DSN-BR-Based Online Inspection Method and Application for Surface Defects of Pharmaceutical Products in Aluminum-Plastic Blister Packages

Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing,as it is subject to rigorous regulatory practices.This study presents a research focused on the development of an on-line detection method and system for identifying surface defects in pharmaceutical products packaged in aluminum-plastic blisters.Firstly,the aluminum-plastic blister packages exhibit multi-scale features and inter-class indistinction.To address this,the deep semantic network with boundary refinement(DSN-BR)model is proposed,which leverages semantic segmentation domain knowledge,to accurately segment the defects in pixel level.Additionally,a specialized image acquisition module that minimizes the impact of ambient light is established,ensuring high-quality image capture.Finally,the image acquisition module,image detection module,and data management module are designed to construct a comprehensive online surface defect detection system.To validate the effectiveness of our approach,we employ a real dataset for instance verification on the implemented system.The experimental results substantiate the outstanding performance of the DSN-BR,achieving the mean intersection over union(MIoU)of 90.5%.Furthermore,the proposed system achieves an inference speed of up to 14.12 f/s,while attaining an F1-Score of 98.25%.These results demonstrate that the system meets the actual needs of the enterprise and provides theoretical and methodological support for intelligent inspection of product surface quality.By standardizing the control process of pharmaceutical manufacturing and improving the management capability of the manufacturing process,our approach holds significant market application prospects.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Analytical Modeling and Mechanism Analysis of Time-Varying Excitation for Surface Defects in Rolling Element Bearings

Surface defects,including dents,spalls,and cracks,for rolling element bearings are the most common faults in rotating machinery.The accurate model for the time-varying excitation is the basis for the vibration mechanism analysis and fault feature extraction.However,in conventional investigations,this issue is not well and fully addressed from the perspective of theoretical analysis and physical derivation.In this study,an improved analytical model for time-varying displacement excitations(TVDEs)caused by surface defects is theoretically formulated.First and foremost,the physical mechanism for the effect of defect sizes on the physical process of rolling element-defect interaction is revealed.According to the physical interaction mechanism between the rolling element and different types of defects,the relationship between time-varying displacement pulse and defect sizes is further analytically derived.With the obtained time-varying displacement pulse,the dynamic model for the deep groove bearings considering the internal excitation caused by the surface defect is established.The nonlinear vibration responses and fault features induced by surface defects are analyzed using the proposed TVDE model.The results suggest that the presence of surface defects may result in the occurrence of the dual-impulse phenomenon,which can serve as indexes for surface-defect fault diagnosis.

Building surface defects by doping with transition metal on ultrafine TiO_2 to enhance the photocatalytic H_2 production activity

Inefficient charge separation and limited light absorption are two critical issues associated with high‐efficiency photocatalytic H2production using TiO2.Surface defects within a certain concentration range in photocatalyst materials are beneficial for photocatalytic activity.In this study,surface defects(oxygen vacancies and metal cation replacement defects)were induced with a facile and effective approach by surface doping with low‐cost transition metals(Co,Ni,Cu,and Mn)on ultrafine TiO2.The obtained surface‐defective TiO2exhibited a3–4‐fold improved activity compared to that of the original ultrafine TiO2.In addition,a H2production rate of3.4μmol/h was obtained using visible light(λ>420nm)irradiation.The apparent quantum yield(AQY)at365nm reached36.9%over TiO2‐Cu,significantly more than the commercial P25TiO2.The enhancement of photocatalytic H2production activity can be attributed to improved rapid charge separation efficiency andexpanded light absorption window.This hydrothermal treatment with transition metal was proven to be a very facile and effective method for obtaining surface defects.

Tuning the crystallite size of monoclinic ZrO_(2) to reveal critical roles of surface defects on m–ZrO_(2) catalyst for direct synthesis of isobutene from syngas

The effects of crystallite size on the physicochemical properties and surface defects of pure monoclinic ZrO_(2) catalysts for isobutene synthesis were studied.We prepared a series of monoclinic ZrO_(2) catalysts with different crystallite size by changing calcination temperature and evaluated their catalytic performance for isobutene synthesis from syngas.ZrO_(2) with small crystalline size showed higher CO conversion and isobutene selectivity,while samples with large crystalline size preferred to form dimethyl ether(DME)instead of hydrocarbons,much less to isobutene.Oxygen defects(ODefects)analyzed by X-ray photoelectron spectroscopy(XPS)provided evidence that more ODefectsoccupied on the surface of ZrO_(2) catalysts with smaller crystalline size.Electron paramagnetic resonance(EPR)and ultraviolet–visible diffuse reflectance(UV–vis DRS)confirmed the presence of high concentration of surface defects and Zr3+on mZrO_(2)-5.9 sample,respectively.In situ diffuse reflectance infrared Fourier transform spectroscopy(in situ DRIFTS)analysis indicated that the adsorption strength of formed formate species on catalyst reduced as the crystalline size decreased.These results suggested that surface defects were responsible for CO activation and further influenced the adsorption strength of surface species,and thus the products distribution changed.This study provides an in-depth insight for active sites regulation of ZrO_(2) catalyst in CO hydrogenation reaction.

