Disclosed is an injection molding system including: a state observation section observing, when injection molding is performed, physical-amounts relating to the injection molding that is being performed; a physical-amount data storage section storing the physical-amount data; a reward-conditions setting section setting reward conditions for machine learning; a reward calculation section calculating a reward based on the physical-amount data and the reward conditions; an operating-conditions adjustment learning section performing machine learning of adjusting operating conditions based on the reward calculated by the reward calculation section, the operating conditions, and the physical-amount data; a learning-result storage section storing a learning result of the machine learning by the operating-conditions adjustment learning section; and an operating-conditions adjustment-amount output section determining and outputting an operating condition to be adjusted and an adjustment amount based on the machine learning by the operating-conditions adjustment learning section.

A central plant that generates and provides resources to a building. The central plant includes an electrical energy storage subplant configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The central plant includes a plurality of generator subplants that consume one or more input resources. The central plant includes a controller configured to determine, for each time step within a time horizon, an optimal allocation of the input resources and the output resources for each of the subplants in order to optimize a total monetary value of operating the central plant over the time horizon. The total monetary value includes revenue from participating in incentive-based demand response programs as well as costs associated with resource consumption, equipment degradation, and losses in battery capacity.

A motion generation device may be for generating a movement for changing the robot from a first orientation to a second orientation, and include a first acquisition unit that acquires first orientation information that specifies the first orientation and second orientation information that specifies the second orientation, a second acquisition unit that acquires at least one priority item regarding the movement for changing from the first orientation to the second orientation, and a movement generation unit that generates a motion of the robot that includes a movement path along which the robot moves from the first orientation to the second orientation, based on the first orientation information, the second orientation information, and the priority item that were acquired.

The invention relates to a method (100) for orientation of a workpiece (20) to be processed, comprising the steps of: a) providing a processing path (27) fixed on the workpiece for processing the workpiece (20); b) selecting a rigid transformation (30) of the positioning of the workpiece (20); c) simulating the processing path (27) taking account of the rigid transformation (30) of the positioning of the workpiece (20); d) determining at least one process variable (40) of the machining of the workpiece (20); wherein the steps b) to d) are repeated by modifying the at least one rigid transformation (30) of the positioning of the workpiece (20) until the at least one process variable (40) reaches a target value (43).

A method of controlling a non-productive movement of a tool from a starting position to an end position in a travel envelope of a machine tool includes the steps of a) providing a collision-free first trajectory for the non-productive movement of the tool, b) determining a second trajectory that is improved over the first trajectory with regard to a selectable target parameter using an algorithm, and c) checking the second trajectory for collisions and, if the second trajectory is free of collisions, providing an instruction corresponding to the second trajectory. The first trajectory in step a) includes plural rectilinear segments and the second trajectory in step b) includes a polynomial segment and, if the second trajectory is not free of collisions in step c), steps b) to c) are repeated so that the algorithm is provided with a modified model of the travel envelope in a repeat of step b).

Methods for CAD, simulation, and corresponding systems and computer-readable mediums. A method includes receiving inputs including one or more of robot information, operation information, position information, and constraint information. The method includes generating a list of candidate positions of a robot. The method includes, for each candidate position, determining a time value of the candidate position and when the time value of the candidate position does not meet a threshold cycle time value, removing the candidate position. The method includes, for each candidate position, determining an energy consumption value of the candidate position. The method includes, for each candidate position, determining one or more of a rating and a ranking for the candidate position based on the time value and the energy consumption value. The method includes determining the optimal position of the robot based on the ranking of each candidate position.

A scheduling system includes first circuitry and second circuitry. The first circuitry stores, in a memory, information on a plurality of tasks to be executed by at least one mobile device. The information on the plurality of tasks includes information on an estimated amount of battery consumption of the at least one mobile device in executing each of the plurality of tasks. The second circuitry receives designation of the plurality of tasks to be executed by the at least one mobile device. The second circuitry further causes a display to display a screen having a schedule in which the plurality of tasks is arranged for the at least one mobile device based on the information on the estimated amount of battery consumption.

Various disclosed embodiments include methods, systems, and computer-readable media for identifying a motion path for an industrial robot. According to one embodiment, a method includes identifying a plurality of points at which at least one component of the industrial robot is positioned during performance of a task. The identified points include at least a starting point and an ending point of the component for performing the task. The method also includes generating one or more motion paths for the industrial robot to perform the task based on the identified points. The method further includes identifying and predicting energy consumption by the industrial robot for the one or more generated motion paths. The method also includes selecting the motion path for the industrial robot based on the identified energy consumption. Additionally, the method includes storing information about the energy consumption by the industrial robot for the selected motion path.

A method for managing electric power consumption of a client device, the method being characterized in that it comprises the steps of: receiving (701) a request to start an operating program associated with an energy consumption plan comprising at least one step; exchanging (703) data with an external device, regarding the operation request as well as the selected energy consumption plan of the device; receiving (705) a modified energy consumption plan wherein said modified energy consumption plan has been set by taking into account energy consumption plan of at least one other device as well as a maximum power consumption limit; and executing (707) said at least one step of the energy consumption plan.

A central plant that generates and provides resources to a building. The central plant includes an electrical energy storage subplant configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The central plant includes a plurality of generator subplants that consume one or more input resources. The central plant includes a controller configured to determine, for each time step within a time horizon, an optimal allocation of the input resources and the output resources for each of the subplants in order to optimize a total monetary value of operating the central plant over the time horizon. The total monetary value includes revenue from participating in incentive-based demand response programs as well as costs associated with resource consumption, equipment degradation, and losses in battery capacity.