Support structures are necessary for many AM (additive manufacturing) processes to maintain the overhanging areas or resist deformation caused by thermal stress in printing. The design of support ...structures affects not only the printing quality but also material consumption and post-processing time. Current research had proposed many support structure designs and optimization methods to meet varying optimization requirements. However, no research has investigated yet how to determine the support points, or contact points for optimal support design. The number and position of support points will directly affect the support structure’s performance and volume, and therefore the final printing quality. To fill this gap, this paper proposes a determination method, which integrates overhang detection, analysis of periodic support point patterns and AM constraints to optimize support point distribution with ensured manufacturability. It is particularly critical for complex and porous structures in medical applications. The proposed method is illustrated and validated through a complex dental component. It can be used with support structure design methods to further improve the support structure performance and reduce support volume in printing, especially for metallic AM processes.
ABSTRACTThe laser power bed fusion (LPBF) forming process introduces heat accumulation and variations in powder layer thickness, which can destabilize the melt track and reduce surface quality. This ...phenomenon is especially more serious in the early printing stage. To tackle this stability problem, we proposed a novel approach optimizing process parameters on a layer-specific basis. At first, a numerical database was constructed through a set of numerical simulations. Then, a neural network prediction model was trained based on the database. Finally, this prediction model was embedded into a genetic algorithm for layer-based prediction. To verify the prediction results on processing parameters and calibrate the prediction model, physical experiments were prepared. The developed model consistently exhibited relative errors mostly within 6%. It is noteworthy that the relative errors between the numerical simulation results and the expected values were only 0.77% in width and 1.11% in depth. Printing optimization test was applied for an LPBF machine with a Invar alloy powder. The proposed method yielded positive results in both numerical simulations and the printing test. It can be further adopted for new material printing parameter optimization due to an efficient printing stability in the early-stage for LPBF process.
Manufacturability analysis is a critical step before manufacturing to reduce costs and risks. It is used widely in conventional manufacturing (CM) processes. However, to the best of our knowledge, ...there is no natural method to evaluate the manufacturability of additive manufacturing (AM) processes that have more uncertainty-derived risks and costs than CM processes. A clear definition of the manufacturability of AM processes has not been established, and there is no standard to check whether a component is manufactured successfully by an AM process, particularly for porous complex components. This study introduces the development of a new machine learning-based method to solve the problem mentioned above. It is based on the statistical measurement of experimental samples. The proposed method can be used to perform the manufacturability analysis for periodic cellular structures printed by a selective laser melting (SLM) process. A novel definition of the manufacturability of the SLM-ed periodic cellular structure was proposed. Experimental results indicate that the developed learning model (ANN model) can achieve up to 94% classification accuracy and 96% prediction accuracy, which satisfies the application requirements of the AM industry. Moreover, the developed model can be adapted for the manufacturability analysis of different AM processes.
While additive manufacturing (AM) provides design flexibility, challenges persist in handling intricate freeform shapes, especially those laden with fine details. Conventional AM processes, such as ...slicing stereolithography (STL) format models, generating line segment toolpaths, and polyline-based printing, prove costly and compromise accuracy. This paper proposes a solution: the spline scanning generative design method. Utilizing spline patterns to construct smooth toolpaths directly, it enables seamless curved printing, significantly reducing computational expenses while maintaining high accuracy through spline control points. Experimental implementation, supported by dedicated algorithms, attests to its efficacy, emphasizing its potential for intricate freeform structure design and printing.
Deep 3D human pose estimation: A review Wang, Jinbao; Tan, Shujie; Zhen, Xiantong ...
Computer vision and image understanding,
September 2021, 2021-09-00, Volume:
210
Journal Article
Peer reviewed
Open access
Three-dimensional (3D) human pose estimation involves estimating the articulated 3D joint locations of a human body from an image or video. Due to its widespread applications in a great variety of ...areas, such as human motion analysis, human–computer interaction, robots, 3D human pose estimation has recently attracted increasing attention in the computer vision community, however, it is a challenging task due to depth ambiguities and the lack of in-the-wild datasets. A large number of approaches, with many based on deep learning, have been developed over the past decade, largely advancing the performance on existing benchmarks. To guide future development, a comprehensive literature review is highly desired in this area. However, existing surveys on 3D human pose estimation mainly focus on traditional methods and a comprehensive review on deep learning based methods remains lacking in the literature. In this paper, we provide a thorough review of existing deep learning based works for 3D pose estimation, summarize the advantages and disadvantages of these methods and provide an in-depth understanding of this area. Furthermore, we also explore the commonly-used benchmark datasets on which we conduct a comprehensive study for comparison and analysis. Our study sheds light on the state of research development in 3D human pose estimation and provides insights that can facilitate the future design of models and algorithms.
