3D Printing and Additive Manufacturing最新文献

The additive manufacturing (AM) technique has received considerable industrial attention, as it is capable of producing complex functional parts in the aerospace and defense industry. Selective laser melting (SLM) technology is a relatively mature AM process that can manufacture complex structures both directly and efficiently. However, the quality of SLM parts is affected by many factors, resulting in a lack of repeatability and stability of this method. Therefore, several common and advanced in situ monitoring as well as defect detection methods are utilized to improve the quality and stability of SLM processes. This article aims at documenting the various defects that occurred in SLM processes and their influences on the final parts. Various types of in situ monitoring and defect detection methods and their applications are reviewed, and their integrations with the SLM processes are also discussed.

A single screw extruder is used in this study to efficiently transport SiC slurry in direct ink writing (DIW) technology. The deposits caused by low viscosity and the agglomerations resulting from the nonuniform mixing form the obstacles in the channel, which affect the normal flow of the slurry, theoretical outlet velocity, and interaction with other printing parameters. Therefore, it is necessary to study the effect mechanism of the obstacles on the flow. The obstacles are always irregular, which makes it difficult to directly analyze them. Irregular geometries are always composed of linear and/or arcuate elements; therefore, the obstacles can be simplified into regular geometries. In the present work, interactive elements, including line-line, line-arc, arc-arc situations are analyzed. Then, an improved multiple relaxation time lattice Boltzmann method (MRT LBM) with a pseudo external force is proposed for the flow analysis. The improved MRT LBM is combined with rheological test data to investigate cases with interactive elements, and the results are applied to reveal the general mechanism. The results show that the positions are common influencing factors, which affect the streamlines, outflow directions, and outlet velocity distributions. In addition, in different situations, different factors are considered to affect SiC slurry flow. It is obvious that the existed obstacles inevitably change the theoretical flow direction and outlet velocity, which has a synergistic effect on the printing parameters. It is necessary to understand the effect mechanism of the obstacles on the flow.

Additive manufacturing (AM) that is currently being used to process micromixers has many issues regarding the structural integrity of the micromixers. To solve these issues, in this article, we propose a cross-sectional contour extraction algorithm based on computed tomography (CT) scan data to nondestructively detect the size deviation of micromixers generated by AM. Herein, we take a square wave micromixer and a three-dimensional (3D) circular micromixer as examples to characterize the size deviation. We reconstruct the surface model of the micromixer from CT scan data, which is referred to as the reconstructed model, and extract the central axis of the micromixer reconstructed model. Subsequently, a dividing plane perpendicular to the central axis is established, which is then used to cut the reconstructed model to obtain the cross-sectional contour of the channel. Finally, size inspection is conducted on the extracted cross-sectional contour. The standard deviations of the channel width and height for the square wave micromixer are 0.0271 and 0.0175, respectively, and those for the 3D circular micromixer are 0.0122 and 0.0144, respectively. Through uncertainty analysis, the errors calculated based on the design size are -1.70%, +0.48%, +0.23%, -1.86%, -5.23%, and -0.90%, respectively, which shows that this method can meet the needs of measurement.

[This corrects the article DOI: 10.1089/3dp.2022.0138.].

Copper was manufactured by using a low-cost 3D printing device and copper oxide water-based colloids. The proposed method avoids the use of toxic volatile solvents (used in metal-based robocasting), adopting copper oxide as a precursor of copper metal due to its lower cost and higher chemical stability. The appropriate rheological properties of the colloids have been obtained through the addition of poly-ethylene oxide-co-polypropylene-co-polyethylene oxide copolymer (Pluronic P123) and poly-acrylic acid to the suspension of the oxide in water. Mixing of the components of the colloidal suspension was performed with the same syringes used for the extrusion, avoiding any material waste. The low-temperature transition of water solutions of P123 is used to facilitate the homogenization of the colloid. The copper oxide is then converted to copper metal through a reductive sintering process, performed at 1000°C for a few hours in an atmosphere of Ar-10%H₂. This approach allows the obtainment of porous copper objects (up to 20%) while retaining good mechanical properties. It could be beneficial for many applications, for example current collectors in lithium batteries.

This article evaluates an active thermography algorithm to detect subsurface defects in materials made by additive manufacturing (AM). It is based on the techniques of thermographic signal reconstruction (TSR), thermal contrast, and the physical principles of heat transfer. The subsurface defects have different infill, depth, and size. The results obtained from this algorithm are compared with state-of-the-art TSR technique and show the high performance of the proposed algorithm even for subsurface defects done by 3D AM. The resulting images are better shown using the absolute difference in the place of variance. The proposed algorithm has higher contrast, better sensitivity to the defect depths, and lower noise than the TSR. The resultant image is quite clean and gives no doubt where the subsurface defects are.

