The present disclosure is intended to provide a technique which enables a direct visual comparison between a computationally-machined surface profile determined based on motor position information and a machined surface profile obtained by actual measurement of a machined surface. A display unit includes: a motor position information acquirer that acquires motor position information including at least one of a command position or a real position of a motor; a machine information acquirer that acquires machine information including a drive shaft configuration, a tool geometry, and a shape of an unmachined workpiece; a machined surface profile simulator that performs a simulation of machining a workpiece based on a machining program, and determines a computationally-machined surface profile of the workpiece simulated to be machined, based on the motor position information and the machine information; a machined surface profile measurer that measures a machined surface profile of a machined workpiece that has actually been machined based on the machining program; and a machined surface profile display that displays the computationally-machined surface profile determined by the machined surface profile simulator, in parallel with the machined surface profile measured by the machined surface profile measurer.

A method of controlling a real production process, wherein the method includes: a) receiving initial condition data from an on-line simulator system simulating the real production process, and b) performing an optimization based on the initial condition data and on an objective function to obtain set points for controlling the real production process.

A method for training an operator of a spray drying plant, the spray drying plant including a plurality of plant elements including pre-processing elements, a spray drying element, post-processing elements, powder recovery elements, and a programmable logic controller, PLC. The method including the steps of: obtaining a transient model of the spray drying plant, wherein the transient model includes transient sub-models of the plurality of plant elements; calculating repeatedly simulated sensor data based on the obtained transient model, using a processing unit; displaying on a display a training human machine interface, tHMI, configured to communicate with the transient model of the spray drying plant, and to display the simulated sensor data and control data for the transient model; and updating the transient model based on operator input on the tHMI, for controlling the transient model of the spray drying plant.

Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

One or more aspects of the present invention relate to a computer-implemented method for part analytics, in particular for analyzing the quality, the machining process and preferably the engineering process, of a workpiece machined by at least one CNC machine. According to these aspects, the method may include providing a digital machine model of the CNC machine with realtime and non-realtime process data of the at least one CNC machine, the realtime and non-realtime process data being recorded during the machining process of the workpiece under consideration; and subsequently simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime and non-realtime process data.

A method and a device for simulating a machining process of a workpiece on an NC-controlled machine tool by means of a self-learning artificial neural network. Process parameters both from a machining process on a real machine tool located in a manufacturing section and a digital machine model implemented in a simulation section are provided to the artificial neural network to learn the behavior of the machine tool including the tools and workpieces used and are reformatted into input parameters by means of mathematical transformation. By learning the behavior of the machining process, the artificial neural network ca, send output files back to the simulation software of the simulation section and optimally adapt the behavior of the digital machine model to the conditions of the real machine tool by adapting the simulation parameters and make it more efficient in order to optimize the machining process on the machine tool.

Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

The present disclosure provides a cloud-based method and a system for optimizing tuning of an industrial plant. The method includes receiving plant engineering data associated with an industrial plant from a plant environment. Further, the method includes generating a cloud-based virtual simulation environment synchronous to the industrial plant based on the plant engineering data. The cloud-based virtual simulation environment includes one or more virtual machines for virtually simulating the plant engineering data. Further, the method includes tuning the raw process variables of the industrial plant in the cloud-based virtual simulation environment to obtain optimized tuned process variables of the industrial plant. Additionally, the method includes rendering the optimized tuned process variables for the industrial plant to a client device.

Various embodiments relate to a method for motion simulation for a manipulator, such as an NC-controlled manipulator, in a machining environment, wherein the manipulator is moved in an operating mode by a control apparatus and the machining environment is at least partly mapped in an environment model and wherein the method comprises computation of a trajectory plan by the control apparatus from a setpoint movement of the manipulator starting from an initial situation and based on a kinematic model of the manipulator, performance of a kinematic collision check based on the trajectory plan, the kinematic model and the environment model, and production of a prediction result based on the kinematic collision check. The method is characterized in that the initial situation corresponds to the current manipulator state. Further, some embodiments relate to a corresponding computer program with program code and to a corresponding system for motion simulation for a manipulator.