CN112935465B - Technological parameter optimization method for improving welding quality and fusion width - Google Patents
Technological parameter optimization method for improving welding quality and fusion width Download PDFInfo
- Publication number
- CN112935465B CN112935465B CN202110186408.2A CN202110186408A CN112935465B CN 112935465 B CN112935465 B CN 112935465B CN 202110186408 A CN202110186408 A CN 202110186408A CN 112935465 B CN112935465 B CN 112935465B
- Authority
- CN
- China
- Prior art keywords
- welding
- robot
- width
- seam
- layer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/02—Seam welding; Backing means; Inserts
- B23K9/028—Seam welding; Backing means; Inserts for curved planar seams
- B23K9/0282—Seam welding; Backing means; Inserts for curved planar seams for welding tube sections
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/095—Monitoring or automatic control of welding parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/16—Arc welding or cutting making use of shielding gas
- B23K9/173—Arc welding or cutting making use of shielding gas and of a consumable electrode
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Plasma & Fusion (AREA)
- Mechanical Engineering (AREA)
- Butt Welding And Welding Of Specific Article (AREA)
Abstract
The invention discloses a technological parameter optimization method for improving welding quality and weld width.A full-position welding robot samples the width of a welding layer of a groove to be welded, the height of the welding layer and the current crawling angle of the welding robot in real time; inputting the width of a welding layer, the height of the welding layer and the current crawling angle of the welding robot into a technological parameter control formula to obtain a welding voltage U and the staying time T of a welding gun on the edge of the welding seam; the stay time T of the welding gun at the edge of the welding seam controls the all-position welding robot, and the welding voltage U controls the digital welding machine, so that the all-position welding of the pipeline is realized. The technological parameter optimization method for improving the welding quality and the fusion width is integrated in the welding robot, and during welding operation, the technological parameters can be automatically matched according to the real-time working state of the robot, so that the all-position pipeline welding operation can be efficiently completed with high quality.
Description
Technical Field
The invention relates to a technological parameter optimization method for improving welding quality and fusion width, and belongs to the technical field of automatic welding.
Background
In the past, the petrochemical industry of China has a very important influence on the national economic development, and the pipeline welding operation amount related to the petrochemical industry is huge, the field operation environment is severe, and the automation degree is low. In recent years, young welders are less and less, manual welders are in short of resources, and cost increase is a common problem facing many chemical construction units. Petrochemical engineering pipeline welding engineering puts high requirements on the quality, efficiency and automation level of pipeline welding. However, when the pipeline to be welded is welded on site, the pipeline to be welded is fixed in position and cannot rotate, the requirement for welding the circular seam at the opening of the pipeline is full-position welding, the full-position welding process is complex, the welding specification is difficult to control, and therefore the stability of the quality of the welding seam is difficult to guarantee.
During the actual all-position welding process, the influence of gravity causes the weld pool to be stressed and unbalanced in different spatial positions, thereby causing unstable formation of the weld pool. The welding position of the pipeline at all positions is shown in figure 1, and in the non-flat welding position, such as vertical downward welding, overhead welding and vertical upward welding, a molten pool flows, so that the phenomena of poor weld formation, incomplete fusion between welding layers and high projection of the center position of the weld are easy to occur, and are shown in figure 2.
In the prior art, an MAGW welding mode is commonly used for welding pipelines at all positions, however, the welding method is complex in specification and various in important parameters, and the problem of welding defects caused by insufficient fusion width in all-position welding is solved by technical personnel in the field.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a technological parameter optimization method for improving the welding quality and the fusion width.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a technological parameter optimization method for improving welding quality and fusion width comprises the following steps:
the all-position welding robot samples the width of a welding layer of a groove to be welded, the height of the welding layer and the current crawling angle of the welding robot in real time;
inputting the width of a welding layer, the height of the welding layer and the current crawling angle of the welding robot into a process parameter control formula to obtain welding voltage U and the staying time T of a welding gun on the edge of the welding seam;
the stay time T of the welding gun at the edge of the welding seam controls the all-position welding robot, and the welding voltage U controls the digital welding machine, so that the all-position welding of the pipeline is realized.
As a preferred scheme, the width and the height of a welding layer of the groove to be welded are obtained by a machine vision detection device on a welding robot.
As the preferred scheme, the current crawling angle of the welding robot is acquired through a crawling angle detection module on the welding robot.
