Abstract: A Vacuum Arc Remelting controller apparatus wherein process control is accomplished primarily through control of pool power. Pool power being defined as the total energy flux into a top ingot surface of the VAR ingot. The controller apparatus of the present invention comprises a controller computer that transmits commands to the host furnace through an ethernet connection.

Abstract: A Vacuum Arc Remelting controller apparatus wherein process control is accomplished primarily through control of pool power. Pool power being defined as the total energy flux into a top ingot surface of the VAR ingot. The controller apparatus of the present invention comprises a controller computer that transmits commands to the host furnance through an ethernet connection.

Abstract: An apparatus for controlling a vacuum remelting furnace comprising a controller capable of adjusting electrical current to an electrode and a controller capable of adjusting the electrode's drive speed. The controllers adjust the current and drive speed based on a predetermined pool power reference value. The apparatus may also comprise a third controller that receives the adjusted current and drive speed as inputs and sends estimated electrode drive speed bias as output to the drive speed controller and estimated current bias as output to the current controller.

Abstract: An apparatus for and method of controlling a remelting furnace comprising adjusting current supplied to an electrode based upon a predetermined pool power reference value and adjusting the electrode drive speed based upon the predetermined pool power reference value.

Abstract: An apparatus and method for controlling an electroslag remelting furnace comprising adjusting electrode drive speed by an amount proportional to a difference between a metric of electrode immersion and a set point, monitoring impedance or voltage, and calculating the metric of electrode immersion depth based upon a predetermined characterization of electrode immersion depth as a function of impedance or voltage.

Abstract: A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra.

Abstract: A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra.

Abstract: A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra.

Abstract: A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra.

Abstract: A method of and apparatus for controlling an electroslag remelting furnace by driving the electrode at a nominal speed based upon melting rate and geometry while making minor proportional adjustments based on a measured metric of the electrode immersion depth. Electrode drive speed is increased if a measured metric of electrode immersion depth differs from a set point by a predetermined amount, indicating that the tip is too close to the surface of a slag pool. Impedance spikes are monitored to adjust the set point for the metric of electrode immersion depth based upon one or more properties of the impedance spikes.

Abstract: A method of and apparatus for controlling an electroslag remelting furnace by driving the electrode at a nominal speed based upon melting rate and geometry while making minor proportional adjustments based on a measured metric of the electrode immersion depth. Electrode drive speed is increased if a measured metric of electrode immersion depth differs from a set point by a predetermined amount, indicating that the tip is too close to the surface of a slag pool. Impedance spikes are monitored to adjust the set point for the metric of electrode immersion depth based upon one or more properties of the impedance spikes.

Abstract: An apparatus and method of controlling a remelting process by providing measured process variable values to a process controller; estimating process variable values using a process model of a remelting process; and outputting estimated process variable values from the process controller. Feedback and feedforward control devices receive the estimated process variable values and adjust inputs to the remelting process. Electrode weight, electrode mass, electrode gap, process current, process voltage, electrode position, electrode temperature, electrode thermal boundary layer thickness, electrode velocity, electrode acceleration, slag temperature, melting efficiency, cooling water temperature, cooling water flow rate, crucible temperature profile, slag skin temperature, and/or drip short events are employed, as are parameters representing physical constraints of electroslag remelting or vacuum arc remelting, as applicable.

Abstract: A method and apparatus for controlling vacuum arc remelting (VAR) furnaces by estimation of electrode gap based on a plurality of secondary estimates derived from furnace outputs. The estimation is preferably performed by Kalman filter. Adaptive gain techniques may be employed, as well as detection of process anomalies such as glows.