- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE NAECON 2019 - IEEE National Aerospace and Electronics Conference - Dayton, OH, USA (2019.7.15-2019.7.19)] 2019 IEEE National Aerospace and Electronics Conference (NAECON) - In Situ Process Monitoring for Laser-Powder Bed Fusion using Convolutional Neural Networks and Infrared Tomography
摘要： Additive Manufacturing (AM) is a growing field for various industries of avionics, biomedical, automotive and manufacturing. The onset of Laser Powder Bed Fusion (LPBF) technologies for metal printing has shown exceptional growth in the past 15 years. Quality of parts for LPBF is a concern for the industry, as many parts produced are high risk, such as biomedical implants. To address these needs, a LPBF machine was designed with in-situ sensors to monitor the build process. Image processing and machine learning algorithms provide an efficient means to take bulk data and assess part quality, validating specific internal geometries and build defects. This research will analyze infrared (IR) images from a Selective Laser Melting (SLM) machine using a Computer Aided Design (CAD) designed part, featuring specific geometries (squares, circles, and triangles) of varying sizes (0.75-3.5 mm) on multiple layers for feature detection. Applying image processing to denoise, then Principal Component Analysis (PCA) for further denoising and applying Convolution Neural Networks (CNN) to identify the features and identifying a class which does not belong to a dataset, where a dataset are created from CAD images. Through this automated process, 300 geometric elements detected, classified, and validated against the build file through CNN. In addition, several build anomalies were detected and saved for end-user inspection.
关键词： Laser Powder Bed Fusion (LPBF),Principal Component Analysis (PCA),infrared image (IR),Convolution Neural Networks (CNN),Additive Manufacturing (AM),Computer Aided Design (CAD)
更新于2025-09-23 15:21:01
-
Laser Operating Windows Prediction in Selective Laser-Melting Processing of Metallic Powders: Development and Validation of a Computational Fluid Dynamics-Based Model
摘要： The rapidly ascending trend of additive manufacturing techniques requires a tailoring of existing solidification models and the development of new numerical tools. User-friendly numerical models can be a valid aid in order to optimize operating parameter ranges with the scope to extend the modelling tools to already existing or innovative alloys. In this paper a modelling approach is described simulating the generation of single tracks on a powder bed system in a selective laser melting process. The approach we report attains track geometry as a function of: alloy thermo-physical properties, laser speed and power, powder bed thickness. Aim of the research is to generate a numerical tool able to predict laser power and speed ranges in manufacturing porosity-free printed parts without lack of fusion and keyhole pores. The approach is based on a simplified description of the physical aspects. Main simplifications concern: the laser energy input, the formation of the pool cavity, and the powder bed thermo-physical properties. The model has been adjusted based on literature data providing the track’s geometry (width and depth) and relative density. Such data refer to different alloys. In particular, Ti6Al4V, Inconel625, Al7050, 316L and pure copper are considered. We show that the printing process presents features common to all alloys. This allows the model to predict the printing behavior of an alloy from its physical properties, avoiding the need to perform specific experimental activities.
关键词： metallic alloys,numerical model,selective laser melting,additive manufacturing,laser operating window
更新于2025-09-23 15:21:01
-
Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing
摘要： Additive manufacturing (AM) enables the fabrication of complex designs that are di?cult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is quali?ed for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatibility. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This paper describes a fast, scalable method for part-scale process optimization of arbitrary geometries. The computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of ?delity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.
关键词： control,DMLS,DMLM,optimization,3D printing,additive manufacturing,powder bed fusion
更新于2025-09-23 15:21:01
-
The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment
摘要： The additive manufacturing (AM) process induces high uncertainty in the mechanical properties of 3D-printed parts, which represents one of the main barriers for a wider AM processes adoption. To address this problem, a new time-efficient microstructure prediction algorithm was proposed in this study for the laser powder bed fusion (LPBF) process. Based on a combination of the melt pool modeling and the design of experiment approaches, this algorithm was used to predict the microstructure (grain size/aspect ratio) of materials processed by an EOS M280 LPBF system, including Iron and IN625 alloys. This approach was successfully validated using experimental and literature data, thus demonstrating its potential efficiency for the optimization of different LPBF powders and systems.
关键词： laser powder bed fusion,additive manufacturing,microstructure,process optimization,analytical model
更新于2025-09-23 15:21:01
-
Surface roughness and densification correlation for direct metal laser sintering
摘要： The increasing use of metal additive manufacturing (AM) technologies, such as direct metal laser sintering (DMLS), requires an in-depth understanding of how the optimum DMLS process parameters can be determined to achieve the target properties, such as reduced defect densities and/or desired surface characteristics. To this end, it is important to develop simple strategies that assess part quality and are fast and cost-effective. In this study, the in-plane surface roughness of components fabricated with AM is correlated with the DMLS process parameters and fractional density, enabling rapid and accurate indirect determination of the fractional density of AM components through surface roughness measurements. To this end, two sets of DMLS process parameters and a geometrical parameter are utilized to fabricate more than 150 rectangular cubic samples with varying parameters. All the samples are fabricated using Ti-6Al-4 V powder, which is a frequently used metal alloy for DMLS. Second, two line roughness parameters are defined and measured for all the samples, and their correlations with the DMLS and geometrical parameters are reported. Third, the fractional densities of all the samples are measured and their correlations with the DMLS process parameters are demonstrated. Lastly, a thorough analysis of the observed correlations between the line roughness parameters and fractional density are discussed.
