Julio Molleda

Infrared thermography for temperature measurement and non-destructive testing.

The intensity of the infrared radiation emitted by objects is mainly a function of their temperature. In infrared thermography, this feature is used for multiple purposes: as a health indicator in medical applications, as a sign of malfunction in mechanical and electrical maintenance or as an indicator of heat loss in buildings. This paper presents a review of infrared thermography especially focused on two applications: temperature measurement and non-destructive testing, two of the main fields where infrared thermography-based sensors are used. A general introduction to infrared thermography and the common procedures for temperature measurement and non-destructive testing are presented. Furthermore, developments in these fields and recent advances are reviewed.

Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.

Real-time flatness inspection of rolled products based on optical laser triangulation and three-dimensional surface reconstruction

Flatness is a major geometrical feature of rolled products specified by both production and quality needs. Real-time inspection of flatness is the basis of automatic flatness control. Industrial facilities where rolled products are manufactured have adverse environments that affect artificial vision systems. We present a low-cost flatness inspection system based on optical triangulation by means of a laser stripe emitter and a CMOS matrix camera, designed to be part of an online flatness control system. An accurate and robust method to extract a laser stripe in adverse conditions over rough surfaces is proposed and designed to be applied in real time. Laser extraction relies on a local and a global search. The global search is based on an adjustment of curve segments based on a split-and-merge technique. A real-time recording method of the input data of the flatness inspection system is proposed. It stores information about manufacturing conditions for an offline tuning of the laser stripe extraction method using real data. Flatness measurements carried out over steel strips are evaluated quantitatively and qualitatively. Moreover, the real-time performance of the proposed system is analyzed.

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.

An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry

Measurement, inspection and quality control in industry have benefited from 3D techniques for imaging and visualization in recent years. The development of machine vision devices at decreased costs, as well as their miniaturization and integration in industrial processes, have accelerated the use of 3D imaging systems in industry. In this paper we describe how to improve the performance of a 3D imaging system for inline dimensional quality inspection of long, flat-rolled metal products manufactured in rolling mills we designed and developed in previous works. Two dimensional characteristics of rolled products are measured by the system: width and flatness. The system is based on active triangulation using a single-line pattern projected onto the surface of the product under inspection for range image acquisition. Taking the system calibration into account the range images are transformed into a calibrated point cloud representing the 3D surface reconstruction of the product. Two approaches to improve the line detection and extraction method used in the original system are discussed, one intended for high-speed processing with lower accuracy, and the other providing high accuracy while incurring higher computational time expenses. A mechanism to remove, or at least reduce, the effects of product movements while manufacturing, such as bouncing and flapping, is also proposed to improve the performance of the system.

On-Line Flatness Measurement in the Steelmaking Industry

Shape is a key characteristic to determine the quality of outgoing flat-rolled products in the steel industry. It is greatly influenced by flatness, a feature to describe how the surface of a rolled product approaches a plane. Flatness is of the utmost importance in steelmaking, since it is used by most downstream processes and customers for the acceptance or rejection of rolled products. Flatness sensors compute flatness measurements based on comparing the length of several longitudinal fibers of the surface of the product under inspection. Two main different approaches are commonly used. On the one hand, most mechanical sensors measure the tensile stress across the width of the rolled product, while manufacturing and estimating the fiber lengths from this stress. On the other hand, optical sensors measure the length of the fibers by means of light patterns projected onto the product surface. In this paper, we review the techniques and the main sensors used in the steelmaking industry to measure and quantify flatness defects in steel plates, sheets and strips. Most of these techniques and sensors can be used in other industries involving rolling mills or continuous production lines, such as aluminum, copper and paper, to name a few. Encompassed in the special issue, State-of-the-Art Sensors Technology in Spain 2013, this paper also reviews the most important flatness sensors designed and developed for the steelmaking industry in Spain.

Structured-light sensor using two laser stripes for 3D reconstruction without vibrations.

3D reconstruction based on laser light projection is a well-known method that generally provides accurate results. However, when this method is used for inspection in uncontrolled environments, it is greatly affected by vibrations. This paper presents a structured-light sensor based on two laser stripes that provides a 3D reconstruction without vibrations. Using more than one laser stripe provides redundant information than is used to compensate for the vibrations. This work also proposes an accurate calibration process for the sensor based on standard calibration plates. A series of experiments are performed to evaluate the proposed method using a mechanical device that simulates vibrations. Results show excellent performance, with very good accuracy.

Uncertainty Propagation Analysis in 3-D Shape Measurement Using Laser Range Finding

Quality evaluation of rolling processes in the metal industry involves an inspection of the shape of the outgoing products in real time during manufacturing. Shape measurement systems are usually based on 3-D reconstructions of the surface of rolled products. As surface properties are crucial, these systems favor contactless techniques. Using 3-D measurements of the surface of rolled products, several geometric properties can be analyzed. In this paper, we analyze how uncertainty is propagated in a contactless shape measurement system designed and developed by the authors and presented in previous published works. This measurement system is based on active triangulation, and it is able to provide inline width and flatness measurements of long, flat-rolled products in harsh industrial environments. The camera model used to calibrate the vision system is described, and it is used to estimate the uncertainty of the reprojected 3-D points on the scene. The system uses the reprojected 3-D points, and the speed of the product movement in the production line to reconstruct its surface. Thus, the uncertainty of the speed is also estimated. Finally, the propagation of both the uncertainty of the 3-D reprojection, and the uncertainty of the speed into the final width and flatness measurements is analyzed. This paper comprises a detailed uncertainty propagation analysis in 3-D shape measurements computed indirectly through functional relationships.

Machine Vision System for Flatness Control Feedback

Quality control is very important in the iron and steel industry to ensure that products meet customer requirements. Flatness is one of the most important features of rolled products, and it is used to estimate the final quality of the resulting product. Therefore, flatness control, which requires precise flatness measurements, is of vital importance during rolling. This work proposes a machine vision system for flatness measurement based on the projection of a laser stripe over the surface of the steel strip. The flatness measured cannot be used as easily as the feedback of the flatness control system due to the huge amount of information it contains. In order to solve this problem, a feature extraction method based on Legendre polynomial fit is also proposed.

Compensation for Uneven Temperature in Flatness Control Systems for Steel Strips

Uneven temperature across the width of steel strips during rolling generates flatness defects due to differences in the contraction of the longitudinal fibers that make up a strip. In this work, a compensation process to modify the objective flatness using information about the temperature differences is designed. Compensation for uneven temperature is accomplished in two successive phases: a real time calculation of the temperature profile which best describes the temperature differences, and the modification of the objective flatness to take the temperature profile into account. To calculate the temperature profile, an adaptive moving-average filter is proposed. This information is used to modify the objective flatness carrying out the temperature compensation