Defect Inspection Technology for Steel Truss Suspension Bridges

Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension bridge,it is necessary to inspect for defects promptly,understand the cause of the defect,and locate it through the use of inspection technology.By promptly addressing defects,the suspension bridge’s safety can be ensured.The author has analyzed the common defects and causes of steel truss suspension bridges and proposed specific inspection technologies.This research is intended to aid in the timely discovery of steel truss suspension bridge defects.

A new method for specular curved surface defect inspection based on reflected pattern integrity

Defect inspection of specular curved surface is a challenging job. Taking steel balls for example, a new method based on reflected pattern integrity recognition is put forward. The specular steel ball surface will totally reflect the patterns when it is placed inside a dome-shaped light source, whose inner wall is modified by patterns with certain regular. Distortion or intermittence of reflected pattern will occur at the defective part, which indicates the pattern has lost its integrity. Based on the integrity analysis of reflected pattern images？ surface defects can be revealed. In this paper, a set of concentric circles are used as the pattern and an image processing algorithm is customized to extract the surface defects. Results show that the proposed method is effective for the specular curved surface defect inspection

Optical wafer defect inspection at the 10 nm technology node and beyond

The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond.Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas:(a) the defect detectability evaluation,(b) the diverse optical inspection systems,and(c) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.

Realtime Vision-Based Surface Defect Inspection of Steel Balls

In the proposed system for online inspection of steel balls, a diffuse illumination is developed to enhance defect appearances and produce high quality images. To fully view the entire sphere, a novel unfolding method is put forward based on geometrical analysis, which only requires one-dimensional movement of the balls and a pair of cameras to capture images from different directions. Moreover, a realtime inspection algorithm is customized to improve both accuracy and efficiency. The precision and recall of the sample set were 87.7% and 98%, respectively. The average time cost on image processing and analysis for a steel ball was 47 ms, and the total time cost was less than 200 ms plus the cost of image acquisition and balls' movement. The system can sort 18 000 balls per hour with a spatial resolution higher than 0.01 mm.

Defect inspection technologies for additive manufacturing

Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity,internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.

Research on automatic inspection system for defects on precise optical surface based on machine vision

In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

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.

Automatic Fabric Defects Inspection Machine

The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on eye inspection.Famously,human visual assessment is drawn-out,tiring,and an exhausting errand,including perception,consideration and experience to recognize the fault occurrence.The precision of human visual assessment declines with dull positions and vast schedules.Some of the time slow,costly,and sporadic review is the outcome.In this manner,the programmed automatic visual review safeguards both the fabric quality inspector and the quality.This examination has exhibited that Textile Defect Recognition System is fit for distinguishing fabrics’imperfections with endorsed exactness with viability.With some products 100%inspection is important to ensure the stipulated quality or standard.The classifications for the automated fabric inspection approaches are expanding as the work is vast and complex.According to the algorithm used,the texture analysis problem is classified into different approaches.They are Structural,spectral,model-based methods,Unfortunately,the optimal plan does not yet exist for these vast numbers of applied methods,as each of them has some advantages and disadvantages.

Rail fastener defect inspection method for multi railways based on machine vision

Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.

Intelligent Camera for Surface Defect Inspection

An intelligent camera for surface defect inspection is presented which can pre-process the surface image of a rolled strip and pick defective areas out at a spead of 1 600 meters per minute. The camera is made up of a high speed line CCD, a 60 Mb/s CCD digitizer with correlated double sampling function, and a field programmable gate array(FPGA), which can quickly distinguish defective areas using a perceptron embedded in FPGA thus the data to be further processed would dramatically be reduced. Some experiments show that the camera can meet high producing speed, and reduce cost and complexity of automation surface inspection systems.