Creation of surface defects on carbon nanofibers by steam treatment

A direct strategy for the creation of defects on carbon nanofibers (CNFs) has been developed by steam treatment.Nitrogen physisorption,XRD,Raman spectra,SEM and TEM analyses proved the existence of the new defects on CNFs.BET surface area of CNFs after steam treatment was enhanced from 20 to 378 m2/g.Pd catalysts supported on CNFs were also prepared by colloidal deposition method.The different activity of Pd/CNFs catalysts in the partial hydrogenation of phenylacetylene further demonstrated the diverse surfaces of CNFs could be formed by steam treatment.

Impact of Methanol Photomediated Surface Defects on Photocatalytic H2 Production Over Pt/TiO2

Co-catalysts play a critical role in enhancing the efficiency of inorganic semiconductor photocatalysts;however,synthetic approaches to tailoring cocatalyst properties are rarely the focus of research efforts.A photomediated route to control the dispersion and oxidation state of a platinum(Pt)cocatalyst through defect generation in the P25 titania photocatalyst substrate is reported.Titania photoirradiation in the presence of methanol induces longlived surface defects which subsequently promote the photodeposition of highly dispersed(2.2±0.8 nm)and heavily reduced Pt nanoparticles on exposure to H2 PtCl6.The optimal methanol concentration of 20 vol%produces the highest density of metallic Pt nanoparticles.Photocatalytic activity for water splitting and associated hydrogen(H2)production under UV irradiation mirrors the methanol concentration employed during the P25 photoirradiation pretreatment and resulting Pt loading resulting in a common mass-normalized H2 productivity of 3800±130 mmol gpt-1 h-1.Photomediated surface defects(arising in the presence of a methanol hole scavenger)provide electron traps that regulate subsequent photodeposition of a Pt co-catalyst over P25,offering a facile route to tune photocatalytic efficiency.

Contribution of Surface Defects to the Interface Conductivity of SrTiO_3/LaAlO_3

Based on the first-principles method, the structural stability and the contribution of point defects such as O, Sr or Ti vacancies on two-dimensional electron gas of n- and p-type LaAlO3/SrTiO3 interfaces are investigated. The results show that O vacancies at p-type interfaces have much lower formation energies, and Sr or Ti vacancies at n-type interfaces are more stable than the ones at p-type interfaces under O-rich conditions. The calculated densities of states indicate that O vacancies act as donors and give a significant compensation to hole carriers, resulting in insulating behavior at p-type interfaces. In contrast, Sr or Ti vacancies tend to trap electrons and behave as acceptors. Sr vacancies are the most stable defects at high oxygen partial pressures, and the Sr vacancies rather than Ti vacancies are responsible for the insulator-metal transition of n-type interface. The calculated results can be helpful to understand the tuned electronic properties of LaAlO3 /SrTiO3 heterointerfaces.

AN EFFICIENT LABELLING ALGORITHM FOR WOODPANELS SURFACE DEFECTS

This paper describes an efficient approach for labeling images using a combination of pipeline (Datacube) and (general purpose computer) processing. The output of the algorithm is coordinate list of labeled object pixels that facilitates further high level operations. It is an efficient labeling algorithm for a automatic classification of surface defects on wood boards.

Effects of Surface Defects on Adsorption of CO and Methyl Groups on Rutile TiO_(2)(110)

The interaction of reactants with catalysts has always been an important subject for catalytic reactions.As a promising catalyst with versatile applications,titania has been intensively studied for decades.In this work we have investigated the role of bridge bonded oxygen vacancy(O_(v))in methyl groups and carbon monoxide(CO)adsorption on rutile TiO_(2)(110)(R-TiO_(2)(110))with the temperature programmed desorption technique.The results show a clear different tendency of the desorption of methyl groups adsorbed on bridge bonded oxygen(O_(b)),and CO molecules on the five coordinate Ti^(4+)sites(Ti_(5c))as the Ovconcentration changes,suggesting that the surface defects may have crucial influence on the absorption of species on different sites of R-TiO_(2)(110).

Surface defects in 4H-SiC homoepitaxial layers

Although a high-quality homoepitaxial layer of 4H‑silicon carbide(4H-SiC)can be obtained on a 4°off-axis substrate using chemical vapor deposition,the reduction of defects is still a focus of research.In this study,several kinds of surface defects in the 4H-SiC homoepitaxial layer are systemically investigated,including triangles,carrots,surface pits,basal plane dislocations,and step bunching.Themorphologies and structures of surface defects are further discussed via optical microscopy and potassium hydroxide-based defect selective etching analysis.Through research and analysis,we found that the origin of surface defects in the 4H-SiC homoepitaxial layer can be attributed to two aspects:the propagation of substrate defects,such as scratches,dislocation,and inclusion,and improper process parameters during epitaxial growth,such as in-situ etch,C/Si ratio,and growth temperature.It is believed that the surface defects in the 4H-SiC homoepitaxial layer can be significantly decreased by precisely controlling the chemistry on the deposition surface during the growth process.

