Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.

Defect Engineering with Rational Dopants Modulation for High‑Temperature Energy Harvesting in Lead‑Free Piezoceramics

High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.

Defect Engineering:Can it Mitigate Strong Coulomb Effect of Mg^(2+)in Cathode Materials for Rechargeable Magnesium Batteries?

Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Masquelet technique using an allogeneic cortical bone graft for a large bone defect:A case report

BACKGROUND The induced-membrane technique was initially described by Masquelet as an effective treatment for large bone defects,especially those caused by infection.Here,we report a case of chronic osteomyelitis of the radius associated with a 9 cm bone defect,which was filled with a large allogeneic cortical bone graft from a bone bank.Complete bony union was achieved after 14 months of follow-up.Previous studies have used autogenous bone as the primary bone source for the Masquelet technique;in our case,the exclusive use of allografts is as successful as the use of autologous bone grafts.With the advent of bone banks,it is possible to obtain an unlimited amount of allograft,and the Masquelet technique may be further improved based on this new way of bone grafting.CASE SUMMARY In this study,we reported a case of repair of a long bone defect in a 40-year-old male patient,which was characterized by the utilization of allograft cortical bone combined with the Masquelet technique for the treatment of the patient's long bone defect in the forearm.The patient's results of functional recovery of the forearm were surprising,which further deepens the scope of application of Masquelet technique and helps to strengthen the efficacy of Masquelet technique in the treatment of long bones indeed.CONCLUSION Allograft cortical bone combined with the Masquelet technique provides a new method of treatment to large bone defect.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Association of Congenital Heart Defects (CHD) with Factors Related to Maternal Health and Pregnancy in Newborns in Puerto Rico

Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

In-doping collaboratively controlling back interface and bulk defects to achieve efficient flexible CZTSSe solar cells

Focusing on the low open circuit voltage(V_(OC))and fill factor(FF)in flexible Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)solar cells,indium(In)ions are introduced into the CZTSSe absorbers near Mo foils to modify the back interface and passivate deep level defects in CZTSSe bulk concurrently for improving the performance of flexible device.The results show that In doping effectively inhibits the formation of secondary phase(Cu(S,Se)_(2))and VSndefects.Further studies demonstrate that the barrier height at the back interface is decreased and the deep level defects(Cu_(Sn)defects)in CZTSSe bulk are passivated.Moreover,the carrier concentration is increased and the V_(OC) deficit(V_(OC,def))is decreased significantly due to In doping.Finally,the flexible CZTSSe solar cell with 10.01%power conversion efficiency(PCE)has been obtained.The synergistic strategy of interface modification and bulk defects passivation through In incorporation provides a new thought for the fabrication of efficient flexible kesterite-based solar cells.

Built defects of homogeneous junction to enhance the lithium storage capacity of niobium pentoxide materials

Niobium pentoxide(Nb_(2)O_(5))is deemed one of the promising anode materials for lithium-ion batteries(LIBs)for its outstanding intrinsic fast Li-(de)intercalation kinetics.The specific capacity,however,is still limited,because the(de)intercalation of excessive Li-ions brings the undesired stress to damage Nb_(2)O_(5) crystals.To increase the capacity of Nb_(2)O_(5) and alleviate the lattice distortion caused by stress,numerous homogeneous H-and M-phases junction interfaces were proposed to produce coercive stress within theNb_(2)O_(5)crystals.Such interfaces bring about rich oxygen vacancies with structural shrinkage tendency,which pre-generate coercive stress to resist the expansion stress caused by excessive Li-ions intercalation.Therefore,the synthesized Nb_(2)O_(5) achieves the highest lithium storage capacity of 315 mA h g−1 to date,and exhibits high-rate performance(118 mA h g^(-1) at 20 C)as well as excellent cycling stability(138 mA h g^(-1) at 10 C after 600 cycles).

Right Axillary Thoracotomy Should Be the Standard of Care for Repair of Non-Complex Congenital Heart Defects in Infants and Children

Minimally invasive approaches for cardiac surgery in children have been lagging in comparison to the adult world.A wide range of the most common congenital heart defects in infants and children can be repaired suc-cessfully through a variety of non-sternotomy incisions.This has been shown to be associated with superior cos-metic results,shorter hospital stays,and rapid return to full activity compared to sternotomy.These approaches have been around for decades,but they have not been widely adopted for a variety of reasons.Right axillary thor-acotomy is one of these approaches that we believe should be the new standard for the repair of a wide variety of heart defects in children and will be the focus of our current review.

Visualizing extended defects at the atomic level in a Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire

The microstructure significantly influences the superconducting properties.Herein,the defect structures and atomic arrangements in high-temperature Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire are directly characterized via stateof-the-art scanning transmission electron microscopy.Interstitial oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped superconductor.Inclusion phases with varied numbers of CuO_(2) layers are found,and twist interfaces with different angles are identified.This study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.

