Automotive manufacturing: Intelligent resistance welding

Abstract

Resistance spot welding (RSW) is an important process in the automotive industry. The advantages of spot welding are many: an economical process, adaptable to a wide variety of materials (including low carbon steel, coated steels, stainless steel, aluminum, nickel, titanium, and copper alloys) and thicknesses, a process with short cycle times, and overall, a relatively robust process with some tolerance to fit-up variations. Although used in mass production for several decades, RSW poses several major problems, most notably, large variation in weld quality. Given the variation and uncertainty in weld quality (attributed to factors such as tip wear, sheet metal surface debris, and fluctuations in power supply), it is a common practice in industry to add a significant number of redundant welds to gain confidence in the structural integrity of the welded assembly. In recent years, global competition for improved productivity and reduced nonvalue added activity, is forcing automotive OEMs and others to eliminate these redundant spot welds. The emphasis on reduction of the redundant welds significantly increases the need for monitoring of weld quality and minimizing weld process variability. Traditionally, destructive and nondestructive tests for weld quality evaluation are predominantly off-line or end-of-line processes. While this test information is useful and valuable for quality and process monitoring, it cannot be utilized in process control because of the significant delays that are associated with the off-line test analysis. In order to minimize the number of spot welds and still satisfy essential factors such as strength and surface integrity, weld quality has to be monitored and controlled in real-time. Advances over the last decade in the area of non-intrusive electronic sensors, signal processing algorithms, and computational intelligence, coupled with drastic reductions in computing and networking hardware costs, have now made it possible to develop non-intrusive intelligent resistance welding systems that overcome the above shortcomings. © 2008 Springer-Verlag Berlin Heidelberg.