3D Printing and Additive Manufacturing最新文献

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.

Traditional defect detection methods for metal additive manufacturing (AM) have the problems of low detection efficiency and accuracy, while the existing machine learning detection algorithms are of poor adaptability and complex structure. To address the above problems, this article proposed an improved You Only Look Once version 3 (YOLOv3) algorithm to detect the surface defects of metal AM based on multispectrum. The weighted k-means algorithm is used to cluster the target samples to improve the matching degree between the prior frame and the feature layer. The network structure of YOLOv3 is modified by using the lightweight MobileNetv3 to replace the Darknet-53 in the original YOLOv3 algorithm. Dilated convolution and Inceptionv3 are added to improve the detection capability for surface defects. A multispectrum measuring system was also developed to obtain the AM surface data with defects for experimental verification. The results show that the detection accuracy in the test set by YOLOv3-MobileNetv3 network is 11% higher than that by the original YOLOv3 network on average. The detection accuracy for cracking defects of the three types of defects is significantly increased by 23.8%, and the detection speed is also increased by 18.2%. The experimental results show that the improved YOLOv3 algorithm realizes the end-to-end surface defect detection for metal AM with high accuracy and fast speed, which can be further applied for online defect detection.

Postprocessing of additively manufactured (AM) metal parts to remove support structures or improve the surface condition can be a manually intensive process. One novel solution is a two-step, self-terminating etching process (STEP), which achieves both support removal and surface smoothing. While the STEP has been demonstrated for laser powder bed fusion (L-PBF) 316L stainless steel, this work evaluates the impact of pre-STEP heat treatments and resulting changes in dislocation density and microstructure on the resulting surface roughness and amount of material removed. Two pre-STEP heat treatments were evaluated: stress relief at 470°C for 5 h and recrystallization-solution annealing at 1060°C for 1 h. Additionally, one set of specimens was processed without the pre-STEP heat treatment (as-printed condition). Dislocation density and phase composition were quantified using X-ray diffraction along with standard, metallurgical stain-etching techniques. This work, for the first time, highlights the mechanisms of sensitization of AM L-PBF 316L stainless steel and provides fundamental insights into selective etching of these materials. Results showed that the sensitization depth decreased with increasing dislocation density. For samples etched at a STEP bias of 540 mV_SHE, material removal terminated at grain boundaries; therefore, the fine-grained stress-relieved specimen had the lowest post-STEP surface roughness. For surface roughness optimization, parts should be stress relived pre-STEP. However, to achieve more material removal, pre-STEP solution annealing should be performed.

In recent days, the additive manufacturing process plays a vital role in the production of tool electrodes, which are used in the electrical discharge machining (EDM) process. In this work, the copper (Cu) electrodes prepared using the direct metal laser sintering (DMLS) process are used for the EDM process. The performance of the DMLS Cu electrode is studied by machining the AA4032-TiC composite material using the EDM process. Then the performance of the DMLS Cu electrode is compared with the conventional Cu electrode. Three input parameters, such as peak current (A), pulse on time (s), and gap voltage (v), are selected for the EDM process. The performance measures, which are determined during the EDM process, are material removal rate (MRR), tool wear rate, surface roughness (SR), microstructural analysis of machined surface, and residual stress. At a higher pulse on time, more material was removed from the workpiece surface and thus MRR is enhanced. Likewise, at a higher peak current, the SR is amplified and thus wider craters are formed on the machined surface. The residual stress on the machined surface has influenced the formation of craters, microvoids, and globules. Lower SR and residual stress are attained by using DMLS Cu electrode, whereas MRR is higher when using conventional Cu electrode.

The austenitic 316L stainless steel (SS) is used extensively for marine applications as well as in construction, processing, and petrochemical industries due to its outstanding corrosion resistance properties. This study investigates the density, microhardness, and microstructural development of 316L SS samples fabricated by selective laser melting (SLM) under high laser energy densities. The selective laser melted (SLMed) specimens were fabricated under high laser energy densities (500, 400, and 333.33 J/mm³) and their metallurgical and mechanical properties were compared with the wrought specimen. SLMed 316L SS showed excellent printability, thereby enabling the fabrication of parts near full density. The porosity content present in the SLMed specimens was determined by both the image analysis method and Archimedes method. SLMed 316L specimens fabricated by the SLM process allowed observation of a microhardness of 253 HV_1.0 and achieved relative density up to 98.022%. Microstructural analysis using optical microscopy and phase composition analysis by X-ray diffraction (XRD) has been performed. Residual stresses were observed using the XRD method, and compressive stress (-68.9 MPa) was noticed in the as-printed specimen along the surface of the build direction. The microstructure of the as-built SLMed specimens consisted of a single-phase face-centered cubic solid solution with fine cellular and columnar grains along the build direction. The SLMed specimens seemed to yield better results than the wrought counterpart. IRB approval and Clinical Trial Registration Number are not applicable for this current work.