A non-transitory computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive a first dataset associated with a mover system having a track and a plurality of mover assemblies independently movable along the track, identify a subset of the first dataset associated with a normal operating state of the mover system based on state data associated with the mover system, receive a second dataset associated with the mover system after receiving the first dataset and having a first set of differences from the subset of the first dataset, determine whether the second dataset is indicative of an anomaly state of the mover system based on a relationship between an anomaly signature dataset and the second dataset, and adjusting operation of the mover system in response to determining that the second dataset corresponds to the anomaly signature dataset.

A method may include obtaining acquired process data regarding a plant process that is performed by a plant system. The method may further include obtaining from a process model, simulated process data regarding the plant process. The method may further include determining drift data for the process model based on a difference between the acquired process data and the simulated process data. The drift data may correspond to an amount of model drift associated with the process model. The method may further include determining whether the drift data satisfies a predetermined criterion. The method further includes determining, in response to determining that the drift data fails to satisfy the predetermined criterion, a model update for the process model.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

A manufacturing device inputs design information including three-dimensional structure data, generates a manufacturing process flow, and displays the manufacturing process flow on a screen for a user to check, modify, and confirm the flow based on design information and setting information. A process method includes a first process method of a direct modeling method having an FIB method and a second process method of a semiconductor manufacturing process method which is a non-FIB method. The manufacturing device generates a plurality of manufacturing process flows by a combination of cases where each of the process methods is applied to each of the regions of the three-dimensional data. The manufacturing process flow includes a process device, the process method, a control parameter value, a process time, and a total process time for each of process steps. An output unit outputs a manufacturing process flow having, for example, the shortest total process time.

Disclosed herein is a worksite automation process that involves: generating a first sequence of tasks to build the product according to a model. The process further involves causing one or more robotic devices to build the product by beginning to execute the first sequence of tasks. Further, during the execution of the first sequence of tasks, performing a buildability analysis to determine a feasibility of completing the product by executing the first sequence of tasks. Based on the analysis, determining that it is not feasible to complete the product by executing the first sequence of tasks, and in response, generating a second sequence of tasks to complete the product according to the model. Then, causing the one or more robotic devices to continue building the product by beginning to execute the second sequence of tasks.

A base software application and an operation software application are installed in a machine control computer. The base software application includes a first communication unit being a program portion, a second communication unit being a program portion, and an information conversion unit being a program portion. The information conversion unit enables a CPU of the computer to convert first machine information specific to a machine into second machine information specific to the operation software application, based on a data model or the like corresponding to a category of the machine or the like.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

There is provided a method of generating instructions for manufacturing a microstructure by a manufacturing device, comprising: providing a representation of a freeform tile, providing a representation of a deformation map that maps a parameter space to a space of an object, arranging a plurality of instances of the freeform tile within at least a portion of a domain of the deformation map to create a tile arrangement, creating a representation a microstructure of the object by composing the tile arrangement into the deformation map, and providing code instructions for execution by a manufacturing device controller of a manufacturing device for manufacturing the microstructure.