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.

A method of developing a product is provided that includes generating a digital system model (DSM) that describes the product and a manufacturing process for the product, and generating a digital twin (DTw) of an instance of a component of the manufacturing process, the DTw including a digital replica of the instance. The method includes receiving attribute data for attributes of process/process characteristics subject to sources of variation that affect product quality. The digital replica is executed with input of the attribute data, and thereby updating the DTw to replicate the instance of the component of the manufacturing process as performed. A data analysis of the manufacturing process is performed based on the DTw as updated, and the manufacturing process is modified based on the data analysis to reduce variability in one or more product characteristics. The DTws of multiple instances may be used to generate further improvement.

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.

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.

Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

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.

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.

Provided is a system including a planning section that generates a production plan for a production site, using a planning model; a simulating section that simulates operation of at least a portion of the production site, based on a simulation model of the at least a portion of the production site; a monitoring section that monitors actual operation of the at least a portion of the production site; a calibrating section that calibrates the simulation model, based on a difference between the simulated operation and the actual operation; and a control section that controls the at least a portion of the production site, based on a simulation result obtained by simulating the operation of the at least a portion of the production site in accordance with the production plan, using the simulation model that has been calibrated.

When operating a production site, it is preferable to maintain accurate models continuously. Provided is a system including a simulating section that simulates operation of at least a portion of a production site, based on a simulation model of the at least a portion of the production site; a monitoring section that monitors actual operation of the at least a portion of the production site; a calibrating section that calibrates the simulation model, based on a difference between the simulated operation and the actual operation; and an updating section that updates a planning model used to generate a production plan for the production site, according to the calibration of the simulation model.