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.

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.

Methods, systems, and non-transitory computer readable medium for scheduling a substrate processing sequence in an integrated substrate processing system are disclosed herein. A processing device generates a processing model for a batch of semiconductor substrates. The processing model defines a corresponding start time for each semiconductor substrate in each processing chamber in the integrated substrate processing system. One or more graphics processing units (GPUs) generates parallel inputs based on the processing model and concurrently processes, by a plurality of cores of the one or more GPUs, the parallel inputs to generate parallel outputs for the batch of semiconductor substrates. Each of the parallel inputs is processed on a distinct core of the plurality of cores of the one or more GPUs to generate a corresponding parallel output. The processing device causes the batch of semiconductor substrates to be processed in the integrated substrate processing system based on the parallel outputs.

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.

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 production system between processes is automatically determined using work performance data acquired at a production site, and a process model in which a flow shop and a job shop are mixed is automatically generated. A process model automatic generation system 210 reads work performance data from work performance data 120 and extracts a process flow for each product (130). Next, a synthesis flow obtained by combining a plurality of process flows is created, and a production system between processes corresponding to a closed path is changed to a job shop to automatically determine a production system (140). Finally, a process model in which a flow shop and a job shop are mixed is generated (150) and displayed on a display screen 160.

A sensing system provided with a target detecting unit which applies predetermined processing to data obtained by a detecting device and thereby detects a target object, the sensing system including an augmented-reality-image creating unit which creates an image corresponding to a candidate in a plurality of candidates that appear in the processed data to which the predetermined processing has been applied, a predetermined parameter of the candidate being equal to or greater than a detection threshold, and creates a candidate image corresponding to a candidate in the plurality of candidates, the predetermined parameter of the candidate for the candidate image is less than the detection threshold and equal to or greater than a detection candidate threshold, and a display device that displays the image and the candidate image.

A method for computer-aided control of an automation system is provided by use of a digital simulation model which simulates the automation system and which is specified by a number parameters comprising a number of configuration parameters) describing the configuration of the automation system and a number of state parameters describing the operational state of the automation system. Simulated operation runs of the automation system based on the simulation model can be performed with the aid of a computer, where a simulation run predicts a number of performance parameters of the automation system.

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.

Methods, systems, and non-transitory computer readable medium for scheduling a substrate processing sequence in an integrated substrate processing system are disclosed herein. A processing device generates a processing model for a batch of semiconductor substrates. The processing model defines a corresponding start time for each semiconductor substrate in each processing chamber in the integrated substrate processing system. One or more graphics processing units (GPUs) generates parallel inputs based on the processing model and concurrently processes, by a plurality of cores of the one or more GPUs, the parallel inputs to generate parallel outputs for the batch of semiconductor substrates. Each of the parallel inputs is processed on a distinct core of the plurality of cores of the one or more GPUs to generate a corresponding parallel output. The processing device causes the batch of semiconductor substrates to be processed in the integrated substrate processing system based on the parallel outputs.