dimensional‐printed polycaprolactone (PCL) scaffold and determined whether this system improved bone regeneration outcomes. in vitro experiments indicated that Asp@Lipo/BFP‐1at a 3:7 ratio was the best option for enhancing the osteogenic efficiency of human mesenchymal stem cells (hMSCs). This was confirmed in an in vivo cranial defect animal model. In addition, RNA‐Seq was applied for preliminarily exploration of the mechanism of action of this composite scaffold system, and the results suggested that it mainly improved bone regeneration via the PI3K/AKT signaling pathway. This approach will have potential for application in bone tissue engineering and regenerative medicine.
Additive manufacturing (AM) technology has rapidly evolved with research advances related to AM processes, materials, and designs. The advantages of AM over conventional techniques include an ...augmented capability to produce parts with complex geometries, operational flexibility, and reduced production time. However, AM processes also face critical issues, such as poor surface quality and inadequate mechanical properties. Therefore, several post-processing technologies are applied to improve the surface quality of the additively manufactured parts. This work aims to document post-processing technologies and their applications concerning different AM processes. Various types of post-process treatments are reviewed and their integrations with AM process are discussed.
NGCs are considered as an alternative treatment method for treating peripheral nerve injuries in place of nerve autografts. Biomimicry, conductivity, and biodegradability are the properties expected ...of an ideal NGC. PCL/PAA NGCs with three different concentrations of PAA (2.5, 5 and 7.5%) were fabricated using EHD-jet 3D printing. The mechanical properties of the PCL/PAA NGCs mimic the native human nerve properties (ultimate tensile strength of 6.5 to 11.7 MPa) and the conductivity match that of the amphibian motor nerve fiber myelin sheath (10−6 S/cm). The in vitro degradation studies reveal that they are biodegradable and injury/site-specific biodegradability can be obtained by tuning the PCL/PAA concentration ratio. In addition, PAA being a polyanionic polymer has the potential to act as a cation-exchanger, mimicking the functions of the nerve cortical gel layer, thereby influencing the electrophysiological phenomena called nerve excitation and conduction. Neural differentiation studies with PC12 cells assessed by the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and immunocytochemistry showed enhanced gene expression with the presence of PAA. Our results suggest that the EHD-jet 3D printed porous conductive PCL/PAA NGCs has the potential to be used in the treatment of peripheral nerve injuries.
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•Ideal Nerve Guide Conduits with biomimicry and conductivity were fabricated using Electrohydrodynamic Jet 3D Printing•The mechanical properties and conductivity of the scaffolds mimic the native human nerve properties•Poly(acrylic acid) as cation-exchanger mimics nerve cortical gel layer functions, influencing nerve excitation & conduction•PC12 in vitro neural differentiation studies showed enhanced gene expression with the presence of Poly(acrylic acid)•Can potentially be used to treat peripheral nerve injuries & neurodegenerative conditions (Alzheimer’s and Parkinson’s)
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•A Janus film with a ridge-like rough surface and a flat surface was fabricated via cryogenic electrospinning.•The rough and flat surface of the Janu film mimic the natural anatomical ...surface texture of epithelium and lamina propria, respectively.•The Janus film allows the paracrine action between epithelial cells and fibroblasts while keeping the insulation between them.
Substantial mucosa defects in gastrointestinal tracts are difficult to repair, causing high mortality. Existing artificial mucosa studies overlooked the natural surface texture and the paracrine action between epithelial cells and fibroblasts, leading to an incomplete repair of the mucosa. Here, a Janus film with an exterior surface having a ridge-like texture and an interior flat surface to mimic the anatomical pattern of natural mucosa in digestive tracts is obtained through cryogenic electrospinning (e-spinning). Inspired by the natural mucosa surface textures, biopolymer nanofiber assemblies are employed to replicate the micro-ridges of the exterior layer of mucosa, while the flat surface mimics the morphology of the inferior layer. Hence, the exterior surface of Janus film bolsters epithelial cells' proliferation through the macro pores between fiber assemblies and the nanopores on fibers. In addition, the Janus film realizes a bilayer epithelium/lamina propria reconstruction by culturing epithelial cells and fibroblasts on different sides of the Janus film. Moreover, the Janus film assists the paracrine action between epithelial cells and fibroblasts while insulating their direct contact, an essential mimicry of the natural counterpart. In summary, it is proved that the Janus film will be a promising repair solution for mucosa defects in digestive tracts.
In recent years, metal cellular structures have drawn attentions in various industrial sectors due to their design freedoms and abilities to achieve multi-functional mechanical properties. However, ...metal cellular structures are difficult to fabricate due to their complex geometries, even with modern additive manufacturing technologies such as the direct metal laser sintering (DMLS) process. Assessing the manufacturability of metal cellular structures via a DMLS process is a challenging task as the geometric features of the structures are complex. Besides, via a DMLS process, the manufacturability also depends on the cumulative deformation of the layers during the manufacturing process. Existing methods on Design for Additive Manufacturing (DFAM) provide design guidelines that are based on past successful printed designs. However, they are not effective in predicting the manufacturability of metal cellular structures. In this paper, we propose a semi-supervised deep learning based manufacturability assessment (SSDLMA) framework to assess whether a metal cellular structure can be successfully manufactured from a given DMLS process. To enable efficient learning, we represent the complex cellular structures as 3D binary arrays with a simple yet efficient voxelisation method. We then train a deep learning based classifier using only a small amount of experimental data by adopting a semi-supervised learning approach. By running real experiments and comparing with existing DFAM methods and machine learning models, we demonstrate the advantages of the proposed SSDLMA framework. The proposed framework can be extended to predict the manufacturability of various other complex geometries beyond cellular structure in a reliable way even with a small number of training data.
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