C. Spinola

Calibration of thickness measurement instruments based on twin laser sensors. Isoline bilinear look up tables

We present a procedure for the calibration of an instrument for noncontact thickness measurement of stainless steel sheets, based on a pair of triangulation laser sensors. Firstly, we carry out a general review of the architecture of this kind of instrument and of the problems associated with noncontact measurement based on commercial laser sensors in an industrial production environment. Secondly, we introduce a method for the calibration and characterization of this instrument based on the piecewise linear approximation of the isoline curves obtained in the calibration process (isoline bilinear look-up tables). We follow with a description of the searching and calculation of thickness in these types of tables.

Object recognition and inspection in difficult industrial environments

The manufacturing environment is characterized by rapid change, originating new challenges and problems to the production and operation manager in the industry. In response to the need for fast and flexible manufacturing, increasing attention is being given to integration of computing technologies with the manufacturing systems leading to the development of fast and flexible manufacturing systems aided with high performance vision capabilities. This paper presents an inspection and recognition system for manufacturing applications in difficult industrial environments. In such difficult environments, where objects to be recognized can be dirty and illumination conditions cannot be sufficiently controlled, the required accuracy and rigidity of the system are critical features. The purpose of the real-time system is to inspect and recognize air-conditioning objects for avoiding deficiency in the manufactured process and erroneous identifications due to a large variety of size and kinds of objects. The proposed system is composed by five USB cameras (640times480 pixel resolution, 30 Hz frame rates and 8 mm lenses) plug into a conventional computer (Pentium IV 3 GHz). The edged images are features which are relatively robust against illumination changes, so the system can work well in difficult environments mentioned. Experimental results on inspection and recognition of a large variety of air-conditioning objects are provided to show the performance of the system in a difficult environment.

Real-time image processing for edge inspection and defect detection in stainless steel production lines

An image acquisition and processing system to measure the width and inspect the quality of the stainless steel strip in a production line is presented in this paper. It is based on the real-time processing of the images acquired by a twin linear camera system. Image processing algorithms to detect defects and anomalies in the edges have been implemented. The core of the image processing is submitted to the GPU of the graphic card to reduce the total processing time. A system like this has been proved and installed in a stainless steel production line for quality control purposes.

Contactless real time measuring system for the length of sheets in stainless steel production lines

A real time system to measure without contact the length of stainless steel sheets in a production line is presented in this paper. The items are measured in real time as they are cut in the production line and are moved forward by a conveyor passing down a differential laser sensor device which measures and integrates its perpendicular speed. One challenge of the system is to deal with different length and speed maintaining the accuracy. Also installation, calibration and supervision procedures have been established. In addition the designed system must be robust enough to be installed in the hard industrial environment of the iron and steel works. The presented system has been designed and installed for quality control purpose in a stainless steel factory.

Image processing for surface quality control in stainless steel production lines

In this paper we present an image processing algorithm to detect and measure the amount of residual oxide remaining on the surface of stainless steel coils for quality control in a production line. This algorithm processes in real time the images taken by the acquisition system which we have designed for this purpose and which has been installed in the finish line of a stainless steel production factory. We present here a more robust and reliable algorithm than the initial one, which has been adapted to deal with non-ideal conditions such as non-perfect homogeneous lighting, different surface finish, water marks, etc., which usually occur in practice.

Residual oxides detection and measurement in stainless steel production lines

In this paper, we present a system to detect and measure the amount of residual oxide stains remaining in the surface of stainless steel coils after the pickling process in a production line. The system is able to acquire clear images of the stainless steel surface with the appropriate illumination and magnification, while it is being produced. These images are processed and analyzed in real time in order to detect and measure the oxide stains which typically are between 50 and 200 microns in size. We present here an outline of the acquisition system and the image processing algorithm which has been designed to detect this sort of defect.

Filtering of impulse noise from laser sensor signals in the process of non-contact thickness measurement in a stainless steel sheet production line

The filtering of noise in data coming from measuring systems in the production environment is an area worthy of special attention. In this article we will introduce the detection and elimination of impulse noise in a series of time measurements adapting techniques of median based filters. First of all, we will describe the impulse noise phenomenon, more important when a sensor pair is considered, and analysed the problems associated with the application of these techniques in a series of time measurements. Subsequently we will develop and apply a progressive iterative method for the detection and elimination of impulse noise, that obtains more than acceptable results, overcoming problems normally associated with these types of filters.

Continuous real-time optical measuring of strip width and edge inspection in stainless steel production lines

An optical system to continuously measure the width of stainless steel strip in a production line is presented in this paper. It is based on the real-time processing of the images acquired by a twin linear camera system. The geometrical model and corrections due to different strip thickness are presented. Detection of defects and anomalies in the edges are also implemented along with width measurement. This system has been installed for quality control purposes in a stainless steel production line.

Supervision model for the annealing process in a stainless steel production line

Annealing is an important process in the production of stainless steel coils. In this paper we present a system for supervising this process in a production line. To implement it, we need to acquire continuously the temperatures along the annealing furnace and the speed of the line and to correlate them with each production units or coils. In order to manage all that information, a simple model of the furnace has been built and simple annealing functions have also been proposed whose integration along a space-time trajectory provides a powerful supervision tool for the quality control engineer.

An Empirical Model of the Decarburization Process in Stainless Steel Production

The argon-oxygen decarburization (AOD) is the refining process of stainless steel to get its final chemical composition through several stages where tons of materials are added and oxygen and inert gas are blown. We present in this paper the design of an empirical model of this process to predict critical values of the decarburization process in order to automate it and enhance the production performance of the AOD. We show that it is possible to build an empirical model, simpler than analytical parameterized models, based on Multilinear Regression or Neural Network Perceptron to predict the amount of oxygen to be blown and the temperature to be reached.