Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications

In this report, the research on welding defect detection and classification using radiograph images is presented. The first part of the report describes work on collection of digital radiograph images while the second part covers work on image processing and analysis using the collected images. The radiograph images from the Fuji DynamIX DynaView Workstation are custom-exported with the help of the NDT specialist. The collection of interpreted images is diverted from radiograph images captured using the old X-ray tube {Tube A) to the new X-ray tube (Tube B). Tube B images are needed to evaluate the performance of the developed defect detection algorithm under different radiography conditions. However, the total number of requested images remains approximately the same so that no extra workload is imposed to the NDT specialist. In the image processing stage, a flaw map, as described in the previous report, is used. Six welding defect types, namely Porosity{POR), Drop Through{DT), and Lack of Fusion{LOF), Lack of Penetration{LOP), Linear Indication{LI) and Undercut{UC), have been investigated. DT is detected using the background subtraction technique along with some heuristic rules as described in the previous report. For other detects, a set of image features including shape and texture information is extracted to characterize the welding defect flaws at the regions of interest (ROl). For POR, a series of sub-regions are further segmented in order to better represent the characteristics of POR at different locations in the ROl. To perform classification of the welding defects, an artificial intelligence (AI) technique, i.e., the Fuzzy ARTMAP (FAM) neural network, is applied. A series of experiments has been conducted by using the sample images collected from Tubes A and B. The overall performance is around 73% for accuracy, sensitivity, and specificity for both CF6-80 Connector Weld and Cover Weld programs. The only exception is that the sensitivity rate of the Connector Weld program stands around 63%. Further work will focus on ascertaining the stability of the FAM network in defect classification, as well as on improving the overall performance of the defect detection algorithms developed in this project. Il

Development Of A Computed Radiography-Based Weld Defect Detection And Classification System [RC78.7.D35 K75 2008 f rb].

Dalam penyelidikan ini, satu sistem bersepadu yang terdiri daripada satu peta kecacatan dan satu pengelas pelbagai rangkaian neural bagi peruasan, pengesanan dan pengesanan kecacatan kimpalan telah direkabentuk dan dibangun. In this research, an integrated system consisting of a flaw map and a multiple neural network classifier for weld defect segmentation, detection, and classification is designed and developed

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

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

Machine learning approach to thermite weld defects detection and classification.

Masters Degree. University of KwaZulu- Natal, Durban.The defects formed during the thermite welding process between two sections of rails require the welded joints to be inspected for quality purpose. The commonly used non-destructive method for inspection is Radiography testing. However, the detection and classification of various defects from the generated radiography imagesremains a costly, lengthy and subjective process as it is purely conducted manually by trained experts. It has been shown that most rail breaks occur due to a crack that initiated from the weld joint defect that was not detected. To meet the requirements of the modern technologies, the development of an automated detection and classification model is significantly demanded by the railway industry. This work presents a method based on image processing and machine learning techniques to automatically detect and classify welding defects. Radiography images are first enhanced using the Contrast Limited Adaptive Histogram Equalisation method; thereafter, the Chan-Vese Active Contour Model is applied to the enhanced images to segment and extract the weld joint as the Region of Interest from the image background. A comparative investigation between the Local Binary Patterns descriptor and the Bag of Visual Words approach with Speeded Up Robust Features descriptor was carried out for extracting features in the weld joint images. The effectiveness of the aforementioned feature extractors was evaluated using the Support Vector Machines, K-Nearest Neighbours and Naive Bayes classifiers. This study’s experimental results showed that the Bag of Visual Words approach when used with the Support Vector Machines classifier, achieves the best overall classification accuracy of 94.66%. The proposed method can be expanded in other industries where Radiography testing is used as the inspection tool

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast