Intelligent steel plate welding system based on numerical control machine tool

Abstract

The present invention relates to the technical field of laser welding of CNC machine tools, and discloses a steel plate intelligent welding system based on CNC machine tools, comprising a laser beam welding data acquisition module, a machine tool welding temperature acquisition module, a machine tool welding spatter data acquisition module, a machine tool welding analysis module, and a machine tool welding adjustment control module. The laser beam welding data acquisition module comprises a laser beam power acquisition unit and a laser beam fluctuation acquisition unit. The machine tool welding analysis module sends data based on the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module, and performs analysis and calculation to obtain three different analysis and evaluation results. The system evaluates welding quality by constructing corresponding coefficients from different key factors by respectively constructing a processing deviation coefficient, a fluctuation deviation coefficient, and a spatter deviation coefficient. By combining these three coefficients, the welding quality can be comprehensively evaluated, and the root causes of problems that occur during the welding process can be quickly located.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical control machine tool laser welding, in particular to a steel plate intelligent welding system based on a numerical control machine tool. BACKGROUND

[0002] With the updating of technology, the development of laser welding industry, laser welding technology has been applied in more and more fields. In order to meet more and more needs, laser welding machine is also developing towards high precision, high composite, high reliability and high automation, which requires continuous improvement of the original design technology.

[0003] At present, it is difficult to detect and evaluate the abnormal operation of the laser and give reasonable feedback and early warning during the laser welding process. The management personnel cannot take corresponding control measures in time, which is not conducive to ensuring the welding quality, and checking the welding quality one by one seriously affects the processing quality. SUMMARY

[0004] In view of the shortcomings of the prior art, the present application provides a steel plate intelligent welding system based on a numerical control machine tool, which has the advantages of warning the risks in the laser welding process, and solves the above technical problems.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a steel plate intelligent welding system based on a numerical control machine tool, comprising a laser beam welding data acquisition module, a machine tool welding temperature acquisition module, a machine tool welding spatter data acquisition module, a machine tool welding analysis module and a machine tool welding adjustment control module.

[0006] The laser beam welding data acquisition module comprises a laser beam power acquisition unit and a laser beam fluctuation acquisition unit, the laser beam power acquisition unit is used for acquiring the laser beam power deviation in the steel plate welding process, and the laser beam fluctuation acquisition unit is used for acquiring the fluctuation data of the laser beam in the welding process and sending to the machine tool welding analysis module respectively.

[0007] The machine tool welding temperature acquisition module is used for acquiring the welding temperature deviation in the steel plate welding process and sending to the machine tool welding analysis module.

[0008] The machine tool welding spatter data acquisition module is used for acquiring the spatter data in the steel plate welding process and sending to the machine tool welding analysis module.

[0009] The machine tool welding analysis module analyzes and calculates three different analysis evaluation results based on the data sent by the laser beam welding data acquisition module, the machine tool welding temperature acquisition module and the machine tool welding spatter data acquisition module respectively, and judges whether to execute the corresponding adjustment instruction. If yes, the machine tool welding adjustment control module is called to execute control.

[0010] As a preferred technical solution of the present application, the specific expression of the laser beam power collection unit for collecting the laser beam power deviation during the welding process of the steel plate is as follows:

[0011] JGSGL=[GLPC1,…,GLPC i ,…,GLPC n ]

[0012] Wherein, JGSGL represents the laser beam power deviation set during the welding process of the steel plate, GLPC1,…,GLPC i ,…,GLPC n respectively represent the first sampling time laser beam power deviation value, …, the i-th sampling time laser beam power deviation value, …, the n-th sampling time laser beam power deviation value, i∈[1,n], and the specific expression of the i-th laser beam power deviation value GLPC i is as follows:

[0013]

[0014] Wherein, GL0 represents the preset standard processing laser beam power, GL i represents the i-th sampling time laser beam power, GLPC i represents the i-th laser beam power deviation value.

[0015] As a preferred technical solution of the present application, the specific steps of the laser beam fluctuation degree collection unit for collecting the fluctuation data of the laser beam during the welding process are as follows:

[0016] Step A1: The image is taken by the shooting device arranged in the numerical control machine tool every sampling period, and the center point coordinates of the laser beam in the sampling period are stored as the trajectory path point set of the laser beam;

[0017] Step A2: The trajectory straight line is constructed based on the coordinates of the first sampling point and the last sampling point of the laser beam trajectory path point set of the laser beam;

[0018] Step A3: The distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the laser beam is calculated, and the offset coefficient PYXS is obtained;

[0019] Step A4: The noise of the laser beam emitting part of the numerical control machine tool in the displacement process is obtained.

[0020] As a preferred technical solution of the present application, the specific expression of the step A3 for calculating the distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the laser beam, and obtaining the offset coefficient PYXS is as follows:

[0021]

[0022] Wherein, PYXS represents the offset coefficient, L represents the set composed of the distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the calculated laser beam, max{L} represents the maximum value in L, represents the mean value of L, L0 represents the preset offset distance.

[0023] As a preferred technical scheme of the present application, the machine tool welding temperature acquisition module is used to acquire the specific expression of the welding temperature deviation during the welding process of the steel plate as follows:

[0024] HJWD = [WDPC1, …, WDPC i , WDPC n ]

[0025] Wherein, HJWD represents the set of temperature deviations acquired during the welding process of the steel plate, WDPC1, …, WDPC i , WDPC n respectively represent the welding temperature deviation value at the 1st sampling time, the welding temperature deviation value at the i-th sampling time, and the welding temperature deviation value at the n-th sampling time, i∈[1,n], and the specific expression of the welding temperature deviation value WDPC i at the i-th sampling time is as follows:

[0026]

[0027] Wherein, WD0 represents the welding temperature standard value, WD i represents the welding temperature at the i-th sampling time, and WDPC i represents the welding temperature deviation value at the i-th sampling time.

[0028] As a preferred technical scheme of the present application, the specific steps of the machine tool welding spatter data acquisition module for acquiring the spatter data during the welding process of the steel plate are as follows:

[0029] Step B1: identifying the welding area before welding;

[0030] Step B2: measuring the distance l between the farthest spatter point after the end of welding and the center of the machine tool.

[0031] As a preferred technical scheme of the present application, the specific steps of the machine tool welding analysis module based on the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module for sending data and respectively performing analysis and calculation to obtain three different analysis and evaluation results are as follows:

[0032] Step C1: based on the laser beam power deviation in the laser beam welding data acquisition module, the welding temperature deviation is used to construct the processing deviation coefficient JGPCXS;

[0033] Step C2: based on the laser beam fluctuation acquisition unit, the fluctuation data of the laser beam during the welding process is collected, and the fluctuation deviation coefficient BDPCXS is constructed;

[0034] Step C3: based on the distance l between the farthest spatter point after the welding is completed and the center of the machine tool, the spatter deviation coefficient FJPCXS is constructed;

[0035] Step C4: when any one of the processing deviation coefficient JGPCXS, the fluctuation deviation coefficient BDPCXS and the spatter deviation coefficient FJPCXS exceeds the preset judgment value, a warning is given, reminding the staff that there is a quality risk, and the quality of the currently welded steel plate needs to be monitored.

[0036] As a preferred technical scheme of the present application, the specific expression of the processing deviation coefficient JGPCXS constructed based on the laser beam power deviation in the laser beam welding data acquisition module in step C1 is as follows:

[0037] JGPCXS=w1*max{|JGSGL|}+w2*max{|HJWD|}

[0038] Wherein, JGSGL represents the laser beam power deviation set during the welding process of the steel plate, |JGSGL| represents the absolute value of the laser beam power deviation set during the welding process of the steel plate, w1 and w2 represent the weight coefficients whose sum is 1, and max{} represents the maximum value function.

[0039] As a preferred technical scheme of the present application, the specific expression of the fluctuation deviation coefficient BDPCXS constructed based on the laser beam fluctuation acquisition unit in step C2 is as follows:

[0040]

[0041] Wherein, PYXS represents the offset coefficient, and ZS represents the maximum noise of the numerical control machine tool laser beam emitting element during displacement.

[0042] As a preferred technical scheme of the present application, the specific expression of the spatter deviation coefficient FJPCXS in step C3 is as follows:

[0043]

[0044] Wherein, l represents the distance between the farthest spatter point after the welding is completed and the center of the machine tool, and FJPCXS represents the spatter deviation coefficient, The distance mean value representing the standard splatter point.

[0045] Compared with the prior art, the steel plate intelligent welding system based on a numerical control machine tool has the following beneficial effects:

[0046] The welding quality is evaluated by constructing the machining deviation coefficient, the fluctuation deviation coefficient and the splashing deviation coefficient respectively, and the welding quality is comprehensively evaluated by comprehensively considering the three coefficients, potential quality problems are found in time, the problem source in the welding process is quickly located, the welding steel plate with problems is quickly located, and further detection is performed. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a system framework schematic diagram of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0049] Please refer to Figure 1 A steel plate intelligent welding system based on a numerical control machine tool, comprising a laser beam welding data acquisition module, a machine tool welding temperature acquisition module, a machine tool welding splashing data acquisition module, a machine tool welding analysis module and a machine tool welding adjustment control module.

[0050] The laser beam welding data acquisition module comprises a laser beam power acquisition unit and a laser beam fluctuation acquisition unit, the laser beam power acquisition unit is used for acquiring the laser beam power deviation in the welding process of the steel plate, and the laser beam fluctuation acquisition unit is used for acquiring the fluctuation data of the laser beam in the welding process and sending the fluctuation data to the machine tool welding analysis module respectively.

[0051] The machine tool welding temperature acquisition module is used for acquiring the welding temperature deviation in the welding process of the steel plate and sending the welding temperature deviation to the machine tool welding analysis module.

[0052] The machine tool welding splashing data acquisition module is used for acquiring the splashing data in the welding process of the steel plate and sending the splashing data to the machine tool welding analysis module.

[0053] The machine tool welding analysis module sends data based on the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module, and performs analysis and calculation to obtain three different analysis and evaluation results, and determines whether to execute the corresponding adjustment instructions. If executed, the machine tool welding adjustment control module is called to execute control.

[0054] The specific expression of the laser beam power acquisition unit used to acquire the laser beam power deviation during the steel plate welding process is as follows:

[0055] JGSGL=[GLPC1,…,GLPC i ,…,GLPC n ]

[0056] Where JGSGL represents the laser beam power deviation set during the steel plate welding process, GLPC1,…,GLPC i ,…,GLPC n They represent the laser beam power deviation value at the 1st sampling moment, ..., the laser beam power deviation value at the i-th sampling moment, ..., the laser beam power deviation value at the b-th sampling moment, i∈[1,n], and the i-th laser beam power deviation value GLPC i The specific expression is as follows:

[0057]

[0058] Among them, GL0 represents the laser beam power during the preset standard processing, GL i represents the laser beam power at the i-th sampling moment, GLPC i Represents the power deviation value of the i-th laser beam.

[0059] The specific steps of the laser beam fluctuation acquisition unit for collecting the fluctuation data of the laser beam during the welding process are as follows:

[0060] Step A1: capturing images at each sampling period using a camera installed in the CNC machine tool, and storing the coordinates of the center point of the laser beam in the sampling period as a trajectory path point set of the laser beam;

[0061] Step A2: Constructing a trajectory line based on the coordinates of the first and last sampling points of the laser beam based on the trajectory path point set of the laser beam. By clarifying the straight line path between the starting and ending points, a benchmark is provided for subsequent evaluation of whether the laser beam has deviated.

[0062] Step A3: calculate the distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the laser beam, and obtain the offset coefficient PYXS, which can accurately reflect the offset of the laser beam at each position in the welding process. For each sampling point, the distance from the trajectory straight line can quantify the real-time offset degree of the laser beam;

[0063] The specific expression of the offset coefficient PYXS calculated from the distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the laser beam is as follows:

[0064]

[0065] Wherein, PYXS represents the offset coefficient, L represents the set composed of the distance from the first sampling point and the last sampling point of the laser beam to the trajectory straight line in the trajectory path point set of the laser beam, max{L} represents the maximum value in L, represents the mean value of L, and L0 represents the preset offset distance.

[0066] Step A4: obtain the noise of the laser beam emitting component of the numerical control machine tool in the displacement process. Through monitoring the noise of the laser beam emitting component and the laser beam travel path, the real-time running stability can be reflected in time.

[0067] The specific expression of the machine tool welding temperature collection module for collecting the welding temperature deviation in the welding process of the steel plate is as follows:

[0068] HJWD = [WDPC1, …, WDPC i , …, WDPC n ]

[0069] Wherein, HJWD represents the set of collected temperature deviations in the welding process of the steel plate, WDPC1, …, WDPC i , …, WDPC n respectively represent the welding temperature deviation value at the 1st sampling time, the welding temperature deviation value at the i th sampling time, and the welding temperature deviation value at the n th sampling time, i ∈ [1, n], and the specific expression of the welding temperature deviation value WDPC i at the i th sampling time is as follows:

[0070]

[0071] Wherein, WD0 represents the welding temperature standard value, WD i represents the welding temperature at the i th sampling time, and WDPC i represents the welding temperature deviation value at the i th sampling time.

[0072] The specific steps of the machine tool welding spatter data acquisition module for collecting spatter data during steel plate welding are as follows:

[0073] Step B1: Identify the welding area before welding;

[0074] Step B2: Measure the distance l between the farthest spatter point and the center of the machine tool after welding.