Defects-rich MgFe LDH:A high-capacity adsorbent for methyl orange wastewater

Dye pollution is a common pollutant in wastewater that poses a serious threat to human health.Layered double hydroxide(LDH)is a commonly used adsorbent for dye removal.However,its adsorption efficiency is significantly limited by the limited adsorption active sites of the adsorbent.In this paper,a defects-rich MgFe LDH adsorbent for anionic dye wastewater was synthesized by a simple hydrothermal method and alkaline etching.Different analytical techniques,such as XRD,FT-IR,SEM,TEM,XPS,and N2 adsorption-desorption isotherm,were used to verify the chemical composition and surface characteristics of the materials,and the effects of pH,temperature,and contact time on the adsorption effect of methyl orange and the adsorption mechanism were analyzed.Alkaline etching of Al and Zn in the laminate generated defects that expose unsaturated coordination centers and create abundant adsorption sites,which can electrostatically attract and coordinate with dye ions.At 25℃,the adsorption capacity of MgFe LDH with Al etched and MgFe LDH with Zn etched for methyl orange dye reached 1722 mg·g^(-1 ) and 1685 mg·g^(-1 ),respectively,much higher than that of MgFe LDH(544 mg·g^(-1 )).This work provides a promising method for the removal of dye wastewater by adsorption and a new idea for the design and development of high-performance dye wastewater adsorbents.

Effects of Initial Defects on Effective Elastic Modulus of Concrete with Mesostructure

An exquisite mesostructure model was presented to predict the effective elastic modulus of concrete,in which concrete is realized as a four-phase composite material consisting of coarse aggregates,mortar matrix,interfacial transition zone(ITZ),and initial defects.With the three-dimensional(3D)finite element(FE)simulation,the highly heterogeneous composite elastic behavior of concrete was modeled,and the predicted results were compared with theoretical estimations for validation.Monte Carlo(MC)simulations were performed with the proposed mesostructure model to investigate the various factors of initial defects influencing the elastic modulus of concrete,such as the shape and concentration(pore volume fraction or crack density)of microspores and microcracks.It is found that the effective elastic modulus of concrete decreases with the increase of initial defects concentration,while the distribution and shape characteristics also exert certain influences due to the stress concentration caused by irregular inclusion shape.

Effect of Vacancy Defects on the Properties of CoS_(2) and FeS_(2)

In order to explore the effect of vacancy defects on the structural,electronic,magnetic and optical properties of CoS_(2) and FeS_(2),first-principles calculation method was used to investigate the alloys.The calculated results of materials without vacancy are consistent with those reported in the literatures,while the results of materials with vacancy defect were different from those of literatures due to the difference vacancy concentration.The Co vacancy defect hardly changes the half-metallic characteristic of CoS_(2).The Fe vacancy defect changes FeS_(2) from semiconductor to half-metal,and the bottom of the spin-down conduction band changes from the p orbital state of S to the d(t_(2g))orbital state of Fe,while the top of the valence band remains the d orbital d(eg)state of Fe.The half-metallic Co vacancy defects of CoS_(2) and Fe vacancy defects of FeS_(2) are expected to be used in spintronic devices.S vacancy defects make both CoS_(2) and FeS_(2) metallic.Both the Co and S vacancy defects lead to the decrease of the magnetic moment of CoS_(2),while both the Fe and S vacancy defects lead to the obvious magnetic property of FeS_(2).Vacancy defects enhance the absorption coefficient of infrared band and long band of visible light obviously,and produce obvious red shift phenomenon,which is expected to be used in photoelectric devices.

Theoretical Design of Defects as a Driving Force for Ion Transport in Li_(3)OBr Solid Electrolyte

Due to ever-increasing concerns about safety issues in using Li ionic batteries,solid electrolytes have extensively explored.The Li-rich antiperovskite Li_(3)OBr has been considered as a promising solid electrolyte candidate,but it still suffers challenges to achieve a high ionic conductivity owing to the high intrinsic symmetry of the crystal lattice.Herein,we presented a design strategy that introduces various point defects and grain boundaries to break the high lattice symmetry of Li_(3)OBr crystal,and their effect and microscopic mechanism of promoting the migration of Li-ion were explored theoretically.It has been found that Li_(i)are the dominant defects responsible for the fast Li-ion diffusion in bulk Li_(3)OBr and its surface,but they are easily trapped by the grain boundaries,leading to the annihilating of the Frenkel defect pair V'_(Li)+Li_(i),and thus limits the V'_(Li)diffusion at the grain boundaries.The V_(Br)defect near the grain boundaries can effectively drive V'_(Li)across the grain boundary,thereby converting the carrier of Li^(+)migration from Li,in the bulk and surface to V'_(Li)at the grain boundary,and thus improving the ionic conductivity in the whole Li_(3)OBr crystal.This work provides a comprehensive insight into the Li^(+)transport and conduction mechanism in the Li_(3)OBr electrolyte.It opens a new way of improving the conductivity for all-solid-state Li electrolyte material through the defect design.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

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.

Simulation on dynamic characteristics of TC4 cutting with crack defects

Titanium alloys play an important role in aerospace and other fields.However,after precision forging and cold rolling process,some defects will appear on the subsurface of titanium alloy bars,thus reducing the surface quality and precision of turning process.This study aimed at exploring the effect of crack defects on TC4 cutting.Firstly,the finite element cutting simulation model of TC4 material with crack defects was established in ABAQUS.Then,the cutting parameters such as cutting force,stress concentration,chip morphology,residual stress were obtained by changing the variables such as the size and height of crack defects.Finally,the turning experiment was carried out on centerless lathe.The results show that the cutting force changes abruptly when the defect position is located on the cutting path,the maximal stress occurs at the tip of the defect,and the mutation of stress value is more serious with the increase of defect size;the buckling deformation of chip morphology occurs and becomes less serious with the increase of the distance between the defect position and the workpiece surface;the surface residual stress near the defect is related to the stress when the tool is close to the defect,the larger defect size and the closer to the machined surface,the greater the residual stress.Therefore,under certain processing conditions,the TC4 material should avoid large size defects or increase the distance between defects and the machined surface,so as to obtain better and stable surface quality.