Methods and systems are disclosed for simulating a fabrication process based on real time sensor measurements obtained during the process. In one embodiment, a first simulation of the process computes a set of predicted physical responses based on a first set of assumed boundary conditions, and then, during the fabrication process sensor measurements are obtained and used to compute a second set of boundary conditions. A second simulation, based on the second set of boundary conditions, can then be performed to compute an updated set of predicted physical responses that can be compared to the previously computed set of physical responses. The difference(s) can be used to determine line, surface or volumetric response distribution from point, line or surface boundary conditions respectively, whether and how to modify the fabrication process (or other processes) and how to take additive and other manufacturing process decisions real-time using simulation. Other examples are also described.

There are provided methods and systems for making or repairing a specified part. For example, there is provided a method for creating a manufacturing process to make or repair the specified part. The method includes receiving data from a plurality of sources, the data including as-designed, as-manufactured, as-simulated, as-operated, as-inspected, and as-tested data relative to one or more parts similar to the specified part. The method includes updating, in real time, a surrogate model corresponding with a physics-based model of the specified part, wherein the surrogate model forms a digital twin of the specified part. The method includes generating a multi-variant distribution including component performance and manufacturing variance, the manufacturing variance being associated with at least one of an additive manufacturing process step and a reductive manufacturing process step. The method includes comparing a performance from the multi-variant distribution with an expected performance of the new part based on the surrogate model. The method includes executing, based on the digital twin, the optimized process to either repair or make the specified part.

A method of controlling a real production process, wherein the method includes: a) receiving initial condition data from an on-line simulator system simulating the real production process, and b) performing an optimization based on the initial condition data and on an objective function to obtain set points for controlling the real production process.

The present invention relates to the technical field of industrial automation, and in particular to a method and system for quick customized-design of an intelligent workshop. The method comprises the following steps: step A: acquiring design requirement information of a production line, and performing modeling in a simulation system according to the design requirement information; step B: performing action planning of a physical stand-alone device, performing logistics and motion planning of articles being processed, and compiling motion and action control scripts; step C: establishing, by the digital twin technology, a communication channel among a PLC system of the workshop digitization model, a PLC system of a physical workshop device and a host computer; and, step D: outputting a three-dimensional digital twin model as a blueprint for follow-up design and development of the stand-alone device, a control system and an execution system.

Methods and systems (100) for modeling operations of a physical plant (110) are presented. For instance, a system (100) includes at least a first component (111) and a second component (112). The first and second components (111), (112) have at least one physical connection (130a). First and second model operational parameters of the physical connection are received from first and second models 121, 122, respectively. The first and second models (121), (122) are updated with the second and first model operational parameters, respectively. In one example, the first and second models (121), (122) run on different computer systems. In another example, real-time operational data is received from the first and second components (111), (112), and the first and second models (121), (122) are updated with the real-time operational data received from the second and first components, respectively. In a further example, the system (100) may receive and process simulation input. In various examples, the physical connections (130a) may include a material stream, a rotating shaft or a control signal.

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.

The purpose of the present invention is to provide a method for constructing a digital twin, enabling real-time monitoring, operation improvement, and coping with the occurrence of an accident in an industrial site by combining reduced order models of a multiphysical system, measurement data and artificial intelligence techniques, and the method for constructing a digital twin, according to the present invention, comprises: a network-defining step of defining a multiphysical engineering system as a network constituted by a combination of element facilities; an element model establishing step of establishing a relation-based 0-dimensional (0-D) model for each of the element facilities; a system model establishing step of closing all relations for a system by reflecting an additional relation by machine learning from a 3-dimensional computer aided engineering reduced order model (3-D CAE ROM) or data for key element facilities in the 0-D models established in the element model establishing step; a system ROM constructing step of constructing a system ROM for the system model established in the system model establishing step from calculation results for conditions sampled in an operating variable parameter space; a system ROM correcting step of minimizing an error between a model predicted value and measured data for the element facility and the system; and a real-time algorithm constructing step of constructing an algorithm for identifying an expected system state or an optimal operating condition in a virtual operating condition based on the real-time monitoring result.

To make it possible to evaluate quantitatively whether there is a problem on a machining surface. A simulator includes a memory unit that stores machining position data to be used when a machine tool machines a machining-target object, a machining surface simulation unit that uses the machining position data that is stored to perform a simulation of a machining surface, a surface texture calculation unit that calculates a surface texture of the machining surface that is simulated through the simulation of the machining surface, and a machining surface evaluation unit that evaluates the surface texture on the basis of an evaluation condition.

Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.

Methods and systems (100) for modeling operations of a physical plant (110) are presented. For instance, a system (100) includes at least a first component (111) and a second component (112). The first and second components (111), (112) have at least one physical connection (130a). First and second model operational parameters of the physical connection are received from first and second models 121, 122, respectively. The first and second models (121), (122) are updated with the second and first model operational parameters, respectively. In one example, the first and second models (121), (122) run on different computer systems. In another example, real-time operational data is received from the first and second components (111), (112), and the first and second models (121), (122) are updated with the real-time operational data received from the second and first components, respectively. In a further example, the system (100) may receive and process simulation input. In various examples, the physical connections (130a) may include a material stream, a rotating shaft or a control signal.