Abstract: A method for time constraint management at a manufacturing system is provided. The method includes receiving a request to initiate a set of operations to be run at a manufacturing system, wherein the set of operations comprises one or more operations that each have one or more time constraints. The method further includes obtaining current data relating to a current state of the manufacturing system. The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model.

Abstract: A method for time constraint management at a manufacturing system is provided. A first request to initiate a set of operations to be run at the manufacturing system is received. The set of operations include one or more operations that each have one or more time constraints. A first set of candidate substrates to be processed during the set of operations is determined. A first simulation of the set of operations for the first set of candidate substrates is run over a first period of time. The simulation generates a first simulation output indicate a first number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the first time period. The set of operations is initiated at the manufacturing system to process the first number of candidate substrates over the first time period.

Abstract: A feed forward command aiding architecture with corresponding method, system, and computer product are provided. The feed forward command aiding architecture includes generating angle and rate commands from a received inertial data input. The angle command is feed into a proper order position loop producing an intermediate result. An angle feedback is differentiated producing a rate loop feedback. The intermediate result, rate command, and rate loop feedback are then feed into a proper order rate loop producing a torque command. The proper order rate loop is nested inside of the proper order position loop. The torque command being generated moves a beam steering element of an electro-optic sensor to deflect a line of sight of the electro-optic sensor by an angle approximating the received inertial angular input.

Abstract: A method and system for the use of prediction data in monitoring actual production targets is described herein. In one embodiment, a process is provided to receive data from a plurality of source systems in a manufacturing facility and generate a prediction pertaining to a future state of the manufacturing facility based on the data received from the plurality of source systems. A recent state of the manufacturing facility is determined based on the data received from the plurality of source systems and a comparison between the recent state and the prediction is facilitated.

Abstract: A feed forward command aiding architecture with corresponding method, system, and computer product are provided. The feed forward command aiding architecture includes generating angle and rate commands from a received inertial data input. The angle command is feed into a proper order position loop producing an intermediate result. An angle feedback is differentiated producing a rate loop feedback. The intermediate result, rate command, and rate loop feedback are then feed into a proper order rate loop producing a torque command. The proper order rate loop is nested inside of the proper order position loop. The torque command being generated moves a beam steering element of an electro-optic sensor to deflect a line of sight of the electro-optic sensor by an angle approximating the received inertial angular input.

Abstract: A method and system for the use of prediction data in monitoring actual production targets is described herein. In one embodiment, a process is provided to receive data from a plurality of source systems in a manufacturing facility and generate a prediction pertaining to a future state of the manufacturing facility based on the data received from the plurality of source systems. A recent state of the manufacturing facility is determined based on the data received from the plurality of source systems and a comparison between the recent state and the prediction is facilitated.