CN118438040A - Laser tin wire welding optimization method based on cumulative analysis - Google Patents
Laser tin wire welding optimization method based on cumulative analysis Download PDFInfo
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- CN118438040A CN118438040A CN202410920207.4A CN202410920207A CN118438040A CN 118438040 A CN118438040 A CN 118438040A CN 202410920207 A CN202410920207 A CN 202410920207A CN 118438040 A CN118438040 A CN 118438040A
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- 238000003466 welding Methods 0.000 title claims abstract description 364
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 title claims abstract description 252
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000005457 optimization Methods 0.000 title claims abstract description 54
- 238000004458 analytical method Methods 0.000 title claims abstract description 17
- 230000001186 cumulative effect Effects 0.000 title claims abstract description 17
- 229910000679 solder Inorganic materials 0.000 claims abstract description 34
- 238000007418 data mining Methods 0.000 claims abstract description 20
- 239000002245 particle Substances 0.000 claims description 99
- 238000012544 monitoring process Methods 0.000 claims description 23
- 238000012216 screening Methods 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 11
- 238000010606 normalization Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 230000003094 perturbing effect Effects 0.000 claims 1
- 238000005476 soldering Methods 0.000 claims 1
- 230000008685 targeting Effects 0.000 claims 1
- 230000008569 process Effects 0.000 description 35
- 230000000694 effects Effects 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007747 plating Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
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- 238000013178 mathematical model Methods 0.000 description 1
- 238000013433 optimization analysis Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
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- 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
- B23K26/00—Working by laser beam, e.g. welding, cutting or boring
- B23K26/20—Bonding
- B23K26/21—Bonding by welding
- B23K26/22—Spot welding
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- 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
- B23K26/00—Working by laser beam, e.g. welding, cutting or boring
- B23K26/70—Auxiliary operations or equipment
- B23K26/702—Auxiliary equipment
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Abstract
The invention provides a laser tin wire welding optimization method based on cumulative analysis, which relates to the field of laser spot welding and comprises the following steps: after the welding target is positioned in the welding area, receiving a laser tin wire welding optimization request by a user, wherein the laser tin wire welding optimization request comprises a welding target type and target positioning information; performing frequent data mining on the requirement index attribute through the welding target type to generate welding constraint conditions; configuring welding control parameters, wherein the welding control parameters comprise a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence and a spot tin duration sequence of a laser spot tin arm, and a solder paste flow sequence; and according to the multi-constraint objective function, optimizing by combining the objective positioning information, and generating laser tin wire welding optimization control parameters for control. The method solves the technical problems that the welding number intelligent degree is low due to the fact that the prior art cannot autonomously analyze requirements and optimize control.
Description
Technical Field
The invention relates to the technical field of laser spot welding, in particular to a laser tin wire welding optimization method based on cumulative analysis.
Background
The laser tin wire welding technology is widely applied in the field of electronic manufacturing, and particularly in the aspect of welding of micro elements, the high-efficiency and precise welding process is realized by precisely controlling the temperature, the light spot radius, the position and the time length of laser spot tin. The laser tin wire welding technology has the advantages of high heating speed, concentrated energy, small heat affected zone and the like, and becomes an ideal choice for complex electronic circuits and high-density packaging.
However, the prior art lacks multiple constraint optimization analysis in the welding process, resulting in unstable welding quality. When the prior art is used for coping with different welding targets and requirements, the prior art generally depends on preset fixed parameters, and cannot autonomously analyze the requirements and optimally control, so that the defect of low intelligent degree of welding number exists.
Disclosure of Invention
The application provides a laser tin wire welding optimization method based on cumulative analysis, and aims to solve the technical problem that the prior art generally depends on preset fixed parameters when dealing with different welding targets and demands, cannot autonomously analyze the demands and optimally control, and has low welding number intelligence degree.
In view of the above, the present application provides a laser tin wire welding optimization method based on cumulative analysis.
