The invention relates to a system (1) for controlling a production plant (3) consisting of a plurality of plant parts (2), in particular a metallurgical production plant for producing industrial goods such as metal semi-finished products and/or metal end products, wherein each plant part (2) has an input quality window (4), an output quality window (5) and a process window (6), wherein the input quality window (4) of a plant part (2) defines the quality characteristics of the input product that are required by the plant part (2) and the output quality window (5) of a plant part (2) defines the quality characteristics of the output product that are allowed by the plant part (2) after processing the input product, wherein, in a production plant (3) consisting of the plurality of plant parts (2), the output quality window (5) of an upstream plant part (2) corresponds to the input quality window (4) of the downstream plant part (2), wherein the process window (6) defines the setting values (7) that can be implemented by the respective plant part (2) for a plant automation unit of the plant part (2), wherein each plant part (2) detects the current state by means of sensors (8) and adapts the process window (6) of the plant part (2) to the detected current state, and wherein the system (1) for controlling the production plant (3) consisting of the plurality of plant parts (2) determines setting values (7) for the respective plant automation unit for each plant part (2), the setting values being within the process windows (6) and that the product produced in the production plant (3) meets the quality characteristics required by the input quality windows (4) and output quality windows (5) of the plurality of plant parts (2).

The invention further relates to a corresponding method and computer program.

An evaluation value prediction unit predicts an evaluation value at a time point after a given time passes from a predetermined evaluation time based on input data related to an operation of a factory at the evaluation time using a learned prediction model. The prediction model is a learned model that is learned so that the evaluation value related to the operation of the factory at a time point after the given time passes from one time point is output by inputting a plurality of kinds of data related to the operation of the factory at one time point. An evaluation value output unit outputs information related to the evaluation value.

Systems and methods for modeling and controlling entities of a building system are provided. An exemplary method includes comparing an input indicating a new entity or connection between entities of a building system to a data model for the building system to determine whether the new entity or connection is represented in the data model, extending the data model to define the new entity or connection in response to determining that the new entity or connection is not represented in the data model, and using the data model with the new entity or connection in a control strategy to generate control decisions for the entities of the building system.

A method includes recording: a setting value of a setting quantity, a value of a process quantity, and/or a value of an indirect process quantity of the production process obtained from a value of a process quantity by a data recording unit. The method also includes establishing a calculated setting value and/or an electronic message by a computing unit by a set of rules. The input data of the set of rules includes the values recorded and/or a system configuration value of a system configuration quantity and/or classified values from the values. The method also includes deciding whether the calculated setting value should be adopted and/or the recommended action should be followed by a decision-making unit and/or the operator via the operator interface. The set of rules is created by a learning unit by a machine learning process employing training data from production plants and/or production machines.

System and method for optimizing bottling line performance based on data received from a bottle with sensors. The system provides a bottle with sensors in a bottling line or maintenance system, a network gateway, a human machine interface (HMI) device, and a platform server. The HMI device provides for local control of the bottling line while the platform server handles distribution of GUIs, as well as data processing, data storage, and data analysis.

Machine elements can be displaced along disjoined path sections by position-controlled machine axes. Movement guidance of the machine elements from the end of a path section to the beginning of a directly following path section along a previously unknown path is provided, wherein location, velocity and acceleration are continuous along the previously unknown path and at the transitions between the path sections and the previously unknown path. Velocity, acceleration and jerk are limited. A preliminary axis guidance and a corresponding required axis time is determined for each of the axes. A greatest required axis time is determined therefrom which is then set as a final axis guidance for this axis. For other axes whose preliminary axis times are smaller than the greatest required axis time, the respective preliminary axis guidance is matched to the greatest required axis time, which is then adopted for the other axes as final axis guidance.

A method of improving production performance of an additive manufacturing system includes obtaining a first production plan and a second production plan, different from the first production plan, for the manufacture of a plurality of objects using a fleet of additive manufacturing apparatus, automatically generating a first allocation of a first quantity of the plurality of objects to the fleet of additive manufacturing apparatus using the first production plan, automatically generating a second allocation of a second quantity of the plurality of objects to the fleet of additive manufacturing apparatus using the second production plan, comparing a production performance of the first and second quantity of the plurality of objects after being manufactured by the fleet of additive manufacturing apparatus, and based on the comparison of the production performance, automatically regenerating the first and second allocations to change the first and second quantities.

The present disclosure provides an automated warehousing system, a display panel production line and a logistics delivery method. The automated warehousing system includes a plurality of zones, a server host, a controller, and at least one moving device. Each zone corresponds to one manufacturing process for an in-process product. The server host is configured to manage production information of the in-process product and send the production information to the controller. The controller is configured to, based on the received production information, after completion of one corresponding manufacturing process, control the moving device to move the in-process product to the zone that corresponds to the next manufacturing process to be performed at the in-process product.

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.

A calculating method for natural frequency of toolholder-tool system of machine tool spindle is provided. The equivalence of bending stiffness of a toolholder part including that: a toolholder in the toolholder-tool system is a first segment, and section moment of inertia, elastic modulus, density, length and cross section area of the toolholder are I.sub.1, E.sub.1, ρ.sub.1, L.sub.1 and A.sub.1 respectively; a tool part is a second segment, and section moment of inertia, elastic modulus, density, length and cross section area of the toolholder are I.sub.2, E.sub.2, ρ.sub.2, L.sub.2 and A.sub.2 respectively; and assuming that the tool and the toolholder are closely fitted at contact surfaces without shedding and slipping. The calculating method can quickly and accurately calculate the vibration characteristic of toolholder-tool system of machine tool and provide a guide for optimizing the structure of the machine tool spindle or selecting suitable tools and toolholders.