Types Of Defects Research Articles

Frequency and pattern of adult congenital heart disease in a tertiary care cardiac hospital: reasons associated with delayed diagnosis

Objective: To determine the distribution pattern and possible reasons of delayed diagnosis of congenital heart disease in adult population. Method: The analytical, cross-sectional study was conducted at the Department of Paediatric Cardiology, National Institute of Cardiovascular Diseases, Karachi, from July to December 2021, and comprised patients of either gender aged 18 years and above with congenital heart disease confirmed by echocardiographic examination. Age, gender, weight, height, provincial affiliation, marital status, number of children, if any, duration of disease and New York Heart Association classification were noted on a predesigned proforma. Patterns of congenital heart disease types and reasons behind delayed diagnosis, if noted, were recorded. Data was analysed using SPSS 26. Results: Of the 129 patients, 68(52.7%) were males. The overall mean age was 25.8±9.8 years. Mean age at the time of diagnosis was 12.4±14.8 years. There were 91(70.5%) cases with delayed diagnosis. There was no significant gender difference with respect to delayed diagnosis (p>0.05). Delayed referral 39(42.9%), delayed consultation 22(24.2%), delayed diagnosis by the doctor 21(23.1%), and social factors 9(9.9%) were the most frequent reasons behind delayed diagnosis. Ventricular septal defect was the most frequently defect 38(29.5%), followed by tetralogy of fallot 32(24.8%). Right ventricular dysfunction was the most common complication of delayed diagnosis 35(27.1%). Conclusion: Delayed diagnosis of congenital heart disease was reported in over two-thirds of the cases, with ventricular septal defect being the most frequent type of defect, and delayed referral being the most common reason. Key Words: Congenital heart disease, Diagnosis, Echocardiography, Tetralogy of fallot, Ventricular septal defect.

A perspective on guided electrophoretic transport of particles in liquid crystals

Nonlinear electrophoresis in complex fluids like nematic liquid crystals provides new pathways toward achieving precisely controlled motion and assembly of microscopic objects. The nematic host introduces a paradigm shift in the mechanism of electrophoretic transport by generating unbalanced electro-osmotic flows around the colloidal particle due to symmetry breaking of the medium caused by the induced topological defects. Rationally designed particles, which induce various types of defects and asymmetries, provide new opportunities in this regard. In this Perspective article, we discuss how the asymmetry in the shape and interfacial properties help in piloting the particles using an AC electric field. Finally, we propose some feasible strategies to achieve navigational control using magnetic and photo-responsive particles, guided by orthogonal electric, magnetic fields, and light, respectively.

MONITORING THE SAUSAGE PRODUCT USING LANEY DEMERIT CHART BASED ANALYTICAL HIERARCHY PROCESS

Ready-to-eat sausage is a food product that has a limited shelf life. Therefore, regularly monitoring the quality of packaged ready-to-eat sausage products is important to ensure that the products meet the established quality standards. Twelve types of product defects need to be observed in the final checking process to meet the quality standards, namely Wrinkle, Dots, Leaking, Product Stain, Non-standard Form, Poor Print Quality, Vacuum Leaks, Weak Ties, Body Defects, Uneven Length, Broken Node, and Small Stain. This study aims to apply the Laney Demerit Control Chart (LDCC) and Analytical Hierarchy Process-Integrated Statistical Process Control (AHP-ISPC) methods to monitor the quality of packaged ready-to-eat sausage production at XYZ Inc. The data is from the quality testing of ready-to-eat sausage products taken from XYZ Inc. for six months from April 1, 2023, until September 30, 2023. The findings reveal that conventional control charts (u control chart, demerit control chart, and AHP-based demerit control chart) exhibited oversensitivity because it is attributed to the large number of samples produced by the company, prompting the need for a more balanced approach. Implementing the Laney u control chart, Laney demerit control chart, and the AHP-based Laney demerit control charts successfully achieved statistical control in phase I. In contrast, phase II still demonstrated challenges, particularly with the AHP-based Laney Demerit Control Chart detecting the highest number of out-of-control points. This suggests that phase II remains statistically out of control, necessitating further analysis or corrective measures to enhance process stability. Additionally, the process capability analysis indicated that the production process during the specified period lacked capability, as evidenced by a capability index value below one (0.883).

Field test of internal visualization of deep-water pile foundation using ultrasonic waves

Early-age internal visualization is critical for timely assessing the quality of pile foundations, especially as super cross-sea bridges increase the diameter and length of pile foundations beyond the capabilities of traditional cross-hole sonic logging (CSL). This article introduces two innovations. First, ultrasonic instruments are specially developed to transmit waves through a concrete pile with a diameter of 4 m, a length of 92 m, and a water depth of 63 m, before the full hardening of concrete. Second, an imaging method is proposed to upgrade traditional CSL from 1D detection to 3D visualization. Both large-scale experiments and field tests are tested in the first 7 days after the concrete casting. The experiments successfully visualize typical construction defects, achieving a detection resolution of 18 cm for layered defects and 15 cm for void defects. During the field test, a low-pixel region is observed at the pile head, extending approximately 2.1 m deep, attributed to mud floating up during concrete casting into the steel casing. These developments in instrument technology and visualization methods provide essential tools for the quality evaluation of super pile foundations. Furthermore, the visualization data can support digital twin modeling in subsequent stages of construction and maintenance planning.

Studying the Defects in Spinel Compounds: Discovery, Formation Mechanisms, Classification, and Influence on Catalytic Properties.

Spinel ferrites demonstrate extensive applications in different areas, like electrodes for electrochemical devices, gas sensors, catalysts, and magnetic adsorbents for environmentally important processes. However, defects in the real spinel structure can change the many physical and chemical properties of spinel ferrites. Although the number of defects in a crystal spinel lattice is small, their influence on the vast majority of physical properties could be really decisive. This review provides an overview of the structural characteristics of spinel compounds (e.g., CoFe2O4, NiFe2O4, ZnFe2O4, Fe3O4, γ-Fe2O3, Co3O4, Mn3O4, NiCo2O4, ZnCo2O4, Co2MnO4, etc.) and examines the influence of defects on their properties. Attention was paid to the classification (0D, 1D, 2D, and 3D defects), nomenclature, and the formation of point and surface defects in ferrites. An in-depth description of the defects responsible for the physicochemical properties and the methodologies employed for their determination are presented. DFT as the most common simulation approach is described in relation to modeling the point defects in spinel compounds. The significant influence of defect distribution on the magnetic interactions between cations, enhancing magnetic properties, is highlighted. The main defect-engineering strategies (direct synthesis and post-treatment) are described. An antistructural notation of active centers in spinel cobalt ferrite is presented. It is shown that the introduction of cations with different charges (e.g., Cu(I), Mn(II), Ce(III), or Ce(IV)) into the cobalt ferrite spinel matrix results in the formation of various point defects. The ability to predict the type of defects and their impact on material properties is the basis of defect engineering, which is currently an extremely promising direction in modern materials science.

Influence of Synthesis Parameters on Structure and Characteristics of the Graphene Grown Using PECVD on Sapphire Substrate.

The high surface area and transfer-less growth of graphene on dielectric materials is still a challenge in the production of novel sensing devices. We demonstrate a novel approach to graphene synthesis on a C-plane sapphire substrate, involving the microwave plasma-enhanced chemical vapor deposition (MW-PECVD) technique. The decomposition of methane, which is used as a precursor gas, is achieved without the need for remote plasma. Raman spectroscopy, atomic force microscopy and resistance characteristic measurements were performed to investigate the potential of graphene for use in sensing applications. We show that the thickness and quality of graphene film greatly depend on the CH4/H2 flow ratio, as well as on chamber pressure during the synthesis. By varying these parameters, the intensity ratio of Raman D and G bands of graphene varied between ~1 and ~4, while the 2D to G band intensity ratio was found to be 0.05-0.5. Boundary defects are the most prominent defect type in PECVD graphene, giving it a grainy texture. Despite this, the samples exhibited sheet resistance values as low as 1.87 kΩ/□. This reveals great potential for PECVD methods and could contribute toward efficient and straightforward graphene growth on various substrates.

