Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation

Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.This work was partially supported by by FEDER funds through MINECO project TIN2017-85827-P, and ELKARTEK funded projects ENSOL2 and CODISAVA2 (KK-202000077 and KK-202000044) supported by the Basque Governmen

Manifested flatness predictions in thin strip cold rolling

International audienceThe paper deals with flatness defects prediction in thin plates which appear during rolling. Their origin is the roll stack thermo-elastic deformation. The combination of the elastic deflection, the thermal crown and the roll grinding crown results in a non-parallel bite. If the transverse roll profile is not an affinity of the incoming strip profile, differential elongation results and induces high stresses in the outgoing strip. The latter combine with the imposed strip tension force, resulting in a net post-bite stress field which may be sufficiently compressive locally to promote buckling. A variety of non-developable shapes may result, generally occurring as waviness, and classified as flatness defects (center waves, wavy edges, quarterbuckles...). The purpose of the present paper is to present a coupled approach, following [1]: a simple buckling criterion is introduced in the FEM model of strip and roll deformation, LAM3/TEC3 [2]. The post-bite stress field is in much better agreement with experiments if this treatment is used, as will be demonstrated

Validation of Tornio AP3 model based furnace control and grain size calculation

Abstract. Automation for annealing and pickling -line 3 was renewed in 2016, but the model-based control wasn’t good enough that production could rely on it, so doing modifications to the model was necessary. The goal was to get the model working so that automation could control furnaces mostly without input from line operators. Phenomena concerning grain size calculation in the automation system was studied theoretically to acquire sufficient information. The solving of problem was done by inspecting source code for programming errors, examining calculation log files and tables used by automation and finally measuring accuracy of the calculation both statistically and comparing calculated results with measured grain size from production trials. The accuracy was improved by modifying both grain size calculation and set-point calculation. Many changes were suggested for general parameters, grain specific grain growth parameters and source code. After these changes maximum grain size calculation error improved to 0,45 ASTM, but because of poor control of zone temperatures high accuracy in annealing can’t be done. The fixing of automations system is started, and it continues even when this thesis project is finished.Tiivistelmä. Hehkutus- ja peittauslinja 3:n automaatio uusittiin vuonna 2016, mutta uunien mallipohjainen ohjaus ei ollut riittävän hyvällä tasolla, jotta tuotannossa voitaisiin luottaa siihen. Tämän vuoksi mallin validointi oli tarpeellista. Tavoitteena oli saada malli toimimaan siten, että uuneja voitaisiin ohjata pääasiassa pelkästään automaatiolla ilman operaattoreiden panosta. Raekokolaskennan taustalla olevia ilmiöitä ja automaatiota tarkasteltiin kirjallisuuden pohjalta tietopohjan luomiseksi. Ongelmaa lähestyttiin tarkastelemalla lähdekoodia ohjelmointivirheiden osalta, seuraamalla laskennan lokitietoja, tarkastelemalla automaation käyttämiä taulukoita, hyödyntämällä tilastollista tietoa, sekä vertaamalla laskennan tuloksia tuotantokokeiden tuottamaan raekokoon. Laskennan tarkkuutta on mahdollista parantaa modifioimalla sekä raekoko-, että vyöhykkeiden asetusarvojen laskentaa. Laskentaan ehdotettiin useita parannuksia yleisiin parametreihin, laatukohtaisiin rakeenkasvuparametreihin ja lähdekoodiin. Näiden parannusten perusteella laskennan suurin virhe on 0,45 ASTM yksikköä. Tuotantolinjan heikon vyöhykelämpötilan hallinnan takia näidenkään muutosten jälkeen tarkkuutta vaativia hehkutuksia ei voida suorittaa. Automaation korjaus on aloitettu, mutta sitä ei ehditty viemään loppuun tämän projektin puitteissa

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Nondestructive test of regenerative chambers

Flat panels simulating internally cooled regenerative thrust chamber walls were fabricated by electroforming, brazing and diffusion bonding to evaluate the feasibility of nondestructive evaluation techniques to detect bonds of various strength integrities. Ultrasonics, holography, and acoustic emission were investigated and found to yield useful and informative data regarding the presence of bond defects in these structures