As a preferred scheme, the process parameter control formula is obtained by the following steps:
counting the welding voltage U and the retention time T of a welding gun on the edge of a welding seam under the conditions of different welding layer widths d, welding layer heights h and welding robot crawling angles alpha;
taking the welding voltage U, the stay time T of a welding gun on the edge of a welding seam as response values, taking the width d of the welding seam, the height h of the welding seam and the crawling angle alpha of the welding robot as input variables, and then carrying out curve fitting by adopting a multivariate nonlinear regression method to obtain a nonlinear regression equation;
the calculation formula of the nonlinear regression equation is as follows:
wherein the content of the first and second substances,
the welding voltage U and the residence time T, x of the welding gun on the edge of the welding seam corresponding to the kth welding layer are respectively 1 、x 2 、x 3 The welding voltage U nonlinear regression equation coefficient is M1-M7, and the welding voltage N1-N7 are the welding voltage U nonlinear regression equation coefficients, and the welding robot crawling angle alpha is the welding robot crawling angle h, and the welding voltage U nonlinear regression equation coefficients are the welding gun staying time T nonlinear regression equation coefficients.
Preferably, the values of the coefficients M1-M7 are shown in the following table:
preferably, the values of the coefficients N1-N7 are shown in the following table:
has the advantages that: according to the technological parameter optimization method for improving the welding quality and the weld width, provided by the invention, the defect of insufficient weld width can be effectively reduced by setting the welding voltage U of the welding robot and the retention time T of the welding gun on the edge of the weld seam, and the welding quality is improved. The method is integrated in the welding robot, and can automatically match welding process parameters according to the real-time working state of the robot during welding operation, thereby efficiently and high-quality completing all-position pipeline welding operation.
Drawings
FIG. 1 is a schematic diagram of a welding position of a carbon steel pipeline.
FIG. 2 is a schematic diagram of a defect of a weld joint of a carbon steel pipeline.
FIG. 3 is a comparison of weld forming sub-regions.
Fig. 4 is a schematic view of the appearance of the formed weld.
Detailed Description
The present invention will be further described with reference to the following examples.
A technological parameter optimization method for improving welding quality and fusion width comprises the following steps:
acquiring various basic information of a pipeline to be welded, wherein the basic information comprises: the material, the pipe diameter, the wall thickness, the welding process and the groove shape of the pipeline;
acquiring data information of the width and height of a welding layer of a groove to be welded of the pipeline through a machine vision detection device;
acquiring a current crawling angle of the welding robot through a crawling angle detection module;
and establishing a mathematical model, and performing nonlinear regression on welding parameters through a large amount of welding data to obtain welding process parameters under different conditions.
Example 1:
a machine vision detection device for obtaining pipeline groove data information that waits to weld can acquire welding layer width and welding layer height data automatically, provides the information for the automatic adjustment of technological parameter.
And the control system is used for adjusting the technological parameters of the all-position pipeline welding robot in real time, so that the all-position pipeline welding robot carries out welding operation according to the welding technological parameters calculated by the method.
The selection of welding process parameters has a direct relation with the width of a welding layer to be welded, the height of the welding layer and the crawling angle alpha of the welding robot. The traditional univariate experimental method cannot test the interaction of different parameters, and has the problems of low test efficiency and huge workload. According to the invention, through a test design method, a mathematical model is established, the test cost is reduced, the test efficiency is improved, and scientific and reasonable test results are obtained.
Based on the fact that the characteristics of insufficient fusion width are influenced by welding process parameters and have a certain rule, the method comprises the steps of firstly counting the welding voltage U and the welding gun retention time T at the edge of a welding seam under the conditions of different welding layer widths d, welding layer heights h and the welding robot crawling angle alpha through a large number of welding tests, and then carrying out curve fitting by adopting a multivariate nonlinear regression method to establish a mathematical model. The mathematical model describes the quantitative relation between the welding voltage U and the retention time of the welding gun on the edge of the welding seam and the influence factors of the welding gun, and the quantitative relation is compiled into a control system of the all-position pipeline welding robot, and finally the all-position welding of the pipeline is realized.
Two parameters of welding voltage U and the stay time T of the welding gun on the edge of the welding seam are used as response values, and the width d of the welding seam, the height h of the welding seam and the crawling angle alpha of the welding robot are used as input variables. And establishing a nonlinear regression equation to fit a functional relation between the response value and the input variable.
The response value is recorded as
The welding voltage U and the welding gun staying time T at the edge of the welding seam corresponding to the kth welding layer are respectively input into the welding gunThe quantity is denoted as x 1 、x 2 、x 3 The weld width d, the weld height h and the welding robot crawling angle alpha are respectively adopted, and the functional relationship between the response value and the input variable can be expressed as follows:
wherein, i is 1, 2, k is a natural number.
The nonlinear regression equation is calculated as follows:
M1-M7 are welding voltage U nonlinear regression equation coefficients, and N1-N7 are welding torch edge retention time T nonlinear regression equation coefficients.
