CN113449442B - Spray coating thickness prediction method, device, equipment and medium for complex curved surface - Google Patents
Spray coating thickness prediction method, device, equipment and medium for complex curved surface Download PDFInfo
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Abstract
The application discloses a spraying thickness prediction method, a spraying thickness prediction device, spraying thickness prediction equipment and a spraying thickness prediction medium for complex curved surfaces, wherein the method comprises the following steps: in the effective spraying range of the spray gunSpraying distance D of internal adjustment N timeshObtaining N different spraying distances DhLower coating thickness profile function; establishing a thickness distribution grid corresponding to preset spraying process parameters according to a coating thickness distribution curve function; according to the thickness distribution grid, determining a spraying track passing through a point P on any point P on the surface of a spraying object based on the coordinate of the point P, the coordinate of the motion track of the spray gun and a direction vector, and calculating the variation value of the coating thickness caused by all the spraying tracks related to the point P; the final coating thickness at the point P is obtained by summing the coating thickness variation values, and the method has the advantage that the coating thickness and uniformity can be accurately and reliably predicted in the face of complex curved surfaces.
Description
Technical Field
The application relates to the technical field of automatic spraying, in particular to a spraying thickness prediction method, a device, equipment and a medium for a complex curved surface.
Background
For automatic spraying, the accuracy of the coating thickness and the uniformity of the distribution of the coating thickness are key evaluation indexes of the coating quality, and particularly in the field of aviation manufacturing, the uniformity and the accuracy of the coating thickness directly determine the functional characteristics of an airplane. The related influence factors of the coating thickness and the uniformity are numerous, and the comprehensive influence of the spraying quality of each parameter is complicated. Meanwhile, for spray objects with complex curvatures, such as automobile and airplane surfaces, the coating thickness and uniformity cannot be predicted.
Disclosure of Invention
The application mainly aims to provide a spraying thickness prediction method, a spraying thickness prediction device, spraying thickness prediction equipment and a spraying thickness prediction medium for a complex curved surface, and aims to solve the technical problem that the existing spraying method cannot accurately and reliably predict the thickness and the uniformity of a coating for the complex curved surface.
In order to achieve the above object, the present application provides a method for predicting a spraying thickness of a complex curved surface, including the following steps:
within the effective spraying range of the spray gunAdjusting spraying distance D for N timeshObtaining N different spraying distances DhLower coating thickness profile function
(h =1, …, n); wherein,
for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,
on the distribution curve of the thickness of the coating
The corresponding thickness value, N > 2;
according to the coating thickness distribution curve function
Establishing a thickness distribution grid corresponding to preset spraying process parameters;
according to the thickness distribution grid, for any point P on the surface of the spraying object, determining a spraying track passing through the point P based on the coordinate of the point P, the coordinate of the motion track of the spray gun and the direction vector, and calculating the variation value of the coating thickness caused by all spraying tracks related to the point P
；
For the value of the variation of the thickness of the coating
The sum is summed to give the final coating thickness at point P
Optionally, said function according to said coating thickness profile
Establishing a thickness distribution grid corresponding to preset spraying process parameters, which specifically comprises the following steps:
establishing a plane coordinate system OJK with the center O of the spray gun as the original point on the normal plane of the motion track of the spray gun;
according to the different spraying distances DhLower corresponding coating thickness distribution curve function
Carrying out grid segmentation on the spray distance, and recording the thickness value on a grid node as delta (j, k); where j, k is the index of the grid corner point under the planar coordinate system OJK.
Optionally, the value of the coating thickness variation caused by all spraying tracks related to the point P is calculated
The method specifically comprises the following steps:
recording the number of the spraying track as Li (i =1, …, n);
determining 4 corner points of the thickness distribution grid where the P point is located according to the position of the P point under the plane coordinate system OJK, and respectively recording the distances from the P point to the 4 corner points as d along the clockwise direction1、d2、d3、d4；
Calculating the thickness change value of the P point coating caused by the spraying track Li by adopting an inverse distance weighting method
。
Optionally, the corresponding P-point coating thickness variation value caused by the spraying track Li is calculated by adopting an inverse distance weighting method
The method specifically comprises the following steps:
obtaining the thickness change value of the P point coating caused by the spraying track Li according to the following relational expression
：
Wherein,
；
in the formula, S is the corner number of the grid around the P point, i.e., S =1, 2, 3, 4.
Optionally, acquiring N different spraying distances DhLower coating thickness profile function
(h =1, …, n), including in particular:
maintaining the spray gun at the spraying distance D under the preset spraying process parametershSpraying at constant speed on a flat surface to form a rectangular coating, measuring the thickness value of the coating on the cross section of the spraying track, and fitting to obtain a coating thickness distribution curve function
（h=1,…,n）。
Optionally, the preset spraying process parameters include a spraying flow rate, a spraying speed, a spraying posture, a spraying distance, a lapping distance, a spraying direction, an atomizing pressure and a fan-shaped pressure.
A spray coating thickness prediction device facing a complex curved surface comprises:
a function acquisition module for adjusting the spraying distance D for N times within the effective spraying range of the spray gunhObtaining N different spraying distances DhLower coating thickness profile function
(h =1, …, n); wherein,
for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,
on the distribution curve of the thickness of the coating
The corresponding thickness value, N > 2;
a thickness distribution grid construction module for constructing a thickness distribution grid according to the coating thickness distribution curve function
Establishing a thickness distribution grid corresponding to preset spraying process parameters;
a coating thickness variation calculating module for determining the spraying track passing through the point P for any point P on the surface of the spraying object based on the point P coordinate, the spray gun motion track coordinate and the direction vector according to the thickness distribution grid, and calculating the coating thickness variation value caused by all the spraying tracks related to the point P
；
A result output module for outputting the coating thickness variation value
The sum is summed to give the final coating thickness at point P
(i=1,…,n)。
A computer device comprising a memory having a computer program stored therein and a processor executing the computer program, implements the method described above.
A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method described above.
The beneficial effect that this application can realize is as follows:
the coating thickness distribution curve function and the thickness distribution grid are obtained based on actual measurement of the preset spraying process parameters, the preset spraying process parameters are one or more parameters with larger influence input at a certain point of the curved surface, and are more targeted, the thickness distribution grids corresponding to different parameters are obtained by changing the spraying process parameters, so that the coating thickness corresponding to various parameters is calculated, the influence of thickness deviation caused by the difference between material attribute setting and model construction and the real environment in simulation prediction methods such as finite elements is avoided, and the reliability of the data for predicting the coating thickness is high; meanwhile, the coating thickness is calculated and predicted based on the geometric relation between the thickness distribution grid and the surface of the spraying object, and the calculation and prediction method is suitable for complex and changeable curved surface characteristics.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings that are needed in the detailed description of the present application or the technical solutions in the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow chart of a complex curved surface-oriented spray thickness prediction method according to the present application;
FIG. 2 is a schematic view of the spray gun of the present application spraying onto a workpiece (the direction of the arrow is the spraying direction);
FIG. 3 is a graphical illustration of a coating thickness profile as a function of the present application;
FIG. 4 is a schematic view of a spray gun varying the spray distance;
FIG. 5 is a schematic diagram of the calculation of the predicted coating thickness point.
Reference numerals:
1-spray gun, 2-workpiece, 3-rectangular coating and 4-thickness distribution grid. The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that all the directional indications (such as up, down, left, right, front, and back … …) in the embodiment of the present application are only used to explain the relative position relationship between the components, the motion situation, and the like in a specific posture, and if the specific posture is changed, the directional indication is changed accordingly.
In this application, unless expressly stated or limited otherwise, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and/or" appearing throughout includes three juxtapositions, exemplified by "A and/or B" including either A or B or both A and B. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
Example 1
Referring to fig. 1 to 5, the present embodiment provides a method for predicting a spraying thickness of a complex curved surface, including the following steps:
adjusting the spraying distance D of N times within the effective spraying range of the spray gun 1hObtaining N different spraying distances DhLower coating thickness profile function
(h =1, …, n); wherein,
for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,
on the distribution curve of the thickness of the coating
The corresponding thickness value, N > 2;
according to the coating thickness distribution curve function
Establishing a thickness distribution grid 4 corresponding to preset spraying process parameters;
according to the thickness distribution grid 4, for any point P on the surface of the spraying object, determining a spraying track passing through the point P based on the coordinate of the point P, the coordinate of the motion track of the spray gun and the direction vector, and calculating the variation value of the coating thickness caused by all the spraying tracks related to the point P
；
For the value of the variation of the thickness of the coating
The sum is summed to give the final coating thickness at point P
(i=1,…,n)。
At present, the common method for simulating the thickness and uniformity of the coating is a finite element method, and a constitutive model of a spraying scene is usually established by using commercial software to simulate a deposition model of the spraying paint. The disadvantages of this method are: (1) the actual spraying scene has a plurality of influence factors, and a finite element method is difficult to obtain an accurate paint deposition model; (2) the curvature change of the sprayed object is complex, the calculation efficiency for large curved surfaces and complex curved surfaces is low, the calculation cost is high, and the method is not suitable for the fast-rhythm requirement in production. For a surface with large curvature and variable curvature, the influence of the curve characteristic change on the coating thickness and uniformity needs to be fully considered in path planning. The thickness of any point on the curved surface of the workpiece 2 is the effect that the spray gun 1 can spray in a plurality of tracks and a plurality of postures. The conventional fluid simulation method is difficult to simulate the coating thickness under the conditions of complex spraying postures and complex curved surfaces. If a thickness distribution rule on a spraying track section is established based on a spraying process test, and the positions of discrete points on the complex curved surface under the coordinate system of the spray gun 1 are combined, the thickness of the predicted points can be interpolated through the existing distribution rule, and the quick calculation of the coating thickness is realized.
Therefore, in this embodiment, the coating thickness distribution curve functions at different spraying distances are obtained based on the preset spraying process parameters, the thickness distribution grid 4 at the specific process parameters is constructed through the coating thickness distribution curve functions at the different spraying distances, and finally, the coating thickness value of the region to be calculated is solved according to the geometric relationship between the thickness distribution grid 4 and the complex curved surface. The method establishes the distribution rule of the coating thickness based on the measured data, has high reliability, is suitable for spraying objects with complex curved surfaces, and has simple calculation method.
It should be noted that the coating thickness distribution curve function and the thickness distribution grid 4 proposed in this embodiment are obtained based on actual measurement of preset spraying process parameters, and the preset spraying process parameters are parameters with a large influence on one or more parameters input at a certain point of the curved surface, and are more targeted, and the thickness distribution grids corresponding to different parameters are obtained by changing the spraying process parameters, so that the coating thickness corresponding to various parameters is calculated, and the influence of thickness deviation caused by the difference between the material attribute setting and the model construction in the simulation prediction methods such as finite elements and the like and the real environment is avoided, so that the reliability of the data for predicting the coating thickness is high; meanwhile, the coating thickness is calculated and predicted based on the geometric relation between the thickness distribution grid 4 and the surface of the spraying object, and the calculation and prediction method can adapt to complex and variable curved surface characteristics; meanwhile, the embodiment performs interpolation calculation based on the geometric information and the actual measurement empirical data of the spraying object, has low calculation cost, and can be flexibly transplanted into automatic spraying control software or other application software.
As an alternative embodiment, the coating thickness is distributed as a function of the profile
Establishing a thickness distribution grid 4 corresponding to preset spraying process parameters, which specifically comprises the following steps:
establishing a plane coordinate system OJK taking the center O of the spray gun 1 as an original point on the normal plane of the motion track of the spray gun 1;
according to the different spraying distances DhLower corresponding coating thickness distribution curve function
Carrying out grid segmentation on the spray distance, and recording the thickness value on a grid node as delta (j, k); where j, k is the index of the grid corner point under the planar coordinate system OJK.
This embodiment is a method for establishing a thickness distribution grid 4, according to which the thickness distribution grid 4 can be established under a planar coordinate system OJK with the center O of the spray gun 1 as the origin, and the accuracy and authenticity of data can be ensured.
As an alternative embodiment, the coating thickness variation values caused by all spraying tracks related to the point P are calculated
The method specifically comprises the following steps:
recording the number of the spraying track as Li (i =1, …, n);
determining 4 corner points of the thickness distribution grid where the P point is located according to the position of the P point under the plane coordinate system OJK, and respectively recording the distances from the P point to the 4 corner points as d along the clockwise direction1、d2、d3、d4；
Calculating the thickness change value of the P point coating caused by the spraying track Li by adopting an inverse distance weighting method
。
Wherein, the corresponding P point coating thickness variation value caused by the spraying track Li is calculated by adopting an inverse distance weighting method
The method specifically comprises the following steps:
obtaining the thickness change value of the P point coating caused by the spraying track Li according to the following relational expression
：
Wherein,
；
in the formula, S is the corner number of the grid around the P point, i.e., S =1, 2, 3, 4.
In the embodiment, the thickness variation value of the P-point coating caused by the spraying track Li is calculated by an inverse distance weighting method
The calculation result is accurate and reliable, and the high-precision coating thickness variation value can be obtained
。
As an optional implementation manner, the obtaining N different spraying distances DhLower coating thickness profile function
(h =1, …, n), including in particular:
maintaining the spray gun at the spraying distance D under the preset spraying process parametershSpraying at constant speed on a flat surface to form a rectangular coating 3, measuring the thickness value of the coating on the cross section of the spraying track, and fitting to obtain a coating thickness distribution curve function
（h=1,…,n）。
In the embodiment, a rectangular coating 3 (shown in fig. 2) is formed by uniformly spraying the surface of the workpiece 2 with preset spraying process parameters, and then the coating thickness value on the cross section of the spraying track is measured and fitted to obtain a coating thickness distribution curve function (shown in fig. 3), which is obtained by actually measuring the preset spraying process parameters, so that the influence of thickness deviation caused by the difference between material attribute setting, model construction and the real environment by simulation prediction methods such as finite elements and the like is avoided, and the reliability of the data of coating thickness prediction is high.
As an alternative embodiment, the preset spraying process parameters include preset spraying process parameters including spraying flow, spraying speed, spraying attitude, spraying distance, overlapping distance, spraying direction, atomizing pressure and fan pressure, and the calculation of the coating thickness is more representative and influential.
Example 2
The embodiment provides a spraying thickness prediction device facing a complex curved surface, which comprises:
function acquisition module for efficient injection at a spray gun 1Adjusting the spraying distance D for N times within the coating rangehObtaining N different spraying distances DhLower coating thickness profile function
(h =1, …, n); wherein,
for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,
on the distribution curve of the thickness of the coating
The corresponding thickness value, N > 2;
a thickness distribution grid construction module for constructing a thickness distribution grid according to the coating thickness distribution curve function
Establishing a thickness distribution grid 4 corresponding to preset spraying process parameters;
a coating thickness change calculating module, determining the spraying track passing through the point P for any point P on the surface of the spraying object based on the point P coordinate, the spray gun motion track coordinate and the direction vector according to the thickness distribution grid 4, and calculating the coating thickness change value caused by all the spraying tracks related to the point P
；
A result output module for outputting the coating thickness variation value
The sum is summed to give the final coating thickness at point P
(i=1,…,n)。
In the embodiment, based on the device, the function obtaining module can accurately obtain the coating thickness distribution curve function, the thickness distribution grid 4 constructing module can construct the thickness distribution grid 4, and the coating thickness distribution curve function and the thickness distribution grid 4 are obtained based on the actual measurement of the preset spraying process parameters, so that the influence of thickness deviation caused by the difference between the material attribute setting and the model construction and the real environment of simulation prediction methods such as a finite element is avoided, and the reliability of the data of coating thickness prediction is high; meanwhile, the coating thickness variation calculation module can calculate the coating thickness according to the geometric relationship between the thickness distribution grid 4 and the surface of the spraying object, and finally the final coating thickness value is obtained through the result output module, so that the effect of adapting to complex and variable curved surface characteristics is realized.
Example 3
The present embodiment provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the method described in embodiment 1.
Example 4
The present embodiment provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement the method of embodiment 1.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (8)
1. A spray coating thickness prediction method for a complex curved surface is characterized by comprising the following steps:
adjusting the spraying distance D for N times within the effective spraying range of the spray gunhObtaining N different spraying distances DhLower coating thickness profile functionf(y h(),z h()) =0, h =1, …, n; wherein,y h()for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,z h()on the distribution curve of the thickness of the coatingy h()The corresponding thickness value, N > 2;
according to the coating thickness distribution curve functionf(y h(),z h()) =0, establishing a thickness distribution grid corresponding to the preset spraying process parameters, specifically comprising: establishing a plane coordinate system OJK with the center O of the spray gun as the original point on the normal plane of the motion track of the spray gun; according to the different spraying distances DhLower corresponding coating thickness distribution curve functionf(y h(),z h()) Carrying out grid segmentation on the spray distance by =0, and recording the thickness value on a grid node as delta (j, k); wherein j, k is an index of a grid corner point under the plane coordinate system OJK;
according to the thickness distribution grid, for any point P on the surface of the spraying object, determining a spraying track passing through the point P based on the coordinate of the point P, the coordinate of the motion track of the spray gun and the direction vector, and calculating the variation value of the coating thickness caused by all spraying tracks related to the point Pλ i ；
For the value of the variation of the thickness of the coatingλ i The sum is summed to give the final coating thickness at point P
，i=1,…,n。
2. The method for predicting the spraying thickness of the complex curved surface as claimed in claim 1, wherein the values of the coating thickness variation caused by all spraying tracks related to the P point are calculatedλ i The method specifically comprises the following steps:
recording the serial number of the spraying track as Li, i =1, …, n;
determining 4 corner points of the thickness distribution grid where the P point is located according to the position of the P point under the plane coordinate system OJK, and respectively recording the distances from the P point to the 4 corner points as d along the clockwise direction1、d2、d3、d4；
Calculating the thickness change value of the P point coating caused by the spraying track Li by adopting an inverse distance weighting methodλ i 。
3. The method for predicting the thickness of the coating facing the complex curved surface as claimed in claim 2, wherein the inverse distance weighting method is adopted to calculate the thickness variation value of the corresponding P-point coating caused by the coating track Liλ i The method specifically comprises the following steps:
obtaining the thickness change value of the P point coating caused by the spraying track Li according to the following relational expressionλ i ：
Wherein,
；
in the formula, S is the corner number of the grid around the P point, i.e., S =1, 2, 3, 4.
4. The method for predicting the spraying thickness of the complex curved surface as claimed in claim 1, wherein the N different spraying distances D are obtainedhLower coating thickness profile functionf(y h(),z h()) =0, h =1, …, n, including in particular:
maintaining the spray gun at the spraying distance D under the preset spraying process parametershSpraying at constant speed on a flat surface to form a rectangular coating, measuring the thickness value of the coating on the cross section of the spraying track, and fitting to obtain a coating thickness distribution curve functionf(y h(),z h())=0，h=1,…,n。
5. The method for predicting the spraying thickness of the complex curved surface as claimed in claim 1 or 4, wherein the preset spraying process parameters comprise spraying flow, spraying speed, spraying attitude, spraying distance, overlapping distance, spraying direction, atomizing pressure and fan-shaped pressure.
6. A spray coating thickness prediction device facing a complex curved surface is characterized by comprising:
a function acquisition module for adjusting the spraying distance D for N times within the effective spraying range of the spray gunhObtaining N different spraying distances DhLower coating thickness profile functionf(y h(),z h()) =0, h =1, …, n; wherein,y h()for the spraying distance DhThe abscissa of the lower coating thickness distribution curve,z h()on the distribution curve of the thickness of the coatingy h()The corresponding thickness value, N > 2;
a thickness distribution grid construction module for constructing a thickness distribution grid according to the coating thickness distribution curve functionf(y h(),z h()) =0, establishing a thickness distribution grid corresponding to the preset spraying process parameters, specifically comprising: establishing a plane coordinate system OJK with the center O of the spray gun as the original point on the normal plane of the motion track of the spray gun; according to the different spraying distances DhLower corresponding coating thickness distribution curve functionf(y h(),z h()) Carrying out grid segmentation on the spray distance by =0, and recording the thickness value on a grid node as delta (j, k); wherein j, k is an index of a grid corner point under the plane coordinate system OJK;
a coating thickness variation calculating module for determining the spraying track passing through the point P for any point P on the surface of the spraying object based on the point P coordinate, the spray gun motion track coordinate and the direction vector according to the thickness distribution grid, and calculating the coating thickness variation value caused by all the spraying tracks related to the point Pλ i ；
A result output module for outputting the coating thickness variation valueλ i Summing to obtain final P pointThickness of coating
，i=1,…,n。
7. A computer arrangement, characterized in that the computer arrangement comprises a memory in which a computer program is stored and a processor which executes the computer program for implementing the method as claimed in any one of claims 1-5.
8. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, performs the method of any one of claims 1-5.
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| CN116237206A (en) * | 2023-05-11 | 2023-06-09 | 成都飞机工业（集团）有限责任公司 | Composite part spraying method, device, storage medium and equipment |
| JP2024170936A (en) * | 2023-05-29 | 2024-12-11 | 株式会社日立製作所 | Coating work management device, coating work management program, and railway vehicle coated using coating work management device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102007025706A1 (en) * | 2007-06-01 | 2008-12-04 | Schott Ag | Workpiece e.g. silicon wafer, coating method, involves determining coating thickness function, which reproduces coating thickness distribution on workpiece and calculating time and/or movement function for controlling spraying device |
| CN103495516A (en) * | 2013-09-24 | 2014-01-08 | 盐城工学院 | Two-pass automatic-spraying track optimization method of complex curved surfaces |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006033721A1 (en) * | 2006-03-21 | 2007-09-27 | Daimlerchrysler Ag | Method and device for predicting the paint layer thickness |
| CN101739488B (en) * | 2009-12-10 | 2012-05-30 | 广西大学 | Method for modeling paint deposition rate model adapting to complex free curved surface spraying |
| CN102831306B (en) * | 2012-08-13 | 2015-10-07 | 东南大学 | A kind of layering spraying profile planing method for spray robot |
| CN108549759B (en) * | 2018-03-30 | 2021-09-28 | 苏州瀚华智造智能技术有限公司 | Part coating thickness calculation and simulation method for automatic spraying process |
| CN112804955B (en) * | 2018-10-05 | 2024-10-11 | Epix疗法公司 | System for lesion formation assessment and display |
| KR102271074B1 (en) * | 2018-12-18 | 2021-06-30 | 원광이엔텍 (주) | System for processing surface of 3D printed sintered product using multiaxial joint robot |
| CN112691864B (en) * | 2020-11-27 | 2022-05-10 | 成都飞机工业（集团）有限责任公司 | Method for improving coating thickness uniformity of automatic spraying coating |
-
2021
- 2021-08-30 CN CN202111002571.5A patent/CN113449442B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102007025706A1 (en) * | 2007-06-01 | 2008-12-04 | Schott Ag | Workpiece e.g. silicon wafer, coating method, involves determining coating thickness function, which reproduces coating thickness distribution on workpiece and calculating time and/or movement function for controlling spraying device |
| CN103495516A (en) * | 2013-09-24 | 2014-01-08 | 盐城工学院 | Two-pass automatic-spraying track optimization method of complex curved surfaces |
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