[0075] The specific steps for the machine tool welding analysis module to analyze and calculate the data sent by the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module to obtain three different analysis and evaluation results are as follows:

[0076] Step C1: constructing a processing deviation coefficient JGPCXS based on the laser beam power deviation and welding temperature deviation in the laser beam welding data acquisition module;

[0077] Step C2: The laser beam fluctuation acquisition unit is used to collect the fluctuation data of the laser beam during the welding process and construct the fluctuation deviation coefficient BDPCXS;

[0078] Step C3: constructing a spatter deviation coefficient FJPCXS based on the distance l between the farthest spatter point and the center of the machine tool after the welding is completed;

[0079] Step C4: When any of the processing deviation coefficient JGPCXS, the fluctuation deviation coefficient BDPCXS, and the spatter deviation coefficient FJPCXS exceeds the preset judgment value, an early warning is issued to remind the staff that there is a quality risk and the quality monitoring of the currently welded steel plate is required;

[0080] Steps C1-C3 construct corresponding coefficients based on different key factors (processing deviation, fluctuation deviation, and spatter deviation) to evaluate weld quality. The processing deviation coefficient (step C1) reflects the difference between the welding parameters (power, temperature) and the ideal settings, which directly affects the weld formation and microstructure. The fluctuation deviation coefficient (step C2) takes into account the stability of the laser beam during the welding process. An unstable laser beam may lead to inconsistent weld width and uneven penetration. The spatter deviation coefficient (step C3) focuses on the spatter generated during the welding process. Excessive spatter may indicate inappropriate welding parameters or a poor welding environment. By combining these three coefficients, the weld quality can be comprehensively evaluated and potential quality issues can be identified in a timely manner.

[0081] In step C1, the specific expression of the processing deviation coefficient JGPCXS based on the laser beam power deviation and welding temperature deviation in the laser beam welding data acquisition module is as follows:

[0082] JGPCXS=w1*max{|JGSGL|}+w2*max{|HJWD|}

[0083] wherein, JGSGL represents the laser beam power deviation set during the welding process of the steel plate, |JGSGL| represents the absolute value of the laser beam power deviation set during the welding process of the steel plate, w1 and w2 respectively represent the weight coefficients and the sum is 1, and max{} represents the maximum value function.

[0084] The fluctuation data of the laser beam during the welding process is collected by the laser beam fluctuation degree acquisition unit in step C2, and the specific expression of the fluctuation deviation coefficient BDPCXS is as follows:

[0085]

[0086] wherein, PYXS represents the offset coefficient, and ZS represents the maximum noise of the numerical control machine tool laser beam emitting element during the displacement process.

[0087] The specific expression of the spatter deviation coefficient FJPCXS is as follows in step C3:

[0088]

[0089] wherein, l represents the distance between the farthest spatter point after welding and the center of the machine tool, FJPCXS represents the spatter deviation coefficient, represents the distance mean of the standard spatter point.

[0090] Embodiment:

[0091] The specific parameters in this embodiment are shown in the following table 1:

[0092] Table 1

[0093]

[0094]

[0095] At this time, JGPCXS is calculated as w1*max{|JGSGL|}+w2*max{|HJWD|}=0.255, which is less than the machining deviation coefficient threshold value=0.95;

[0096] which is greater than the fluctuation deviation coefficient=1.85, which is less than the spatter deviation coefficient=1.45, at this time, a pre-warning is given to remind the staff that there is a quality risk, and the current steel plate being welded needs to be further monitored for quality;

[0097] The above embodiments only give one or more feasible solutions, and do not represent the optimal solution. The size of the threshold is set for the purpose of comparison. The size of the threshold depends on how much sample data is available and the number of bases set by a person skilled in the art for each group of sample data. As long as the proportional relationship between the parameters and the quantized values is not affected, the size of the weight can be determined by a person skilled in the art according to each sample data and the process of multiple experiments. The above formulas are dimensionless and the values are calculated.

[0098] Although embodiments of the present application have been shown and described, it would be appreciated by those skilled in the art that changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent steel plate welding system based on a CNC machine tool, characterized by: It includes laser beam welding data acquisition module, machine tool welding temperature acquisition module, machine tool welding spatter data acquisition module, machine tool welding analysis module and machine tool welding adjustment control module; The laser beam welding data acquisition module includes a laser beam power acquisition unit and a laser beam fluctuation acquisition unit. The laser beam power acquisition unit is used to acquire the laser beam power deviation during the steel plate welding process. The laser beam fluctuation acquisition unit is used to acquire the fluctuation data of the laser beam during the welding process and send them to the machine tool welding analysis module respectively. The machine tool welding temperature acquisition module is used to collect the welding temperature deviation during the welding process of the steel plate and send it to the machine tool welding analysis module; The machine tool welding spatter data acquisition module is used to collect spatter data during the steel plate welding process and send it to the machine tool welding analysis module; The machine tool welding analysis module analyzes and calculates the data sent by the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module to obtain three different analysis and evaluation results, and determines whether to execute the corresponding adjustment instructions. If so, the machine tool welding adjustment control module is called to execute the control; The specific steps of the machine tool welding analysis module to analyze and calculate the data sent by the laser beam welding data acquisition module, the machine tool welding temperature acquisition module, and the machine tool welding spatter data acquisition module to obtain three different analysis and evaluation results are as follows: Step C1: Constructing a processing deviation coefficient based on the laser beam power deviation and welding temperature deviation in the laser beam welding data acquisition module , the specific expression is as follows: ； in, represents the laser beam power deviation set during the steel plate welding process, It represents the absolute value of the laser beam power deviation set during the steel plate welding process, and They represent weight coefficients that sum to 1, =0.55, =0.45, represents the maximum function, represents the temperature deviation set during the welding process of the steel plate; Step C2: The laser beam fluctuation acquisition unit is used to collect the fluctuation data of the laser beam during the welding process and construct the fluctuation deviation coefficient. , the specific expression is as follows: ； in, represents the offset coefficient, Indicates the maximum noise of the laser beam emitting part of the CNC machine tool during the displacement process; Step C3: Based on the distance between the farthest spatter point and the center of the machine tool after welding is completed Constructing the Splash Deviation Coefficient The specific expression is as follows: ； in, Indicates the distance between the farthest spatter point and the center of the machine tool after welding. represents the spatter deviation coefficient, Indicates the mean distance of the standard splash point; Step C4: When the processing deviation coefficient , Fluctuation Deviation Coefficient and splash deviation coefficient If any of the values ​​exceeds the preset judgment value, an early warning will be issued to remind the staff that there is a quality risk and the quality of the currently welded steel plate needs to be monitored.

2. The intelligent steel plate welding system based on a CNC machine tool according to claim 1, characterized in that: The laser beam power acquisition unit is used to acquire the specific expression of the laser beam power deviation during the steel plate welding process as follows: ； in, represents the laser beam power deviation set during the steel plate welding process, Respectively represent the laser beam power deviation value at the first sampling moment, , No. The laser beam power deviation value at each sampling moment, , No. The laser beam power deviation value at each sampling moment is , and the first Laser beam power deviation value The specific expression is as follows: ； in, Indicates the laser beam power during preset standard processing. Indicates the The laser beam power at each sampling moment, Indicates the Laser beam power deviation value.

3. The intelligent steel plate welding system based on a CNC machine tool according to claim 2, characterized in that: The specific steps of the laser beam fluctuation acquisition unit for acquiring the fluctuation data of the laser beam during welding are as follows: Step A1: capturing images at each sampling period using a camera installed in the CNC machine tool, and storing the coordinates of the center point of the laser beam in the sampling period as a trajectory path point set of the laser beam; Step A2: constructing a trajectory line based on the coordinates of the first sampling point and the last sampling point of the laser beam in the trajectory path point set of the laser beam; Step A3: Calculate the distance from the laser beam trajectory path point set except the first sampling point and the last sampling point of the laser beam to the trajectory straight line, and obtain the offset coefficient ; Step A4: Obtain the noise of the laser beam emitting component of the CNC machine tool during the displacement process.

4. The intelligent steel plate welding system based on a CNC machine tool according to claim 3, characterized in that: In step A3, the distance from the laser beam trajectory path point set excluding the first sampling point and the last sampling point of the laser beam to the trajectory straight line is calculated, and the offset coefficient is obtained. The specific expression is as follows: ； in, represents the offset coefficient, It represents the set of calculated laser beam trajectory path points except the distances from the first sampling point and the last sampling point of the laser beam to the trajectory straight line. express The maximum value in express The mean of Indicates the preset offset distance.

5. The intelligent steel plate welding system based on a CNC machine tool according to claim 4, characterized in that: The specific expression of the machine tool welding temperature acquisition module for collecting the welding temperature deviation during the steel plate welding process is as follows: ； in, Indicates the temperature deviation set collected during the welding process of the steel plate. Respectively represent the welding temperature deviation value at the first sampling moment, , No. The welding temperature deviation value at each sampling moment, , No. The welding temperature deviation value at each sampling moment, , and the first Welding temperature deviation value at each sampling moment The specific expression is as follows: ； in, Indicates the standard value of welding temperature, Indicates the The welding temperature at each sampling moment, Indicates the The welding temperature deviation value at each sampling moment.

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