The present invention relates to an apparatus (100) for configuring a production machine based on product data, the apparatus (100) comprising: interface device (10) adapted to provide product data for a product to be produced; computer device (20) adapted to evaluate the product data and to generate configuration data for a machine configuration of the production machine based thereon; and control device (30) adapted to control the production machine based on the configuration data in order to produce the product.

A power tool includes a housing and a sensor, a machine learning controller, a motor, and an electronic controller supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The machine learning controller includes a first processor and a first memory and is coupled to the sensor. The machine learning controller further includes a machine learning control program configured to receive the sensor data, process the sensor data using the machine learning control program, and generate an output based on the sensor data using the machine learning control program. The electronic controller includes a second processor and a second memory and is coupled to the motor and to the machine learning controller. The electronic controller is configured to receive the output from the machine learning controller and control the motor based on the output.

Described are platforms, systems, and methods to discover relationships among equipment in automated industrial or commercial environments by looking for synchrony in state changes among the equipment. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; detect one or more events or one or more state changes in the data sources; store the detected events or state changes; detect synchrony in the detected events or state changes by performing operations comprising: identifying combinatorial pairs of data sources having an event or state change within a predetermined time window; and conducting pairwise testing for each identified combinatorial pair of data sources by applying an algorithm to the stored detected events or state changes; and determine one or more relationships for at least one identified combinatorial pair of data sources.

Techniques for calibrating a high fidelity (HF) model of molten droplet coalescence are disclosed. An example method includes selecting initial HF parameter values for the HF model. The method also includes iteratively refining the HF parameter values until the HF model converges with experimental data. At each iteration, the HF parameter values are applied to the HF model and a plurality of simulations are run using the HF model to generate the simulated numerical data. For each simulation, a Reduced Order Model (ROM) is fitted to the simulated numerical data to generate ROM parameter values for ROM parameters of the ROM. Correlations between the ROM parameters and the HF parameters are identified to narrow the search space to be searched in a next iteration.

Described are platforms, systems, and methods for mapping data found in connected equipment from a manufacturer's selected schema, format, and protocol to a normalized data model. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; retrieve data from at least one of the identified data sources; generate a plurality of data source mapping profiles, each data source mapping profile specific to a particular data source configuration; maintain a data store comprising the plurality of data source mapping profiles; select a data source mapping profile specific to the at least one identified data source configuration; and apply an algorithm to map the retrieved data to a predetermined ontology based on the selected data source mapping profile for the at least one identified data source.

Described are platforms, systems, and methods to discover subtended devices through introspection of executive or supervisory controllers. The platforms, systems, and methods maintain a plurality of introspection directives, each introspection directive comprising a sequence of instructions specific to a communications protocol, the sequence of instructions comprising instructions to send at least one command to at least one controller associated with an automation environment in accordance with the communications protocol, instructions to receive a response to the at least one command, and instructions to parse the response; identify an appropriate introspection directive for the at least one controller; and execute the sequence of instructions with respect to the at least one controller to perform operations comprising: sending at least one command to at least one controller; receiving a response; and parsing the response.

Described are platforms, systems, and methods for real-time enrichment of vertices, edges, and related data within a graph database. The platforms, systems, and methods maintain a graph database comprising a representation of a current state of an automation environment comprising a plurality of data sources, wherein the data sources are represented as vertices in the graph database and relationships between the individual data sources are represented as edges in the graph database; operate a plurality of software agents, each software agent configured to perform operations comprising: applying an algorithm to identify patterns in the graph database; and generating a specific data enrichment based on one or more identified patterns; and contribute the generated data enrichment back to the graph database.

Methods, devices, and systems related to process control in manufacturing are described. In an example, a method can include receiving data from a first process control device affixed to a first manufacturing tool of a first type, identifying one or more attributes of the data via a second processing resource of a second process control device affixed to a second manufacturing tool of a second type different from the first type, determining one or more settings for the second manufacturing tool via the second processing resource in response to identifying the one or more attributes of the data, and sending a command including the one or more settings to the second manufacturing tool from the second process control device.

Methods and systems are disclosed for focus ring thickness measurement and feedback control within process chambers. For disclosed embodiments, in-chamber sensors measure physical parameters associated with focus rings, and these measurements are used to determine thickness for the focus rings. The thickness determinations can be used to detect when a focus ring should be replaced and can also be used as feedback to adjust the position of the focus rings within the chamber. For one embodiment, measurements from ultrasonic sensors are used to make thickness determinations for focus rings. For further embodiments, these ultrasonic sensors are positioned at end portions of focus ring lift pins. Other sensors can also be used such as capacitive sensors, resistive sensors, and/or other desired sensors. Further variations and implementations can also be achieved using in-chambers sensors to facilitate focus ring thickness determinations.

Methods, devices, and systems related to process control in manufacturing are described. In an example, a method can include receiving data from a first process control device affixed to a first manufacturing tool of a first type, identifying one or more attributes of the data via a second processing resource of a second process control device affixed to a second manufacturing tool of a second type different from the first type, determining one or more settings for the second manufacturing tool via the second processing resource in response to identifying the one or more attributes of the data, and sending a command including the one or more settings to the second manufacturing tool from the second process control device.