Unraveling the influence of defects in Janus MoSSe and Janus alloys MoS2(1−x)Se2x

We investigate the effect of structural and substitutional defects in Janus MoSSe and the Janus alloys MoS2(1−x)Se2x by a comprehensive analysis. Distinct Raman signatures are associated with various defect types and densities, mirroring the evolution from MoSe2 to Janus alloys to ideal Janus MoSSe. By the corresponding stoichiometrical and structural changes, the band gap can be tuned from 1.50 eV up to 1.68 eV at room temperature. Electrical characterization in a field effect device uncovers the impact of defects on conductivity, mobility (up to 2.42 × 10−3 cm2 V−1 s−1), and threshold voltages. A decrease of n-type doping of 5.3 × 1011 cm−2 in Janus MoSSe compared to the Janus alloy points towards an increased work function and a reduction of defects. Our findings deepen the understanding of defect physics in 2D Janus materials and pave the way for tailored defect engineering strategies for advanced (opto-)electronic applications.

Donor Posterior Atrial Flap Rotation for Left Atrial Cuff Reconstruction in Lung Transplantation.

We describe in detail a novel surgical technique for repairing donor anterior left atrial wall defect in vivo by rotating the posterior atrial flap during lung transplantation. This method can safely and effectively address the most common type of donor left atrial cuff defect: the anterior wall defect, with the intact posterior wall, typically retained when the donor heart is also used. During atrial cuff anastomosis in lung transplantation, the excess donor posterior atrial wall is trimmed into an atrial flap. After the posterior wall anastomosis is completed, the atrial flap is rotated 180° and used as a patch for anterior wall reconstruction anastomosis. After restoring blood flow, the flow rate of the pulmonary vein is normal and smooth as confirmed by transesophageal echocardiography. Compared with the traditional patch reconstruction method, the new method effectively reduces the atrial cuff anastomosis time. Our results showed no significant difference in pulmonary vein obstruction after reconstruction. Pulmonary artery systolic blood pressure was significantly lower and pulmonary function improved postoperatively in all groups, with no significant differences among the groups. The new technique provides a feasible strategy for the reconstruction of left atrial cuff defects and can improve the effective utilization rate of donor lungs.

Quantitative characterization of rubber three-body abrasion wear: multi-scale testing and analysis methods based on defect detection

Abrasive wear is one of the main causes of rapid deterioration of rubber serviceability. Therefore, it is necessary to obtain information on the degree of rubber abrasion and the wear mechanism. Due to the complex nature of abrasive surfaces, obtaining accurate information on rubber abrasion is often difficult and provides limited quantitative parameters. This study presents a method to quantify rubber abrasion through defect detection and analysis. Accurate and fast identification of typical abrasion defects is achieved, and in addition, macro- and microscopic characterization data are provided based on the distribution of defects to understand the degree of abrasion and the wear mechanism. Experimental validation demonstrated the fast and accurate characterization capability of the method, especially the advanced advantages at the microscopic level. The method achieves accurate and efficient characterization of rubber abrasion, which helps to advance the study of rubber tribological behavior and is important for guiding engineering applications and improving design.

Research on Surface Defect Positioning Method of Air Rudder Based on Camera Mapping Model

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.

Self-passivation of Halide Interstitial Defects by Organic Cations in Hybrid Lead-Halide Perovskites: Ab Initio Quantum Dynamics.

Halide interstitial defects severely hinder the optoelectronic performance of metal halide perovskites, making research on their passivation crucial. We demonstrate, using ab initio nonadiabatic molecular dynamics simulations, that hydrogen vacancies (Hv) at both N and C atoms of the methylammonium (MA) cation in MAPbI3 efficiently passivate iodine interstitials (Ii), providing a self-passivation strategy for dealing with the Hv and Ii defects simultaneously. Hv at the N site (Hv-N) introduces a defect state into the valence band, while the state contributed by Hv at the C site (Hv-C) evolves from a shallow level at 0 K to a deep midgap state at ambient temperature, exhibiting a high environmental activity. Both Hv-N and Hv-C are strong Lewis bases, capable of capturing and passivating Ii defects. Hv-C is a stronger Lewis base, bonds with Ii better, and exhibits a more pronounced passivation effect. The charge carrier lifetimes in the passivated systems are significantly longer than in those containing either Hv or Ii, and even in pristine MAPbI3. Our demonstration of the Hv and Ii defect self-passivation in MAPbI3 suggests that systematic control of the relative concentrations of Hv and Ii can simultaneously eliminate both types of defects, thereby minimizing charge and energy losses. The demonstrated defect self-passivation strategy provides a promising means for defect control in organic-inorganic halide perovskites and related materials and deepens our atomistic understanding of defect chemistry and charge carrier dynamics in solar energy and optoelectronic materials.

