Image Recognition of Shape Defects in Hot Steel Rolling

A frequently occurring issue in hot rolling of steel is so-called tail pinching. Prominent features of a pinched tail are ripple-like defects and a pointed tail. In this report two algorithms are presented to detect those features accurately in 2D gray scale images of steel strips. The two ripple detectors are based on the second order Gaussian derivative and the Gabor transform, a localized Fourier transform, yielding the so-called rippleness measures. Additionally a parameter called tail length is defined which indicates to what extent the overall shape of the tail deviates from an ideal rectangular shape. These methods are tested on images from the surface inspection system at Tata Hot Strip Mill 2 in IJmuiden, it is shown that by defining a simple criterion in the feature space spanned by these two parameters a given set of strips can correctly be classified into pinched and non-pinched strips. These promising results open the way for the development of an automatic pinch detection system

Design of online classifier for surface defect detection and classification of cold rolled steel coil

The target to be achieved through this project was primarily aimed at detecting the surface defects belonging to different classes in cold rolled steel coils. This was achieved through grabbing the images from the camera, here line scan camera is used which grabs 20 frames per second. Carrying out defect detection on these images and later classifying them. We present a method to automatically detect and localize defects occurring on the surface. Defect regions are segmented from background images using their distinguishing texture characteristics. This method locates candidate defect regions directly in the DCT (Discrete cosine transform) domain using the intensity variation information encoded in the DCT coefficients. More precisely, defect detection employs DCT analysis of each individual non-overlapping region of the image to determine potentially defective blocks, which are further grown and merged to form a defect region on the image. In this thesis a computer vision based, a framework for steel surface defects detection and classification of cold rolled steel strips is implemented. We have designed online classifier for automatic defect detection and classification of defects. In this we measured statistical textural features using gray level co-occurrence matrix presented by Haralick and geometrical features are also calculated. The final decision SVM (Support Vector Machine) handles the problem of classification of the defect types. We also proposed SVM voting strategy for the final decision that handles the problem of multiple outputs of a given input image with a specific defect type. In addition, this approach improves the classification performance. Experimental results demonstrate the effectiveness of the proposed method on steel surface defects detection and classification. In addition, the defect information is encoded in the image. An image viewer application is designed for decoding the defect information

Novel methods of object recognition and fault detection applied to non-destructive testing of rail’s surface during production

A series of rail image inspection algorithms have been developed for Tata Steels Scunthorpe rail production line. The following thesis describes the contributions made by the author in the design and application of these algorithms. A fully automated rail inspection system that has never been implemented before in any such company or setup has been developed. An industrial computer vision system (JLI) already exists for the image acquisition of rails during production at a rail manufacturing plant in Scunthorpe. An automated inspection system using the same JLI vision system has been developed for the detection of rail‟s surface defects during manufacturing process. This is to complement the human factor by developing a fully automated image processing based system to recognize the faults with an improved efficiency and to allow an exhaustive detection on the entire rail in production. A set of bespoke algorithms has been developed from a plethora of available image processing techniques to extract and identify components in an image of rail in order to detect abnormalities. This has been achieved through offline processing of the rail images using the blended use of different object recognition and image processing techniques, in particular, variation of standard image processing techniques. Several edge detection methods as well as adapted well known Artificial Neural Network and Principal Component Analysis techniques for fault detection on rail have been developed. A combination of customised existing image algorithms and newly developed algorithms have been put together to perform the efficient defect detection. The developed system is fast, reliable and efficient for detection of unique artefacts occurring on the rail surface during production followed by fault classification on the rail imaging system. Extensive testing shows that the defect detection techniques developed for automated rail inspection is capable of detecting more than 90% of the defects present in the available data set of rail images, which has more than 100,000 images under investigation. This demonstrates the efficiency and accuracy of the algorithms developed in this work

Fabric Defect Detection with Deep Learning and False Negative Reduction

Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative - FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple.publishersversionpublishe

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Proceedings of the 84th European Study Group Mathematics with Industry (SWI 2012), Eindhoven, January 30 - February 3, 2012

