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.

An operation state display device displays an operation state of a board production facility in which a board is sequentially conveyed to multiple board work machines, which perform predetermined board work on the board, and the board work is sequentially performed to produce a board product, and includes an acquisition section and a display section. The acquisition section acquires an execution time zone in which setup changing work required for switching a type of the board product to be produced is performed, for each board work machine, based on production information acquired by the board production facility during production of the board product. The display section graphically displays at least one of the execution time zone for each of the multiple board work machines and the execution time zone for a predetermined board work machine of the board production facility in time series.

An automation server accesses a task for a robotic device. The automation server generates motor control commands for the robotic device to complete the task. The automation server transmits, via a network and in a format defined by an Application Program Interface (API), the motor control commands from the automation server to a fleet manager associated with the robotic device, the motor control commands for forwarding from the fleet manager to the robotic device. The automation server receives, from one or more sensors attached to the robotic device and via the network, robotic device sensor data. The automation server receives, from multiple remote sensors and via the network, remote sensor data. The automation server adjusts the generated motor control commands to complete the task based on the robotic device sensor data and the remote sensor data.

A method for optimizing manufacturing data in a subtractive manufacturing system includes obtaining a model of and tolerance requirements for an object to be manufactured and performing measurements on the manufacturing system in a static condition. The method further includes simulating a manufacturing process in the manufacturing system using the obtained static information to obtain runtime information, determining which manufacturing data is responsible for producing each feature of the manufactured object, simulating manufacturing of the object, and comparing the simulated finished product of the object with the three-dimensional model of the object, detecting a deviation between the simulated finished product and the obtained tolerance requirements included in the model, and based on the detected deviation and the determination of which manufacturing data is responsible for producing each feature, optimizing the manufacturing data such that an object manufactured based on the optimized manufacturing data complies with the obtained tolerance requirements.

To accurately obtain operating conditions of a production facility that satisfies desired criteria. A control apparatus includes a process data acquisition unit that reads process data from a storage device that stores the process data obtained from a production facility, and a control unit that obtains an optimum solution for a predetermined adjustment target of the process data and controls the production facility on the basis of the optimum solution. The optimum solution satisfies constraint conditions and optimizes an objective function. The constraint conditions are defined by using at least some of the process data. The objective function is defined by using at least some of the process data. A learned model having learned features of the process data obtained from the production facility is further stored in a storage device on the basis of causality information that defines a combination of first process data and second process data. The first process data is used as an explanatory variable. The second process data is used as a response variable. The constraint conditions include a condition that the second process data or a value corresponding to the second process data is a value calculated by using the first process data and the learned model, and an upper limit or a lower limit for at least some of the process data.

The invention relates to a system and a method for legally compliant documentation of health-relevant and safety-relevant process events in a food processing plant. The system comprises a workstation, an acquisition device, an auditor device, a report generator and a signing device. The acquisition device is assigned to the workstation and is configured to automatically acquire plant-related process variables. The auditor device, on the other hand, is configured to analyze the acquired process variables and identify a trigger event. Further, the auditor device is capable of generating a first data set including at least one of the acquired process variables and/or an information value about the trigger event. The report generator is adapted to determine a signing device responsible for the first data set. In addition, the report generator may generate a requirement data set comprising a signature requirement and at least part of the first data set. This requirement data set may be transmitted to the responsible signing device. The signing device is adapted to generate a requirement data set comprising an electronic signature for at least part of the requirement data set.

A management method for managing a production line including a plurality of steps includes carrying out a first displaying process and carrying out a second displaying process, wherein, among a first indicator group (I1), an indicator corresponding to a certain step functions as a GUI element configured to change a screen to be displayed on a display from a top screen (G1) to a second screen.

An information technology system for supporting additive manufacturing and value chain workflows includes a cloud-based metal additive manufacturing management platform including an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from one or more additive manufacturing nodes to optimize additive manufacturing and value chain processes and workflows. The information technology system includes a distributed ledger system configured to store data related to the manufacturing nodes.

Systems and methods for production line optimization include: receiving, from each of a plurality of equipment along the production line, equipment data at each interval of a plurality of intervals, the equipment data including equipment speed data, equipment state data, and equipment fault data; analyzing the equipment data with one or more optimization rules to identify an optimization setting; and, outputting the optimization setting to a production line device for modification of the production line.

A data analysis device, for analyzing efficiency of an assembly line operation including processes, includes: an input interface configured to acquire log data indicating a history of the processes respectively performed for each time of the assembly line operation; and a control circuit configured to generate analysis information indicating an analysis result of the history indicated by the log data, based on information generated by a probability model to calculate probability distribution for the assembly line operation. The probability model is configured to: generate process efficiency distribution indicating probability distribution of efficiency for each process in the assembly line operation, and variation factor distribution indicating probability distribution of a factor probable to vary the efficiency of the assembly line operation in each process; and generate work efficiency distribution indicating probability distribution of the efficiency of the assembly line operation, based on the process efficiency distribution and the variation factor distribution.