Quality Inspection Method Research Articles

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

Vision-based inspection of prefabricated components using camera poses: Addressing inherent limitations of image-based 3D reconstruction

Modular construction can lead to additional cost overruns and delays when a defect is found on the construction site and is not easily repairable. Researchers have developed various methods that use image-based 3D reconstruction for quality assessment, but they have inherent limitations, such as inconsistency and dealing with surfaces with reflectivity and limited visual features. Therefore, this paper presents a vision-based quality assessment method using cameras for prefabricated components by addressing these limitations. Specifically, this paper proposes a novel quality inspection method with sub-millimeter accuracy using cameras focused on leveraging camera poses (as opposed to 3D point clouds that are often not consistent in quality) from the image-based 3D reconstruction. The 3D point estimation by computing triangulation was used for achieving accurate measurement. The proposed method is validated using six different variances and two case studies – an aluminum pipe with a reflective surface and a fabricated concrete column. The results demonstrate the accuracy and effectiveness of the proposed method.

Real-time batch inspection system for surface defects on circular optical filters.

Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instrument's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.

Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network

Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2–1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

Hybrid-Learning-Based Operational Visual Quality Inspection for Edge-Computing-Enabled IoT System

Deep learning-enhanced Internet of Things (IoT) plays a pivot role in advancing the transformation toward smart manufacturing, and an essential component in many smart manufacturing IoT systems is the quality inspection. However, challenges, such as expensive data labeling, innumerable types of defects, and high costs for iterative optimization, hinder the industrial applicability of previous visual surface quality inspection methods. In this article, we present an edge-computing-enabled IoT system based on an innovative hybrid learning method for visual surface quality inspection using only few labeled data and minimum iterative optimization efforts. Our hybrid learning method first employs a deep neural network to synthesize global representations of real-world industrial images, which are subsequently analyzed via an unsupervised clustering algorithm for anomaly detection. Besides, enhancement strategies, such as fine-tuning and data augmentation, are proposed to improve the robustness against the noisy data set and support low-cost inference in multiple edge devices for manufacturing operation. On a holdout data set collected from real-world factories, our method achieves classification accuracies between 90% and 98%, outperforming the benchmark method by 7%–12%. Moreover, this hybrid learning method demonstrates the effectiveness in detecting new types of surface defects and achieves test recalls between 86% and 97%, outperforming the benchmark method by 11%–34%.

Installation Quality Inspection for High Formwork Using Terrestrial Laser Scanning Technology

Current inspection for installation quality of high formwork is conducted by site managers based on personal experience and intuition. This non-systematic inspection is laborious and it is difficult to provide accurate dimension measurements for high formwork. The study proposed a method that uses terrestrial laser scanning (TLS) technology to collect the full range measurements of a high formwork and develop a genetic algorithm (GA) optimized artificial neutral network (ANN) model to improve measurement accuracy. First, a small-scale high formwork model set was established in the lab for scanning. Then, the collected multi-scan data were registered in a common reference system, and RGB value and symmetry of the structure were used to extract poles and tubes of the model set, removing all irrelevant data. Third, all the cross points of poles and tubes were generated. Next, the model set positioned on the moving equipment was scanned at different specified locations in order to collect sufficient data to develop an GA-ANN model that can generate accurate estimates of the point coordinates so that the accuracy of the dimension measurements can be achieved at the millimetre level. Validation experiments were conducted both on another model set and a real high formwork. The successful applications suggest that the proposed method is superior to other common techniques for obtaining the required data necessary for accurately measuring the overall structure dimensions, regarding data accuracy, cost and time. The study proposed an effective method for installation quality inspection for high formwork, especially when the inspection cannot be properly operated due to cost factors associated with common inspection methods.