Scanning Electron Microscopic Evaluation of Surface Defects of Remover Retreatment File After Single and Multiple Uses.

This study aimed to evaluate surface defects of Remover rotary Nickel-Titanium (NiTi) files after single and multiple uses in conventional endodontic retreatment procedures using scanning electron microscopy (SEM). Eighty acrylic blocks, simulating root canals with a 1.5 mm internal diameter, a 5 mm radius of curvature, and a 55° curvature, were utilized. After chemomechanical preparation and obturation, 24 new Remover files (N30, 7%, L23) were randomly assigned to three groups: single use, triple use, and six uses. The files were operated at 600 rpm with a torque of 2.5 Ncm, cleaned, and sterilized after each use. SEM analysis at magnifications of 100x, 250x, and 500x revealed surface defects, including tip deformation, microcracks, fracture, unwinding, surface pitting, and blade disruption. Deformation was observed in 75% of the files after a single use and in 100% of the files after three and six uses. Microcracks were absent after single use but appeared in 25% and 87.5% of files after three and six uses, respectively, showing a statistically significant increase (p < 0.001). Surface pitting also significantly increased among groups (p = 0.004). No fractures were observed in any group. The most common defects were tip deformation (91.7%) and surface pitting (70.8%). The findings suggest that repeated use of NiTi files significantly increases surface defects, elevating the risk of fatigue fractures. Thus, the results recommend limiting the reuse of Remover files to a maximum of 3x. Further research is needed to correlate defect types with anatomical factors and to assess file effectiveness in retreatment scenarios.

АНАЛІЗ ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПІДВИЩЕННЯ НАДІЙНОСТІ У РОБОТІ ФЕС

This work is dedicated to the study of applying convolutional neural networks (CNN) for automatic recognition of photovoltaic panel conditions based on images. The focus is placed on the impact of CNN hyperparameter tuning, such as the number of layers, kernel size, learning rate, and activation function, on the accuracy of classi-fying various panel conditions, including physical damage, electrical defects, dust contamination, and clean pan-els. The use of CNNs is driven by their ability to automatically detect complex spatial patterns in images, which is crucial for accurately identifying different types of defects and anomalies on the panels. The main challenges include the variability in panel conditions and external factors, such as weather conditions, that affect data quali-ty. The influence of CNN hyperparameters on classification accuracy was analyzed, and their optimal values were determined to achieve high model accuracy. The relevance of the research lies in the growing role of CNNs in monitoring photovoltaic panel conditions, which enables timely defect detection and optimization of mainte-nance. The results demonstrate that proper hyperparameter tuning of CNNs significantly improves classification accuracy, contributing to increased efficiency and stability of photovoltaic stations. The paper emphasizes the importance of using neural networks for image analysis in the field of photovoltaic system maintenance, ensuring their effective integration into energy networks. The goal of the study is to improve the accuracy of photovoltaic panel condition classification by developing and tuning CNN models with optimal hyperparameters, enabling timely detection of panel defects and enhancing the efficiency of photovoltaic system maintenance.

Prediction of mass adhesive damage based on the Rousselier model: Experimental and numerical analysis

The study of the mechanical strength of adhesives remains an important area of research for researchers. These adhesives must be prepared in the form of mass test pieces to characterize them under different mechanical stresses. However, during the preparation of the test pieces several defects are likely to be present, namely air bubbles, cavities, or impurities. The behavior of the adhesive differs depending on the presence of one of these defects and, in most cases, the real behavior of the adhesive is not precisely known. For this purpose, several tests are necessary to have a close estimate of the adhesive's behavior. To numerically model the behavior of the adhesive it is necessary to consider the presence of these types of defects. This paper proposes a damage criterion based on the Rousselier model, which describes the damage due to crack growth from the presence of cavities in an adhesive, assumed as a ductile material. The proposed damage model was developed and implemented in a user-defined subroutine in the ABAQUS finite element code. Other damage models integrated into ABAQUS were used. In addition, the extended finite element method (XFEM) was used in the numerical simulations to study automatic damage modelling by the appearance and propagation of cracks in highly stressed areas. The main objective of this work is an analysis by the finite element method to determine the elastoplastic behavior coupled with the damage in the mass adhesive, considering the size, position, and shape of the defect (porosities) by the proposed models. Initially, experimental tests were carried out on mass specimens of adhesive to characterize the tensile response and to determine their mechanical properties depending on the position and size of the defect, which may exist in the specimen following its fabrication. The numerical results were validated by uniaxial tensile tests on the mass adhesive. Comparisons with the damage models integrated into ABAQUS have proven their effectiveness in predicting the behavior of the adhesive in the presence of a cavity.