关键词： Ti-6Al-4 V,Surface roughness,Densification,Process parameters,Metal additive manufacturing
更新于2025-09-23 15:21:01
-
Characterisation of elemental analysis, carbon sulphur analysis and impact test of stent manufacturing using medical grade ASTM F75 cobalt chromium (CoCrMo) by selective laser melting (SLM) technology
摘要： This paper explains and demonstrates the capabilities of metal additive manufacturing (MAM) technology in producing intricate stent structure with a customise design by using ASTM F75 cobalt chromium powder. The elemental analysis (EDX-SEM), carbon sulphur analysis and Impact Test are being develop and tested and thus exploring the potential area of MAM process for future proof stent manufacturing. By alternatively switching to MAM, the step of production can be minimised and thus customisation of stent can be carried out according to the patient’s need. The suggested model of the stent was taken from the third-party vendor and fabrication was carried out using EOSINT M280 metal printer with the aid of Materialise Magics 19.0 software for support generation.
关键词： stent,scanning electron microscope (SEM),selective laser melting (SLM),cobalt chromium (CoCrMo),energy-dispersive X-ray spectroscopy (EDX),Metal additive manufacturing (MAM)
更新于2025-09-23 15:21:01
-
The effects of laser peening on laser additive manufactured 316L steel
摘要： Laser peening has an extensive application in traditional manufacturing industry. However, in additive manufacturing, the initial stresses on the parts often reduce the effects of laser peening and make it hard to achieve a desirable residual stress distribution. In this investigation, the interaction of initial residual stress and laser peening-induced stress was studied through numerical simulation and experimental tests. A finite element model (FEM) model was built to predict the stress distribution on laser-deposited sample, and its changed state is affected by laser peening. The microstructure and mechanical properties were also characterized experimentally. The result turned out that the thermal-induced tensile residual stress in laser-deposited sample can affect the laser peening result in both horizontal and longitudinal directions. Some mechanical properties of the LAMed sample were changed after LSP treatment. The hardness on the surface and 1-mm depth have been increased by 7% and 22%, respectively, and the yield strength was increased by 16%, while there is no significant change in the tensile strength and elongation rate.
关键词： Finite element analysis,Laser peening,Laser additive manufacturing,Residual stress
更新于2025-09-23 15:21:01
-
Experimental and numerical investigation of selective laser meltinga??induced defects in Tia??6Ala??4V octet truss lattice material: the role of material microstructure and morphological variations
摘要： The remarkable progress in additive manufacturing has promoted the design of architected materials with mechanical properties that go beyond those of conventional solids. Their realization, however, leads to architectures with process-induced defects that can jeopardize mechanical and functional performance. In this work, we investigate experimentally and numerically as-manufactured defects in Ti–6Al–4V octet truss lattice materials fabricated with selective laser melting. Four sets of as-manufactured defects, including surface, microstructural, morphological, and material property imperfections, are characterized experimentally at given locations and orientations. Within the characterized defects, material property and morphological defects are quanti?ed statistically using a combination of atomic force microscopy and micro–computed tomography to generate representative models that incorporate individual defects and their combination. The models are used to assess the sensitivity to as-manufactured defects. Then, the study is expanded by tuning defects amplitude to elucidate the role of the magnitude of as-designed defects on the mechanical properties of the lattice material.
关键词： mechanical properties,octet truss lattice,additive manufacturing,defects,Ti–6Al–4V,selective laser melting
更新于2025-09-23 15:21:01
-
[IEEE 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Guangzhou, China (2019.5.19-2019.5.22)] 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Design of Substrate Integrated Waveguide Based Filtering Antennas
摘要： This paper reports the design, fabrication, and characterization of arrays of miniaturized, internally fed, polymer electrospray emitters fabricated with stereolithography. The freeform additive manufacturing process used to make the devices has associated two orders of magnitude reduction in the fabrication cost per device and fabrication time (from thousands of dollars to tens of dollars, and from months to hours, respectively) and a two orders of magnitude reduction in the cost of the manufacturing infrastructure (from millions of dollars to tens of thousands of dollars) compared with a silicon MEMS multiplexed electrospray source. The 3-D printed devices include features not easily attainable with other microfabrication methods, e.g., tapered channels and threaded holes. Through the optimization of the fabrication process 10-mm tall, isolated, straight, solid columns with diameter as small as 300 μm, and 12-mm long, straight tubes with inner diameter as small as 400 μm and wall thickness as small as 150 μm were demonstrated. Arrays with as many as 236 internally fed electrospray emitters (236 emitters in 1 cm2) were made, i.e., a twofold increase in emitter density and a sixfold increase in array size compared with the best reported values from multiplexed, internally fed, electrospray sources made of polymer. The characterization of devices with a different array size suggests a uniform emitter operation.
关键词： electrospray,Additive manufacturing of MEMS,stereolithography,multiplexed liquid ionizers
更新于2025-09-23 15:21:01
-
Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process
摘要： 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 dif?cult 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 ef?cient learning, we represent the complex cellular structures as 3D binary arrays with a simple yet ef?cient voxelisation method. We then train a deep learning based classi?er 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.
关键词： Design for additive manufacturing,Manufacturability analysis,Direct metal laser sintering,Semi-supervised deep learning,Cellular structures
更新于2025-09-23 15:21:01