Robot welding process control

This final report documents the development and installation of software and hardware for Robotic Welding Process Control. Primary emphasis is on serial communications between the CYRO 750 robotic welder, Heurikon minicomputer running Hunter & Ready VRTX, and an IBM PC/AT, for offline programming and control and closed-loop welding control. The requirements for completion of the implementation of the Rocketdyne weld tracking control are discussed. The procedure for downloading programs from the Intergraph, over the network, is discussed. Conclusions are made on the results of this task, and recommendations are made for efficient implementation of communications, weld process control development, and advanced process control procedures using the Heurikon

Identification of Hidden Failures in Process Control Systems Based on the HMG Method

We will continue here the research work, the goal of which was to introduce the notion of nondeterministic aggregation operators and study their properties, even in relation to classification systems and the associated learning problem. Here we will concentrate mostly on the notion of the nondeterministic aggregation system and its relation with deterministic ones. We will also see how such a model extends a discretized version of a model of participatory learning with an arousal background mechanism

Adaptive process control in rubber industry

This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples

Principal alarms in multivariate statistical process control

This paper describes a methodology for the simulation of multivariate out of control situations using in-control data. The method is based on finding the independent factors of the variability of the process, and shifting these factors one by one. These shifts are then translated in terms of the observed variables. The shifts provoked by the most important factors are called principal alarms. The principal alarms are plotted, visualizing the main deviations of the process. Also, a resampling procedure for ARL estimation using principal alarms is proposed. An application using a real industrial process, illustrates the usefulness of the methodology