The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges.

A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally, such a device is capable of continuous and reversible response and should not damage the sample. Nanosensor refers to a system in which at least one of the nanostructures is used to detect gases, chemicals, biological agents, electric fields, light, heat, etc. in its construction. The use of nanomaterials significantly increases the sensitivity of the system. In biosensors, the part of the system used to attach to the analyte and specifically detect it is a biological element (such as a DNA strand, antibody, enzyme, whole cell). The “Nano Biosensors” series reviews various types of biosensors and biochips (including an array of biosensors), emphasizing the role of nanostructures, developed for medical and biological applications. Nano Biosensors Electrochemical sensors are sensors that use the biological element as a diagnostic component and the electrode as a transducer. The use of nanostructures in these systems is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Given the nature of the biomaterial detection process, electrochemical biosensors are divided into catalytic and propulsion. Common electrochemical techniques common in sensors include potentiometric, chronometry, voltammetry, impedance measurement, and field effect transistor (FET). Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power. The use of nanostructures in these sensors is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Various types of nanostructures including nanoparticles, nanotubes and nanowires, nanopores, self-adhesive monolayers and nanocomposites can be used to improve the performance and efficiency of sensors in their structure. Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power.

Nano Biosensors: Properties, applications and electrochemical techniques

FPGAN: Face de-identification method with generative adversarial networks for social robots.

The burning of fossil fuels produces large amounts of exhaust gases containing carbon dioxide (CO2). The emission of CO2 into the atmosphere is widely known as the leading cause of global warming and climate change. The separation processes are responsible for capturing the CO2 to reduce its undesirable effects on the environment. Since the conventional processes have their drawbacks, it is crucial to find a more environment-friendly process for CO2 capture. Recently, ionic liquids (ILs) have become an interesting candidate for CO2 capture. In this study, the solubility of CO2 in the 1-n-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) is estimated using six different artificial intelligence (AI) techniques, including four artificial neural networks (ANN), support vector machines (LS-SVM), adaptive neuro-fuzzy interface system (ANFIS). The cascade feed-forward neural network has been found as the best model for the considered matter. This model predicts overall experimental datasets with excellent accuracy of AARD = 6.88%, MSE = 8 × 1 0 − 4 , and R 2 = 0 . 98808 . The maximum mole fraction of CO2 in the ionic liquid (i.e., 0.8) can be obtained at the highest pressure and the lowest temperature.

Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms

We adopted actual intelligent production requirements and proposed a tiny part defect detection method to obtain a stable and accurate real-time tiny part defect detection system and solve the problems of manually setting conveyor speed and industrial camera parameters in defect detection for factory products. First, we considered the important influences of the properties of tiny parts and the environmental parameters of a defect detection system on its stability. Second, we established a correlation model between the detection capability coefficient of the part system and the moving speed of the conveyor. Third, we proposed a defect detection algorithm for tiny parts that are based on a single short detector network (SSD) and deep learning. Finally, we combined an industrial real-time detection platform with the missed detection algorithm for mechanical parts based on intermediate variables to address the problem of missed detections. We used a 0.8 cm darning needle as the experimental object. The system defect detection accuracy was the highest when the speed of the conveyor belt was 7.67 m/min.

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Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

The complexity of the background and the similarities between different types of precision parts, especially in the high-speed movement of conveyor belts in complex industrial scenes, pose immense challenges to the object recognition of precision parts due to diversity in illumination. This study presents a real-time object recognition method for 0.8 cm darning needles and KR22 bearing machine parts under a complex industrial background. First, we propose an image data increase algorithm based on directional flip, and we establish two types of dataset, namely, real data and increased data. We focus on increasing recognition accuracy and reducing computation time, and we design a multilayer feature fusion network to obtain feature information. Subsequently, we propose an accurate method for classifying precision parts on the basis of non-maximal suppression, and then form an improved You Only Look Once (YOLO) V3 network. We implement this method and compare it with models in our real-time industrial object detection experimental platform. Finally, experiments on real and increased datasets show that the proposed method outperforms the YOLO V3 algorithm in terms of recognition accuracy and robustness.

Zheng Wang

Papers

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges.

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Real-Time Recognition Method for 0.8 cm Darning Needles and KR22 Bearings Based on Convolution Neural Networks and Data Increase