The application provides a laser tin wire welding optimization method based on cumulative analysis, which is applied to a laser tin wire welding optimization system based on cumulative analysis, wherein the system is deployed on an industrial control upper computer of the laser tin wire welding device, the laser tin wire welding device also comprises a tin paste needle tube and a laser tin arm, and the laser tin wire welding optimization method comprises the following steps:
after the welding target is positioned in the welding area, receiving a laser tin wire welding optimization request by a user, wherein the laser tin wire welding optimization request comprises a welding target type and target positioning information;
Performing frequent data mining on the requirement index attribute through the welding target type to generate welding constraint conditions;
Configuring welding control parameters, wherein the welding control parameters comprise a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence of a spot tin paste needle tube of a laser spot tin arm;
constructing a multi-constraint objective function according to the welding constraint conditions;
according to the multi-constraint objective function, the target positioning information is combined to perform optimizing on the spot tin temperature sequence, the spot tin spot radius sequence, the spot tin position sequence and the spot tin duration sequence, and the solder paste flow sequence is used for generating laser tin wire welding optimizing control parameters;
and controlling the spot tin paste needle tube and the laser spot tin arm through an industrial control upper computer based on the optimized control parameters of the laser tin wire welding.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
After the welding target is positioned in the welding area, a laser tin wire welding optimization request is received by a user, wherein the request contains the type of the welding target and target positioning information; carrying out frequent data mining on the welding target type to generate welding constraint conditions; configuring welding control parameters, including a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence; constructing a multi-constraint objective function according to welding constraint conditions; optimizing welding control parameters according to the multi-constraint objective function and the objective positioning information to generate laser tin wire welding optimization control parameters; based on the optimized control parameters, the technical scheme of controlling the point solder paste needle tube and the laser point solder arm by the industrial control upper computer, and controlling and optimizing according to the multi-constraint conditions by accumulating and analyzing the multi-constraint conditions, the problems that the requirements cannot be analyzed autonomously and the control parameters cannot be optimized due to the dependence on fixed parameter setting in the prior art are effectively solved, and the technical effects of high stability and high precision of the welding process are ensured.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic diagram of a possible flow of a laser tin wire welding method based on cumulative analysis according to an embodiment of the present application;
Fig. 2 is a schematic flow chart of a possible frequent data mining in a laser tin wire welding method based on cumulative analysis according to an embodiment of the present application.
Detailed Description
As shown in fig. 1, an embodiment of the present application provides a laser tin wire welding optimization method based on cumulative analysis, which is applied to a laser tin wire welding optimization system of cumulative analysis, where the system is deployed in an industrial control host computer of a laser tin wire welding device, and the laser tin wire welding device further includes a spot solder paste needle tube and a laser spot solder arm, and includes:
Specifically, the cumulative analysis refers to a process of collecting and analyzing various data in the welding process; the laser tin wire welding optimization system refers to a hardware and/or software system for optimizing laser tin wire welding, and is used for implementing any step of the laser tin wire welding optimization method based on cumulative analysis provided by the embodiment of the application. The laser tin wire welding device refers to equipment for performing a laser tin wire welding operation; the solder paste dispensing needle tube refers to a needle tube device for precisely applying solder paste in a welding process; the laser spot welding arm refers to a mechanical arm for spot welding by a laser technology. The industrial control upper computer refers to an upper computer in an industrial control system and is used for managing and controlling the welding device. Preferably, the laser tin wire welding optimization system is deployed in an industrial control upper computer of the laser tin wire welding device in a hardware or software mode.
After the welding target is positioned in the welding area, receiving a laser tin wire welding optimization request by a user, wherein the laser tin wire welding optimization request comprises a welding target type and target positioning information;
Specifically, the welding target refers to an object, such as an electronic component, a minute circuit, or the like, that needs to perform a welding operation; the welding area positioning means that a welding target is accurately placed at a preset welding position, so that the welding target is in an optimal welding state; the user end refers to an interface or equipment for a user to operate and control the welding process, and can be a computer, a tablet or other intelligent equipment; the laser tin wire welding optimization request refers to a request sent by a user through a user side, and aims to optimize control parameters of a welding process; the welding target type refers to a specific category of a welding object, such as a specific model number and the like; the target positioning information refers to specific position data of the welding target in the welding area, ensuring accurate welding operation.
After the welding target is positioned in the welding area, the welding target can be ensured to be positioned at the correct position, then a user inputs a laser tin wire welding optimization request through an operation interface of the user side, and any one laser tin wire welding optimization request at least comprises specific type and accurate positioning data of the target. The back step requirement matching is facilitated, and the control of spot welding is performed.
Performing frequent data mining on the requirement index attribute through the welding target type to generate welding constraint conditions;
In particular, the demand index attribute refers to various technical indices and requirements related to the welding process, such as, for example: the thickness of the welding spot requirement, the diameter of the welding spot requirement, the firmness of the welding spot requirement and the like, wherein the firmness of the welding spot requirement refers to the maximum bearable stripping external force. The welding constraint condition refers to a result of assigning a requirement index attribute through a welding target type, and is used for constraining the optimization convergence condition of welding control. The frequent data mining refers to a feature value of high-frequency occurrence of a corresponding requirement index attribute based on the accumulated statistics of big data according to the type of the welding target, and because the high-frequency occurrence in the big data, the high probability is the standard requirement corresponding to the type of the welding target, and therefore the welding constraint condition can be set. Through frequent data mining, compared with the traditional expert subjective setting, the automation degree is higher, and the subjective consciousness interference is eliminated due to the objectivity of big data analysis, so that the objectivity of the welding optimization in the later step is ensured.