Laser ultrasonic technique for surface defects detection of 6061 aluminum alloy

In order to estimate and detect the surface defect depth of metals, the transmission method of laser ultrasonic surface waves is used in this work. The laser ultrasonic detection platform taking use of thermoelastic mechanism as acoustic signal excitation method and interference receiver as acoustic signal receiver method was built, by which B-scan images of detected specimens with surface defects were collected to establish the relationship between the transmission coefficient and depth of the surface defect. Experimental results show that the amplitude of transmitted acoustic signal is related to the depth of surface defect. At last, a fitted curve of transmission coefficient using measured experimental data is obtained to estimate depth of surface defect on the 6061 aluminum alloy. Furthermore, a surface defect depth of 0.3 mm is estimated by the fitting curve with an estimated error of 16%. Therefore, a experimental method using the transmission method by laser ultrasonic is presented in this paper.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

Surface defects incorporated diamond machining of silicon

This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the surface defect machining(SDM)method.The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating,cryogenic cooling,electric pulse or ultrasonic elliptical vibration.The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode.This paper made use of Raman spectroscopy data,average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional single-point diamond turning(SPDT)and SDM while incorporating surface defects in the(i)circumferential and(ii)radial directions.Complementary 3D finite element analysis(FEA)was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer.It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness(Ra)of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters.The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases.FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance.The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard,brittle materials such as SiC,ZnSe and GaAs as well as hard steels.

Neutral and metallic vs.charged and semiconducting surface layer in acceptor doped CeO_(2)

The monomolecular surface layer of acceptor doped CeO_(2) may become neutral and metallic or charged and semiconducting.This is revealed in the theoretical analysis of the oxygen pressure dependence of the surface defects concentration in acceptor doped ceria with two different dopant types and operated under different oxygen pressures.Recently published experimental data for highly reduced Sm0.2Ce0.8O1.9-x(SDC)containing a fixed valence dopant Sm3+are very different from those published for Pr0.1Ce0.9O_(2)-x(PCO) with the variable valence dopant Pr4+/Pr3+being reduced under milder conditions.The theoretical analysis of these experimental results fits very well the experimental results of SDC and PCO.It leads to the following predictions:the highly reduced surface of SDC is metallic and neutral,the metallic surface electron density of state is gs=0.9×10^(38)J-1·m^(-2)(1.4×1015eV^(-1)·cm^(-2)),the electron effective mass is meff,s=3.3me,and the phase diagram of the reduced surface has theα(fcc)structure as in the bulk.In PCO a double layer is predicted to be formed between the surface and the bulk with the surface being negatively charged and semiconducting.The surface of PCO maintains high Pr^(3+) defect concentration as well as relative high oxygen vacancy concentration at oxygen pressures higher than in the bulk.The reasons for the difference between a metallic and semiconducting surface layer of acceptor doped CeO_(2) are reviewed,as well as the key theoretical considerations applied in coping with this problem.For that we make use of the experimental data and theoretical analysis available for acceptor doped ceria.

Facile Surface Engineering of NiCo_(2)O_(4) to Boost Propane Oxidation Activity

Spinel oxide(NiCo_(2)O_(4))has demonstrated great potential to replace noble metal catalysts for the oxidation reaction of air pollutants.To further boost the oxidation ability of such catalysts,in this study,a facile surface-engineering strategy wherein NiCo_(2)O_(4) was treated with different alkali solvents was developed.The obtained catalyst(NiCo_(2)O_(4)-OH)showed a higher surface alkalinity and more surface defects compared to the pristine spinel oxide,including enhanced structural distortion as well as promoted oxygen vacancies.The propane oxidation ability of NiCo_(2)O_(4)-OH was greatly enhanced,with a propane conversion rate that was approximately 6.4 times higher than that of pristine NiCo_(2)O_(4) at a reaction temperature 193℃.This work sets a valuable paradigm for the surface modulation of spinel oxide via alkali treatment to ensure a high-performance oxidation catalyst.

Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+Deep Learning Model

The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.

Surface repair of wide-bandgap perovskites for high-performance all-perovskite tandem solar cells

Wide-bandgap(WBG)perovskite solar cells(PSCs)play a fundamental role in perovskite-based tandem solar cells.However,the efficiency of WBG PSCs is limited by significant open-circuit voltage losses,which are primarily caused by surface defects.In this study,we present a novel method for modifying surfaces using the multifunctional S-ethylisothiourea hydrobromide(SEBr),which can passivate both Pb^(-1)and FA^(-1)terminated surfaces,Moreover,the SEBr upshifted the Fermi level at the perovskite interface,thereby promoting carrier collection.This proposed method was effective for both 1.67 and 1.77 eV WBG PSCs,achieving power conversion efficiencies(PCEs)of 22.47%and 19.90%,respectively,with V_(OC)values of 1.28 and 1.33 V,along with improved film and device stability.With this advancement,we were able to fabricate monolithic all-perovskite tandem solar cells with a champion PCE of 27.10%,This research offers valuable insights for passivating the surface trap states of WBG perovskite through rational multifunctional molecular engineering.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.