Fatigue properties of a Ti–5Al–5Mo-5 V–3Cr alloy manufactured by electron beam powder bed fusion

AbstractAdditive manufacturing (AM) is a modern way of manufacturing structures, which tends to have fewer design limitations than those manufactured by conventional processes such as casting or forging. A combination of high-strength materials and small and complex structures opens up a wide range of potential applications, especially in the fields of medicine and aerospace. Titanium and its alloys show a very beneficial combination of density and mechanical properties. One of these alloys is the metastable β titanium alloy Ti– 5Al–5Mo–5 V–3Cr (Ti-5553), which is currently used mainly for large forged structures like landing gears of airplanes. In this study, for the first time the fatigue behavior of electron beam powder bed fused (PBF-EB) Ti-5553 was investigated with a focus on the defects created by the layer wise manufacturing. To understand the defect structure and its respective influence on the fatigue behavior, all specimens were scanned prior to fatigue testing using a state-of-the-art µ-focus CT. The specimens were subjected to two heat treatment procedures commonly used in technical applications, which were aiming for high strength (solution treated and aged—STA) as well as high ductility (beta annealed, slow cooled and aged—BASCA). Results indicate that the fatigue strength of PBF-EB manufactured Ti-5553 is significantly reduced compared to conventionally manufactured Ti-5553. The main reason for this are defects, which have varying critical effects depending on the heat treatment of the specimen and the defect size, shape, location and type.

Post‐Mortem Analysis of Building‐Integrated Flexible Thin Film Modules

ABSTRACTFlexible, lightweight thin film (TF) photovoltaic (PV) modules offer a unique opportunity for integration into non‐planar surfaces unable to support heavy weights. While such applications increase the potential for PV in urban areas, the reliability implications are yet to be investigated. Here, prototypes of corrugated rooftiles with integrated Cu (In,Ga)Se2 (CIGS) modules were investigated after 3 years of outdoor operation. Their performance before and after the outdoor exposure was compared and defects were localized. An unpackaging method was developed, allowing access to the solar cells for more detailed characterization of present defects without causing additional damage or changes to existing defects. To our knowledge, this was the first time such an unpackaging method was successfully applied to flexible TF PV modules. The relative efficiency loss ranged from 17% to 43%, mostly due to short‐circuit current (ISC) loss and series resistance (RS) increase. The predominant cause of the RS increase was the delamination at the interconnects, ascribed to thermomechanical stresses caused by outdoor temperature fluctuations. The ISC loss was mainly caused by localized delamination of CIGS from the molybdenum (Mo) back‐contact. The occurrence of such delaminated areas pointed to presence of high local stresses during outdoor operation, possibly due to thermal fluctuations, applied deformation and/or mechanical impact. Two other types of delamination defects were found with no observable impact on performance. These results show the necessity for further optimization in the material choice and processing of TF flexible modules, to avoid mechanical stress related failures upon integration into curved surfaces.

Vibration Analysis of a Centrifugal Pump with Healthy and Defective Impellers and Fault Detection Using Multi-Layer Perceptron

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while the second ones are associated with cavitation. It is possible to use computational algorithms to monitor both failures in CPs. In this study, two different problems in pumps, the defective impeller and cavitation, have been considered. When a CP is working in a faulty condition, it generates vibrations that can be measured using piezoelectric sensors. Collected data can be analyzed to extract time- and frequency-domain data. Interpreting the time-domain data showed that distinguishing the type of defect is not possible. However, indicators like kurtosis, skewness, mean, and variance can be used as input for the multi-layer perceptron (MLP) algorithm to classify pump faults. This study presents a detailed discussion of the vibration-based method outcomes, emphasizing the benefits and drawbacks of the multi-layer perceptron method. The results show that the suggested algorithm can identify the occurrence of different faults and quantify their severity during pump operation in real time.