A large amount of welding data are obtained through experiments, the relation between response values and input variables is integrated, and various regression coefficients in a second-order nonlinear regression equation are obtained through a least square method and are shown in tables 1-2:
TABLE 1 welding Voltage U second order nonlinear regression equation coefficients
TABLE 2 residence time T of welding gun at weld edge, each coefficient of the second order nonlinear regression equation
Example 2:
in the all-position welding process, the action control of the whole system is realized by adopting a technological parameter optimization method for improving the welding quality and the fusion width through a wireless remote controller. After the system starts to operate, the vision module periodically collects and processes images to obtain width and height data of a welding layer and sends the width and height data to the control module, the pose detection sensor is arranged in the all-position welding robot to obtain crawling angle alpha information of the welding robot in real time, the control module combines the three data, welding process parameters are obtained through internal algorithm calculation and sent to the execution module, and the all-position welding robot and the digital welding machine receive and execute instructions sent by the control module, so that pipeline all-position welding with automatic matching of the welding process parameters is realized.
Example 3:
in order to quantitatively verify the precision and the stability of the method, the same groove of the same pipeline is welded by three modes of not changing process parameters, manually adjusting process parameters and automatically matching the process parameters by using the all-position control system, the width and the thickness of a welding seam after the welding seam is formed are shown in figure 3, the all-position welding control system designed by the method is adopted, the fusion width is sufficient, the welding seam quality is good, the maximum value of the four-layer accumulated thickness deviation is 0.5mm, the deviation of the width of the welding seam is 0.2mm, and the actual welding effect of each part of the circumference of the pipeline is shown in figure 4.
The invention aims to reduce the welding labor intensity, improve the welding efficiency and effectively solve the problem that the welding quality is difficult to ensure because a professional welder needs to observe a welding seam in real time in the welding process and frequently adjusts process parameters when the pipeline welding engineering in the petrochemical industry is operated on a welding site.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (3)
1. A technological parameter optimization method for improving welding quality and fusion width is characterized in that: the method comprises the following steps:
the all-position welding robot samples the width of a welding layer of a groove to be welded, the height of the welding layer and the current crawling angle of the welding robot in real time;
inputting the width of a welding layer, the height of the welding layer and the current crawling angle of the welding robot into a process parameter control formula to obtain welding voltage U and the staying time T of a welding gun on the edge of the welding seam;
the welding gun stays at the edge of the welding seam for a time T to control the all-position welding robot, and a welding voltage U controls the digital welding machine to realize all-position welding of the pipeline;
the process parameter control formula is obtained by the following steps:
counting the welding voltage U and the retention time T of a welding gun on the edge of a welding seam under the conditions of different welding layer widths d, welding layer heights h and welding robot crawling angles alpha;
taking the welding voltage U, the stay time T of the welding gun on the edge of the welding seam as response values, taking the width d of the welding seam, the height h of the welding seam and the crawling angle alpha of the welding robot as input variables, and then carrying out curve fitting by adopting a multivariate nonlinear regression method to obtain a nonlinear regression equation;
the calculation formula of the nonlinear regression equation is as follows:
wherein the content of the first and second substances,
the welding voltage U and the retention time T, x of the welding gun on the edge of the welding seam corresponding to the k-th welding layer 1 、x 2 、x 3 Respectively the width d of the welding seam, the height h of the welding seam and the creeping angle alpha of the welding robot, wherein M1-M7 are nonlinear regression equation coefficients of welding voltage U, and N1-N7 are nonlinear regression equation coefficients of the staying time T of the welding gun on the edge of the welding seam;
the values of the M1-M7 coefficients are shown in the following table:
the values of the N1-N7 coefficients are shown in the following table:
2. the method for optimizing process parameters for improving weld quality and weld width according to claim 1, wherein the method comprises the following steps: and the width and the height of the welding layer of the groove to be welded are obtained by a machine vision detection device on the welding robot.
