Abstract
Resistance spot welding is the principal method of welding sheet steel products. In practice, the manufacture of welds of acceptable quality depends on the definition of optimum welding parameters and the implementation of suitable controls to ensure constant weld quality over a long production run. The ability to make a weld is best defined by a weldability lobe outlining the available manufacturing tolerances between minimum and maximum limits. Both two- and three-dimensional weldability lobes exist defined in terms of weld time, welding current and electrode force. Production variables influencing weld growth are discussed, particularly the effect of electrode diameter. The importance of welding machine characteristics relative to weld growth is highlighted. In particular, the rigidity of the machine coupled with the weight and frictional effects developed in the electrode head assembly are shown to be important factors influencing weld growth. Also important are the electrical characteristics of the machine, including transformer configuration current waveform and current 'off' time in the non-conducting part of the waveform. A reliable control philosophy is an essential requirement of any in-process feedback system if high quality spot welds are to be produced in high volume. Various model simulations indicate that changes, with time, in electrode/sheet and sheet/sheet interfacial resistances control weld nugget formation and growth. The relative contributions of these resistances are discussed. Heat generation and temperature distribution in the weld are determined by the current and force distribution across these interfaces. One-, two- and three-dimensional models have been developed to describe the temperature distribution in the weld zone and weld nugget growth. These models have limited application due to inadequate input data to describe the transient conditions appertaining in the weld zone. Current waveform, inductance effects, and friction/rigidity of the electrode head assembly, are not considered. Neural networks and fuzzy logic control have shown promise in classifying weld quality into predetermined groupings. The way forward is to adopt a multidisciplinary approach, taking into account the various interactions between the thermal, electrical, mechanical and metallurgical phenomena developed during the welding process. Models describing these phenomena should be interlinked to simulate heat generation and growth in the weld zone. The resulting model could be coupled with experimentally developed trends to optimise inputs to a neural network, the output of which is used, through a fuzzy logic controller, to take appropriate corrective action to ensure the required weld quality. It is stressed that any mathematical simulation or control system must take into account changes that occur at the welding electrodes as a consequence of electrode wear. The latter are discussed in Part 2 of this review.
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