CN-121980770-A - Spraying process optimization method and system based on digital twin

CN121980770ACN 121980770 ACN121980770 ACN 121980770ACN-121980770-A

Abstract

The invention discloses a spray process optimization method and a spray process optimization system based on digital twinning, which are characterized in that a preliminary spray track and reference process parameters are planned according to a grid model of a workpiece, a spray gun is controlled to perform sectional spray, a digital twinning system of the spray gun is constructed, the digital twinning system acquires a hit point of a spray center of the spray gun on the surface of the workpiece, the digital twinning system outputs spray amplitude characterization parameters through a proxy model according to the reference process parameters of the spray gun, film thickness increment of a hit point neighborhood is calculated according to the spray amplitude characterization parameters, reference statistical film thickness of each section is sprayed in a sectional mode according to film thickness increment statistics, the reference statistical film thickness is corrected into equivalent film thickness through equivalent deposition coefficients, section errors, spray thickness trends and strip balance items are calculated, the reference process parameters of the spray gun are updated, actual process parameters are obtained, and spray gun spray is controlled through the actual process parameters. And the geometric consistency correction is introduced for the curved surface/structural surface, so that the reconstruction precision and the mobility are improved.

Inventors

XU JIANGMIN
SHI TONGJUN
JING XUWEN
LIU JINFENG
WANG HUI

Assignees

江苏科技大学

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The spray coating process optimization method based on digital twinning is characterized by comprising the following steps of: Step 1, acquiring a grid model of a workpiece; Step 2, planning a preliminary spraying track and reference process parameters according to a grid model of the workpiece; Step 3, controlling the spray gun to carry out sectional spraying according to the preliminary spraying track and the reference process parameters, constructing a digital twin system of the spray gun, acquiring a hit point of a spraying center of the spray gun on the surface of the workpiece by the digital twin system, outputting a spray amplitude characterization parameter through a proxy model according to the reference process parameters of the spray gun, and calculating a film thickness increment of a hit point neighborhood according to the spray amplitude characterization parameter; Step 4, spraying the reference statistical film thickness of each section in a sectionalized mode according to the film thickness increment statistics, correcting the reference statistical film thickness into an equivalent film thickness through an equivalent deposition coefficient, calculating a section error between the equivalent film thickness and a target film thickness, obtaining a spraying thickness trend according to the section error and the section error of the last section, and obtaining a strip balance item according to the section error; And 5, updating the reference process parameters of the spray gun according to the section errors, the spray thickness trend and the strip balance item to obtain actual process parameters, and controlling the spray of the spray gun through the actual process parameters.
2. The spray process optimization method according to claim 1, wherein the reference process parameters include a reference speed of a spray gun Gun distance Pressure and force And equivalent deposition coefficient The deposition coefficient The confirmation method comprises the steps of dividing the preliminary spraying track into a plurality of sections, carrying out structural partition on each section of the workpiece to obtain section structural characteristics of the workpiece, and determining equivalent deposition coefficients of each section of the workpiece according to the section structural characteristics of the workpiece, wherein the equivalent deposition coefficients of different structural partitions are obtained through experiments or simulations.
3. The spray process optimization method according to claim 1, wherein the reference process parameters input by the proxy model include a gun pitch of a spray gun On-line actual speed active_v of the lance and pressure of the lance The agent model outputs spray amplitude characterization parameters including spray amplitude peak characterization quantity A and diffusion width characterization quantity 。
4. The spray process optimization method according to claim 3, wherein the calculation formula for calculating the film thickness increment of the hit point neighborhood according to the spray characteristic parameter is: Wherein, the Represents the radial decay deposition kernel with the center as the peak, and d is the distance from the neighborhood pixel to the center pixel.
5. The spray process optimization method according to claim 4, wherein the speed factor of the spray gun is updated according to segment errors, spray thickness trends, strip balance terms Calculating updated control speed of the spray gun according to the speed factor : Wherein, the The reference speed of the spray gun in the j+1th section is v min which is the lower speed limit, v max which is the upper speed limit, Limited for rate of change of speed adjacent segments.
6. The spray process optimization method according to claim 5, wherein the speed factor of the spray gun speed is updated according to segment errors, spray thickness trend, strip equalization term: Wherein, the For the updated speed factor of the j +1 th segment, As a reference speed factor And the update amount of the speed factor S min is the lower speed factor limit, s max is the upper speed factor limit, In order to limit the rate of change of the speed factor, The weight of the segment error for the j+1th segment, For the normalized segment error of the j-th segment, The weight of the trending item for the j+1st segment, As normalized trend term for the j-th segment, The weights of the terms are balanced for the stripe of the j +1 th segment, And (5) balancing the term for the normalized j-th band.
7. The spray process optimization method according to claim 1, wherein the calculation formula of the equivalent film thickness is: Wherein, the For the equivalent deposition coefficient, Counting the film thickness as a reference; the calculation formula of the segment error is as follows: Wherein, the Is the target film thickness; the calculation formula of the trend term is as follows: the calculation formula of the stripe equalization term is as follows: Wherein, the Is the set of time segment numbers for all stripe numbers b.
8. A digital twinning-based spray process optimization system, comprising: the off-line preparation module is used for acquiring a grid model of the workpiece, planning a preliminary spraying track and reference process parameters according to the grid model of the workpiece; The on-line synchronization module is used for controlling the spray gun to carry out sectional spraying according to the preliminary spraying track and the reference process parameters, constructing a digital twin system of the spray gun, acquiring a hit point of a spraying center of the spray gun on the surface of a workpiece by the digital twin system, outputting a spray amplitude characterization parameter through a proxy model according to the reference process parameters of the spray gun, and calculating a film thickness increment of a hit point neighborhood according to the spray amplitude characterization parameter; the soft measurement reconstruction module is used for sectionally spraying the reference statistical film thickness of each section according to the film thickness increment statistics, correcting the reference statistical film thickness into an equivalent film thickness through an equivalent deposition coefficient, calculating a section error between the equivalent film thickness and a target film thickness, obtaining a spraying thickness trend according to the section error and the section error of the last section, and obtaining a strip equalization term according to the section error; And the online decision module and the execution module update the reference process parameters of the spray gun according to the section error, the spray thickness trend and the strip balance item to obtain actual process parameters, and control the spray of the spray gun through the actual process parameters.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

Description

Spraying process optimization method and system based on digital twin Technical Field The invention relates to surface spraying, in particular to a spraying process optimization method and system based on digital twinning. Background In marine segment automated spraying, coating uniformity and qualification rate are commonly affected by trajectory planning, process parameters (e.g., gun pitch, speed, pressure/flow), and curved/structural surface geometry (angle of incidence, curvature, shielding). The prior art generally suffers from the following disadvantages: 1) The film thickness is difficult to obtain on line, the field operation is more dependent on post off-line spot inspection or limited contact thickness measurement, a continuous film thickness field can not be obtained, a defect area is difficult to position in time, and the process depends on experience; 2) The complex geometry causes systematic deviation that a flat plate calibration or an empirical model is difficult to migrate to structural areas such as curved surface transition areas, rib plate root corners and the like, and local overspray/underspray is easy to occur; 3) The high-precision simulation is difficult to realize in real time, the numerical simulation calculation amount such as CFD is large, and the on-site real-time monitoring and on-line decision making are difficult to meet; 4) The twin is 'just looking and not controlling', namely the existing digital twin stays in the equipment state/track playback display layer, lacks soft measurement and out-of-tolerance assessment which are strongly coupled with film thickness quality, and can dynamically adjust closed loops by using technological parameters. Disclosure of Invention Aiming at the problems, the invention aims to provide a digital twin-based spraying process optimization method and system for improving spraying quality. The spraying film thickness distribution can be quickly rebuilt under the condition of no online thickness measurement or thickness measurement sparseness, and can be visualized in real time in digital twinning, and meanwhile, the film thickness deviation can be converted into executable technological parameter correction quantity and sent to a controller, so that the closed-loop regulation and control of the spraying quality can be realized. The spray coating process optimization method based on digital twinning comprises the following steps of: Step 1, acquiring a grid model of a workpiece; Step 2, planning a preliminary spraying track and reference process parameters according to a grid model of the workpiece; Step 3, controlling the spray gun to carry out sectional spraying according to the preliminary spraying track and the reference process parameters, constructing a digital twin system of the spray gun, acquiring a hit point of a spraying center of the spray gun on the surface of the workpiece by the digital twin system, outputting a spray amplitude characterization parameter through a proxy model according to the reference process parameters of the spray gun, and calculating a film thickness increment of a hit point neighborhood according to the spray amplitude characterization parameter; Step 4, spraying the reference statistical film thickness of each section in a sectionalized mode according to the film thickness increment statistics, correcting the reference statistical film thickness into an equivalent film thickness through an equivalent deposition coefficient, calculating a section error between the equivalent film thickness and a target film thickness, obtaining a spraying thickness trend according to the section error and the section error of the last section, and obtaining a strip balance item according to the section error; And 5, updating the reference process parameters of the spray gun according to the section errors, the spray thickness trend and the strip balance item to obtain actual process parameters, and controlling the spray of the spray gun through the actual process parameters. Further, the reference process parameter includes a reference speed of the lanceGun distancePressure and forceAnd equivalent deposition coefficientThe deposition coefficientThe confirmation method comprises the steps of dividing the preliminary spraying track into a plurality of sections, carrying out structural partition on each section of the workpiece to obtain section structural characteristics of the workpiece, and determining equivalent deposition coefficients of each section of the workpiece according to the section structural characteristics of the workpiece, wherein the equivalent deposition coefficients of different structural partitions are obtained through experiments or simulations. Further, the reference process parameters input by the agent model comprise the gun distance of the spray gunOn-line actual speed active_v of the lance and pressure of the lanceThe agent model outputs spray amplitude characterization parameters including spray am