Introduction There are a few welldefined moments when mathematicians can get in contact with relevant unsolved problems proposed by the industry. One such a moment is the socalled "Study Group". The concept of the Study Group is rather simple and quite efficient: A group of mathematicians (of very different expertise) work together for one week. As a rule, on a Monday the industrial problems are presented by their owners, then few research groups selforganize around the proposed problems and work intensively until Friday, when the main findings are presented. The insight obtained via mathematical modeling together with the transfer of suitable mathematical technology usually lead the groups to adequate approximate solutions. As a direct consequence of this fact, the problem owners often decide to benefit more from such knowledge transfer and suggest related followup projects. In the period January 31– February 3, 2012, it was the turn of the Department of Mathematics and Computer Science of the Eindhoven University of Technology to organize and to host the "Studiegroep Wiskunde met de Industrie/Study Group Mathematics with the Industry" (shortly: SWI 2012, but also referred to as ESG 84, or as the 84th European Study Group with Industry). This was the occasion when about 80 mathematicians enjoyed working on six problems. Most of the participants were coming from a Dutch university, while a few were from abroad (e.g. from UK, Germany, France, India, Russia, Georgia, Turkey, India, and Sri Lanka). The open industrial problems were proposed by Endinet, Philips Lighting, Thales, Marin, Tata Steel, and Bartels Engineering. Their solutions are shown in this proceedings. They combine ingenious mathematical modeling with specific mathematical tools like geometric algorithms, combinatorial optimization of networks, identification of parameters and model structures, probability theory, and statistical data analysis. It is worth mentioning that this scientific proceedings is accompanied by a popular proceedings, written by Ionica Smeets, containing layman’s descriptions of the proposed problems and of the corresponding results

Proceedings of the 84th European Study Group Mathematics with Industry (SWI 2012), Eindhoven, January 30 - February 3, 2012

Introduction There are a few welldefined moments when mathematicians can get in contact with relevant unsolved problems proposed by the industry. One such a moment is the socalled "Study Group". The concept of the Study Group is rather simple and quite efficient: A group of mathematicians (of very different expertise) work together for one week. As a rule, on a Monday the industrial problems are presented by their owners, then few research groups selforganize around the proposed problems and work intensively until Friday, when the main findings are presented. The insight obtained via mathematical modeling together with the transfer of suitable mathematical technology usually lead the groups to adequate approximate solutions. As a direct consequence of this fact, the problem owners often decide to benefit more from such knowledge transfer and suggest related followup projects. In the period January 31– February 3, 2012, it was the turn of the Department of Mathematics and Computer Science of the Eindhoven University of Technology to organize and to host the "Studiegroep Wiskunde met de Industrie/Study Group Mathematics with the Industry" (shortly: SWI 2012, but also referred to as ESG 84, or as the 84th European Study Group with Industry). This was the occasion when about 80 mathematicians enjoyed working on six problems. Most of the participants were coming from a Dutch university, while a few were from abroad (e.g. from UK, Germany, France, India, Russia, Georgia, Turkey, India, and Sri Lanka). The open industrial problems were proposed by Endinet, Philips Lighting, Thales, Marin, Tata Steel, and Bartels Engineering. Their solutions are shown in this proceedings. They combine ingenious mathematical modeling with specific mathematical tools like geometric algorithms, combinatorial optimization of networks, identification of parameters and model structures, probability theory, and statistical data analysis. It is worth mentioning that this scientific proceedings is accompanied by a popular proceedings, written by Ionica Smeets, containing layman’s descriptions of the proposed problems and of the corresponding results

Automatic surface defect quantification in 3D

Three-dimensional (3D) non-contact optical methods for surface inspection are of significant interest to many industrial sectors. Many aspects of manufacturing processes have become fully automated resulting in high production volumes. However, this is not necessarily the case for surface defect inspection. Existing human visual analysis of surface defects is qualitative and subject to varying interpretation. Automated 3D non-contact analysis should provide a robust and systematic quantitative approach. However, different 3D optical measurement technologies use different physical principles, interact with surfaces and defects in diverse ways, leading to variation in measurement data. Instrument s native software processing of the data may be non-traceable in nature, leading to significant uncertainty about data quantisation. Sub-millimetric level surface defect artefacts have been created using Rockwell and Vickers hardness testing equipment on various substrates. Four different non-contact surface measurement instruments (Alicona InfiniteFocus G4, Zygo NewView 5000, GFM MikroCAD Lite and Heliotis H3) have been utilized to measure different defect artefacts. The four different 3D optical instruments are evaluated by calibrated step-height created using slipgauges and reference defect artefacts. The experimental results are compared to select the most suitable instrument capable of measuring surface defects in robust manner. This research has identified a need for an automatic tool to quantify surface defect and thus a mathematical solution has been implemented for automatic defect detection and quantification (depth, area and volume) in 3D. A simulated defect softgauge with a known geometry has been developed in order to verify the implemented algorithm and provide mathematical traceability. The implemented algorithm has been identified as a traceable, highly repeatable, and high speed solution to quantify surface defect in 3D. Various industrial components with suspicious features and solder joints on PCB are measured and quantified in order to demonstrate applicability

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics