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.

Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

A non-transitory computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive sensory datasets associated with an industrial automation system from sensors, receive positioning data via an extended reality device associated with a user, determine a first virtual positioning of the user in a virtual coordinate system based on the positioning data, determine a second virtual positioning of an industrial automation system in the virtual coordinate system based on the sensory datasets, determine output representative data to be presented by the extended reality device based on the plurality of sensory datasets and in accordance to the first virtual positioning relative to the second virtual positioning, and instruct the extended reality device to present the output representative data.

Various embodiments of the present technology relate to digital twins of devices and assemblies. More specifically, some embodiments relate to the orchestration of digital twin models for representing industrial systems based on characteristics of digital twins. In an embodiment, a method of operating an orchestration engine in an industrial automation environment comprises identifying a targeted outcome for modeling the industrial automation environment, configuring a digital twin environment corresponding to the industrial automation environment based at least on the targeted outcome, and executing a process associated with the industrial automation environment using the digital twin environment.

A travel control system of an automated guided vehicle including, an actual guided-vehicle travel controller including an actual control program causing a plurality of actual guided vehicles to travel along a plurality of travel routes crossing one another in an actual conveyance area, a virtual guided-vehicle travel controller including a virtual control program causing a plurality of virtual guided-vehicles to travel by imitating a shape and a motion of the actual guided vehicles in a virtual conveyance area, a virtual guided-vehicle travel monitoring processor which, in a case of occurrence of a collision or stop abnormality by the virtual guided vehicles, detects and records the abnormal phenomenon, a monitoring-result output transmitter which outputs the detected abnormal phenomenon to outside, and an update-data input receiver which receives update data modified by referring to the output abnormal phenomenon from outside and finally reflects it in the actual control program.

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.

A simulation method and system for real-time simulation using digital twin agent are disclosed. The simulation method may include generating a digital twin object cyberizing a manufacturing resource required for a process based on manufacturing resource information, mapping a learning model onto the digital twin object and transmitting the learning model mapped onto the digital twin object to a digital twin agent, receiving information analyzed by using the learning model from the digital twin object of the digital twin agent, and performing a simulation based on the received information.

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.

A cyber-physical system type machining system includes: a machine tool disposed in a real world and including a machine body and a control device; and a computer device connected to communicate with the control device and including a processor and a memory storing a program for generating, in a virtual world, a virtual machining phenomenon corresponding to an actual machining phenomenon with regard to a workpiece and the machine body. The program, when executed by the processor, causes the computer device to perform: acquiring a command value in synchronization with the control device, the command value for controlling the machine body by the control device; generating a future virtual machining phenomenon, which is the virtual machining phenomenon in a future, based on the command value; and outputting, to the control device, an optimal command value for correcting the command value based on the future virtual machining phenomenon.