Configuring welding control parameters, wherein the welding control parameters comprise a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence of a spot tin paste needle tube of a laser spot tin arm;
Specifically, the welding control parameters refer to various parameters that need to be controlled and adjusted during the welding process, including: the spot tin temperature sequence refers to the temperature sequences of different spot welding positions reached by the laser spot tin arm in the welding process; the spot tin spot radius sequence refers to a sequence that the radius of a laser spot changes along with the spot welding position in the welding process of the laser spot tin arm; the tin spot position sequence refers to the position change sequence of the welding point in the welding process of the laser tin spot arm; the tin-plating time sequence refers to the laser irradiation time sequence of each welding point in the welding process of the laser tin-plating arm; solder paste flow sequence: and pointing the sequence that the flow rate of the solder paste changes along with the spot welding position in the welding process of the solder paste needle tube. And the welding control parameters can be configured and adjusted in detail in the later step, so that the system can realize high automation, reduce manual intervention and improve production efficiency.
Constructing a multi-constraint objective function according to the welding constraint conditions;
Specifically, the multi-constraint objective function is a self-defined mathematical model, and comprises a plurality of constraint conditions for optimizing various parameters in the welding process so as to achieve the optimal welding effect. In the multi-constraint objective function, the plurality of welding constraints are logical and relational, and convergence is optimized only if all are satisfied.
According to the multi-constraint objective function, the target positioning information is combined to perform optimizing on the spot tin temperature sequence, the spot tin spot radius sequence, the spot tin position sequence and the spot tin duration sequence, and the solder paste flow sequence is used for generating laser tin wire welding optimizing control parameters;
and controlling the spot tin paste needle tube and the laser spot tin arm through an industrial control upper computer based on the optimized control parameters of the laser tin wire welding.
Specifically, the laser tin wire welding optimization control parameters refer to control parameters which are generated after the tin paste flow sequence is optimized and used for guiding a welding process and comprise a tin spot temperature sequence optimizing value, a tin spot radius sequence optimizing value, a tin spot position sequence optimizing value, a tin spot duration sequence optimizing value and a tin paste flow sequence optimizing value.
The optimization flow is that a welding target type is used as constraint, and a welding history parameter is obtained through assignment of big data to a tin spot radius sequence, the tin spot position sequence and the tin spot duration sequence, and the solder paste flow sequence; and then sorting the welding history parameters through the multi-constraint objective function to obtain welding history parameters meeting the multi-constraint objective function, and setting the welding history parameters as laser tin wire welding optimization control parameters. Through the optimized control parameters, the industrial control upper computer can accurately control the solder paste needle tube and the laser solder paste dispensing arm, reduce welding defects and rework rate, and improve production efficiency and automation degree.
Further, the laser tin wire welding device also comprises a vision component, comprising:
Collecting a tin spot monitoring position, a tin spot monitoring time length and a tin spot monitoring radius through a visual assembly;
Extracting a reference point tin position, a reference point tin duration and a reference point tin spot radius according to the laser tin wire welding optimization control parameters;
calculating a first deviation vector of the tin spot monitoring position and the tin datum point position;
Calculating a second deviation vector of the tin spot monitoring time length and the tin datum point time length;
Calculating a third deviation vector of the spot tin spot monitoring radius and the reference spot tin spot radius;
And carrying out welding real-time correction according to the first deviation vector and the second deviation vector until the third deviation vector.
Firstly, the spot tin monitoring position, the spot tin monitoring time length and the spot tin spot monitoring radius are collected in real time through a visual assembly. The vision component is used for collecting images and data in the welding process, including the position of the welding point, the laser irradiation time length and the radius of the laser spot.
And then, extracting the reference point tin position, the reference point tin duration and the reference point tin spot radius according to the laser tin wire welding optimization control parameters. The optimization control parameters are ideal parameters for guiding the welding operation generated by the foregoing optimization process. The reference point tin position, the reference point tin duration and the reference point tin spot radius are the optimized control parameter characteristic values of the current real-time welding spot number.
Then, calculating a first deviation vector of the point tin monitoring position and the reference point tin position, wherein the distance deviation value of the reference point tin position and the point tin monitoring position is the size of the first deviation vector, and the azimuth difference of the point tin monitoring position at the reference point tin position is the direction of the first deviation vector; the second deviation vector of the point tin monitoring duration and the reference point tin duration is the calculated result of subtracting the reference point tin duration from the point tin monitoring duration, and the third deviation vector of the point tin spot monitoring radius and the reference point tin spot radius is the calculated result of subtracting the reference point tin spot radius from the point tin spot monitoring radius.
