Quality Inspection System Research Articles

Development of Automated 3D LiDAR System for Dimensional Quality Inspection of Prefabricated Concrete Elements

The dimensional quality inspection of prefabricated concrete (PC) elements is crucial for ensuring overall assembly quality and enhancing on-site construction efficiency. However, current practices remain heavily reliant on manual inspection, which results in high operator dependency and low efficiency. Existing Light Detection and Ranging (LiDAR)-based methods also require skilled professionals for scanning and subsequent point cloud processing, thereby presenting technical challenges. This study developed a 3D LiDAR system for the automatic identification and measurement of the dimensional quality of PC elements. The system consists of (1) a hardware system integrated with camera and LiDAR components to acquire 3D point cloud data and (2) a user-friendly graphical user interface (GUI) software system incorporating a series of algorithms for automated point cloud processing using PyQt5. Field experiments comparing the system’s measurements with manual measurements on prefabricated bridge columns demonstrated that the system’s average measurement error was approximately 5 mm. The developed system can provide a quick, accurate, and automated inspection tool for dimensional quality assessment of PC elements, thereby enhancing on-site construction efficiency.

Design and Implementation of Quality Inspection System for Image on National Platform for Geospatial Information Services in China

Abstract. This article is based on the data update characteristics of the online service for National Platform for Geospatial Information Services (TIANDITU) imagery data, and it designs and implements a full-process imagery data quality inspection system specifically for imagery tile data. The development and application of this system have made the quality inspection and update process of TIANDITU imagery data more scientific and efficient, significantly enhancing the efficiency of the quality inspection and update process for TIANDITU imagery data. The article provides a detailed introduction to the system’s architecture, design, implementation, characteristics and application. It also proposes optimizations to be made in algorithm optimization, user interface, and system stability. This article provides new ideas for quality inspection and publishing based on image tile data.

Development and Design of an Online Quality Inspection System for Electric Car Seats.

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats.

A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines

Visual sensing has been widely adopted for quality inspection in production processes. This article presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system that leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system that additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction compared with the manual inspection approach.

Optimization of reliability and speed of the end-of-line quality inspection of electric motors using machine learning

Consistently maintaining high-end product quality in the production process is challenging. End-quality inspection must be highly sensitive to detect even minimal deviations, while being fast and accurate. However, quality inspection systems often face calibration intricacies, are time-consuming, and rely heavily on expert knowledge. They handle substantial data flows and inspect numerous features, some of which contribute minimally to the final grade. To address these challenges, the paper proposes employing statistically supervised machine learning methods for classification. Decision trees, Random forests, Bagging, and Gradient boosting classifiers are recommended for feature selection and accurate diagnosis, particularly for electric motor classification. By utilizing the feature importance attribute for feature selection, the proposed approach compares model accuracies, reducing rampup and commission times significantly. The study found that all suggested classifiers achieved high accuracy in classifying electric motors in end-of-line quality inspection system. Moreover, they effectively reduced the number of features and optimize database operations. Utilizing a reduced feature set streamlined diagnostic algorithms, accelerated learning, and improved model interpretability, enhancing overall efficiency and comprehension. Furthermore, analysing the feature importance attribute could simplify diagnostic hardware and expedite quality inspection by eliminating unnecessary steps. Newly generated models can also verify expert decisions on feature selection and limit adjustments, enhancing efficiency in production processes.

SISTEM DETEKSI MUTU TELUR AYAM RAS MENGGUNAKAN SENSOR LDR (LIGHT DEPENDENT RESISTOR) BERBASIS ARDUINO UNO

Eggs are a food ingredient with excellent nutritional value, but their quality can deteriorate when stored for a long time. As consumers, it is crucial to be cautious in determining the quality of eggs to be consumed, as eggs may contain harmful pathogens that can cause diseases. The traditional manual detection process, typically done using a flashlight and water, is time-consuming and not always accurate. To address this issue, there is a need for an accurate egg quality inspection system to ensure that the eggs consumed are in a suitable condition. Based on this problem, an automated egg quality detection device has been developed using a Light Dependent Resistor (LDR) sensor. The device is composed of an Arduino Uno as the main microcontroller. When the LDR sensor detects light intensity emitted from the egg with a value <600, the egg is considered good; conversely, if the LDR value is >600, the egg is deemed bad. The output of the detection process is displayed on an LCD screen, accompanied by indicator lights. This device not only aids consumers in selecting high-quality eggs but also provides advantages for the egg industry. The implementation of this technology can enhance the efficiency of the mass egg selection process, reducing the risk of spreading diseases present in substandard eggs.

