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A statistical learning method for image-based monitoring of the plume signature in laser powder bed fusion
摘要： The industrial breakthrough of metal additive manufacturing processes mainly involves highly regulated sectors, e.g., aerospace and healthcare, where both part and process qualification are of paramount importance. Because of this, there is an increasing interest for in-situ monitoring tools able to detect process defects and unstable states since their onset stage during the process itself. In-situ measured quantities can be regarded as “signatures” of the process behaviour and proxies of the final part quality. This study relies on the idea that the by-products of laser powder bed fusion (LPBF) can be used as process signatures to design and implement statistical monitoring methods. In particular, this paper proposes a methodology to monitor the LPBF process via in-situ infrared (IR) video imaging of the plume formed by material evaporation and heating of the surrounding gas. The aspect of the plume naturally changes from one frame to another following the natural dynamics of the process: this yields a multimodal pattern of the plume descriptors that limits the effectiveness of traditional statistical monitoring techniques. To cope with this, a nonparametric control charting scheme is proposed, called K-chart, which allows adapting the alarm threshold to the dynamically varying patterns of the monitored data. A real case study in LPBF of zinc powder is presented to demonstrate the capability of detecting the onset of unstable conditions in the presence of a material that, despite being particularly interesting for biomedical applications, imposes quality challenges in LPBF because of its low melting and boiling points. A comparison analysis is presented to highlight the benefits provided by the proposed approach against competitor methods.
关键词： Process plume,Metal additive manufacturing,Laser powder bed fusion,Infrared imaging,In-situ monitoring,Zinc
更新于2025-11-28 14:24:20
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Laser additive manufacturing of biodegradable magnesium alloy WE43: a detailed microstructure analysis
摘要： WE43, a magnesium alloy containing yttrium and neodymium as main alloying elements, has become a well-established bioresorbable implant material. Implants made of WE43 are often fabricated by powder extrusion and subsequent machining, but for more complex geometries laser powder bed fusion (LPBF) appears to be a promising alternative. However, the extremely high cooling rates and subsequent heat treatment after solidification of the melt pool involved in this process induce a drastic change in microstructure, which governs mechanical properties and degradation behaviour in a way that is still unclear. In this study we investigated the changes in the microstructure of WE43 induced by LPBF in comparison to that of cast WE43. We did this mainly by electron microscopy imaging, and chemical mapping based on energy-dispersive X-ray spectroscopy in conjunction with electron diffraction for the identification of the various phases. We identified different types of microstructure: an equiaxed grain zone in the center of the laser-induced melt pool, and a lamellar zone and a partially melted zone at its border. The lamellar zone presents dendritic lamellae lying on the Mg basal plane and separated by aligned Nd-rich nanometric intermetallic phases. They appear as globular particles made of Mg3Nd and as platelets made of Mg41Nd5 occurring on Mg prismatic planes. Yttrium is found in solid solution and in oxide particles stemming from the powder particles’ shell. Due to the heat influence on the lamellar zone during subsequent laser passes, a strong texture developed in the bulk material after substantial grain growth.
关键词： Rapid solidification,Microstructure,Bone scaffolds,Electron microscopy,Biodegradable implants,WE43,Laser powder bed fusion,Magnesium
更新于2025-11-21 11:20:48
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Rapid Alloy Development of Extremely High-Alloyed Metals Using Powder Blends in Laser Powder Bed Fusion
摘要： The design of new alloys by and for metal additive manufacturing (AM) is an emerging field of research. Currently, pre-alloyed powders are used in metal AM, which are expensive and inflexible in terms of varying chemical composition. The present study describes the adaption of rapid alloy development in laser powder bed fusion (LPBF) by using elemental powder blends. This enables an agile and resource-efficient approach to designing and screening new alloys through fast generation of alloys with varying chemical compositions. This method was evaluated on the new and chemically complex materials group of multi-principal element alloys (MPEAs), also known as high-entropy alloys (HEAs). MPEAs constitute ideal candidates for the introduced methodology due to the large space for possible alloys. First, process parameters for LPBF with powder blends containing at least five different elemental powders were developed. Secondly, the influence of processing parameters and the resulting energy density input on the homogeneity of the manufactured parts were investigated. Microstructural characterization was carried out by optical microscopy, electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS), while mechanical properties were evaluated using tensile testing. Finally, the applicability of powder blends in LPBF was demonstrated through the manufacture of geometrically complex lattice structures with energy absorption functionality.
