Jose L. Rendueles

Temperature Measurement of Molten Pig Iron With Slag Characterization and Detection Using Infrared Computer Vision

Accurate temperature measurement in industrial environments is as important as it is challenging. Precise control over temperature measurement is crucial when processing metals, such as iron or steel, where temperature monitoring is critical to productivity and product quality. In the steel manufacturing process, temperature measurement of molten pig iron is particularly important, as it is a required parameter of the physical models used to control operations in steel furnaces. However, measuring the temperature of molten pig iron is not an easy task. Conventional methods using thermocouples or pyrometers present serious drawbacks which limit their applicability and do not provide accurate measurements. In this paper, an infrared computer vision system is proposed to measure the temperature of molten pig iron while it is being poured. The proposed system confronts two challenges: The stream must be detected in the infrared images, and the slag, which can partially cover the stream of molten pig iron, must be detected and removed from the stream. Fast, robust, and accurate methods are proposed. A calibration procedure for the emissivity of the molten pig iron and for the temperature level is also proposed and applied. This procedure makes it possible to differentiate molten pig iron from slag in the stream. Tests indicate that the results meet production needs.

A fast and robust decision support system for in-line quality assessment of resistance seam welds in the steelmaking industry

Assessing the quality of a weld in the steelmaking industry is a complex task. The level of complexity increases when the assessment is based on non-destructive tests. Skilled technicians are often required to make a decision based on automatic assessments of welds. Technicians consider the results of the automatic assessments and use their expert knowledge in order to make a final decision about the quality of the weld. In this paper we propose a decision support system to assess the quality of resistance seam welds of steel strips based on statistical analysis of both the mechanical and electrical variables involved in the welding process to be assessed as well as previously recorded historical data of similar welds. The proposed system is designed following component model based software architecture. The system consists of a set of orthogonal modules: welding variable measurement, welding variable processing and welding quality assessment, communicated by means of dedicated interfaces. The proposed system has been installed in three steel manufacturing lines. With the reduction in the time spent by technicians to make a decision about each weld, the productivity of the manufacturing line has greatly improved. Furthermore, production costs have been reduced since the number of defective welds assessed as non-defective was reduced, and thus the failures in the manufacturing lines due to weld breakages. The experimental results after two years of use in a steel strip galvanizing line are shown.

Vision-based technique for periodical defect detection in hot steel strips

This document presents a technique to detect a particularly serious problem: periodical defects. Periodical defects can cause serious damage to steel strips, and so should be corrected as quickly as possible. The technique proposed, which is based on information provided by an artificial vision system, reports on periodical defects detected in one strip before starting to roll the next.

Thickness quality control

This article describes a series of methods to detect thickness defects in DWI tinplate. These methods provide information to identify the origin of the defects, allowing fast corrections and, therefore, improving mill performance. In this approach, an adapted thickness signal is spatially sampled from the input stages of the mills last X-ray gauge. The system is also able to detect other problems related to thickness quality such as third-octave chatter using only the thickness measures from an X-ray gauge. The usage of this system resulted in an improvement in the factory performance and considerable money savings.

Periodic defects in steel strips: Detection through a vision-based technique

This article proposes a technique to detect the defects caused by defective work roll. This system inspects the surface (top and bottom) of each strip. The images captured by the cameras are then processed. It includes a way to store the information necessary for detection as well as a flexible algorithm that uses this information efficiently. To train the algorithm to obtain the best possible outcome for a set of test strips, a way to quantify each solution is also proposed. Finally, the results obtained are compared with those obtained by a commercial tool, and the improvement achieved using the new technique is discussed.

Interactive data visualization of chatter conditions in a cold rolling mill

Abstract Rolling of flat steel products is an industrial process in the field of metalworking where two or more pairs of rolls reduce the thickness of a steel strip to produce a uniform thickness material. Despite it has been studied for many years, there are still unpredictable problems that can affect the final quality of the product. One of them is the so-called chatter, that is a powerful self-excited vibration that appears suddenly and limits the productivity of the process. In this paper, a visual analytics approach is considered for exploratory analysis in order to discover and understand the factors and conditions under which chatter appears. An interactive web-based interface is presented here which allows the user to explore a map of dynamical conditions and visualize relevant details of each chatter onset. A validation case is performed using real data where normal/fault conditions have been identified automatically. By means of interactive exploration, the tool allows to refine an automatic chatter detection method. Moreover, it is shown to reveal correlations between variables, providing in some expected cases data-based confirmation, but also revealing less obvious relationships. Finally, it provides context, allowing to carry out comparative analysis, both qualitative and quantitative, for different subsets of coils (e.g. different years) as well as for different working conditions.

Thickness quality control and diagnosis system for tinplate rolling mills

The draw wall ironed (DWI) tinplate market clearly trends to demand a reduction in thickness, which implies stricter tolerance levels as small variations could result in problems in the manufacturing process and therefore customers dissatisfaction in a highly competitive market. This paper describes a series of methods to detect thickness defects in DWI tinplate. In addition, these methods provide information to identify the origin of the defects, allowing fast corrections and therefore improving mill performance. In our approach an adapted thickness signal is spatially sampled from the input stages of the mills last X-ray gauge.