Research on Quality Inspection Method of Main Structure of Building Engineering

Research on Quality Inspection Method of Main Structure of Building Engineering

Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning

AbstractVisual inspection of photovoltaic modules using electroluminescence (EL) images is a common method of quality inspection. Because human inspection requires a lot of time, object detection algorithm to replace human inspection is a popular research direction in recent years. To solve the problem of low accuracy and slow speed in EL image detection, we propose a YOLO‐based object detection algorithm YOLO‐PV, which achieves 94.55% of AP (average precision) on the photovoltaic module EL image data set, and the interference speed exceeds 35 fps. The improvement of speed and accuracy benefits from the targeted design of the network architecture according to the characteristics of EL image. First, we weaken the backbone's ability to extract deep‐level information so that it can focus on extracting the low‐level defect information. Second, the PAN network is used for feature fusion in the Neck part. But, only the single‐size feature map output is retained, which significantly reduces the amount of calculation. Also, we analyze the impact of data enhancement methods on model overfitting and performance. Finally, we give effective data enhancement methods. The results show that the object detection algorithm in this paper can meet the requirements for high‐precision and real‐time processing on the PV module production line.

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

Assembly Quality Inspection of Combine Harvester Based on Whale Algorithm Optimization LSSVM

In order to detect the assembly quality of the combine harvester accurately and effectively, a method for the assembly quality inspection of the combine harvester based on the improved whale algorithm (IWOA) to optimize the least square support vector machine is proposed. Aiming at the characteristics of whale optimization algorithm’s weak search ability and easy maturity, this paper introduces the cosine control factor and the sine time-varying adaptive weight to improve it and uses the benchmark function to verify the general adaptability of the algorithm. Combined with the local mean decomposition (LMD), the assembly quality inspection model of the combine harvester was established and applied to the Dongfanghong 4LZ-9A2 combine harvester for experimental verification. The experimental results show that the IWOA proposed in this paper has better optimization ability and adaptability. The average accuracy of the IWOA model proposed in this paper reaches 90.5%, which is 4% higher than that of the WOA model, and the standard deviation of the average accuracy is reduced by 0.15%, which indicates that the IWOA model has better stability.

NDT model study of crown pear based on near infrared spectroscopy

With the growing area and production of pear, as well as the people for the fruit quality requirements are growing. The traditional methods for pear quality inspection are expensive and less accurate, and cannot be done for every pear. In response to the current production environment and market demand, a rapid and non-destructive method of pear quality testing is explored, using near infrared spectroscopy to establish the quality prediction model of crown pear. The main quality indicators for crown pears are soluble solids (SSC) and hardness, using NIR spectroscopy to establish a hardness prediction model and NIR spectroscopy to establish a soluble solids prediction model to build a spectral measurement system. The method was to measure pear spectral images in the NIR band (400nm to 1050nm). The spectra were pre-processed with multiple scattering correction (MSC) and standard normal variables exchange (SNV) to eliminate the influence of extraneous factors, and then downscaled by continuous projection algorithm (SPA) and principal component analysis (PCA) to extract the number of principal factors respectively. Partial Least Squares (PLS) was used to build the regression model and to predict the soluble solids (SSC) and hardness. The best model developed was a hardness prediction model by SNV + principal component analysis with a correlation coefficient of 0.64116 and a soluble solids prediction model by SNV + principal component analysis with a correlation coefficient of 0.73566.

Development of nondestructive testing technique for adhesively bonded materials based on thermographic transient temperature measurement after pulse heating and data analysis

Currently, the automobile industry is promoting weight reduction of car bodies by using high-strength thin steel sheets to improve fuel efficiency. On the other hand, since the rigidity of high-strength steel sheets alone is reduced, the weld bond method is attracting attention as a way to compensate for this. The weld bond method is a joining method that uses both adhesives and spot welding to improve the rigidity of steel plates while keeping them lightweight. The weld bond method can improve the rigidity only when the adhesive is applied to the required area. In this study, a new non-destructive inspection method using infrared thermography is developed as a high efficiency and high quality inspection method. The adhesive section is detected by measuring the difference in temperature fluctuation between the adhesive and non-adhesive section that occurs after heating the inspection target. However, the adhesive cannot be detected clearly with the time-series infrared data measured by temperature fluctuation. To solve this problem, we proposed PCA processing for time-series infrared data. The PCA process extracts each of the feature values present in the data as independent variables, and the changes in the feature values can be confirmed by images. It was found that the adhesive section, which cannot be confirmed by the infrared temperature image, can be clearly detected by the PCA processing.