3. The method for optimizing the process parameters for improving the welding quality and the fusion width as claimed in claim 1, wherein the method comprises the following steps: the current crawling angle of the welding robot is acquired through a crawling angle detection module on the welding robot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110186408.2A CN112935465B (en) | 2021-02-08 | 2021-02-08 | Technological parameter optimization method for improving welding quality and fusion width |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110186408.2A CN112935465B (en) | 2021-02-08 | 2021-02-08 | Technological parameter optimization method for improving welding quality and fusion width |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112935465A CN112935465A (en) | 2021-06-11 |
| CN112935465B true CN112935465B (en) | 2022-08-23 |
Family
ID=76245903
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110186408.2A Active CN112935465B (en) | 2021-02-08 | 2021-02-08 | Technological parameter optimization method for improving welding quality and fusion width |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112935465B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113664414A (en) * | 2021-07-22 | 2021-11-19 | 中国化学工程第六建设有限公司 | Welding process parameter optimization method suitable for different pipe diameters |
| CN113441818B (en) * | 2021-08-16 | 2022-06-24 | 哈尔滨焊接研究院有限公司 | Welding method for tube plate of sodium-cooled fast reactor evaporator tube |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105345237A (en) * | 2015-12-10 | 2016-02-24 | 河北沧海核装备科技股份有限公司 | Device and process method for automatically controlling welding seam shape in longitudinal submerged arc welding |
| CN110026652A (en) * | 2019-04-26 | 2019-07-19 | 湖北文理学院 | A kind of pipeline welding parameter determines method, system and storage medium |
| CN110210127A (en) * | 2019-05-31 | 2019-09-06 | 山东大学 | Welding condition and welding bead molding parameter correlation model method for building up and system |
| CN111451606A (en) * | 2020-03-31 | 2020-07-28 | 北京博清科技有限公司 | Welding control method and device |
| CN111985094A (en) * | 2020-08-06 | 2020-11-24 | 河海大学常州校区 | Method for predicting weld penetration of underwater wet welding |
| CN112199632A (en) * | 2020-09-03 | 2021-01-08 | 大连理工大学 | Laser-textured aluminum alloy surface hardness prediction method |
-
2021
- 2021-02-08 CN CN202110186408.2A patent/CN112935465B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105345237A (en) * | 2015-12-10 | 2016-02-24 | 河北沧海核装备科技股份有限公司 | Device and process method for automatically controlling welding seam shape in longitudinal submerged arc welding |
| CN110026652A (en) * | 2019-04-26 | 2019-07-19 | 湖北文理学院 | A kind of pipeline welding parameter determines method, system and storage medium |
| CN110210127A (en) * | 2019-05-31 | 2019-09-06 | 山东大学 | Welding condition and welding bead molding parameter correlation model method for building up and system |
| CN111451606A (en) * | 2020-03-31 | 2020-07-28 | 北京博清科技有限公司 | Welding control method and device |
| CN111985094A (en) * | 2020-08-06 | 2020-11-24 | 河海大学常州校区 | Method for predicting weld penetration of underwater wet welding |
| CN112199632A (en) * | 2020-09-03 | 2021-01-08 | 大连理工大学 | Laser-textured aluminum alloy surface hardness prediction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112935465A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112935465B (en) | Technological parameter optimization method for improving welding quality and fusion width | |
| CN112958871B (en) | Welding process parameter optimization method for improving welding efficiency and quality | |
| CN105345237B (en) | A kind of vertical masonry joint submerged-arc welding automatically controls the device and its process of seam center | |
| CN101310909B (en) | Automatic welding process of skewed tube fillet of boiler separator | |
| CN103480991B (en) | Thin steel plate narrow welding joint online visual inspection and control device | |
| CN105618976B (en) | A kind of intelligent weld seam system | |
| CN102009275B (en) | Automatic welding system for rack and base of marine diesel engine | |
| CN112935466B (en) | Technological parameter optimization method for improving welding stability and appearance quality | |
| CN103962808B (en) | Fabrication and installation method for last furnace shell | |
| CN103934693B (en) | The processing technology of box-structure post/beam shaping | |
| CN102413987A (en) | Method for manufacturing a door frame, welding arrangement and structure of a door frame | |
| CN105618898A (en) | Intelligent line welding method | |
| CN113664414A (en) | Welding process parameter optimization method suitable for different pipe diameters | |
| CN109590599A (en) | A kind of double end Friction Stir Welding device and method | |
| CN109986255A (en) | Mix visual servo parallel robot and operational method | |
| CN106780655B (en) | Manual decision method and system for automatic welding path | |
| KR200401599Y1 (en) | Manufacture device of a spiral wounding gasket | |
| CN103551705A (en) | Chongqing monorail roof assembly welding process method | |
| CN110202238A (en) | Reinforced steel bar perforation plug welding method | |
| CN206869283U (en) | A kind of robot circular weld automated system based on PLC controls | |
| CN115533380A (en) | Welding seam defect identification and automatic repair welding method | |
| CN113523500A (en) | Intelligent control system and method for rail vehicle welding | |
| CN205614232U (en) | Intelligence welding seam system | |
| CN113000985B (en) | Automatic surfacing process for surface of membrane type wall tube panel | |
| CN204771065U (en) | Steam injection boiler unit convection zone box automatic weld workstation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |