Query: defect phone glass

title: Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning
abstract: Defect detection based on machine vision and machine learning techniques has drawn much attention in recent years. Deep learning is very suitable for such segmentation and detection tasks and has become a promising research area. Surface quality inspection is essentially important in the manufacturing of mobile phone back glass (MPBG) Different types of defects are produced because of the imperfection of the manufacturing technique. Unlike general transparent glass, screen printing glass has totally different reflection and scattering characteristics, which means the traditional dark field imaging system is not suitable for this task. Meanwhile, the imaging system requires high resolution since the minimum defect size can be 0.005 mm2. According to the imaging characteristics of screen printing glass, this paper proposes a coaxial bright field (CBF) imaging system and low angle bright field (LABF) imaging system, and 8K line scan complementary metal oxide semiconductor(CMOS) cameras are utilized to capture images with the resolution size of 16,000*8092. The CBF system is applied for the weak scratch and discoloration defects while the LABF system is applied for the dent defect. A symmetric convolutional neural network composed of encoder and decoder structures is proposed based on U net, which produces a semantic segmentation with the same size as the original input image. More than 10,000 original images were captured, and more than 30,000 defective and non defective images were manually annotated in the glass surface defect dataset (GSDD) Verified by the experiments, the results showed that the average precision reaches more than 91% and the average recall rate reaches more than 95% The method is very suitable for the surface defect inspection of screen printing mobile phone back glass.

title: A Fast Surface Defect Detection Method Based on Background Reconstruction
abstract: In this paper, we propose an unsupervised background reconstruction method to detect defects on surfaces with unevenly distributed textures. An improved deep convolutional autoencoder is utilized to reconstruct the textured background of the original image as a defect free reference. Specifically, a weighted loss function based on structural similarity (SSIM) is utilized to adapt to the unevenly distributed texture background and improve the reconstruction accuracy. Furthermore, combined with the reconstructed defect free reference, a novel difference analysis method based on the discrete cosine transform (DCT) is given to accurately segment the defect regions from the original image. A series of experiments for the defect detection on mobile phone cover glass (MPCG) are conducted. The processing time for an image of 512 x 512 pixels is only 20 ms, which satisfies the requirement of online detection. The experimental results verify the effectiveness of the proposed method.

title: Anomaly Detection Neural Network with Dual Auto Encoders GAN and Its Industrial Inspection Applications
abstract: Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion) Therefore, in this study, an anomaly detection neural network, dual auto encoder generative adversarial network (DAGAN) was developed to solve the problem of sample imbalance. With skip connection and dual auto encoder architecture, the proposed method exhibited excellent image reconstruction ability and training stability. Three datasets, namely public industrial detection training set, MVTec AD, with mobile phone screen glass and wood defect detection datasets, were used to verify the inspection ability of DAGAN. In addition, training with a limited amount of data was proposed to verify its detection ability. The results demonstrated that the areas under the curve (AUCs) of DAGAN were better than previous generative adversarial network based anomaly detection models in 13 out of 17 categories in these datasets, especially in categories with high variability or noise. The maximum AUC improvement was 0.250 (toothbrush) Moreover, the proposed method exhibited better detection ability than the U Net auto encoder, which indicates the function of discriminator in this application. Furthermore, the proposed method had a high level of AUCs when using only a small amount of training data. DAGAN can significantly reduce the time and cost of collecting and labeling data when it is applied to industrial detection.

