Francisco G. Bulnes

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.

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.

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.

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.

Monitoring Sintering Burn-Through Point Using Infrared Thermography

Sintering is a complex industrial process that applies heat to fine particles of iron ore and other materials to produce sinter, a solidified porous material used in blast furnaces. The sintering process needs to be carefully adjusted, so that the combustion zone reaches the bottom of the material just before the discharge end. This is known as the burn-through point. Many different parameters need to be finely tuned, including the speed and the quantities of the materials mixed. However, in order to achieve good results, sintering control requires precise feedback to adjust these parameters. This work presents a sensor to monitor the sintering burn-through point based on infrared thermography. The proposed procedure is based on the acquisition of infrared images at the end of the sintering process. At this position, infrared images contain the cross-section temperatures of the mixture. The objective of this work is to process this information to extract relevant features about the sintering process. The proposed procedure is based on four steps: key frame detection, region of interest detection, segmentation and feature extraction. The results indicate that the proposed procedure is very robust and reliable, providing features that can be used effectively to control the sintering process.

Vibrations in steel strips: Effects on flatness measurement and filtering

Vibrations are periodic or random motion from an equilibrium position. In the steel industry, vibrations are an undesirable phenomenon, as they waste energy and create unwanted noise. For example, vibrations affect steel strips during rolling and transportation, producing random or periodic vertical movements of the strips as they move forward along a roll path. The consequences of these vibrations are particularly harmful for 3D reconstruction and flatness measurement based on non-contact techniques. Vibrations corrupt the height profiles, causing an erroneous reconstruction of the 3D surface. The flatness measurement is also distorted because the estimated lengths of the strip fibers with vibrations are different. This paper analyzes how vibrations in steel strips affect flatness measurement, and proposes methods to remove or reduce these effects. The shape of steel strips is firstly modeled including the two most common flatness defects: wavy edges and center buckle. Then, different types of vibrations are added to the strip models, and their effects on the resulting flatness measurement are analyzed. The method proposed to reduce the effects of vibrations is a combination of a low-pass filter and geometric transformations. Finally, these methods are applied to data from real strips.

Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials

During the production of web materials such as plastic, textiles or metal, where there are rolls involved in the production process, periodically generated defects may occur. If one of these rolls has some kind of flaw, it can generate a defect on the material surface each time it completes a full turn. This can cause the generation of a large number of surface defects, greatly degrading the product quality. For this reason, it is necessary to have a system that can detect these situations as soon as possible. This paper presents a vision-based sensor for the early detection of this kind of defects. It can be adapted to be used in the inspection of any web material, even when the input data are very noisy. To assess its performance, the sensor system was used to detect periodical defects in hot steel strips. A total of 36 strips produced in ArcelorMittal Avilés factory were used for this purpose, 18 to determine the optimal configuration of the proposed sensor using a full-factorial experimental design and the other 18 to verify the validity of the results. Next, they were compared with those provided by a commercial system used worldwide, showing a clear improvement.

Temperature Measurement Using the Wedge Method: Comparison and Application to Emissivity Estimation and Compensation

Temperature measurement based on infrared radiation depends on correctly adjusted emissivity. However, emissivity configuration is complex as emissivity is not normally known with precision; it is influenced by radiation reflections and can also vary with the temperature. The wedge method is a temperature measurement method which assures the selection of the correct emissivity configuration using an infrared camera to take images of a wedge region. Within the wedge, a virtually closed cavity for radiation is created. Although this method outperforms traditional infrared measurement, no comparison has yet been carried out under real industrial conditions which would provide information about emissivity variations. This paper proposes a method to measure temperature using the wedge method in industrial environments and compares the results with the temperature measurement acquired from a calibrated infrared line scanner. Using the wedge method, it is possible to accurately estimate emissivity profiles under real working conditions. A method to apply these profiles for emissivity compensation is also proposed in this paper. Conclusions give the analysis of the strengths and weaknesses of the two methods and provide recommendations and guidelines for technicians interested in temperature measurement and emissivity estimation and compensation.

An efficient method for defect detection during the manufacturing of web materials

Defect detection is becoming an increasingly important task during the manufacturing process. The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the economic impact caused by discarding defective products. This point is especially important in the case of products that are very expensive to produce. In this paper, the authors propose a method to detect a specific type of defect that may occur during the production of web materials: periodical defects. This type of defect is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale. To run the proposed method, two different functions must be executed a large number of times. Since the time available to perform the detection of these defects may be limited, it is very important to consume the least amount of time possible. In order to reduce the overall time required for detection, an analysis of how the method accesses the input data is performed. Thus, the most efficient data structure to store the information is determined. At the end of the paper, several experiments are performed to verify that both the proposed method and the data structure used to store the information are the most suitable to solve the aforementioned problem.