The Development of a Smart Mobile App for Building Façade Defects Inspections

The manual visual inspections of façade building defects are posing a high and increasing cost for building asset managers,particularly when inspections delay projects or require asset outages,visits to decommissioned sites or work within hazardous environments.This paper reports on the development,testing and delivery of a working mobile app prototype to facilitate the inspections and documentation of building facade condition monitoring.The work presented builds upon the development of an online platform for remote building inspection based on the integration of methodologies and tools,including VR(virtual reality),and digital photogrammetry to collect real-time data that support automated decision making.The mobile app:(i)allows the user to import 3D models and 2D building plans;(ii)provides the means of first-person exploration of models via a VR headset;and(iii)captures,records and catalogues images of façade defect types,and the date and time.An inspection case study was used to demonstrate and evaluate the mobile app prototype.The Building Inspector app allows building professionals to manage inspections and to track past and ongoing monitoring of the condition of building façades.

High Precision Ultrasonic Guided Wave Technique for Inspection of Power Transmission Line

Due to the merits of high inspection speed and long detecting distance, Ultrasonic Guided Wave（UGW） method has been commonly applied to the on-line maintenance of power transmission line. However, the guided wave propagation in this structure is very complicated, leading to the unfavorable defect localization accuracy. Aiming at this situation, a high precision UGW technique for inspection of local surface defect in power transmission line is proposed. The technique is realized by adopting a novel segmental piezoelectric ring transducer and transducer mounting scheme, combining with the comprehensive characterization of wave propagation and circumferential defect positioning with multiple piezoelectric elements. Firstly, the propagation path of guided waves in the multi-wires of transmission line under the proposed technique condition is investigated experimentally. Next, the wave velocities are calculated by dispersion curves and experiment test respectively, and from comparing of the two results, the guided wave mode propagated in transmission line is confirmed to be F（1,1） mode. Finally, the axial and circumferential positioning of local defective wires in transmission line are both achieved, by using multiple piezoelectric elements to surround the stands and send elastic waves into every single wire. The proposed research can play a role of guiding the development of highly effective UGW method and detecting system for multi-wire transmission line.

Application of automatic surface inspection system in cold-rolled strip production

The detection and classification of real-time surface defects play an important role in automotive sheet inspection and production. In this paper, an automatic surface inspection system （ASIS） based on signal processing in Baosteel NO. 4 cold-rolled plant is briefly presented. We demonstrate that the strip surface defect properties such as image, type, pitch, and position can be accurately calculated and classified by the automatic surface inspection system. In the manufacturing of the high-quality cold-rolled strips, it is necessary that the real-time surface defects can be detected and transferred by the automatic surface inspection system combined with annealing lines and recoiling lines.

Wafer bin map inspection based on DenseNet

Wafer bin map(WBM)inspection is a critical approach for evaluating the semiconductor manufacturing process.An excellent inspection algorithm can improve the production efficiency and yield.This paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model,the structure and training loss function are improved according to the characteristics of the WBM.In addition,a constrained mean filtering algorithm is proposed to filter the noise grains.In model prediction,an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model decision.The experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect patterns.Analyzing the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

Backlit Keyboard Inspection Using Machine Vision

A robust system for backlit keyboard inspection is revealed. The backlit keyboard not only has changeable diverse colors but also has the laser marking keys. The keys on the keyboard can be divided into regions of function keys, normal keys, and number keys. However, there might have some types of defects： incorrect illuminating area, non-uniform illumination of specified inspection region（IR）, and incorrect luminance and intensity of individual key. Since the illumination features of backlit keyboard are too complex to inspect for human inspector in the production line, an auto-mated inspection system for the backlit keyboard is proposed in this paper. The system was designed into the operation module and inspection module. A set of image processing methods were developed for these defects inspection. Some experimental results demonstrate the robustness and effectiveness of the proposed system.

Development of an automatic weld surface appearance inspection system using machine vision

In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.

A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

Inspection of pipeline girth welds with ultrasonic phased array technique

A novel automatic ultrasonic system used for the inspection of pipeline girth welds is developed, in which a linear phased array transducer using electronic scan is adopted. Optimal array parameters are determined based on a mathematical model of acoustic field for linear phased army derived from Huygens＇ principle. The testing method and the system structure are introduced. The experimental results show that the phased array transducer system has the same detectability as that of conventional ultrasonic transducer system, but the system architecture can be simplified greatly, and the testing flexibility and the testing speed can be improved greatly.

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.