In-situ quality inspection system of injection parts based on transfer learning

PurposeThis paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.Design/methodology/approachThe proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.FindingsUsing the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.Originality/valueThe proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture

Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture

Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills. According to the quality inspection process, a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded. Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming. The main goal of this work is to develop an automated quality inspection framework to differentiate between ‘pass’ and ‘fail’ categories based on baby corn images. A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models. Particularly, Convolutional neural networks, specific sub-types of deep learning models, were implemented. Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared. A Shapley additive explanations (SHAP) framework was further utilized for network interpretation purposes. The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06% accuracy as the best performance against 95.28% obtained from traditional image processing. Incorporating a region of interest identification, several model experiments, data application on baby corn images, and the SHAP framework are our main contributions. Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.

Predicting Defect Rates of Printed Circuit Board Assemblies: Towards Zero Defect Manufacturing and Zero-Maintenance Strategies

Printed Circuit Boards (PCB) manufacturing is a critical part of volatile supply chains for a wide variety of products and high value assets. PCBs are expected to exhibit zero defects and be subject to zero-maintenance. However low the defect rates, defects are highly disruptive and costly. Such defects can be introduced by a multitude of reasons, including faulty parts or sub-standard manufacturing processes. While sophisticated and dedicated quality inspection systems are typically in place in production environments, they still leave room for erroneous quality control outcomes. Besides in-line or post-production quality inspection, manufacturers can exploit experience gained from historical records of past inspections to predict future defect rates. This paper presents the development of a predictive quality modelling approach, which capitalises on such historical data and domain knowledge, to predict defect rates in new production orders. Employing appropriate encoding of knowledge through data pre-processing and applying regression type of machine learning, the proposed approach is validated on a real case study from an electronics manufacturing company. The developed approach can positively contribute towards optimising consequent maintenance and warranty services and become part of a zero-defect production strategy.

Learning-based defect inspection of laptop covers with a laser profile sensor

Quality inspection is a crucial process in the manufacturing industry, which significantly impacts the productivity and profits of a factory. To reduce costs and enhance quality control, companies are increasingly driving automation of this process due to the limitations of manual labour. However, the lack of flexible solutions that can adapt to the changing environment in factories has been a challenge. Computer-vision-based inspection methods often require hefty mechanisms and strict lighting conditions, which limit the investment in such solutions. Furthermore, with increasing demands for precision, traditional algorithms are struggling to cope with the problem, while data-driven solutions like deep learning require significant effort in acquiring and labelling data. To address these problems, this paper proposes an eye-in-hand system, i.e., a sensor attached to a robotic manipulator, integrated with unsupervised learning algorithms. One typical product, laptop bottom covers, is tested and discussed with regard to the system to validate our method since it has tiny defects and light-absorbing coatings. With only nine samples of the product, our system solved the case and demonstrated high-precision results and a flexible workflow. In this paper, our main contributions are: 1) we propose a flexible quality inspection system and workflow that needs extremely small volumes of data with unsupervised learning algorithms; and 2) we first propose using a laser profile sensor for collecting 2D intensity images, that are resilient to ambient light disturbance, of matt laptop cover data without lighting equipment.

Optimizing efficiency and zero-defect manufacturing with in-process inspection: challenges, benefits, and aerospace application

In this paper, we present a comprehensive study on the implementation of machine vision-enabled in-process quality inspection systems in machining operations. Our objective is to enable zero-defect manufacturing by maximizing efficiency and effectiveness in production processes. We utilize machine vision technology as a transformative tool, playing a vital role in our comparative analysis which reveals its superiority over traditional in-situ approaches in both ideal and challenging real-world scenarios. Through simulation, we demonstrate how machine vision improves the performance of in-line process systems. We also discuss substantial challenges of implementation, such as managing environmental contamination, optimizing machine coordination, accommodating a range of part sizes, and configuring effective coolant delivery systems. Our in-depth analysis of essential factors includes the robustness of machine vision equipment, operator training for machine vision technology, and cost-benefit analysis of its implementation. The research emphasizes machine vision's crucial role in transforming manufacturing setups and enhancing advanced automation systems. The study underscores the immense potential of machine vision in in-process quality inspection to significantly reduce production costs and time, fostering higher manufacturing sustainability and competitive advantage in the era of Industry 4.0.

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications.

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product's properties, it lowers customers' confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.

