Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzuständen zu unterscheiden und eine Prognose bezüglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsüberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz für den Bandlauf werden entwickelt. Eine Literaturübersicht gibt einen Überblick über die bisherige Forschung zu verwandten Themen. Es wird gezeigt, dass die große Anzahl vorheriger Arbeiten diese Thematik nicht gelöst hat und dass weitere Untersuchungen erforderlich sind, um eine zufriedenstellende Lösung der behandelten Probleme zu erhalten. Die Entwicklung einer neuen Signalverarbeitungskette und die Signalverarbeitungsschritte sind detailliert dargestellt. Die Klassifikationsaufgabe wird in Fehlerdiagnose, Fehleridentifikation und Fehlerprognose differenziert. Der vorgeschlagene Ansatz kombiniert fünf verschiedene Methoden zur Merkmalsextraktion, nämlich Short-Time Fourier Transformation, kontinuierliche Wavelet Transformation, diskrete Wavelet Transformation, Wigner-Ville Distribution und Empirical Mode Decomposition, mit zwei verschiedenen Klassifikationsalgorithmen, nämlich Support-Vektor Maschine und eine Variation der Kreuzkorrelation, wobei letztere in dieser Arbeit entwickelt wurde. Kombinationen dieser Merkmalsextraktion und Klassifikationsverfahren werden an Walzkraft-Daten aus einer Warmbreitbandstraße angewendet.The approach introduced here is able to detect two specific severe faults, to identify them, to distinguish between four different system states, and to give a prognosis on the system behavior. The presented work investigates the condition monitoring of the complex production process of a hot strip rolling mill. A signal-based fault diagnosis and fault prognosis approach for strip travel is developed. A literature review gives an overview about previous research on related topics. It is shown that the great amount of previous work does not cope with the problems treated in this work and that further investigation is necessary to provide a satisfactory solution. The design of a new signal processing chain is presented and the signal processing steps are detailed. The classification task is differentiated into fault detection, fault identification and fault prognosis. The proposed approach combines five different methods for feature extraction, namely short time Fourier transform, continuous wavelet transform, discrete wavelet transform, Wigner-Ville distribution, and empirical mode decomposition, with two different classification algorithms, namely support vector machine and a variation of cross-correlation, the latter developed in this work. Combinations of these feature extraction and classification methods are applied to rolling force data originating from a hot strip mill

Strip tracking in hot strip mills

In the finishing mill, steel strip is rolled from thick slabs through pairs of rollers housed in a continuous train of seven roll stands. As the strip is rolled, unwanted lateral movement, known as strip tracking, can cause the strip to collide with the edge of the mill. Strip tracking control is currently a manual operation, relying on the skill of the operators. When tracking is observed, the stand tilt is adjusted asymmetrically, causing a camber in the strip, steering it towards the centreline. Tracking control can be automated if a reliable measurement of position is available. A vision-based system was developed to measure strip position. Cooling water, steam, high temperatures and electrical noise create a hazardous environment for electronic equipment and hamper image analysis. Hardware was specified to protect all equipment against the environment. A novel image analysis method combining predictive elements, filtering and Bezier curve fitting was created to allow measurements to be made with large amounts of cooling water obscuring the strip edges. The measurement system was designed to integrate with the existing mill systems, using the OPC protocol for communication. The system was created as a development system with only two cameras included, but allowed for additional cameras to be easily added and automatically detected. The results of the system showed that the image analysis techniques were effective, providing an estimated final resolution of 3.5mm/pixel, with measurements ±2mm within 60% confidence. Hardware performance provided good protection of the equipment against the environment but poor quality installation limited overall system performance. A computer model was developed to simulate tracking behaviour in the mill with non-linear variations of strip properties across the strip. The model was not completed to a satisfactory standard capable of producing useful results but the theories described could be developed further.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Strip tracking in hot strip mills

In the finishing mill, steel strip is rolled from thick slabs through pairs of rollers housed in a continuous train of seven roll stands. As the strip is rolled, unwanted lateral movement, known as strip tracking, can cause the strip to collide with the edge of the mill. Strip tracking control is currently a manual operation, relying on the skill of the operators. When tracking is observed, the stand tilt is adjusted asymmetrically, causing a camber in the strip, steering it towards the centreline. Tracking control can be automated if a reliable measurement of position is available. A vision-based system was developed to measure strip position. Cooling water, steam, high temperatures and electrical noise create a hazardous environment for electronic equipment and hamper image analysis. Hardware was specified to protect all equipment against the environment. A novel image analysis method combining predictive elements, filtering and Bezier curve fitting was created to allow measurements to be made with large amounts of cooling water obscuring the strip edges. The measurement system was designed to integrate with the existing mill systems, using the OPC protocol for communication. The system was created as a development system with only two cameras included, but allowed for additional cameras to be easily added and automatically detected. The results of the system showed that the image analysis techniques were effective, providing an estimated final resolution of 3.5mm/pixel, with measurements ±2mm within 60% confidence. Hardware performance provided good protection of the equipment against the environment but poor quality installation limited overall system performance. A computer model was developed to simulate tracking behaviour in the mill with non-linear variations of strip properties across the strip. The model was not completed to a satisfactory standard capable of producing useful results but the theories described could be developed further

Texture evolution in Ti-6AI-4V

Crystallographic orientation and microstructural morphology control properties in engineering materials. Titanium alloys are used extensively in commercial turbofan aircraft engines, due to their high strength and excellent corrosion resistance. The formation of preferred crystallite orientations during manufacturing must be understood in order to maximise component lifespan and avoid failure. In this thesis, I present a methodology which generates virtual 3D microstructures representing a material, conforming to an approximation of a 2D reference surface characterised by electron backscatter diffraction (EBSD). The subsurface grains of this microstructure are instanced using statistical information taken from the map, controlling grain size and texture. The subsurface texture is controlled through optimisations of an orientation distribution function (ODF) and misorientation distribution function (MDF). The influence of this control is shown through simulating deformation within the DAMASK crystal plasticity fast Fourier transform (CP-FFT) solver, to demonstrate the effect of subsurface texture on the stress and strain partitioning on the reference surface. The textures of Ti-6Al 4V formed through hot-rolling at temperatures between 750 and 950 °C are characterised by EBSD. As this method measures spatial and orientation information describing a 2D surface the material, I investigate the mechanisms through which lattice orientations of crystallites evolve during processing. EBSD maps are segmented by preferred orientation to demonstrate the spatial distribution of texture fibres. By measuring phase composition through direct backscatter spectroscopy (DBS) and elemental composition through energy dispersive x-ray spectroscopy (EDS), I demonstrate the influence of the β phase on the formation of texture fibres during rolling, with weak evolution of all existing texture fibres as the Ti-6Al-4V bar plastically deforms through slip. Through CP-FFT simulations of synthetic textured polycrystals in DAMASK, using the orientations of the texture fibres observed by EBSD, I simulate the texture evolution during deformation by hot rolling. Through examination of lattice rotation, slip shear rates and twinning shear rates, I demonstrate that the texture evolution resulting from plastic deformation at high temperatures is conducted entirely by crystal slip, resulting in only small lattice rotations and weak texture evolution. This is in agreement with the textures obtained through EBSD of hot-rolled Ti-6Al-4V.Open Acces

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems