Optical In-Process Measurement Systems

Information is key, which means that measurements are key. For this reason, this book provides unique insight into state-of-the-art research works regarding optical measurement systems. Optical systems are fast and precise, and the ongoing challenge is to enable optical principles for in-process measurements. Presented within this book is a selection of promising optical measurement approaches for real-world applications

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

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens

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

Hybrid/tandem laser-arc welding of thick low carbon martensitic stainless steel plates

High efficiency and long-term life of hydraulic turbines and their assemblies are of utmost importance for the hydropower industry. Usually, hydroelectric turbine components are made of thick-walled low carbon martensitic stainless steels. The assembly of large hydroelectric turbine components has been a great challenge. The use of conventional welding processes involves typical large groove design and multi-pass welding to fill the groove which exposes the weld to a high heat input creating relatively large fusion zone and heat affected zone. The newly-developed hybrid/tandem laser-arc welding technique is believed to offer a highly competitive solution to improve the overall hydro-turbine performance by combining the high energy density and fast welding speed of the laser welding technology with the good gap bridging and feeding ability of the gas metal arc welding process to increase the productivity and reduce the consumable material. The main objective of this research work is to understand different challenges appearing during hybrid laser-arc welding (HLAW) of thick gauge assemblies of low carbon 13%Cr-4%Ni martensitic stainless steel and find a practical solution by adapting and optimizing this relatively new welding process in order to reduce the number of welding passes necessary to fill the groove gap. The joint integrity was evaluated in terms of microstructure, defects and mechanical properties in both as-welded and post-welded conditions. A special focus was given to the hybrid and tandem laser-arc welding technique for the root pass. Based on the thickness of the low carbon martensitic stainless steel plates, this work is mainly focused on the following two tasks: • Single pass hybrid laser-arc welding of 10-mm thick low carbon martensitic stainless steel. • Multi-pass hybrid/tandem laser-arc welding of 25-mm thick martensitic stainless steel

Laser welding of metallic glass to crystalline metal in laser-foil-printing additive manufacturing

The application of metallic glasses has been traditionally limited to parts with small dimensions and simple geometries, due to the requirement of fast cooling during the conventional process of casting. In addition, joining metallic glass to crystalline metal is of interest for many applications that require locally tailored functions and properties, but it is challenging. This research describes a promising additive manufacturing technology, i.e., laser-foil-printing, to make high-quality metallic glass parts with large dimensions and complex geometries and to fabricate multi-material components from metallic glass and crystalline metal. In this research, Zr52.5Ti5Al10Ni14.6Cu17.9 metallic glass parts are fabricated on different crystalline metal substrates, including pure Zr metal, Ti-6Al-4V alloy, and 304L stainless steel. The dissimilar bonding between the metallic glass part and the crystalline metal substrate is studied and then improved through the use of appropriate intermediate layers. The microstructure and properties of the fabricated metallic glass parts are also investigated. The results show that Zr can form a crack-free bonding with Zr-based metallic glass owing to the formation of ductile α-Zr phase, whereas direct joining of Zr-based metallic glass to Ti alloy or stainless steel fails due to the formation of various brittle intermetallic compounds. By using Zr intermediate layers for Ti substrates and V/Ti/Zr intermediate layers for stainless steel substrates, the formation of deleterious intermetallics is suppressed and thus the bonding between metallic glass and crystalline metal is significantly improved. Additionally, fully amorphous and nearly fully dense (~99.9%) metallic glass parts with comparable mechanical properties to as-cast parts have been successfully fabricated --Abstract, page iv