Harbin Institute of Technology

Yongmeng Liu

Jiubin Tan

Chuanzhi Sun

JianWei Wu

Huilin Wu

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.

Large high-speed rotary device gap stacking assembly apparatus and assembly method based on digital twin

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. The device include a base, an air flotation shaft system, an aligning and tilt regulating workbench, precise force sensors, a static balance measuring platform, an upright column, a lower transverse measuring rod, a lower telescopic inductive sensor, an upper transverse measuring rod and an upper lever type inductive sensor.

Large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization of geometry center, mass center, center of gravity and center of inertia

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.

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

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. The device comprises a base, an air flotation shaft system, an aligning and tilt regulating workbench, precise force sensors, a static balance measuring platform, a left upright column, a right upright column, a left lower transverse measuring rod, a left lower telescopic inductive sensor, a left upper transverse measuring rod, a left upper telescopic inductive sensor, a right lower transverse measuring rod, a right lower lever type inductive sensor, a right upper transverse measuring rod and a right upper lever type inductive sensor. The method and the device can perform effective measuring and accurate regulation and control on large-scale high-speed rotary equipment.

Large-scale high-speed rotary equipment measuring and neural network learning regulation and control method and device based on rigidity vector space projection maximization

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. According to the calculation method of the coaxiality, amount of unbalance, rigidity and vibration amplitude of multiple stages of rotor/stator, an objective function taking assembly phases as variables is established, a Monte Carlo method is used to solve the objective function, and a probability density function is solved according to a drawn distribution function to obtain the probability relationship between the contact surface runout of the rotor/stator of the aircraft engine and the final coaxiality, amount of unbalance, rigidity and vibration amplitude of multiple stages of rotor/stator, thereby realizing assembly optimization and distribution of tolerances of multiple stages of rotor/stator.

Deep learning regulation and control and assembly method and device for large-scale high-speed rotary equipment based on dynamic vibration response properties

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. The apparatus comprises a support frame (), a balance mass block (), magnetic levitation workpiece stages (), a workpiece stage measurement apparatus, wireless energy transmission apparatuses () and a wireless energy receiving apparatus (), wherein the two workpiece stages work between a measurement site () and an exposure site (); a laser interferometer () is used to measure the positions of the workpiece stages; the wireless energy transmission apparatuses () are used to provide energy for a sensor () in a micro-drive stage; the workpiece stages are driven using a magnetic levitation planar electrical motor; and during a double-workpiece-stage switching process, the planar electrical motor is used to drive the two workpiece stages so as to achieve single-beat arc quick switching. By using the method and apparatus, the problem that an existing stage switching scheme has many beats, a long track, many start-stop links and a long time for stabilization is solved, thereby reducing the stage switching links, shortening the stage switching time, and improving the productivity of a lithography machine.

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Harbin, China

Yongmeng Liu