And finally, carrying out welding real-time correction according to the first deviation vector, the second deviation vector and the third deviation vector. And the welding parameters are adjusted in real time through the calculated deviation vector so as to correct the deviation in the welding process and ensure the welding quality.
Further, frequent data mining is performed on the requirement index attribute through the welding target type, and welding constraint conditions are generated, including:
The requirement index attribute comprises a welding spot requirement radius attribute and a welding spot requirement thickness attribute;
And carrying out joint frequent data mining on the welding spot required radius attribute and the welding spot required thickness attribute according to the welding target type, generating a welding spot required radius and the welding spot required thickness, and adding the welding spot required radius and the welding spot required thickness into the welding constraint condition.
Specifically, the demand index attributes include a weld spot demand radius attribute and a weld spot demand thickness attribute. The weld spot demand radius attribute refers to the radius of the weld spot during welding, and the weld spot demand thickness attribute refers to the thickness of the weld spot during welding. According to the welding target type, joint frequent data mining is carried out on welding requirement radius attributes and welding spot requirement thickness attributes, the frequent data mining is a data analysis method, and the requirement radius and thickness with higher occurrence frequency under different welding target types are found out through analyzing a large amount of welding data. Then, based on the data mining results, a weld spot demand radius and a weld spot demand thickness are generated and added to the weld constraints. Used for guiding the control optimization of the subsequent step.
Further, performing joint frequent data mining on the welding spot required radius attribute and the welding spot required thickness attribute according to the welding target type to generate a welding spot required radius and the welding spot required thickness, including:
Performing positive sampling according to the welding target type, and collecting historical laser tin wire welding record information, wherein the historical laser tin wire welding record information comprises a welding spot radius record information set and a welding spot thickness record information set, and the welding spot radius record information set corresponds to the welding spot thickness record information set one by one;
Traversing the welding spot radius record information set and the welding spot thickness record information set which are in one-to-one correspondence to perform normalization processing, and constructing a two-dimensional coordinate set;
extracting first coordinates of first historical laser tin wire welding record information of the historical laser tin wire welding record information from the two-dimensional coordinate set;
Calculating the Euclidean distance mean value of the first coordinates and the preset number of adjacent coordinates, and setting the Euclidean distance mean value as a first local discrete parameter;
calculating a local discrete parameter mean value of a local discrete parameter set of the history laser tin wire welding record information, setting a ratio of the first local discrete parameter to the local discrete parameter mean value as a first discrete coefficient, and adding the first discrete coefficient into a discrete coefficient set;
And extracting the minimum value of the discrete coefficient set to generate the welding spot required radius and the welding spot required thickness.
Specifically, positive sampling is carried out according to the type of a welding target, and historical laser tin wire welding record information is collected. The recorded information comprises a welding spot radius recorded information set and a welding spot thickness recorded information set, which are in one-to-one correspondence, and positive sampling is the process of collecting data with qualified welding result quality. Further, traversing and correspondingly processing the welding spot radius record information set and the welding spot thickness record information set, carrying out normalization processing, normalizing data to ensure that the data are compared on a uniform scale, constructing a two-dimensional coordinate system, and distributing two-dimensional coordinates formed by the welding spot radius record information set and the welding spot thickness record information set which are subjected to one-to-one correspondence normalization processing in the two-dimensional coordinate system to obtain the two-dimensional coordinate set.
Further, a first coordinate of a certain history laser tin wire welding record information is extracted from the two-dimensional coordinate set. The Euclidean distance average value of the first coordinates and the preset number of adjacent coordinates is calculated and is set as a first local discrete parameter, the preset number of adjacent coordinates refer to screening a preset number of coordinate sets which are close to and far from the first coordinates in a two-dimensional coordinate set, the Euclidean distance between each coordinate of the preset number of adjacent coordinates and the first coordinates is calculated respectively, and the average value is calculated to obtain the first local discrete parameter.
Further, calculating the average value of local discrete parameters of all the historical laser tin wire welding record information, setting the ratio of the first local discrete parameter to the average value as a first discrete coefficient, and adding the first discrete coefficient into a discrete coefficient set. And finally, extracting the minimum value from the discrete coefficient set, setting the welding spot radius record information and the welding spot thickness record information of the coordinates corresponding to the minimum value of the discrete coefficient as the welding spot required radius and the welding spot required thickness, and adding the welding spot radius record information and the welding spot thickness record information into welding constraint conditions.
