یک سیستم پشتیبانی سریع و قوی تصمیم برای ارزیابی کیفیت در خط مقاومت جوش درز در صنعت فولاد

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.

Welding is a process of utmost importance in the metal industry. The quality of a welded joint determines whether the weld is suitable for subsequent manufacturing processes, or if the joint must be re-welded. Weld quality assessment is a complex task due to the great number of variables involved, such as the mechanics of the welding process, the chemical composition of the workpieces to be welded, and oxides and inclusions dragged into the welding zone. When the weld is part of the final product, or even when it is required for subsequent processes, destructive tests in order to inspect the weld are not possible. In these scenarios non-destructive evaluation must be carried out. Several non-destructive techniques for different types of welding processes have been developed in the recent years, such as ultrasonic [1], X-ray [2] and machine vision [3]. In steel strip manufacturing lines where continuous operation is required one of the most widely used welding processes is resistance seam welding (RSEW), an automated process where two strips are overlapped and the weld is formed progressively in the overlapped area. In these lines the time available for weld inspection is very scarce. The welded strips cannot be stopped more than a few seconds in order not to affect the downstream manufacturing stages. Therefore, in-line non-destructive tests performed while the weld is carried out are required to meet the deadline imposed by the manufacturing process. RSEW is a welding process classified in the group of electric resistance welding (ERW) which produces coalescence of faying surfaces by means of the heat generated by the resistance of the strips to the welding current. The heat generated by the current causes partial melting of the strips, which are compressed in the area of the weld. Two disc-shaped electrodes, called welding sheaves, are used simultaneously to clamp the strips together and to apply pressure and electric current as they rotate along the overlapped area. After them, another pair of sheaves, called flattening sheaves, apply pressure along the lap joint to finish the weld. RSEW can be seen as a succession of spot welds along the seam weld. In ERW, resistance spot welding (RSPW) is a broadly studied process. Most of the systems to automatically assess the quality of welds in ERW have been developed for RSPW. Several techniques have been applied to inspect RSPW processes, such as statistical analysis of the variables involved in the welding process [4], measuring the dynamic resistance of the weld [5], and measuring the electrode displacement during welding [6]. Most of these systems use pattern classification techniques to recognize defects in the welds automatically, such as fuzzy logic [7], neural networks [5] and [8], support vector machines [9] and [10], and ant colony optimization [11]. To the best of our knowledge, automatic quality assessment of welds carried out by RSEW is a relatively unexplored field compared with automatic quality assessment of RSPW. In recent years we have developed a number of prototypes to automatically detect defects in RSEW. In [12] we presented an early prototype which detects defects in RSEW processes of steel strips based on fuzzy logic analysis. In [13] we presented a prototype which is able to estimate the reliability of RSEW of steel strips based on an analysis of the heat reached along the seam weld. In this work we deal with the task of assessing the quality of the RSEW processes of steel strips, providing a decision support system for technicians of the manufacturing line to determine whether the weld fulfills the required specifications. The main issue concerning in-line testing of RSEW processes is the great amount of electric current required by the welding process (between 10 kA and 100 kA). Ultrasound or X-ray welding inspection systems are affected by the electromagnetical noise generated by the welding current, thus they are not useful for in-line weld inspection of RSEW processes. Therefore, these systems must inspect the weld after the welding process is finished, delaying the reception of the assessment by the technicians or other systems of the manufacturing line. Machine vision systems for weld inspection, which are not affected by electromagnetical noise and can be used in-line, only detect defects on the surface of the weld while internal defects are unnoticed [3] and [14]. This paper presents a technique for in-line quality assessment of electric resistance welds, which is immune to the electromagnetical noise generated during the welding process. In addition, a system based on this technique to work as a decision support tool for in-line quality assessment of resistance seam welds is proposed. The system has been developed and deployed in three steel manufacturing lines: two steel strip galvanizing lines and one tin manufacturing line. The proposed technique detects not only external defects of the weld but also internal defects based on comparing the welding variables of the weld to be assessed with the welding variables gathered from previous processes in which the same materials and the same welding settings were used, that is, with previously recorded historical data. This paper is organized as follows. Section 2 describes the decision support system for quality assessment of resistance seam welds proposed in this paper. In Section 3, we describe the implementation of the proposed system. In Section 4, the results provided by the system in a steel strip manufacturing line are presented. Finally, some concluding remarks are shown.

In this paper we propose a fast and robust system for quality assessment of electric resistance welds. The system implements a technique which assesses the quality of the welds based on statistical analysis of both the mechanical and the electrical variables involved in the welding process to be assessed, as well as previously historical data gathered from similar welds. Once the foreground of each variable is extracted, it is assessed. The assessment of the input variables aids in diagnosing problems and failures and can also be used for preventive maintenance. In general, the assessments of the input variables are used to validate that the welding process has been carried out under acceptable conditions. The assessments of the output variables are used to provide the final quality assessment of the weld. The signal processing tasks carried out by the system are real-time compliant in order to meet the deadline imposed by the manufacturing line. The proposed system has been installed in three steel manufacturing lines: two strip galvanizing lines and one tin manufacturing line of the ArcelorMittal Steel Company in Asturias, Spain. Since their installation they have assessed more than 75,000 welds, providing support for the technicians of the manufacturing line making the final decision to accept or reject each weld. The use of the system improves the productivity of the manufacturing line since type I errors, a positively assessed low-quality weld, are minimized. In addition, the system reduces production costs due to the great reduction of weld breakages in the manufacturing line.