A control device, a system and methods for optimizing and providing movement profiles, wherein the movement profiles serve for determining the movement of tools of a press and the movement of a receiving element for a workpiece of a transfer system, where transfer movement profiles are synchronized with one another via press movement profiles, the synchronization of the transfer movement profiles particularly occurs by chronologically shifting synchronization points via boundary conditions such that an offset of the press movement profiles can be determined via the synchronization, so that the workpiece can be processed as quickly as possible through the system.

A vehicle scheduling apparatus acquires a task list information that indicates tasks assigned to a target person to whom a target vehicle is assigned. The vehicle scheduling apparatus determines an execution order of the tasks of the target vehicle using a cost matrix that indicates a minimum cost for each pair of sites, selects a path to travel for each task, generates schedule information, and detects a conflict between the target vehicle and a temporary obstacle. When the conflict is detected, the vehicle scheduling apparatus updates the cost matrix so as to update the minimum cost of the pair of sites, and repeatedly generating the schedule information until no detection is detected.

In one embodiment, a model predictive control system for an industrial process includes a processor to execute an optimization module to determine manipulated variables for the process over a control horizon based on simulations performed using an objective function with an optimized process model and to control the process using the manipulated variables, to execute model modules including mathematical representations of a response or parameters of the process. The implementation details of the model modules are hidden from and inaccessible to the optimization module. The processor executes unified access modules (UAM). A first UAM interfaces between a first subset of the model modules and the optimization module and adapts output of the first subset for the optimization module, and a second UAM interfaces between a second subset of the model modules and the first subset and adapts output of the second subset for the first subset.

A method determines an optimized production schedule of a production line including a hybrid multi-cluster tool formed by a plurality of single-arm tools and dual-arm tools interconnected with each other. The method includes determining time for individual operations of a robotic arm and a processing module in the plurality of single-arm tools and dual-arm tools; determining robot waiting time of the single-arm tools and dual-arm tools based on the time for individual operations and different connection relationships of the plurality of single-arm tools and dual-arm tools; determining whether the optimized production schedule exists using the determined waiting time, wherein the optimized production schedule only exists if the hybrid multi-cluster tool is process-dominant where the robot activity time of the plurality of single-arm tools and dual-arm tools is substantially shorter than processing time at the processing module; and determining the optimized production schedule if the optimized production schedule exists.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes generating a time-sequenced data structure associated with a manufacturing process and transforming the time-sequenced data structure to a positionally-dimensioned data structure by identifying a zone for each parameter of a plurality of parameters, determining a time delay factor for each zone, and generating the positionally-dimensioned data structure using a data matrix transformation based on the time-sequenced data structure, each zone, and each time delay factor. The method also includes identifying a set of empty entries in the time-sequenced data structure or the positionally-dimensioned data structure and imputing data. The method further includes determining a new value for a process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes generating a time-sequenced data structure associated with a manufacturing process and transforming the time-sequenced data structure to a positionally-dimensioned data structure by identifying a zone for each parameter of a plurality of parameters, determining a time delay factor for each zone, and generating the positionally-dimensioned data structure using a data matrix transformation based on the time-sequenced data structure, each zone, and each time delay factor. The method also includes identifying a set of empty entries in the time-sequenced data structure or the positionally-dimensioned data structure and imputing data. The method further includes determining a new value for a process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.

A system and method include dividing, by a processor of a manufacturing execution system (MES), a time axis associated with a time window into a plurality of time slots, assigning an integer value to an integer variable indexed by a slot identifier, a machine identifier, and a wafer lot identifier, specifying one or more constraints based on the integer variable, wherein the one or more constraints comprise a wafer quantity constraint and a Q-time constraint, executing an optimization solver under the one or more constraints to determine a time and a quantity of wafer lots to be provided to each machine associated with the time window, and issuing a request to a controller to cause provision of the quantity of wafer lots to each machine associated with each step in the time window.