Further, constructing a multi-constraint objective function according to the welding constraint condition, including:
Traversing the welding constraint conditions to perform fault-tolerant configuration, and generating a constraint condition fault-tolerant interval;
Traversing the welding constraint condition to perform normalization processing to construct a first space coordinate, wherein any one dimension coordinate is a normalized value of the welding constraint condition;
the multi-constraint objective function comprises a first sub-function, wherein the first sub-function characterizes the number of constraint conditions which meet the constraint condition fault tolerance interval;
the multi-constraint objective function comprises a second sub-function, wherein the second sub-function characterizes Euclidean distance from the first spatial coordinates;
the multi-constraint objective function comprises a third sub-function, wherein the third sub-function=welding firmness parameter normalized value, the first weight-welding duration normalized value, the second weight, and the welding firmness parameter refers to the maximum peeling external force bearable by a welding spot;
The first sub-function and the second sub-function are threshold functions in a logical AND relationship, and the third sub-function is a maximum valued function.
Firstly, traversing welding constraint conditions to perform fault-tolerant configuration, and generating a constraint condition fault-tolerant section. The welding constraint conditions are welding parameters and rules generated by the data mining, and the fault-tolerant configuration refers to setting an allowable error range for each constraint condition to form a fault-tolerant section. Further, the welding constraint conditions are traversed to carry out normalization processing, all conditions are standardized, and a first space coordinate is constructed. Each dimension coordinate represents a normalized value of a welding constraint. The normalization process ensures that different conditions can be compared and analyzed on a uniform scale.
And secondly, constructing a multi-constraint objective function, wherein the multi-constraint objective function comprises a first sub-function used for representing the constraint condition quantity ratio of the constraint condition fault tolerance interval. This sub-function is used to measure how many welding constraints fall within the fault tolerance interval.
The method also comprises a second sub-function, which represents Euclidean distance with the first space coordinate and is used for measuring the distance between the current parameter configuration and the ideal configuration.
The welding method further comprises a third sub-function for measuring the relation between the welding firmness parameters and the welding duration, wherein the calculation formula is (the welding firmness parameters normalize value x the first weight) - (the welding duration normalize value x the second weight). The sub-function is used to balance the weld firmness and time efficiency during the optimization process, and the weld firmness parameter refers to the maximum peel force that the weld joint can withstand.
Furthermore, when the multi-constraint objective function is used, the corresponding welding duration, welding radius, welding thickness and welding firmness parameters are determined by carrying out frequent data mining on each group of control parameters, so that the parameters can be compared with each constraint condition.
In the multi-constraint objective function, the first sub-function and the second sub-function are combined through a threshold function of a logical AND relationship, and the two conditions are required to be met simultaneously. The third sub-function is the maximum value function, and the condition that the value is the maximum is selected as the optimization result.
And the constraint conditions are comprehensively considered through the multi-constraint objective function, particularly, the satisfaction condition of the constraint conditions and the merits of parameter configuration are measured through the first sub-function and the second sub-function, and the welding firmness and the welding efficiency are balanced through the third sub-function, so that the comprehensive optimization is realized.
Further, according to the multi-constraint objective function, the optimizing is performed on the tin spot temperature sequence, the tin spot light spot radius sequence, the tin spot position sequence and the tin spot duration sequence by combining the target positioning information, and the solder paste flow sequence generates a laser tin wire welding optimizing control parameter, which comprises the following steps:
Step one: taking the target positioning information and the welding target type as constraints, and assigning values to the spot tin temperature sequence, the spot tin spot radius sequence, the spot tin position sequence and the spot tin duration sequence based on big data, wherein the solder paste flow sequence generates a laser solder wire welding control particle set;
step two: configuring a first sub-function threshold and a second sub-function threshold;
Step three: screening a first laser tin wire welding control particle set with a first sub-function output value greater than or equal to the first sub-function threshold and a second sub-function output value greater than or equal to the second sub-function threshold from the laser tin wire welding control particle set based on the first sub-function and the second sub-function;
step four: expanding the first laser tin wire welding control particle set to generate a second laser tin wire welding control particle set;
Repeating the third step to the fourth step for N times, and then executing the third step again to generate an Nth laser tin wire welding control particle set;
and extracting the maximum value of the Nth laser tin wire welding control particle set based on the third sub-function, and setting the maximum value as the laser tin wire welding optimization control parameter.
Firstly, taking target positioning information and a welding target type as retrieval constraint, and acquiring record data sets of a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence under the same positioning condition and welding target type scene based on big data to generate a laser tin wire welding control particle set, wherein any one particle represents record data of a group of spot tin temperature sequence, spot tin spot radius sequence, spot tin position sequence, spot tin duration sequence and solder paste flow sequence.
Next, a first sub-function threshold and a second sub-function threshold are configured. The first sub-function is used for representing the constraint condition quantity ratio of the constraint condition fault tolerance interval, the second sub-function represents the Euclidean distance between the first sub-function and the first space coordinate, and the first sub-function threshold and the second sub-function threshold refer to screening threshold values.
And then, based on the first sub-function and the second sub-function, screening out a first laser tin wire welding control particle set with the output value of the first sub-function being greater than or equal to a first sub-function threshold value and the output value of the second sub-function being greater than or equal to a second sub-function threshold value from the laser tin wire welding control particle set. This process ensures that the screened parameter set, while satisfying a number of constraints, is as close as possible to the ideal parameter configuration.
And then expanding the first laser tin wire welding control particle set to generate a second laser tin wire welding control particle set. The screening and expansion process is repeated multiple times (N times) to gradually optimize the parameter set.
And finally, executing the screening process once again to generate an Nth laser tin wire welding control particle set. And based on the third sub-function, extracting particles with the maximum calculated value of the third sub-function from the Nth laser tin wire welding control particle set, outputting the stored control parameters of the particles, and setting the control parameters as laser tin wire welding optimization control parameters.
By optimizing and screening welding parameters for a plurality of times, the system can find the optimal parameter combination, and the accuracy and stability of the welding process are improved.
Further, expanding the first laser tin wire welding control particle set to generate a second laser tin wire welding control particle set, including:
Processing the second laser tin wire welding control particle set according to the third sub-function to generate a third sub-function output value set;
extracting a first number of preferred particles from the second laser tin wire welding control particle set according to the third sub-function output value set;
Extracting a second number of inferior particles from the second laser tin wire welding control particle set according to the third subfunction output value set;
And taking the first quantity of better particles as a target, and carrying out disturbance on the second quantity of worse particles for preset times to generate the second laser tin wire welding control particle set.
First, the second laser tin wire welding control particle set is processed according to the third sub-function, and a third sub-function output value set is generated. The third sub-function is used to measure the relationship between the weld-on parameter and the weld duration.
And then, extracting the first quantity of better particles and the second quantity of worse particles from the second laser tin wire welding control particle set according to the third sub-function output value set. The preferred particles are particles with higher output values of the third sub-function, while the inferior particles are particles with lower output values.
Then, the first number of better particles is targeted, and the second number of worse particles is disturbed for a preset number of times. The disturbance refers to random or directional adjustment of the parameters of the inferior particles, so that the parameters of the inferior particles are close to the parameter values of the superior particles, and a new second laser tin wire welding control particle set is generated.
And the third sub-function is used for processing the particle set, so that better and inferior particles are extracted, the important attention in the optimization process is ensured, and the parameter optimization efficiency is improved.
Further, with the first number of preferred particles as a target, disturbing the second number of inferior particles for a preset number of times, to generate the second laser tin wire welding control particle set, including:
Randomly screening first preferred particles from the first number of preferred particles, and randomly screening disturbance attributes of the first preferred particles to obtain a first attribute characteristic value;
Randomly screening first inferior particles from the second number of inferior particles, and extracting second attribute characteristic values of the first inferior particles, wherein the first attribute characteristic values and the second attribute characteristic values have the same attribute;
calculating a deviation vector of the second attribute characteristic value minus the first attribute characteristic value;
Obtaining a random number and the deviation vector to obtain a product, and generating a disturbance distance, wherein the random number is 0-1;
Adding the disturbance distance to the first attribute characteristic value to generate a second attribute characteristic value updating value;
and generating first expansion particles after updating at least the preset number of attribute characteristic values of the first inferior particles, and adding the second laser tin wire welding control particle set.
First, randomly screening a first preferred particle from a first number of preferred particles, and randomly screening disturbance attributes of the first preferred particle to obtain a first attribute characteristic value. The disturbance attribute refers to randomly selecting one of the attributes of the preferred particles to process, and the first attribute characteristic value is a specific value of the attribute. Next, randomly screening a first inferior particle from the second number of inferior particles, and extracting the characteristic value of the same attribute of the inferior particle, which is called a second attribute characteristic value. This ensures that the compared properties are consistent between the superior and inferior particles. Then, a deviation vector of the second attribute feature value minus the first attribute feature value is calculated. This vector represents the difference between the two attribute values. Next, a random number between 0 and 1 is generated and multiplied by the bias vector to obtain the disturbance distance. The perturbation distance is used to adjust the attribute value of the inferior particle so that it approaches the superior particle. An updated value of the second attribute feature value is generated by adding the perturbation distance to the first attribute feature value. This new value is the result of disturbing the inferior particles. And generating a first expansion particle after updating at least a preset number of attribute characteristic values of the first inferior particles, and adding the first expansion particle to the second laser tin wire welding control particle set.