Fused filament fabrication (FFF) has been widely used in various industries, and the adoption of technology is growing significantly. However, the FFF process has several disadvantages like inconsistent part quality and print repeatability. The occurrence of manufacturing-induced defects often leads to these shortcomings. This study aims to develop and implement an on-site monitoring system, which consists of a camera attached to the print head and the laptop that processes the video feed, for the extrusion-based 3D printers incorporating computer vision and object detection models to detect defects and make corrections in real-time. Image data from two classes of defects were collected to train the model. Various YOLO architectures were evaluated to study the ability to detect and classify printing anomalies such as under-extrusion and over-extrusion. Four of the trained models, YOLOv3 and YOLOv4 with "Tiny" variation, achieved a mean average precision score of >80% using the AP50 metric. Subsequently, two of the models (YOLOv3-Tiny 100 and 300 epochs) were optimized using Open Neural Network Exchange (ONNX) model conversion and ONNX Runtime to improve the inference speed. A classification accuracy rate of 89.8% and an inference speed of 70 frames per second were obtained. Before implementing the on-site monitoring system, a correction algorithm was developed to perform simple corrective actions based on defect classification. The G-codes of the corrective actions were sent to the printers during the printing process. This implementation successfully demonstrated real-time monitoring and autonomous correction during the FFF 3D printing process. This implementation will pave the way for an on-site monitoring and correction system through closed-loop feedback from other additive manufacturing (AM) processes.

In additive manufacturing (AM), the mechanical properties of manufactured parts are often insufficient due to complex defects and residual stresses, limiting their use in high-value or mission-critical applications. Therefore, the research and application of nondestructive testing (NDT) technologies to identify defects in AM are becoming increasingly urgent. This article reviews the recent progress in online detection technologies in AM, a special introduction to the high-speed synchrotron X-ray technology for real-time in situ observation, and analysis of defect formation processes in the past 5 years, and also discusses the latest research efforts involving process monitoring and feedback control algorithms. The formation mechanism of different defects and the influence of process parameters on defect formation, important parameters such as defect spatial resolution, detection speed, and scope of application of common NDT methods, and the defect types, advantages, and disadvantages associated with current online detection methods for monitoring three-dimensional printing processes are summarized. In response to the development requirements of AM technology, the most promising trends in online detection are also prospected. This review aims to serve as a reference and guidance for the work to identify/select the most suitable measurement methods and corresponding control strategy for online detection.

Metal additive manufacturing (AM) is known to produce internal defects that can impact performance. As the technology becomes more mainstream, there is a growing need to establish nondestructive inspection technologies that can assess and quantify build quality with high confidence. This article presents a complete, three-dimensional (3D) solution for automated defect recognition in AM parts using X-ray computed tomography (CT) scans. The algorithm uses a machine perception framework to automatically separate visually salient regions, that is, anomalous voxels, from the CT background. Compared with supervised approaches, the proposed concept relies solely on visual cues in 3D similar to those used by human operators in two-dimensional (2D) assuming no a priori information about defect appearance, size, and/or shape. To ingest any arbitrary part geometry, a binary mask is generated using statistical measures that separate lighter, material voxels from darker, background voxels. Therefore, no additional part or scan information, such as CAD files, STL models, or laser scan vector data, is needed. Visual saliency is established using multiscale, symmetric, and separable 3D convolution kernels. Separability of the convolution kernels is paramount when processing CT scans with potentially billions of voxels because it allows for parallel processing and thus faster execution of the convolution operation in single dimensions. Based on the CT scan resolution, kernel sizes may be adjusted to identify defects of different sizes. All adjacent anomalous voxels are subsequently merged to form defect clusters, which in turn reveals additional information regarding defect size, morphology, and orientation to the user, information that may be linked to mechanical properties, such as fatigue response. The algorithm was implemented in MATLAB™ using hardware acceleration, that is, graphics processing unit support, and tested on CT scans of AM components available at the Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) at Penn State's Applied Research Laboratory. Initial results show adequate processing times of just a few minutes and very low false-positive rates, especially when addressing highly salient and larger defects. All developed analytic tools can be simplified to accommodate 2D images.

Fused filament fabrication (FFF) is an additive manufacturing process where a thermoplastic polymeric material, provided in the form of a filament, is extruded to create layers. Achieving a consistent flow of the extruded material is key to ensure quality of the final part. Extrudate flow depends on many factors; among these, the rate at which the filament is fed into the extruder. In a conventional FFF machine, filament transport is achieved through the use of a drive gear. However, slippage between the gear and the filament may occur, leading to reduced transport and the consequent local decrease of extrudate flow rate, which in turn leads to a series of imperfections in the fabricated part due to underextrusion, including reduced density. In this work, we propose a closed-loop control system to ensure the correct filament transport to the extruder. The system works through the comparison between the nominal transport of the filament and the actual filament transport measured using an encoder. The measured value is used to correct the filament feed rate in real time, ensuring a material flow close to the nominal one, regardless of the other process parameters. In this work, an instrumented FFF machine prototype was used to investigate the performance of the approach. For validation, parts were realized using different process parameters, while enabling and disabling the closed-loop control system. Results showed that the relative filament transport error decreased from up to 9% to below 0.25% and a density increase up to ∼10% regardless of the process parameters, as well as the reduction of interlayer and intralayer voids, as highlighted through cross-sectional imaging of realized samples. A reduction of defects on realized parts was observed, especially at higher fabrication feed rates.