Methods of constructing a process model for simulating a characteristic of a product of lithography from patterns produced under different processing conditions. The methods use a deviation between the variation of the simulated characteristic and the variation of the measured characteristic to adjust a parameter of the process model.

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

In one embodiment, a system may include a multi-purpose sensor coupled to a machine operating in an industrial environment. The multi-purpose sensor may include a camera that obtains a first and second set of image data including images of the machine and an environment surrounding the machine. The first set of image data is associated with a baseline of the machine and the environment, and the second set of image data is acquired subsequent to when the first set is acquired. The system may include a computing device that may include a processor to receive the first and second set of image data, determine baseline positions of objects in the first set, determine subsequent positions of the objects in the second set, determine whether the subsequent positions vary from the baseline positions, and perform an action when the subsequent positions vary from the baseline positions.

The system behavior is evaluated by checking the position and the orientation of a target processed by a processing device in accordance with a control instruction. A simulator estimates a behavior of a system including a processing device that processes a target. The simulator includes a measurement unit that performs image measurement of an input image including at least a part of a target as a subject of the image, an execution unit that executes a control operation for generating a control instruction directed to the processing device based on a measurement result obtained by the measurement unit, and a reproduction unit that reproduces, in the system, a behavior of a target detected in the input image together with information about a type and an orientation of the target based on time-series data for the control instruction output from the execution unit and the measurement result from the measurement unit.

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

The system performs a process for creating robotic control code for manufacturing products. A Designer UI displays virtual parts and receives inputs for processing and assembling the virtual parts that are required to create a virtual product. The designer identifies options for processing and assembling the virtual parts which are displayed on the user interface. The robot abilities and the options are selected to optimize a metric of the product manufacturing. The inventive toolset produces the robot control codes for performing a sequence of robotic abilities with the selected options to product the product. The robot control codes are used by a simulator which manipulates virtual robots and virtual parts to create a virtual product to check the robot control code. The verified robot control code is used to control real robots to create the product.

A recoater collision prediction and calibration method for additive manufacturing and a system thereof are provided. The recoater collision prediction and calibration method includes the following steps: loading a printing image file to generate a simulated printing object according to the printing image file; performing a process thermal stress simulation on the simulated printing object to obtain a plurality of simulated deformation variables respectively corresponding to a plurality of prediction results of the simulated printing object in a vertical direction on each layer; obtaining an experimental collision height of an experimental printed object; selecting one of the plurality of simulated deformation variables according to the experimental collision height; calculating a recoater tolerance according to the one of the plurality of simulated deformation variables; calibrating a collision risk formula according to the recoater tolerance; and predicting a collision risk value between the simulated printing object and a recoater according to the collision risk formula.

A recoater collision prediction and calibration method for additive manufacturing and a system thereof are provided. The recoater collision prediction and calibration method includes the following steps: loading a printing image file to generate a simulated printing object according to the printing image file; performing a process thermal stress simulation on the simulated printing object to obtain a plurality of simulated deformation variables respectively corresponding to a plurality of prediction results of the simulated printing object in a vertical direction on each layer; obtaining an experimental collision height of an experimental printed object; selecting one of the plurality of simulated deformation variables according to the experimental collision height; calculating a recoater tolerance according to the one of the plurality of simulated deformation variables; calibrating a collision risk formula according to the recoater tolerance; and predicting a collision risk value between the simulated printing object and a recoater according to the collision risk formula.

A maintenance support system includes a numerical controller that controls the drive of an operating mechanism of an industrial machine and a server that is a support device capable of communicating with the numerical controller, and the maintenance support system supports maintenance of the industrial machine. The numerical controller includes a first information generating unit that acquires, from among data related to a state of operation of the operating mechanism, data related to the state of operation of the operating mechanism when an anomaly is found in an operation of the operating mechanism, and generates first information including the data acquired. The server includes a second information generating unit that generates second information to be proposed to the numerical controller for the maintenance on the basis of the content of the first information.