In summary, the laser tin wire welding optimization method based on cumulative analysis provided by the embodiment of the application has the following technical effects:
1. After the welding target is positioned in the welding area, a laser tin wire welding optimization request is received by a user, wherein the request contains the type of the welding target and target positioning information; carrying out frequent data mining on the welding target type to generate welding constraint conditions; configuring welding control parameters, including a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence; constructing a multi-constraint objective function according to welding constraint conditions; optimizing welding control parameters according to the multi-constraint objective function and the objective positioning information to generate laser tin wire welding optimization control parameters; based on the optimized control parameters, the technical scheme of controlling the point solder paste needle tube and the laser point solder arm by the industrial control upper computer, and controlling and optimizing according to the multi-constraint conditions by accumulating and analyzing the multi-constraint conditions, the problems that the requirements cannot be analyzed autonomously and the control parameters cannot be optimized due to the dependence on fixed parameter setting in the prior art are effectively solved, and the technical effects of high stability and high precision of the welding process are ensured.
2. The real-time monitoring and feedback mechanism enables key data in the welding process to be captured and analyzed in time, and controllability of the welding process is improved. Secondly, through accurate calculation deviation vector and real-time adjustment welding parameter, the deviation in the welding process can be effectively corrected to the system, improves welding quality's uniformity and stability. In addition, the real-time correction mechanism greatly improves the welding precision, reduces the welding defects and improves the welding quality and reliability. Finally, the visual components and the real-time correction mechanism are added, so that the automation and intelligence level of the welding process is improved, the manual intervention is reduced, and the production efficiency is improved. Through the technical improvements, the laser tin wire welding process becomes more efficient, accurate and reliable, and the overall performance of the system is remarkably improved.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. The laser tin wire welding optimization method based on the cumulative analysis is characterized by being applied to a laser tin wire welding optimization system of the cumulative analysis, wherein the system is deployed on an industrial control upper computer of a laser tin wire welding device, the laser tin wire welding device further comprises a solder paste needle tube and a laser solder arm, and the laser tin wire welding optimization method comprises the following steps:
after the welding target is positioned in the welding area, receiving a laser tin wire welding optimization request by a user, wherein the laser tin wire welding optimization request comprises a welding target type and target positioning information;
Performing frequent data mining on the requirement index attribute through the welding target type to generate welding constraint conditions;
Configuring welding control parameters, wherein the welding control parameters comprise a spot tin temperature sequence, a spot tin spot radius sequence, a spot tin position sequence, a spot tin duration sequence and a solder paste flow sequence of a spot tin paste needle tube of a laser spot tin arm;
constructing a multi-constraint objective function according to the welding constraint conditions;
according to the multi-constraint objective function, the target positioning information is combined to perform optimizing on the spot tin temperature sequence, the spot tin spot radius sequence, the spot tin position sequence and the spot tin duration sequence, and the solder paste flow sequence is used for generating laser tin wire welding optimizing control parameters;
and controlling the spot tin paste needle tube and the laser spot tin arm through an industrial control upper computer based on the optimized control parameters of the laser tin wire welding.
2. The method of claim 1, wherein the laser tin wire soldering apparatus further comprises a vision assembly comprising:
Collecting a tin spot monitoring position, a tin spot monitoring time length and a tin spot monitoring radius through a visual assembly;
Extracting a reference point tin position, a reference point tin duration and a reference point tin spot radius according to the laser tin wire welding optimization control parameters;
calculating a first deviation vector of the tin spot monitoring position and the tin datum point position;
Calculating a second deviation vector of the tin spot monitoring time length and the tin datum point time length;
Calculating a third deviation vector of the spot tin spot monitoring radius and the reference spot tin spot radius;
And carrying out welding real-time correction according to the first deviation vector and the second deviation vector until the third deviation vector.
3. The method of claim 1, wherein generating welding constraints by frequent data mining of demand indicator attributes by the welding target type comprises:
The requirement index attribute comprises a welding spot requirement radius attribute and a welding spot requirement thickness attribute;
And carrying out joint frequent data mining on the welding spot required radius attribute and the welding spot required thickness attribute according to the welding target type, generating a welding spot required radius and the welding spot required thickness, and adding the welding spot required radius and the welding spot required thickness into the welding constraint condition.