A prior knowledge-embedded deformable convolutional network for vision-based fabric defect detection

The vision-based defect detection of textile surface is an essential problem in evaluating the appearance quality. In previous studies on vision-based defect detection, the following two points were neglected: (1) The proportion of defects on fabric is small, resulting in a low signal-to-noise ratio of defect images and a lack of sampling features; (2) the irregular shape of defects can overlap in position, leading to localization difficulty. In this paper, we propose a prior knowledge-embedded deformable convolutional network (PKE-DCNet) based on deep learning to address these two issues. First, a feature extraction method with prior information is designed with a shape-matching deep clustering module and a region-biased sampling module for detecting defects with complex shape. Then, a defect boundary boxes adaptive generation method is proposed with an anchor-free search mechanism and irregular edge contour computation to detect the key points in the region. Extensive experiments with mixed fabric defect datasets demonstrated that PKE-DCNet reached an overall mAP of 95.36% for six types of defects within a detection speed of 322 FPS, which was better than state-of-the-art methods.

Lattice Dynamics of Cu2ZnSn(S x ,Se1-x )4 Kesterite Thin-Film Solar Cells Studied by Nuclear Inelastic Scattering.

Phonons play a crucial role in thermalization and non-radiative recombination losses in semiconductors, impacting the power conversion efficiency of solar cells. To shed light on the lattice dynamics in Cu2ZnSn(S x ,Se1-x )4 (CZTSSe) thin-film solar cells and validate the extensive number of theoretical studies, we determine the 119Sn-partial phonon density of states (Sn-PDOS) by nuclear inelastic X-ray scattering. CZTSSe-based devices, one with near-stoichiometric and two with off-stoichiometric compositions, are investigated, and the results are correlated with the corresponding power conversion efficiencies (PCEs) of 3.2, 7.6, and 10.6%, respectively. Compared to the near-stoichiometric cell, the main Sn-PDOS peak of the off-stoichiometric cells broadens and slightly shifts to higher energy; this effect is correlated with the type and concentration of the characteristic defects in the studied samples. Furthermore, the Sn-PDOS of the 10.6% device is also obtained under operando (maximum power point) and open-circuit conditions. A comparison of the Sn-PDOS before and after the operando measurements suggests structural changes, likely due to the formation of metastable defects. In agreement with the theoretical studies, the Sn-PDOS of the CZTSSe absorber shows additional peaks compared to CZTSe attributed to coupling of Sn to the vibrations of Se and S atoms. This work paves the way for a further understanding of the lattice dynamics and subsequent enhancement of the PCEs of thin-film solar cells as well as other applied materials and devices containing elements that are Mössbauer-active and hence suitable for nuclear inelastic scattering.

ISETECH-Analysis Detection of Real-Time Metallic Surface Defect using MobileNetv2 and YOLOv3 on Raspberry Pi

This work presents an innovative solution utilizing a Raspberry Pi detection system to identify any defects on metallic surfaces in real-time. Manual inspection has several limitations, including time-consuming, subjective assessments, and a higher probability of human error could compromise product quality, lead to potential failures, and result in substantial costs for manufacturers. The primary focus of this endeavour is to enhance manufacturing efficiency and reduce labour expenses by automating the defect identification process. This objective is realized by employing the YOLOv3-tiny and MobileNetv2 algorithms which are subsequently deployed on a Raspberry Pi to enable precise and swift defect detection on metallic surfaces. The implementation process involves training and testing the models on a computer, followed by their deployment onto the Raspberry Pi. Upon proper setup, the trained models are employed for real-time inferences, effectively identifying defects. Notably, while the MobileNetv2 exhibits impressive accuracy in classifying defect types above 0.9, it is found to be less efficient for real-time detection on the Raspberry Pi. In contrast, the YOLO model proves to be well-suited for real-time detection on this platform with above probability of 0.8 for selected types of defects. The successful integration of this model significantly transforms quality control and inspection procedures across various industries.