GEM-TPC pre-design technical report

This document contains the pre-design of the beam diagnostics components Tracking Detectors for the Super-FRS. A GEM-TPC detector has been suggested as suitable tracking detector for the ion/fragment beams produced at the in-flight separator Super-FRS under construction at the FAIR facility. The detector concept combines two widely used approaches in gas filled detectors, the Time Projection Chamber (TPC) and the Gas Electron Multiplication (GEM). Three detector generations (prototypes) have been tested in 2011, 2012 and 2014 with relativistic ion beams at GSI. Due to the high-resolution achromatic mode of the Super-FRS, highly homogeneous transmission tracking detectors are crucial to tag the momentum of the ion/fragment beam. They must be able to provide precise information on the (horizontal and vertical) deviation from nominal beam optics, while operated with slow-extracted beam on event-by event basis, in order to provide unambiguous identification of the fragments. The main requirements are a maximum active area horizontally and vertically of (380x80) mm2, a position resolution of < 1 mm, a maximum rate capability of 1 MHz, a dynamic range of about 600 fC. About 32 tracking detectors operating in vacuum are needed along the Super-FRS beam line

Experimental and numerical study of pinching phenomena in sheet metal rolling processes

Steel sheets are an essential raw material for a wide range of applications, such as household appliances, packaging, construction, shipbuilding, industrial machinery, automotive, and energy industries. The worldwide market for steel sheets faces intense competition and increasing demand for light-weight metal components to reduce CO2 emissions. As a result, newly developed grades of advanced high-strength steel (AHSS) have gained attention, especially from the automotive industry. AHSS allows for down-gauging due to its higher strength compared to other conventional steel grades. However, due to the low thickness of the sheets, rolling of AHSS is a critical process that may suffer from instabilities, such as pinching. Pinching represents a complex type of phenomena related to inhomogeneous stress distributions in the strip, which may arise from disruptions during the rolling process. Similarly to other shape defects, pinches can be related to uneven strip deformations in the roll bite, which result in inhomogeneous stress distributions across the strip’s width. Pinching defects in steel sheets appear as surface marks, wrinkling, repetitive rippled areas, and local ruptures. In the most severe cases, the strip breaks completely, causing damage to the rolls and considerable manufacturing downtime.Controlling the stability and enhancing the performance of the rolling process are top priorities for steel manufacturers. These tasks aim to minimize the occurrence of defects, ensure consistent product quality, and enhance the efficiency of the manufacturing process. Therefore, better understanding of instability phenomena like pinching is required for determining suitable solutions to prevent them and to obtain a stable rolling process. However, despite being a commonly reported issue among steel manufacturers, pinching has been poorly understood in terms of its underlying mechanism. Currently, there is a lack of research examining the mechanisms behind pinches, both in terms of experimental and numerical investigations. Without a comprehensive understanding of these phenomena, it is unfeasible to develop effective measures to prevent pinches and ensure stable operations of rolling mills. Therefore, the aims of this study are: firstly, to identify the mechanism and possible causes of pinching, and secondly, to develop a simulation tool that can be used to analyze pinching phenomena and design guidelines for the selection of robust production settings in cold rolling mills. To this end, both experimental study and numerical modelling are performed, as presented in this work.The experimental investigation of pinching phenomena presented in this work provide an in-depth understanding of the circumstances that lead to pinching through a series of cold rolling tests and the analysis and characterization of pinching defects.To study pinching phenomena, an appropriate tool is needed to replicate and investigate actual pinching events. Simulation models are essential for predicting the occurrence of pinching during the rolling process. However, existing numerical models of rolling do not succeed to reproduce the occurrence of pinching. This is because pinching is a complex phenomenon that depends on the strong interplay between local deformations within the roll bite and the stress state outside the roll bite. To capture this complexity, a numerical tool must be capable of modeling the process both at a millimeter (or sub-millimeter) scale within the roll bite and at a meter scale outside the roll bite. Moreover, to effectively study pinching events, a three-dimensional rolling model is necessary, as the distribution of stresses and strains across the strip's width is a crucial factor. The finite element method (FEM) is a well-established numerical tool for simulating metal forming processes, and is therefore a suitable technique for analyzing and predicting defects during rolling. However, accounting for all the relevant physics of the rolling process in a conventional 3D FEM model would result in an unfeasible computational time. This work proposes a numerical strategy to decrease the computational expense of 3D sheet rolling FEM simulations. The method involves coupling a global model, which represents the behavior and stress state of the strip outside the roll bite, with a local model that reproduces the deformation mechanics inside the roll bite. The global model is a shell finite element model of the sheet, while the local model is a high resolution 2D plane strain model of the roll bite. The developed approach has been validated by comparing its results to those of a conventional full 3D rolling model under stable rolling conditions. Additionally, this model has been employed to carry out a qualitative analysis of instability phenomena that arise during thin strip rolling. Such phenomena include flatness defects that result from disruptions in the frictional conditions. The simulation results demonstrate that locally varying friction induce local variations in the thickness strain, which cause stress re-distributions in the rolled sheet, resulting in flatness defects. Therefore, the proposed model offers a cost-effective alternative to more expensive 3D FEM models in the analysis of complex instability phenomena that can lead to defects during sheet metal rolling processes