Process variable

About: Process variable is a research topic. Over the lifetime, 3983 publications have been published within this topic receiving 43130 citations. The topic is also known as: process parameter.

Method for controlling a process device for sequential processing of semiconductor wafers

Abstract: While the first forward processed semiconductor wafer (11) of a batch in a process device (1) is measured for values for structural parameters (30), a second or other semiconductor wafers (12) of the batch is/are processed in said process device (1). A result signal (100) indicates a successfully conducted inspection of the first wafer for example, whereupon the subsequent wafers (12) do not need to be examined. The process parameters (31) of the process device (1) are automatically adjusted according to the measuring results. Events such as maintenance or parameter drift in trend cards etc. are detected in control units (8 or 9) and lead to event-related selection of the structural parameters (30') and/or initiation of a forward wafer (11) by outputting an event signal (102) e.g. in an event data bank (40).A warning signal (101) is issued by a control unit (8) in response to detected threshold value infringements (21) for at least one process parameter (31) and is also fed into the event data base (40).

Process and apparatus for determining the electroerosive completion of a starting hole

Abstract: The completion of a starting hole produced by spark erosion in a workpiece by use of a starter electrode in an electroerosion machine is automatically detected by a sudden change in a sensed process parameter when the starter electrode has passed through the workpiece.

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

Abstract: The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made. This feature selection is made by computing the mutual information between each process variable and the class variable. The performances of this method are evaluated on the data of a benchmark problem: the Tennessee Eastman Process. Three kinds of faults are taken into account on this complex process. The objective is to obtain the minimal recognition error rate for these 3 faults. Results are given and compared with results of other authors on the same data.

Method and device for controlling an output variable of a drive unit in the starting phase

Abstract: A method and a device for controlling an output variable of a drive unit in the starting phase is described. A controller is provided, regulating the actual value of at least one output variable to a setpoint variable of this output variable which is specified as a function of time. When at least one condition marking entry into the starting phase exists, the setpoint value of the output variable is initialized with the actual value of the output variable.

Abrasive jet machining of glass: Experimental investigation with artificial neural network modelling and genetic algorithm optimisation

Abstract: The paper presents an experimental-based study of abrasive jet machining (AJM) considering the effect of changing process parameters. A series of drilling tests were carried out on glass workpieces using sand as the abrasive powder. The influence of each process parameter; applied air pressure, standoff distance, nozzle diameter, particle grain size and impact angle on the machining performance was determined in terms of the resultant material removal rate (MRR). The experimental results revealed that MRR was highly dependent on the kinetic energy of the abrasive particles, with the applied pressure the dominant parameter. The experimental results were compared with an erosion rate model previously published by Jafar et al. Though correct trends were predicted, there was a large discrepancy between model and measured values. An artificial neural network (ANN) was utilised to model the MRR more precisely, particularly to establish relationships between applied machining parameters and experimentall...