3D characterization of Magnetic Flux Leakage signals : a data fusion approach

http://www.worldcat.org/oclc/3946080

System for measuring steel scrap volume using depth imaging

Abstract. Sustainability and green values are major themes in the world today. Companies across all fields are constantly implementing new technologies to reduce emissions and to limit the magnitude of global warming. The steel industry in general is one of the major producers of carbon dioxide emissions. The objective of this thesis was to develop a system to measure the volume of scrap metal being charged to an electric arc furnace. Obtaining the scrap volume would help the furnace operators in timing the charging of scrap baskets, thus avoiding the adverse effects resulting from early and late charging. The intention is to increase the energy efficiency of the process. The theory section of the thesis provides a short overview of the electric arc furnace process and a more detailed description of the charging process. Depth imaging technologies are then explored from a theoretical standpoint to provide the background for the selection and usage of imaging hardware. In this thesis, design science research methodology was utilized to develop the scrap volume measurement system, which consists of imaging hardware and developed software. The actual contribution of this thesis is the algorithm to extract the height of the scrap surface level from a 3-dimensional image of scrap baskets. The development process was iteratively carried out in a steel factory. The system performance was evaluated in a real-world scenario. It was established that the system was able to capture 3-dimensional data from scrap baskets and determine the scrap surface level height according to the algorithm. However, for some cases the image capturing did not perform as expected. These failure cases were a result of either steel dust obstructing the scene or the inability of the camera to capture data from unreflective material. Further research prospects were identified during conducting of the thesis. The failure cases could be addressed either programmatically, with new hardware technology, or a combination of both. Also, research could be conducted on the usage of the information provided by the system in actual charging events with the goal of optimizing charging timing

Data fusion for NDE signal characterization

The primary objective of multi-sensor data fusion, which offers both quantitative and qualitative benefits, is to be able to draw inferences that may not be feasible with data from a single sensor alone. In this study, data from two sets of sensors are fused to estimate the defect profile from magnetic flux leakage (MFL) inspection data. The two sensors measure the axial and circumferential components of the MFL field. Data is fused at the signal level. The two signals are combined as the real and imaginary components of a complex valued signal. Signals from an array of sensors are arranged in contiguous rows to obtain a complex valued image. Signals from the defect regions are then processed to minimize noise and the effects of lift-off. A boundary extraction algorithm is used not only to estimate the defect size more accurately, but also to segment the defect area. A wavelet basis function neural network (WBFNN) is then employed to map the complex valued image appropriately to obtain the geometric profile of the defect. The feasibility of the approach was evaluated using the data obtained from the MFL inspection of natural gas transmission pipelines. The results obtained by fusing the axial and circumferential component appear to be better than those obtained using the axial component alone. Finally, a WBFNN based boundary extraction scheme is employed for the proposed fusion approach. The boundary based adaptive weighted average (BBAWA) offers superior performance compared to three alternative different fusion methods employing weighted average (WA), principal component analysis (PCA), and adaptive weighted average (AWA) methods

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Selected Papers from Experimental Stress Analysis 2020

This Special Issue consists of selected papers from the Experimental Stress Analysis 2020 conference. Experimental Stress Analysis 2020 was organized with the support of the Czech Society for Mechanics, Expert Group of Experimental Mechanics, and was, for this particular year, held online in 19–22 October 2020. The objectives of the conference included identification of current situation, sharing professional experience and knowledge, discussing new theoretical and practical findings, and the establishment and strengthening of relationships between universities, companies, and scientists from the field of experimental mechanics in mechanical and civil engineering. The topics of the conference were focused on experimental research on materials and structures subjected to mechanical, thermal–mechanical, and dynamic loading, including damage, fatigue, and fracture analyses. The selected papers deal with top-level contemporary phenomena, such as modern durable materials, numerical modeling and simulations, and innovative non-destructive materials’ testing

Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Time of flight diffraction and imaging (TOFDI)

Time of flight diffraction and imaging (TOFDI) is based on time of flight diffraction (TOFD), adding cross-sectional imaging of the sample bulk by exploiting the scattering of ultrasonic waves from bulk defects in metals. Multiple wave modes are emitted by a pulsed laser ultrasound ablative source, and received by a sparse array of receiving electromagnetic acoustic transducers (EMATs), for non-contact (linear) scanning, with mode-conversions whenever waves are scattered. Standard signal processing techniques, such as band-pass filters, reduce noise. A B-scan is formed from multiple data captures (A-scans), with time and scan position axes, and colour representing amplitude or magnitude. B-scans may contain horizontal lines from surface waves propagating directly from emitter to receiver, or via a back-wall, and angled lines after reflection off a surface edge. A Hough transform (HT), modified to deal with the constraints of a B-scan, can remove such lines. A parabola matched filter has been developed that identifies the features in the B-scan caused by scattering from point-like defects, reducing them to peaks and minimising noise. Multiple B-scans are combined to reduce noise further. The B-scan is also processed to form a cross-sectional image, enabling detection and positioning of multiple defects. The standard phase correlation technique applied to camera images, has been used to track the relative position between transducer and sample. Movement has been determined to sub-pixel precision, with a median accuracy of 0.01mm of linear movement (0.06 of a pixel), despite uneven illumination and the use of a basic low resolution camera. The prototype application is testing rough steel products formed by continuous casting, but the techniques created to facilitate operation of TOFDI are applicable elsewhere