scholarly journals Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

2020 ◽  
Vol 32 (10) ◽  
pp. 3429
Author(s):  
Chen-Chiung Hsieh ◽  
Ya-Wen Lin ◽  
Li-Hung Tsai ◽  
Wei-Hsin Huang ◽  
Shang-Lin Hsieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inspection System

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Automated defect inspection system for metal surfaces based on deep learning and data augmentation

2020 ◽  
Vol 55 ◽  
pp. 317-324 ◽  
Author(s):  
Jong Pil Yun ◽  
Woosang Crino Shin ◽  
Gyogwon Koo ◽  
Min Su Kim ◽  
Chungki Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Metal Surfaces ◽  
Data Augmentation ◽  
Inspection System ◽  
Defect Inspection

scholarly journals Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 8243
Author(s):  
Jung-Sing Jwo ◽  
Ching-Sheng Lin ◽  
Cheng-Hsiung Lee ◽  
Li Zhang ◽  
Sin-Ming Huang
Keyword(s):  
Deep Learning ◽  
Intelligent System ◽  
Working Hours ◽  
Inspection System ◽  
Practical Case ◽  
Assembly Quality ◽  
Press Fit ◽  
Manual Inspection ◽  
Testing Dataset ◽  
Traditional Manufacturing

Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

scholarly journals A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1895
Author(s):  
Donggyun Im ◽  
Sangkyu Lee ◽  
Homin Lee ◽  
Byungguan Yoon ◽  
Fayoung So ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Data Acquisition ◽  
Type I Error ◽  
Vision System ◽  
Structural Characteristics ◽  
Inspection System ◽  
Type I ◽  
Data Annotation ◽  
Background Removal

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

scholarly journals Real‐time comprehensive glass container inspection system based on deep learning framework

2019 ◽  
Vol 55 (3) ◽  
pp. 131-132 ◽  
Author(s):  
Qiaokang Liang ◽  
Shao Xiang ◽  
Jianyong Long ◽  
Wei Sun ◽  
Yaonan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Glass Container ◽  
Inspection System ◽  
Learning Framework ◽  
Container Inspection

scholarly journals Achieving remanufacturing inspection using deep learning

2020 ◽  
Author(s):  
Chigozie Nwankpa ◽  
Solomon Eze ◽  
Winifred Ijomah ◽  
Anthony Gachagan ◽  
Stephen Marshall
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithms ◽  
Inspection System ◽  
Valuable Insight ◽  
Validation Data ◽  
Learning Techniques ◽  
Starting Point ◽  
Wide Range ◽  
Preliminary Study

Abstract Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

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