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- 摘要
- 关键词
- 实验方案
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过滤筛选
- 2015
- Bessel Function
- Coupling Coefficient
- Fusion temperature and Elongation speed
- Physics
- UIN Suska Riau
- University of Riau
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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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Investigation of Remote Sensing Image Fusion Strategy Applying PCA to Wavelet Packet Analysis Based on IHS Transform
摘要： Further exploration of wavelet packet analysis (WPA) in the area of image fusion has been a hot topic. It is a strategy to combine WPA with such other transforms as intensity–hue–saturation (IHS), principle component analysis (PCA) for image fusion between the panchromatic (PAN) and the multispectral (MS) image. The paper puts forward a distinct fusion method. Its main idea can be stated as three steps. Firstly, intensity component is derived from IHS model of the image after an MS image is transformed from RGB to IHS. Secondly, intensity component and a matched PAN image are decomposed by WPA at the second scale, respectively. The innovational concept with two aspects is applying PCA theory to merge wavelet packet coefficients. One is to detect edge and produce self-adaptive weighted ratios for low-frequency band. The other is to yield another weighted coefficients for high-frequency bands based on standard deviation. Lastly, the new intensity component created by implementing inverse WPA, matching with hue and saturation reserved, makes up a color composition. A fused image is produced when carrying out transformation from IHS to RGB for the composition. It turns out that the presented fusion strategy is effective with experiments.
关键词： Intensity–hue–saturation (IHS),Image fusion,PCA-based fusion rule,Principle component analysis (PCA),Wavelet packet analysis (WPA)
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Sentinel-1 and Sentinel-2 Images for Classification of Agricultural Areas Using a Novel Classification Approach
摘要： A continuously growing world population increases steadily the demand of foods. This results in strong changes that occur on agricultural sites. Remote sensing data provides an excellent opportunity to monitor these changes which is a crucial base to assess the impact of these changes on the climate or the natural resources. In the presented study, we tested the performance of a new crop classification method for a stack of Sentinel-1 (S1) and Sentinel-2 (S2) images taken within one growing season. We proved, that the new PSP method performs better for S1 images revealing an overall accuracy (OA) of 75% compared to 60% for the Random Forest classifier (RF). The PSP method outperformed also for the fused dataset of S1 and S2 images (72% OA for PSP, 62% for RF). The results illustrate the benefits for crop classifications provided by PSP and give crucial insights for the advantages and limits of S1 and S2 data fusion.
关键词： Classification,Fusion,Agriculture,Sentinel-1,Sentinel-2
更新于2025-09-23 15:23:52
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Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
摘要： Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and na?ve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.
关键词： machine learning,multispectral technology,oilseed rape,image fusion,thermal imaging technology,Sclerotinia sclerotiorum
更新于2025-09-23 15:23:52
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FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks
摘要： In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we proposed an image fusion algorithm using convolutional neural networks (FusionCNN). The fusion model implicitly represents a fusion rule whose inputs are a pair of source images and the output is a fused image with end-to-end property. As no datasets can be used to train FusionCNN in remote sensing field, we constructed a new dataset from a natural image set to approximate MS and Pan images. In order to obtain higher fusion quality, low frequency information of MS is used to enhance the Pan image in the pre-processing step. The method proposed in this paper overcomes the shortcomings of the traditional fusion methods in which the fusion rules are artificially formulated, because it learns an adaptive strong robust fusion function through a large amount of training data. In this paper, Landsat and Quickbird satellite data are used to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion algorithm is superior to the comparative algorithms in terms of both subjective and objective evaluation.
关键词： Convolutional neural networks,Deep learning,Remote sensing image fusion,Image enhancement
更新于2025-09-23 15:23:52
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Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion
摘要： Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
关键词： contourlet transform,multi-source remote sensing image registration,multi-direction gray level co-occurrence matrix,multi-scale circle Gaussian combined invariant moment,Feature fusion
更新于2025-09-23 15:23:52
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A change detection framework by fusing threshold and clustering methods for optical medium resolution remote sensing images
摘要： In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximization (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogenous CD map but with some missed detections. Therefore, a framework is designed to improve CD results by fusing the advantages of the threshold and clustering methods. The CD map generated by the clustering method of FLICM is used to remove false alarms in the CD map obtained by EM threshold method by an overlap fusion. Then, the local Markov random field model is implemented to verify the potentially missed detections. Finally, a fused CD map with less false alarms and missed detections is achieved. Two experiments were carried out on two Landsat ETM+ data sets. The proposed method obtained the least errors (1.11% and 3.51%) and the highest kappa coefficient (0.9366 and 0.8834), respectively, when compared with five popular CD methods.
关键词： Change detection,advantage fusion,remote sensing,clustering,threshold
更新于2025-09-23 15:23:52
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || A Hybrid Fusion of Multimodal Medical Images for the Enhancement of Visual Quality in Medical Diagnosis
摘要： In the ?eld of medical imaging, Multimodal Medical Image Fusion (MIF) is a method of extracting complementary information from diverse source images from different modalities such as Magnetic Resonance Imaging, Computed Tomography, Single Photon Emission Computed Tomography, and Positron Emission Tomography and coalescing them into a resultant image. Image fusion of multimodal medical images is the present-day studies in the ?eld of medical imaging, biomedical research, and radiation medicine and is widely familiar by medical and engineering ?elds. In medical image fusion, single method of fusion is not pro?cient as it always lags in information while comparing with other available techniques. Hence, fusion for hybrid image is used to perform the image processing by applying multiple fusion rules. The integration of these results was obtained together as a single image. In proposed system, Shearlet Transform (ST) and Principal Component Analysis (PCA) are used to apply integrated fusion. The fusion technique is applied for CT that is Computed Tomography and Magnetic Resonance Imaging (MRI) images, where these images are ?rst transformed using the Shearlet Transform and PCA is applied to the transformed images. Finally, the fusion image is acquired using Inverse Shearlet transform (IST). The proposed system performance is evaluated by using speci?c metrics, and it is demonstrated that the outcome of proposed integrated fusion performs better when compared to existing fusion techniques.
关键词： Image fusion,Medical image,Shearlet Transform (ST),Principal Component Analysis (PCA)
更新于2025-09-23 15:23:52