关键词： multi-principal element alloys,high-entropy alloys,additive manufacturing,rapid alloy development,powder blends,laser powder bed fusion
更新于2025-11-21 11:01:37
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Correlations between thermal history and keyhole porosity in laser powder bed fusion
摘要： Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls – yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al-4V powder.
关键词： in-situ measurement,keyhole porosity,machine learning,laser powder bed fusion,x-ray imaging
更新于2025-09-23 15:21:01
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[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
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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
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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
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Effect of Scanning Strategy During Selective Laser Melting on Surface Topography, Porosity, and Microstructure of Additively Manufactured Ti-6Al-4V
摘要： The effect of the scanning strategy during selective laser melting (SLM) of Ti-6Al-4V was investigated. An optimized cellular scan strategy (island scan modeled) was compared to a simple cellular scan strategy (island scan stripes) and a simple antiparallel line scanning strategy (line scan). Surface texture was investigated by optical three-dimensional (3D) surface measurements, which when combined with light optical microscopy (LOM), revealed deflections caused by the thermal stresses during the build process. Elevated edges caused by the edge-effect dominate the surface texture of all investigated specimens. The scanning strategy determines the surface texture, and the lowest surface roughness was obtained by the line scan strategy. Porosity was investigated with X-ray computed tomography-imaging. Mainly spherical porosity was observed for the line scan and island scan modeled specimens, while the island scan stripes strategy showed more lack-of-fusion defects and a higher total porosity amount. Microstructure was investigated with LOM and scanning electron microscopy (SEM). The microstructure in Ti-6Al-4V was largely martensitic α’ and prior β grains. The morphology is different for the various scan strategies, and decomposition of α’ into lamellar α/β was observed in the bottom part of the island scan specimen. Accordingly, the hardness decreased in the decomposed part of the specimen.
关键词： scanning strategy,porosity,surface topography,selective laser melting,powder bed fusion,microstructure,SLM,Ti-6Al-4V
更新于2025-09-23 15:21:01
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Detection of powder bed defects in selective laser sintering using convolutional neural network
摘要： The presence of defects in a powder bed fusion (PBF) process can lead to the formation of flaws in consolidated parts. Powder bed defects (PBDs) have different sizes and shapes and occur in different locations in the built area. Those variations pose great challenges to their detection. In this study, a deep convolution neural network was applied to detect three typical types of PBDs in a selective laser sintering (SLS) process, namely warpage, part shifting, and short feed, which were intentionally generated by varying the process conditions. Images of the powder bed were captured using a digital camera, which were split into three single-channel images corresponding to the color channels in the color image. A deep residual neural network was then used to extract multiscale features, and a region proposal network was adopted to detect the object-level defect bounding box. In the final stage, a fully convolutional neural network was proposed to generate instance-level defect regions in the bounding box. Our results demonstrated that this method had higher accuracy and efficiency and was able to cope with geometrical distortion and image blurring, in comparison to the defect detection methods reported previously. Also, the detection system was cost-effective and could be easily installed outside the chamber of a PBF system. This study lays the groundwork for the development of a variety of automated technologies for additive manufacturing, such as real-time powder layer quality inspection and 3D quality certificate generation for finish parts.
关键词： Defect,Neural network,Selective laser sintering,Powder bed fusion,Detection
更新于2025-09-23 15:21:01
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Molecular dynamics simulation of coalescence kinetics and neck growth in laser additive manufacturing of aluminum alloy nanoparticles
摘要： Laser additive manufacturing emerged as an advanced manufacturing process to fabricate components in a layered fashion by fusing the powder particles. This process is multifaceted and pivotal to understand the underlying physics of the coalescence of powder particles during the process, which impacts the structural and mechanical properties of the build component. In this study, a classical molecular dynamics (MD) model is developed for the coalescence of pre-alloyed aluminum alloy (AlSi10Mg) particles during the laser additive manufacturing process. The model is employed to investigate the neck growth and coalescence kinetics of different pairs of particle size with changing the laser energy density from 7 to 17 J/mm2. The simulation results reveal that the unevenly sized particles undergo complete coalescence as compared with even-sized particles, and the neck growth rate of AlSi10Mg particles increases with an increase in laser energy density. Based on the present investigation, it is established that the coalescence kinetics of the AlSi10Mg nanoparticles are governed by the surface and volume diffusion and the surface energy reduction during the joining of particles. This analysis will act as a guideline to design process parameters and quality control for the printing of new components.
关键词： Molecular dynamics,Laser additive manufacturing,Coalescence,Laser energy density,Powder bed fusion
更新于2025-09-23 15:21:01