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

Quality control and inspection methods have become a critical challenge in everyday situations of the engineering profession. This is due to the evolution of the materials used today in industry and also increasingly complex and critical nature of many of the products and structures produced with them. Ultrasonic measurement is widely used especially in oil and gas and aerospace industries. This method is used because it is effective and not involving damaging the original parts. In ultrasonic measurement there are few types of waves emitted and where one of it is Rayleigh wave or mostly known as surface wave. Surface waves are generated when longitudinal waves intersects a surface near to the second critical angle. This review paper will describe about the types of waves emitted and produce and also some of the research that has been done related to the surface wave. This research can contribute to green environment because it reduces waste by suggesting the uses of Perspex.

Quality Inspection Method and Application Analysis of Main Structure of Construction Engineering

Quality Inspection Method and Application Analysis of Main Structure of Construction Engineering

A new method for detecting heteromorphic workpiece brazing layer quality based on thermal probe

Traditional brazing quality inspection methods find it difficult to detect brazing layer defects on heteromorphic workpieces. Thus, a non-destructive testing technology based on a thermal probe is developed in this work. Scanning thermal resistance testing and analysis are carried out for two types of workpiece samples with different structures, and an evaluation calculation method is proposed to effectively characterize the brazing effect of the workpiece. By comparison with standard workpieces, qualified brazing layer products can be selected. In addition, the feasibility of this method is verified by ANSYS thermal simulations. In comparison with the x ray, it also has shown the superiority of this method. Experimental results show that this method can effectively evaluate the brazing layer quality of workpieces with heteromorphic and complex structures, and the reliability of the workpiece is further improved.

Motor brush assembly inspection system based on OpenMV vision technology

Motor brush assembly inspection is an important link in the industrial field. Traditional quality inspection methods have high errors, poor efficiency and low automation. To this end, the OpenMV machine vision module is used, through the MicroPython language, combined with Canny operator and Hough Transform (Hough Transform, Hough) algorithm. A program is designed to collect the image of the motor brush and process the data by itself. After the actual application in the factory, the detection effect is good, and the accuracy rate is more than 90%, which effectively improves the detection efficiency and meets the real-time quality inspection needs of the motor brushes.

Discussion on Lining Quality Inspection Method and NewTechnology ofSichuanTibet Railway Tunnel in Construction Period

Discussion on Lining Quality Inspection Method and NewTechnology ofSichuanTibet Railway Tunnel in Construction Period

QUALITY INSPECTION AND COMMON ISSUES ANALYSIS OF 10 METER RESOLUTION GLOBAL LAND COVER DATA

Abstract. In order to better carry out the environmental monitoring and resource protection, the 10 meter resolution global land cover data (hereinafter referred to as the GLC 10 data) came into being. The production mode of GLC 10 data is to use vector data, topographic map and other related reference data to get land cover based on digital orthophoto. GLC 10 data is a new type of remote sensing data and its classification system and classification index are also set according to the needs of a new project. Therefore, how to verify and control the quality of this kind of data is an urgent issue to be solved. According to the particularity of GLC 10 data and the new requirements of quality inspection technology, this paper puts forward a set of quality inspection contents and methods of GLC 10 data for large-scale production. And through the way of software automatic inspection combined with human-computer interaction, the inspection requirements are summarized one by one. Then, according to the actual quality inspection work from 2018 to 2020, the common quality issues of GLC 10 data are analyzed and sorted out, which can provide technical reference for the inspection and quality control of GLC 10 data.

Research on quality Inspection method of Topographic Map using oblique Photogrammetry

In view of the shortcomings of traditional methods in the quality inspection of large-scale topographic maps in Alpine area and high altitude areas, such as the difficulty of operation and the limited coverage of detection points, this paper puts forward the method of UAV tilt photogrammetry. This paper attempted to replace the traditional quality inspection method in the quality inspection of mathematical accuracy of large-scale topographic maps. By constructing the three-dimensional real scene model of the target area, this paper studied a new method for the quality inspection of large-scale topographic maps, which breaks through the fixed mode that the traditional quality inspection can only be checked by on-the-spot collection, further improve the accuracy and objectivity of the results quality inspection, intensive manpower and material resources, and improve the efficiency and quality of the quality inspection work. Comparing with the traditional check points collected in the field, the UAV tilt photogrammetry method can replace the traditional quality inspection method in the quality inspection of mathematical accuracy of large-scale topographic maps.