A method of defining a manufacturing operation for a workstation includes providing a selected manufacturing operation record from among a plurality of manufacturing operation records for a selected manufacturing operation to be executed in the workstation. The method further includes extracting, by a process allocation system, process element data for a plurality of process elements associated with the selected manufacturing operation record. The process element data includes a textual description of the respective process element and a process time. The method further includes determining, by the process allocation system, a predicted operation time for the selected manufacturing operation based on the process element data and a time prediction model, where the time prediction model is a trained model recognizing sequential patterns among the plurality of process elements of the selected manufacturing operation.

A system and method generating a 3D plant layout model departing from 2D schema of the layout provide access to a plant catalogue of identifiers of 3D plant objects. At least one 3D plant object identifier is associated with a 2D plant object identifier. Data on a given 2D schema of a layout are received as input data. A function trained by machine learning algorithm is applied to the input data for detecting a set of 2D plant objects. A set of identifier and location data on the detected 2D plant object set is provided as output data. A set of 3D plant objects is selected from the plant catalogue with identifiers associated with the set of 2D plant objects identifiers of the output data. A 3D model of the layout is generated by arranging the selected set of 3D plant objects according to location data of the output data.

An optimum combination of a loop unrolling number and a circuit parallel number in a high-level synthesis is determined. A circuit synthesis information generation unit sets, as parameter candidates, a plurality of combinations of a loop unrolling number and a circuit parallel number to generate circuit synthesis information indicating a synthesis circuit obtained by high-level synthesis processing for each of the combinations. An optimum parameter determination unit calculates, for each piece of the generated circuit synthesis information, an estimation processing performance related to the synthesis circuit indicated by the circuit synthesis information, and determines an optimum combination of the loop unrolling number and the circuit parallel number based on the circuit synthesis information based on which a maximum estimation processing performance is obtained.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, where the 3D models of the physical structures are produced so as to facilitate manufacturing of the physical structures using 2.5-axis subtractive manufacturing systems and techniques, include: obtaining a design space, design criteria, and in-use case(s); iteratively modifying a generatively designed shape in the design space in accordance with the design criteria and the in-use case(s) using a density-based representation of the generatively designed shape and including adjusting the density-based representation of the generatively designed three dimensional shape in accordance with a milling direction of a 2.5-axis subtractive manufacturing process in at least two iterations of the iteratively modifying; and providing the generatively designed shape for use in manufacturing a physical structure using computer-controlled manufacturing that employs the 2.5-axis subtractive manufacturing process.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, where the 3D models of the physical structures are produced so as to facilitate manufacturing of the physical structures using 2.5-axis subtractive manufacturing systems and techniques, include: obtaining a design space, design criteria, and in-use case(s); iteratively modifying a generatively designed shape in the design space in accordance with the design criteria and the in-use case(s) using a density-based representation of the generatively designed shape and including adjusting the density-based representation of the generatively designed three dimensional shape in accordance with a milling direction of a 2.5-axis subtractive manufacturing process in at least two iterations of the iteratively modifying; and providing the generatively designed shape for use in manufacturing a physical structure using computer-controlled manufacturing that employs the 2.5-axis subtractive manufacturing process.

An electronic device manufacturing system configured to performing, by manufacturing equipment, a first process on a first substrate according to a process recipe, wherein the process recipe comprises a plurality of setting parameters. The system then generates metrology data associated with a plurality of features and inputs the metrology data into one or more Bayesian probabilistic models. The system then receives an output from the one or more Bayesian probabilistic models based on the metrology data and at least one settings parameter of the plurality of setting parameters. The system then updates, based on the output of the one or more Bayesian probabilistic models, the process recipe by modifying at least one setting parameter of the plurality of setting parameters, and performs, by the manufacturing equipment, a second process on a second substrate according to the updated process recipe.

A generalization and encapsulation method based on a digital twin (DT) model of a workshop includes: classifying a device in a production line according to a basic operation and a functional characteristic of a process of the device; abstracting a commonality in terms of process action mode, process algorithm and action trigger mechanism; encapsulating according to a sequence characteristic of a process; comparing processes, and generalizing and encapsulating; encapsulating according to a time sequence, a space sequence and a logic characteristic of a specific process; storing a generalized and encapsulated process in a database; and calling the generalized and encapsulated process from the database to a device or a process. The generalization and encapsulation system includes an abstract process encapsulation module, a continuous process encapsulation module, a process action encapsulation module, a process algorithm encapsulation module, a database and a fast calling module.

A method of developing a production system that is aligned with the logical and physical requirements of the production system. The method includes building a logical control node as a structured data object containing a hierarchical list of design requirements associated with a portion of a production system. The logical control node is combined with other logical control nodes associated with other portions of the production system based on relationships between the respective requirements stored in the logical control nodes. The logical network is then converted into a physical model of the production system by converting the logical control nodes into their related physical nodes and confirming the arrangement of the physical nodes conforms to the relationships established in the logical network and any other relevant requirements stored in the logical control nodes.

An optimum combination of a loop unrolling number and a circuit parallel number in a high-level synthesis is determined. A circuit synthesis information generation unit sets, as parameter candidates, a plurality of combinations of a loop unrolling number and a circuit parallel number to generate circuit synthesis information indicating a synthesis circuit obtained by high-level synthesis processing for each of the combinations. An optimum parameter determination unit calculates, for each piece of the generated circuit synthesis information, an estimation processing performance related to the synthesis circuit indicated by the circuit synthesis information, and determines an optimum combination of the loop unrolling number and the circuit parallel number based on the circuit synthesis information based on which a maximum estimation processing performance is obtained.

A production facility is provided with: an AGV for transporting a plurality of fuselage panels of multiple types having different shapes in a mixed state on a previously determined transport path; a plurality of A/Rs for riveting the fuselage panels; work areas set so as to correspond to the respective A/Rs in which the A/Rs move to rivet the fuselage panels; and a buffer area, set beforehand in the transport path adjacent to the work area, to which the A/R corresponding to the adjacent work area moves so as to rivet the fuselage panel. When there is no fuselage panel to be riveted in the work area adjacent to the buffer area and the fuselage panel to be riveted is present in the buffer area, a control device moves the A/R corresponding to the work area adjacent to the buffer area to the buffer area to rivet the fuselage panel.