APPLICATION OF THE RED PITAYA PLATFORM IN THE TECHNOLOGY OF PROTOTYPING AUTOMATED EDDY CURRENT TESTING SYSTEMS

Automated eddy current non-destructive testing has an important role in the quality inspection system for products made of conductive materials both at the production stage and during their operation. The appearance of novel alloys, the complexity of product geometry and inspection conditions, and the increased requirements for the reliability and accuracy of inspection results stimulate the acceleration process of creating new eddy current inspection tools. The technology of prototyping makes it possible to solve this problem. The purpose of the article is to review the functionality and technical characteristics of Red Pitaya for use in the technology of prototyping eddy current inspection, as well as to create a prototype architecture of an automated eddy current inspection system based on it.The choice of the Red Pitaya platform as a convenient tool for prototyping automated systems was made due to its functionality, reliability, and availability. In general, the article highlights the advantages and prospects of using the Red Pitaya platform in the prototyping of automated eddy current non-destructive testing systems. The aspects of the Red Pitaya hardware and its capabilities for expanding the functionality, which allows to create highly efficient and flexible control systems, are considered in detail. The compatibility of Red Pitaya with other devices and accessories is considered, which significantly increases its versatility and adaptability to various requirements and applications.The article highlights the importance of using the Jupyter Notebook application in combination with Red Pitaya as a convenient tool for software development. This helps to facilitate the process of creating and programmatically implementing complex signal processing algorithms in eddy current control systems, and provides flexibility in creating new software products.Modern trends and prospects for the development of eddy current testing, market needs, and the ability to adapt to different application scenarios are taken into account in the proposed architecture of the prototype automated eddy current testing system.

Automatic Fruit Quality Inspection System Using Image Processing

Automatic Fruit Quality Inspection System Using Image Processing

An intelligent remote sensing image quality inspection system

AbstractDue to the inevitable presence of quality problems, quality inspection of remote sensing images is indeed an indispensable step between the acquisition and the application of them. However, traditional manual inspection suffers from low efficiency. Hence, we propose a novel deep learning‐based two‐step intelligent system consisting of multiple advanced computer vision models, which first performs image classification by SwinV2 and then accordingly adopts the most appropriate method, such as semantic segmentation by Segformer, to localize the quality problems. Results demonstrate that the proposed method exhibits excellent performance and efficiency, surpassing traditional methods. Furthermore, an initial exploration of applying multimodal models to remote sensing image quality inspection is conducted.

Deep Network-Assisted Quality Inspection of Laser Welding on Power Battery.

Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a two-branch network and conventional image processing is proposed to predict welding quality while outputting corresponding parameter information. The two-branch network consists of a segmentation network and a classification network, which alleviates the problem of large training sample size requirements for deep learning by sharing feature representations among two related tasks. Moreover, coordinate attention is introduced into feature learning modules of the network to effectively capture the subtle features of defective welds. Finally, a post-processing method based on the Hough transform is used to extract the information of the segmented weld region. Extensive experiments demonstrate that the proposed model can achieve a significant classification performance on the dataset collected on an actual production line. This study provides a valuable reference for an intelligent quality inspection system in the power battery manufacturing industry.

SolderNet: Towards trustworthy visual inspection of solder joints in electronics manufacturing using explainable artificial intelligence

AbstractIn electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time‐consuming and error‐prone process. To improve both inspection efficiency and accuracy, in this work, we describe an explainable deep learning‐based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet that we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.

Implementation and Design of Metro Process Quality Inspection System based on Image Processing Technology

In recent years, China’s subway industry has entered a phase of rapid development, and at the same time, the commissioning of large-scale new lines has brought huge challenges to production and operation. In the quality inspection system of subway process, the traditional way is to use human as the carrier of quality inspection, but with the input of large-scale production lines, the manual inspection method is far from meeting the needs of industrial production. Based on the above background, this paper applies image processing technology to the quality inspection process of subway process, and completes the design and implementation of the quality inspection system of subway process based on image processing. In this paper, a lightweight feature-enhanced convolutional neural network model is proposed under the premise of ensuring efficiency. The model deepens the structure of the backbone feature extraction network, adds another prediction scale layer, realizes three-scale prediction, and enhances the semantic information of the shallow feature map, thus improving the detection accuracy of small-scale defects. At the same time, the algorithm has room for optimization and improvement in “identification of anti-loosening wire” and “accurate positioning of through-hole gaskets and washers without missing”.

The product quality inspection scheme based on software-defined edge intelligent controller in industrial internet of things

The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic-based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to product diversity and different industrial scenarios.