Abstract: A method for distributed agent based non-expert simulation of manufacturing process behavior on a single-processor computer comprises the steps of: object modeling a manufacturing technique having a plurality of processes; associating a distributed agent with each the process; and, programming each the agent to respond to discrete events corresponding to the manufacturing technique, wherein each discrete event triggers a programmed response. The method can further comprise the step of transmitting the discrete events to each agent in a message loop. In addition, the programming step comprises the step of conditioning each agent to respond to a discrete event selected from the group consisting of a clock tick message, a resources received message, and a request for output production message.

Abstract: A method for automatically evaluating a manufacturing technique comprises the steps of: receiving from a user manufacturing process step parameters characterizing a manufacturing process; accepting from the user a selection for an analysis of a particular lean manufacturing technique; automatically compiling process step data for each process step in the manufacturing process; automatically calculating process metrics from a summation of the compiled process step data for each process step; and, presenting the automatically calculated process metrics to the user.

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.