Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

A building time for building an additively-manufactured object is calculated on the basis of the inter-pass time and the welding pass time and is compared with a preset upper limit value, and welding conditions in a depositing plan are repeatedly modified until the building time is equal to or less than the upper limit value. Alternatively, corrections are repeatedly performed until the shape difference between a building shape of built-up object shape data relating to the additively-manufactured object created on the basis of the inter-pass time and the inter-pass temperature, and a building shape of three-dimensional shape data, is smaller than a near net value.

A method includes generating a queue of a plurality of operations in a sequence recipe, the plurality of operations being associated with substrate processing. The method further includes sorting the plurality of operations in the queue based on a plurality of completion times corresponding to the plurality of operations. The method further includes, for each operation of the plurality of operations in the queue, obtaining a next operation in the queue and setting a virtual time axis to time leap to a corresponding completion time of the next operation until each operation of the plurality of operations in the queue are completed to generate a schedule for the sequence recipe.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.

One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

Simulation methods for simulating a real control (2) for an industrial process, a plant or a machine shall be able to determine errors occurring in the course of simulation more easily. For this purpose the invention proposes that the simulation system (7) stores intermediate states during the simulation and time-stamps them, wherein a stored intermediate state can be reloaded into the simulation system (7) at a later time and a simulation carried out on the basis thereof. As a result, simulations do not always need to start with the beginning of the control program to be simulated.

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).