title: Analyzing sustainable performance on high precision molding process of 3D ultra thin glass for smart phone
abstract: Ultra thin curved (2.5D, 3D) glass is widely used in the most recently developed smartphones, and the market demand for it is steadily increasing. The high precision ultra thin glass molding process (UTGMP) is very important in the fabrication of high quality 2.5D/3D ultra thin glass for smartphones; however, the UTGMP, with its high energy consumption and low pass rate, is not considered to be environment friendly. Therefore, in this study, the sustainability of the molding process for 3D ultra thin glass was comprehensively investigated. First, the ultra thin 3D glass molding process is described, including a discussion of the glass molding apparatus, and the principle of glass thermos formation is explained. Secondly, a thermo mechanical coupling model is proposed to study the effects of loading heat and pressure on the distributions of temperature and internal stress of ultra thin glass. The results demonstrate that significant amounts of stress were concentrated at the edges, and in holes and slots. A series of experiments were conducted to reveal the respective effect of various UTGMP parameters on the energy consumption, pass rate, and carbon emission level of the process. Consequently, the percentage of the main effect of loading temperature on energy consumption was found to be 23.65% whereas that of the duration of applied pressure on the pass rate was determined to be 52.48% The optimal process parameter combinations were determined by analyzing the above mentioned research results and subsequently implementing them in a hybrid intelligent optimization algorithm that utilizes quantum behaved particle swarm optimization (QPSO) and a back propagation neural network (BPNN) The algorithm revealed that energy consumption and carbon emissions can be respectively decreased by 3.39% and 3.45% at a relatively high pass rate (i.e. over 70% Eventually, the suggested combinations for the UTGMP will be able to achieve at least 60% 76% of the pass rate, as well as consume no more than 0.756 kW h/pcs to meet sustainable manufacturing requirements. Thus, this study contributes to the effective application of a high precision glass molding process for the sustainable manufacturing of ultra thin curved glass.

title: A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform
abstract: Glass bottles must be thoroughly inspected before they are used for packaging. However, the vision inspection of bottle bottoms for defects remains a challenging task in quality control due to inaccurate localization, the difficulty in detecting defects in the texture region, and the intrinsically nonuniform brightness across the central panel. To overcome these problems, we propose a surface defect detection framework, which is composed of three main parts. First, a new localization method named entropy rate superpixel circle detection (ERSCD) which combines least squares circle detection and entropy rate superpixel (ERS) with an improved randomized circle detection, is proposed to accurately obtain the region of interest (ROI) of the bottle bottom. Then, according to the structure property, the ROI is divided into two measurement regions: central panel region and annular texture region. For the former, a defect detection method named frequency tuned anisotropic diffusion super pixel segmentation (FTADSP) that integrates frequency tuned salient region detection (FT) anisotropic diffusion, and an improved superpixel segmentation is proposed to precisely detect the regions and boundaries of defects. For the latter, a defect detection strategy called wavelet transform multiscale filtering (WTMF) based on a wavelet transform and a multiscale filtering algorithm is proposed to reduce the influence of texture and to improve the robustness to localization error. The proposed framework is tested on four data sets obtained by our designed vision system. The experimental results demonstrate that our framework achieves the best performance compared with many traditional methods.

title: In vivo bioactivity assessment of strontium containing soda lime borate glass implanted in femoral defect of rat
abstract: Most of previous works studied the bioactivity of tertiary soda lime borate as in vitro method, but, there have been no previous studies investigated the in vivo compatibility of such glass. This work was mainly aimed at in vivo assessment of bone formation of tertiary soda lime borate bioactive glass doped with Sr. the glass composition was based on 60 B 2 O 3 20 Na 2 O (20 x CaO x SrO (wt% where, x 0 and 10 wt% (samples encoded B0 and BS, respectively) The in vivo test was conducted in femoral bone defect of Sprague Dawley adult male rats after 3 and 6 weeks post surgery using the histological analyses and bone formation markers (alkaline phosphatase (ALP) and osteocalcin (OCN) Moreover, the possible systemic toxicity was studied using different biochemical analyses (alanine transaminase, aspartate transaminase, urea and creatinine) The result of bone markers showed that serum OCN level increased in rat implanted with sample B0 than that observed in sample BS at 3 and 6 weeks post implantation, while this finding was reversed in ALP activity. In vivo bioactivity test showed that implantation of all borate glasses did not demonstrate local or general complications in all rats, and they exhibited nearly complete bone mineralization. However, BS glass was formed more new bone than B0 one in long implantation time. In conclusion, the synthesized bioactive borate glasses were safe materials, and introducing of Sr in the glass was enhanced formation of new bone throughout long time of implantation. Accordingly, this glass can be used as a potential substitute for bone regenerative materials and a hosting for strontium ions delivery.

