Abstract

Resistance spot welding (RSW) is used for the fabrication of sheet metal assemblies. The process is used extensively for joining low carbon steel components for the bodies and chassis of automobiles, trucks, trailers, buses, mobile homes, motor homes and recreational vehicles, and railroad passenger cars, as well as cabinets, office furniture, appliances and many other products. High-strength low-alloy steel, stainless steel, nickel-, aluminum-, titanium and copper alloys are also spot welded commercially. The major advantages of spot welding are high speed and adaptability for automation in high-volume and/or high-rate production. Despite these advantages, RSW suffers from a major problem of inconsistent quality from weld to weld. This problem results from both the complexity of the basic process as well as from numerous sources of variability, noise, and errors. Any or all of these complicate automation, reduce weld quality, demand over-welding (i.e., the production of more welds than are structurally needed, if each were perfect), and drive up production costs. For this reason, ensuring weld quality has been and remains a major challenge and goal. The objective of this research is to explore the phenomenon of how changes in a controllable parameter of (i.e., percentage heat input) affect a measurable output signal indicative of strength and weld quality (i.e., electrode displacement) for various sheet steels used in the automotive industry. The approach of this research is to create a relationship between a key process input variable and the key process output of a quality weld. The input parameter chosen is the percentage heat input, as this directly affects the size and strength of the resulting weld. The output chosen is electrode displacement, as this has been shown to accurately reflect the formation and growth of a weld nugget. A series of experiments will be conducted to explore how changes of the percentage heat input affect the electrode displacement curve for various sheet steels used in the automotive industry.

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