•The recent methods for deep 3D pose estimation are categorized and thoroughly analyzed.•Provide an extensive review of related datasets and evaluation metrics.•Compare the pros and cons of the deep 3D models valuated on the datasets and draw a conclusion.•We discuss the potential research orientations of future.
This brief proposes an ultralow-voltage four-read-port and two-write-port multiported register file with a novel architecture of read word-line sharing strategy for energy/area efficiency. Static ...read circuits and memory cells with nonminimum channel length are introduced to improve the ultralow-voltage performance. The chip of this register file is fabricated in 65-nm LP CMOS process and occupies the area of 0.019 mm^{2} . Test results show that the minimum operation voltage is 320 mV with its corresponding max frequency 110 KHz. The minimum energy consumption is 0.94 pJ/cycle at the point of 400 mV, 850 KHz, corresponding to 0.15 fJ/port/bit/cycle after normalization. Compared with the state-of-the-art designs, it improves energy efficiency by 25% and saves the area by 58.7%.
•.•A hierarchical multiscale periodic homogenization strategy models the equivalent behavior of lattice structure built with additive manufacturing process.•Accounting for inter-bead voids in models ...is of major importance, a high content of voids inside the composite part leading to poor mechanical properties.•Polynomials dependent on the infill densities of honeycomb lattices provide equivalent homogenized properties with low computational cost.•The usefulness of polynomials dependent on the infill density has been demonstrated by a lattice optimization.
Additive manufacturing (AM) is revolutionizing how we create things, offering innovative solutions that rival traditional manufacturing methods. In Fused Filament Fabrication (FFF), incorporating continuous fiber-reinforced filaments has significantly enhanced mechanical properties, making them valuable across diverse sectors like aerospace, automotive, and robotics. However, before printing a part, numerical simulation for design verification is essential. Yet, there’s a lack of suitable tools for modeling the complex material properties, particularly for composite lattice structures. To address this, we propose a three-level periodic homogenization method. The first level focuses on modeling individual filaments, followed by characterizing inter-bead voids at the second level, and finally, modeling collective lattice behavior at the third level. Hexagonal cellular lattice composite structures were modeled and tested to confirm the method’s accuracy and its value in supporting simulation and modular design for the composite FFF process.
Current porous structure design methods in additive manufacturing (AM) lose accuracy in data model transformations along the processing chain and are difficult to consider manufacturability and ...post-processing issues. In addition, the design and printing preparation is costly due to large number of fine features and their related operations. To solve these problems with an aim to save time in design and printing preparation but ensure manufacturability and easy post-processing, this paper proposes an implicit design method using printing toolpaths to construct printable parametric porous structures. Experimental case studies demonstrated the feasibility, efficiency and application potential of the proposed method.
Two types of vapour chamber with integrated three-dimensionally (3D) printed wicks, namely a single-pore 3D printed wick vapour chamber and hybrid-pore 3D printed wick vapour chamber, were designed, ...printed, and tested to demonstrate the feasibility of constructing VCs through metallic additive manufacturing. A water-cooling test system was established to study the temperature distribution, thermal resistance, and maximum heat transfer power of the VCs. The effects of the cooling-water temperature, heat source area, and hybrid-pore wick structure on the heat transfer performance of the VCs were explored. The results show that the vapour chamber with a 3D printed wick had efficient heat transfer in the range of 0–100 W. The cooling-water temperature greatly affected the heat transfer performance of the VCs. The 3D printed hybrid-pore wick improved the temperature uniformity and heat transfer of the VC. When P = 100 W, the thermal resistance of the hybrid-pore 3D printed wick vapour chamber was 0.077 °C/W at a cooling-water temperature Tw = 40 °C. The test results indicate that the application of Additive Manufacturing (AM) in fabricating high-performance VCs is feasible and provides more freedom in terms of customizing the heat transfer performance and integrating with the structural design of the heat source.
•This paper used 3D-printed wicks in vapour chamber fabrication for the first time.•Different evaporator and condenser wicks are designed to enhance the performance.•The influence of cooling condition on the vapour chamber performance is evaluated.•The effects of 3D printed hybrid-pore wick structure on vapour chamber are investigated.
Purpose
The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or ...industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning.
Design/methodology/approach
In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis.
Findings
The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings.
Originality/value
It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.
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