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.

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.

The invention describes a robot (5) and/or robot controller (17) and a method for validation of programmed workflow sequences or teaching programs (20) of a robot (5) preferably with a robot controller (17), wherein the robot (5) is preferably mounted on or next to a processing machine, in particular an injection molding machine (4), and serves for the extraction, handling, manipulation or further processing of injection-molded parts (3) which have just been produced. The travel parameters, equipment features and functionalities of the physical robot (5) are stored in a configuration file (27) on the control side. The robot controller (17) creates a virtual robot model (21) from these stored data. For validation of a workflow sequence, the robot controller (17) uses the current teaching program (20) in the robot controller (17) whereby the visualization of the workflow sequence is displayed directly on an output unit of the robot controller (17).

The present invention relates to the field of intelligent machining, in particular to a parallel control method based on multi-period differential sampling and digital twinning technologies, the method comprising the following steps of: a. detecting machining conditions of dotting machine equipment by using a multi-period differential sampling technology; b. establishing a digital twinning control model; and c. controlling a simulation model of the dotting machine equipment according to a detection judgment result so as to perform parallel control on the dotting machine equipment. According to the parallel control method based on multi-period differential sampling and digital twinning modelling provided by the present invention, for the digital twinning model of the dotting machine equipment, the parallel control method establishes a simulation model and a detection model of the dotting machine equipment by using a virtual-real synchronization technology; simulation dotting machine equipment operates in synchronization with the physical dotting machine equipment.

The present invention relates to the field of intelligent machining, in particular to a parallel control method based on multi-period differential sampling and digital twinning technologies, the method comprising the following steps of: a. detecting machining conditions of dotting machine equipment by using a multi-period differential sampling technology; b. establishing a digital twinning control model; and c. controlling a simulation model of the dotting machine equipment according to a detection judgment result so as to perform parallel control on the dotting machine equipment. According to the parallel control method based on multi-period differential sampling and digital twinning modelling provided by the present invention, for the digital twinning model of the dotting machine equipment, the parallel control method establishes a simulation model and a detection model of the dotting machine equipment by using a virtual-real synchronization technology; simulation dotting machine equipment operates in synchronization with the physical dotting machine equipment.

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 of optimizing a machining simulation condition includes a step of receiving a setting condition of a machine tool at the time of performing a prescribed machining detail, a step of calculating a first machining result that is a machining result assumed when the machine tool performs machining under the received setting condition, a step of acquiring a second machining result that is a machining result when the machine tool performs machining under the received setting condition, and a step of evaluating a degree of coincidence between the first machining result and the second machining result, and repeatedly performs the calculation of the first machining result while changing the precondition of the calculation until the degree of coincidence is equal to or more than a prescribed threshold value.

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.

The invention is concerned with a robot control system and a method of controlling a robot where the robot control system includes a human-machine interface; a real robot control environment including a real robot and a real robot controller controlling the real robot and a cloud-hosted robot control environment including a virtual robot controller, which is a replica of the real robot controller, where the human-machine interface is configured to transfer a robot change instruction from a user to the cloud-hosted robot control environment, the virtual robot controller is configured to validate the robot change instruction and the real robot controller is configured to apply the validated robot change instruction when controlling the real robot.

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.