Abstract: Provided is a large high-speed rotary equipment gap stacking assembly apparatus and assembly method based on digital twin, and relates to the technical field of engine assembly measurement. The disclosure solves the problem of unbalanced rotation of the rotary parts caused by large assembly error during multi-stage rotary parts are stacked in a gap way. The disclosure includes the assembly apparatus entity and the assembly method; the assembly apparatus entity is configured to establish data communication with the upper computer through data acquisition apparatus, and upper computer is configured to establish a virtual assembly model; the virtual assembly model and optimal coaxiality of the multi-stage rotary parts in gap stacking can be obtained according to the assembly method, and the assembly process can be controlled by using the virtual assembly model and the optimal coaxiality. The disclosure is suitable for controlling the assembly process of the rotary parts.

Abstract: A GA-PSO-BP neural network is provided for performing a measurement of a coaxiality error of parts of a rotary equipment and predicting a coaxiality of parts of the rotary equipment in order to solve a problem that a coaxiality error of saddle surface parts is difficult to calculate by building a traditional mathematical model based on a three-dimensional coordinate system transformation due to serious deformation of fitting surfaces of spigots. The GA-PSO-BP neural network method includes the steps of analyzing an influence source of the coaxiality error of multi-stage parts after assembly; then taking an error source as an input and the coaxiality error of the multi-stage parts after assembly as an output; and introducing a genetic algorithm to optimize an initial weight and threshold of a BP neural network, and introducing a particle swarm optimization to find optimal solutions of hyperparameters.

Abstract: The present invention provides a large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization geometry center, mass center, the center of gravity and the center of inertia, belonging to the technical field of mechanical assembly. The method includes the steps of establishing a four-parameter circular profile measuring model for a single stage of rotor, simplifying the established four-parameter circular profile measuring model for the single stage of rotor, and establishing a four-target optimization model of the geometry center, mass center, the center of gravity and the center of inertia of multiple stages of rotors based on the angular orientation mounting position of each stage of rotor.

Abstract: The disclosure provides a stage-by-stage measurement, regulation and distribution method for dynamic characteristics of multi-stage components of large-scale high-speed rotary equipment. Firstly, a single-stage rotor circular contour measurement model is established, and the circular contour measurement model is simplified by using a distance from an ith sampling point of an ellipse to a geometry center to obtain a simplified circular contour measurement model. Then, actually measured circular contour data is taken into the simplified circular contour measurement model to determine a relationship between dynamic response parameters after rotor assembly and eccentricity errors as well as the amount of unbalance of all stages of rotors. Finally, a rotor speed is set according to the relationship between the dynamic response parameters after rotor assembly and the eccentricity errors as well as the amount of unbalance of all stages of rotors to obtain a critical speed parameter objective function.

Abstract: The present invention provides a large-scale high-speed rotary equipment measuring and neural network learning regulation and control method and device based on rigidity vector space projection maximization, belonging to the technical field of mechanical assembly. The method utilizes an envelope filter principle, a two-dimensional point set S, a least square method and a learning neural network to realize large-scale high-speed rotary equipment measuring and regulation and control.

Abstract: The present invention provides a deep learning regulation and control and assembly method and device for large-scale high-speed rotary equipment based on dynamic vibration response properties. The present invention starts from geometrical deviation of multiple stages of rotor/stator of an aircraft engine, amount of unbalance of rotor/stator, rigidity of rotor/stator and vibration amplitude of rotor/stator, considers the influence of the area of the assembly contact surface between two stages of rotors/stators, and sets the rotation speed of rotor/stator to be the climbing rotation speed to obtain vibration amplitude parameters.

Abstract: The present invention provides a large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization geometry center, mass center, the center of gravity and the center of inertia, belonging to the technical field of mechanical assembly. The method includes the steps of establishing a four-parameter circular profile measuring model for a single stage of rotor, simplifying the established four-parameter circular profile measuring model for the single stage of rotor, and establishing a four-target optimization model of the geometry center, mass center, the center of gravity and the center of inertia of multiple stages of rotors based on the angular orientation mounting position of each stage of rotor.

Abstract: The present invention provides a method for distributing relative gap parameters of large-scale high-speed rotary equipment components based on eccentricity vector following measurement and adjustment.

Abstract: The present invention provides a stage-by-stage measurement, regulation and distribution method for dynamic characteristics of multi-stage components of large-scale high-speed rotary equipment based on multi-biased error synchronous compensation and belongs to the technical field of mechanical assembly. Firstly, a single-stage rotor five-parameter circular contour measurement model is established, and the five-parameter circular contour measurement model is simplified by using a distance from an ith sampling point of an ellipse to a geometry center to obtain a simplified five-parameter circular contour measurement model. Then, actually measured circular contour data is taken into the simplified five-parameter circular contour measurement model to determine a relationship between dynamic response parameters after rotor assembly and eccentricity errors as well as the amount of unbalance of all stages of rotors.

Abstract: The present invention provides a large-scale high-speed rotary equipment measuring and neural network learning regulation and control method and device based on rigidity vector space projection maximization, belonging to the technical field of mechanical assembly. The method utilizes an envelope filter principle, a two-dimensional point set S, a least square method and a learning neural network to realize large-scale high-speed rotary equipment measuring and regulation and control.

Abstract: The present invention provides a deep learning regulation and control and assembly method and device for large-scale high-speed rotary equipment based on dynamic vibration response properties. The present invention starts from geometrical deviation of multiple stages of rotor/stator of an aircraft engine, amount of unbalance of rotor/stator, rigidity of rotor/stator and vibration amplitude of rotor/stator, considers the influence of the area of the assembly contact surface between two stages of rotors/stators, and sets the rotation speed of rotor/stator to be the climbing rotation speed to obtain vibration amplitude parameters.

Abstract: The present invention provides a method for optimizing multi-stage components of large-scale high-speed rotary equipment based on Monte Carlo bias evaluation. The method comprises: obtaining an offset of a contact surface between all stages of rotors according to a multi-stage rotor propagation relationship, and calculating coaxiality according to a coaxiality formula; calculating a cross sectional moment of inertia of the contact surface, and obtaining a bending stiffness according to a bending stiffness formula; obtaining the amount of unbalance of a rotor according to a rotor error propagation relationship; and obtaining a probability relationship between the assembly surface runout of all stages of aero-engine rotors and the final geometric concentricity, the amount of unbalance and stiffness of multi-stage rotors by using a Monte Carlo method, and optimizing the tolerance distribution and bending stiffness of the aero-engine multi-stage rotors.

Abstract: A dynamic-magnetic steel magnetic levitation double-workpiece-stage vector arc switching method and apparatus based on wireless energy transmission, falling within the semiconductor manufacturing equipment technology.

Abstract: A dynamic-magnetic steel magnetic levitation double-workpiece-stage vector arc switching method and apparatus based on wireless energy transmission, falling within the semiconductor manufacturing equipment technology.