A method for monitoring machining processes in workpiece processing including steps of planning a processing process on the basis of a predetermined final shape of a workpiece to be achieved in the processing process and of quality features of the final shape of the workpiece and simulating the planned processing process in a computer-aided simulation. Target values of parameters of the simulated processing process occurring during the simulated processing process are detected and stored in the context of the computer-aided simulation. During the real processing process carried out according to the planned and simulated processing process, the parameters considered in the simulation are monitored and the actual values thereof are detected. By comparing actual values of parameters detected during the real processing process with the target values of these parameters detected during the simulation, the quality of the processing process and/or of the processed workpiece is assessed.

Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

A robot device of the present disclosure includes at least one artificial muscle that operates by being supplied with liquid; and a liquid supply device that supplies and discharges the liquid to/from the artificial muscle, and the liquid supply device includes: a liquid storage part that stores the liquid; a pressure regulating valve that regulates pressure of the liquid from the liquid storage part and supplies the liquid to the artificial muscle; and a liquid keeping part that allows the artificial muscle to keep the liquid supplied to the artificial muscle, according to occurrence of an abnormality, and the liquid supply device can allow the artificial muscle that operates by being supplied with liquid to operate properly.

A method and device for simulating a production duration of a silicon-wafer slicer, including: constructing a slicer simulating model, wherein the slicer simulating model includes process-step data of the slicer, and the process-step data include: a loading process step, a cutting process step, a discharging process step, a rinsing process step, a waiting process step, a broken-line replacing process step, a guide-pulley replacing process step, a home-roll replacing process step and a paying-off-wheel replacing process step; in the slicer simulating model, according to a predetermined rule, obtaining a process-step-to-be-executed datum; according to historical data, for the process-step-to-be-executed datum, assigning duration data that individually correspond to the process-step-to-be-executed data, wherein the historical data include: historical duration data that individually correspond to the process-step data of the slicer; and executing sequentially the process steps in the process-step-to-be-executed data, and obtaining a sum of the duration data of the process steps.

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

Active utilization of a robotic simulator in control of one or more real world robots. A simulated environment of the robotic simulator can be configured to reflect a real world environment in which a real robot is currently disposed, or will be disposed. The robotic simulator can then be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The sequence of robotic actions can be applied, to a simulated robot of the robotic simulator, to generate a sequence of anticipated simulated state data instances. The real robot can be controlled to implement the sequence of robotic actions. The implementation of one or more of the robotic actions can be contingent on a real state data instance having at least a threshold degree of similarity to a corresponding one of the anticipated simulated state data instances.

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

Active utilization of a robotic simulator in control of one or more real world robots. A simulated environment of the robotic simulator can be configured to reflect a real world environment in which a real robot is currently disposed, or will be disposed. The robotic simulator can then be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The sequence of robotic actions can be applied, to a simulated robot of the robotic simulator, to generate a sequence of anticipated simulated state data instances. The real robot can be controlled to implement the sequence of robotic actions. The implementation of one or more of the robotic actions can be contingent on a real state data instance having at least a threshold degree of similarity to a corresponding one of the anticipated simulated state data instances.

Embodiments of the disclosure provide for intelligent model integration within a process simulation system. Some embodiments receive data associated with the operation of a plant, determine, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model, train the intelligent model using the at least one qualifying dataset, and deploy the trained intelligent model for use.