4. The method of claim 3, wherein generating the weld spot demand radius and the weld spot demand thickness by joint frequent data mining of the weld spot demand radius attribute and the weld spot demand thickness attribute according to the weld target type comprises:
Performing positive sampling according to the welding target type, and collecting historical laser tin wire welding record information, wherein the historical laser tin wire welding record information comprises a welding spot radius record information set and a welding spot thickness record information set, and the welding spot radius record information set corresponds to the welding spot thickness record information set one by one;
Traversing the welding spot radius record information set and the welding spot thickness record information set which are in one-to-one correspondence to perform normalization processing, and constructing a two-dimensional coordinate set;
extracting first coordinates of first historical laser tin wire welding record information of the historical laser tin wire welding record information from the two-dimensional coordinate set;
Calculating the Euclidean distance mean value of the first coordinates and the preset number of adjacent coordinates, and setting the Euclidean distance mean value as a first local discrete parameter;
calculating a local discrete parameter mean value of a local discrete parameter set of the history laser tin wire welding record information, setting a ratio of the first local discrete parameter to the local discrete parameter mean value as a first discrete coefficient, and adding the first discrete coefficient into a discrete coefficient set;
And extracting the minimum value of the discrete coefficient set to generate the welding spot required radius and the welding spot required thickness.
5. The method of claim 1, wherein constructing a multi-constraint objective function based on the welding constraints comprises:
Traversing the welding constraint conditions to perform fault-tolerant configuration, and generating a constraint condition fault-tolerant interval;
Traversing the welding constraint condition to perform normalization processing to construct a first space coordinate, wherein any one dimension coordinate is a normalized value of the welding constraint condition;
the multi-constraint objective function comprises a first sub-function, wherein the first sub-function characterizes the number of constraint conditions which meet the constraint condition fault tolerance interval;
the multi-constraint objective function comprises a second sub-function, wherein the second sub-function characterizes Euclidean distance from the first spatial coordinates;
the multi-constraint objective function comprises a third sub-function, wherein the third sub-function=welding firmness parameter normalized value, the first weight-welding duration normalized value, the second weight, and the welding firmness parameter refers to the maximum peeling external force bearable by a welding spot;
The first sub-function and the second sub-function are threshold functions in a logical AND relationship, and the third sub-function is a maximum valued function.
6. The method of claim 5, wherein the optimizing the solder paste flow sequence to generate laser solder wire welding optimization control parameters in combination with the target positioning information based on the multi-constraint objective function includes:
Step one: taking the target positioning information and the welding target type as constraints, and assigning values to the spot tin temperature sequence, the spot tin spot radius sequence, the spot tin position sequence and the spot tin duration sequence based on big data, wherein the solder paste flow sequence generates a laser solder wire welding control particle set;
step two: configuring a first sub-function threshold and a second sub-function threshold;
Step three: screening a first laser tin wire welding control particle set with a first sub-function output value greater than or equal to the first sub-function threshold and a second sub-function output value greater than or equal to the second sub-function threshold from the laser tin wire welding control particle set based on the first sub-function and the second sub-function;
step four: expanding the first laser tin wire welding control particle set to generate a second laser tin wire welding control particle set;
Repeating the third step to the fourth step for N times, and then executing the third step again to generate an Nth laser tin wire welding control particle set;
and extracting the maximum value of the Nth laser tin wire welding control particle set based on the third sub-function, and setting the maximum value as the laser tin wire welding optimization control parameter.
7. The method of claim 6, wherein expanding the first set of laser tin wire welding control particles to generate a second set of laser tin wire welding control particles comprises:
Processing the second laser tin wire welding control particle set according to the third sub-function to generate a third sub-function output value set;
extracting a first number of preferred particles from the second laser tin wire welding control particle set according to the third sub-function output value set;
Extracting a second number of inferior particles from the second laser tin wire welding control particle set according to the third subfunction output value set;
And taking the first quantity of better particles as a target, and carrying out disturbance on the second quantity of worse particles for preset times to generate the second laser tin wire welding control particle set.
8. The method of claim 7, wherein the generating the second set of laser tin wire weld control particles by perturbing the second number of inferior particles a preset number of times targeting the first number of superior particles comprises:
Randomly screening first preferred particles from the first number of preferred particles, and randomly screening disturbance attributes of the first preferred particles to obtain a first attribute characteristic value;
Randomly screening first inferior particles from the second number of inferior particles, and extracting second attribute characteristic values of the first inferior particles, wherein the first attribute characteristic values and the second attribute characteristic values have the same attribute;
calculating a deviation vector of the second attribute characteristic value minus the first attribute characteristic value;
Obtaining a random number and the deviation vector to obtain a product, and generating a disturbance distance, wherein the random number is 0-1;
Adding the disturbance distance to the first attribute characteristic value to generate a second attribute characteristic value updating value;
and generating first expansion particles after updating at least the preset number of attribute characteristic values of the first inferior particles, and adding the second laser tin wire welding control particle set.
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