Abstract: The system of the present invention may “solve” a variety of inverse physical problem types by using neural network techniques. In operation, the present invention may generate data sets characterizing a particular starting condition of a physical process (such as data sets characterizing the parameters of an initial metal die), based upon an ending condition of the physical process (such as the parameters of the metal part to be stamped by the die). In one embodiment, the system of the present invention may generate a plurality of training data sets, each training data set characterizing a sample ending condition, the physical process that results in the sample ending condition, and a sample starting condition of the physical process. The training data sets may then be applied to a neural network so as to train the network.

Abstract: A spot welder comprises a neural network for processing, in real time, current and voltage energizing a weld in progress. The neural network generates a predicted time of optimal weld strength and/or nugget size for the weld in progress. A controller terminates the weld in progress at the predicted time. A method for controlling a spot welder comprises the steps of: sensing in real time current and voltage energizing a spot weld in progress; predicting a time of optimal weld strength and/or nugget size with a neural network responsive to the sensed current and voltage; and, terminating the weld in progress at the predicted time. A sensor for electromotive forces (EMF) induced by the spot welder can generate a signal for canceling out a large fraction of EMF components in at least one or both of the current and voltage signals. EMF components are substantially precluded in the current signal if the current sensor uses a buried shunt.