Abstract: A method, a computer program product, and a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model. The method includes receiving a historical dataset of high-frequency sensor signal data and low-frequency sensor signal data. The method also includes generating a Gaussian process regression model using the historical dataset and sensor parameters and outputting high-frequency sensor signal predictions. The method also includes generating a hierarchical Gaussian process model using the historical dataset and the high-frequency sensor signal predictions and predicting low-frequency sensor signal predictions.

Abstract: A method for determining alloy processing parameters is provided. Simulated melt pool temperature and melt pool geometries can be used to create an initial printability map based on laser speed and laser power, and the printability map can include regions with potential manufacturing defects. Single-track experiments can be used to calibrate the printability map, to produce a revised printability map. Finally, contour lines representing hatch spacing can also be added to the revised printability map to produce a final printability map that can be used to configure additive manufacturing machinery.

Abstract: A method, a computer program product, and a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model. The method includes receiving a historical dataset of high-frequency sensor signal data and low-frequency sensor signal data. The method also includes generating a Gaussian process regression model using the historical dataset and sensor parameters and outputting high-frequency sensor signal predictions. The method also includes generating a hierarchical Gaussian process model using the historical dataset and the high-frequency sensor signal predictions and predicting low-frequency sensor signal predictions.

Abstract: A monitoring system for metric data may include devices, each device capable of generating respective metric data. The monitoring system may also include a processor and a memory cooperating therewith. The processor may be configured to monitor the devices via a network to obtain the respective metric data, generate predicted trigger events based on monitoring the devices, and generate a respective adapted monitoring for the devices based upon each predicted trigger event. The processor may also be configured to, upon occurrence of one of the predicted trigger events, implement the respective adapted monitoring to obtain new respective metric data.

Abstract: A monitoring system for metric data may include devices, each device capable of generating respective metric data. The monitoring system may also include a processor and a memory cooperating therewith. The processor may be configured to monitor the devices via a network to obtain the respective metric data, generate predicted trigger events based on monitoring the devices, and generate a respective adapted monitoring for the devices based upon each predicted trigger event. The processor may also be configured to, upon occurrence of one of the predicted trigger events, implement the respective adapted monitoring to obtain new respective metric data.

Abstract: A method for managing heat energy in a metal casting plant includes executing a local control optimization model to control mass of solid metal charges to each modular melting furnace. The local control optimization model is configured to achieve a commanded total mass of molten material and coincidentally minimize waste heat for each of the modular melting furnaces. The method for managing heat energy in the metal casting plant further includes executing a system control optimization model to manage operation of a heat energy recovery system. The system control optimization model is configured to manage the operation of the heat energy recovery system including transferring the waste heat from the modular melting furnaces to a plurality of heat demand centers while minimizing total loss of the waste heat in the metal casting plant.

Abstract: A method for managing heat energy in a metal casting plant includes executing a local control optimization model to control mass of solid metal charges to each modular melting furnace. The local control optimization model is configured to achieve a commanded total mass of molten material and coincidentally minimize waste heat for each of the modular melting furnaces. The method for managing heat energy in the metal casting plant further includes executing a system control optimization model to manage operation of a heat energy recovery system. The system control optimization model is configured to manage the operation of the heat energy recovery system including transferring the waste heat from the modular melting furnaces to a plurality of heat demand centers while minimizing total loss of the waste heat in the metal casting plant.