title: Shear deformation characteristics and defect evolution of the biaxial 45deg and 0/90deg glass non crimp fabrics
abstract: Currently, the shear pre tension coupling behavior and evolution of various mesoscopic defects for the biaxial non crimp fabric (NCF) are not revealed. Here, this work systematically conducted the picture frame shear with applied pre tension and hemisphere forming for the 45deg and 0/90deg glass NCF. In the picture frame shearing, it is identified that the effective shear response of the biaxial NCF in the positive shear mode should be characterized in the cycle 1 due to the irreversible destruction of the NCF architecture. Besides, the applying pre tension is originally demonstrated to be capable of effectively suppressing the wrinkle defect and delaying the triggering shear angle of the wrinkle. Moreover, for the 45deg NCF, the stitching thread suffers tension in positive shear mode, and the wrinkle is the main defect. In the negative shear mode, due to the non pure shearing at high shear angle and compression of stitching thread, the mesoscopic defects, including bending and unit cell buckling of the stitching thread, are generated. In the hemisphere forming, it is revealed that the shear deformation is certainly arose in the tensile region of the 45deg NCF due to the asymmetrical yarn shrink. The same fabric wrinkle and mesoscopic stitching thread defects are also originally identified, demonstrating the hemisphere forming is highly correlated to the large shear deformation. The results here provide a basic reference to suppress the defects in the performing process of the biaxial NCF.

title: Evaluating the Effect of Non cellular Bioactive Glass Containing Scaffolds on Osteogenesis and Angiogenesis in in vivo Animal Bone Defect Models
abstract: The use of bone scaffolds to replace injured or diseased bone has many advantages over the currently used autologous and allogeneic options in clinical practice. This systematic review evaluates the current evidence for non cellular scaffolds containing bioactive glass on osteogenesis and angiogenesis in animal bone defect models. Studies that reported results of osteogenesis via micro CT and results of angiogenesis via Microfil perfusion or immunohistochemistry were included in the review. A literature search of PubMed, EMBASE and Scopus was carried out in November 2019 from which nine studies met the inclusion and exclusion criteria. Despite the significant heterogeneity in the composition of the scaffolds used in each study, it could be concluded that scaffolds containing bioactive glass improve bone regeneration in these models, both by osteogenic and angiogenic measures. Incorporation of additional elements into the glass network, using additives, and using biochemical factors generally had a beneficial effect. Comparing the different compositions of non cellular bioactive glass containing scaffolds is however difficult due to the heterogeneity in bioactive glass compositions, fabrication methods and biochemical additives used.

title: Advanced cover glass defect detection and classification based on multi DNN model
abstract: Demands for display panels and relevant technologies are rapidly increasing with the recent advances in smart mobile devices. Many manufacturers have begun slowly investing in fully automated inspection systems that enable consistent and objective inspections. Carrying out such undertaking aims to satisfy high user requirements concerning quality while coping with high volumes. Cover glass is one of the important items for inspection because users directly interact with it. Despite the extensive use of typical machine vision based solutions in this field, many manufacturers continue relying on human based judgment because of a deficient understanding of defects or poor confidence in algorithms. To overcome these problems, this study proposes a deep learning neural network (DLNN) based defect inspection system. The DLNN has advantages over traditional computer vision or human based inspection in terms of flexibility and performance. We introduce a weighted multi DLNN inspection system capable of efficiently utilizing multi channel measurement data, with a detection rate of up to 99% and a false pass rate below 1%

title: Effects of cerium doping on dielectric properties and defect mechanism of barium strontium titanate glass ceramics
abstract: The effect of cerium content on phase evolution, dielectric properties and defect mechanism has been investigated in (Ba,Sr)TiO3 glass ceramics. Cerium mainly acts as an isovalent dopant in the B site of ABO3 perovskite structure at low content (1 mol% and then cerium substitution gradually occurs in the A site with increasing cerium content. A compensation mechanism related to variation in oxygen vacancy concentration has been identified. When cerium content increased to 2 mol% the maximum values of dielectric constant and energy storage density were simultaneously achieved. The impedance spectra revealed the highest conductivity. It was due to the increase in the concentration of charge carriers accompanied by the decrease in the activation energy of oxygen vacancy migration. With a further addition of cerium to 3 mol% the opposite trend was observed. The result is related to the presence of more cation vacancies, which, in turn, limits the diffusion rate of oxygen vacancy.