Welding technological process optimization method and system for semitrailer frame

Abstract

The invention provides a welding technological process optimization method and system for a semitrailer frame, and relates to the technical field of welding technology optimizing.The method comprises the steps that a first-state frame profile is input into a main weld joint welding unit to be welded; the second-state frame assembly is input into the auxiliary welding seam welding unit to be welded; the reinforcing rib accessory and the third-state frame assembly are positioned and assembled; the welding current parameters and the welding speed parameters are optimized, and welding current parameter optimization information and welding speed parameter optimization information are generated; and after a main welding seam welding unit is initialized according to the welding current parameter optimization information and an auxiliary welding seam welding unit is initialized according to the welding speed parameter optimization information, welding treatment is conducted on the semi-finished semitrailer frame in the fourth state, and a finished semitrailer frame is generated. The technical problem that in the prior art, the optimization quality of the welding technological process of the semitrailer frame is poor can be solved, and the technical effect of improving the optimization quality of the welding technological process is achieved.

Description

Technical Field

[0001] This application relates to the field of welding process optimization technology, and in particular to a method and system for optimizing the welding process of a semi-trailer frame. Background Technology

[0002] As the semi-trailer transportation industry develops towards higher load capacity, higher strength, and lighter weight, the welding quality and production efficiency of semi-trailer frames have become key factors affecting the overall structural safety and service life of the vehicle. As the main load-bearing structure of a semi-trailer, the quality of its weld connections directly affects the structural stability and fatigue resistance of the vehicle under long-term, high-frequency vibration loads. The rationality of the welding process, the matching of welding parameters, and the stability of the welding process are core indicators determining the production level of the frame.

[0003] Currently, existing semi-trailer frame welding processes mainly rely on fixed process specifications for production control, lacking a dynamic correlation analysis mechanism between welding parameters and actual quality. For example, the welding processes for main welds and auxiliary welds often use the same current and speed settings, without considering the differences in stress characteristics and heat conduction laws of different welds, leading to the risk of over-melting or under-melting in some welds. Although some manufacturing enterprises have introduced automated welding equipment, due to the lack of parameter optimization and quality prediction models, the equipment cannot be intelligently adjusted according to the real-time welding status during operation, resulting in low welding consistency and reliability.

[0004] In summary, the existing technology suffers from a lack of parameter optimization and dynamic control mechanisms for different weld characteristics. As a result, key process parameters such as welding current, welding speed, and heat input cannot be adaptively matched according to the actual stress distribution and heat conduction differences. This further affects the forming stability of the semi-trailer frame welding process, the uniformity of weld quality, and the long-term service reliability of the entire vehicle structure. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing the welding process of a semi-trailer frame, in order to solve the technical problem in the prior art that the lack of parameter optimization and dynamic control mechanisms for different weld characteristics leads to the inability of key process parameters such as welding current, welding speed and heat input to be adaptively matched according to the actual stress distribution and heat conduction differences, which further affects the forming stability of the semi-trailer frame welding process, the uniformity of weld quality and the long-term service reliability of the vehicle structure.

[0006] In view of the above problems, this application provides a method and system for optimizing the welding process of a semi-trailer frame.

[0007] In a first aspect, this application provides a method for optimizing the welding process of a semi-trailer frame, implemented through a welding process optimization system for a semi-trailer frame. The method includes: inputting a first-state frame profile into the main weld seam welding unit for welding to generate a second-state frame assembly; inputting the second-state frame assembly into the auxiliary weld seam welding unit for welding to generate a third-state frame assembly; positioning and assembling the reinforcing rib fittings and the third-state frame assembly to generate a fourth-state semi-finished frame; optimizing welding current parameters and welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information; initializing the main weld seam welding unit according to the welding current parameter optimization information, initializing the auxiliary weld seam welding unit according to the welding speed parameter optimization information, and then welding the fourth-state semi-finished frame to generate a finished semi-trailer frame.

[0008] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: predicting the quality based on welding current parameters and welding speed parameters to obtain a pass probability; when the pass probability is less than or equal to a probability threshold, optimizing the welding current parameters and the welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information.

[0009] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: matching the main weld type, weld thickness parameter, and welding area parameter according to the first state frame profile; selecting main weld welding control parameters based on a first cloud-based process library according to the main weld type, weld thickness parameter, and welding area parameter; and using the main weld welding control parameters to control the main weld welding unit to weld the first state frame profile to generate the second state frame assembly.

[0010] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: inputting the main weld type, weld thickness parameters, and welding area parameters into the first cloud-based process library to determine multiple main weld welding control schemes; and based on the multiple main weld welding control schemes, determining the main weld welding control parameters by screening multiple scheme support.

[0011] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: the welding control schemes for the multiple main welds include welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters; based on the process stability characteristics of the welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters, the reliability of the welding control schemes for the multiple main welds is evaluated, and the support degree of the multiple schemes is generated.

[0012] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: matching auxiliary weld length parameters and welding gap parameters according to the second-state frame assembly; selecting auxiliary weld welding control parameters based on a second cloud-based process library according to the auxiliary weld length parameters and welding gap parameters; and using the auxiliary weld welding control parameters to control the auxiliary weld welding unit to weld the second-state frame assembly to generate the third-state frame assembly.

[0013] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: when the welding current parameter is input into the pass probability evaluation model, activating the current influence evaluation layer and outputting the current adaptation probability; when the welding speed parameter is input into the pass probability evaluation model, activating the speed influence evaluation layer and outputting the speed adaptation probability; and determining the pass probability through the current adaptation probability and the speed adaptation probability.

[0014] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: setting multiple current parameter variable ranges and multiple speed parameter variable ranges based on the welding current parameters and the welding speed parameters; performing variable analysis based on the pass probability evaluation model according to the multiple current parameter variable ranges and multiple speed parameter variable ranges to generate the k-th pass probability; if the k-th pass probability is greater than the probability threshold, setting the k-th welding current parameter as the welding current parameter optimization information and the k-th welding speed parameter as the welding speed parameter optimization information.

[0015] Preferably, the method for optimizing the welding process of a semi-trailer frame further includes: when the k-th pass probability is less than or equal to the probability threshold, determining whether the k-th pass probability is less than the (k-1)-th pass probability; if not less, adding the (k-1)-th welding current parameter and the (k-1)-th welding speed parameter to the elimination dataset, and continuing to iterate based on the k-th welding current parameter and the k-th welding speed parameter, outputting the optimal solution when a preset number of iterations is met, and setting it as the welding current parameter optimization information and the welding speed parameter optimization information.

[0016] Secondly, this application also provides a welding process optimization system for a semi-trailer frame, used to execute a welding process optimization method for a semi-trailer frame as described in the first aspect, comprising: a second-state frame assembly generation module, used to input a first-state frame profile into the main weld seam welding unit for welding to generate a second-state frame assembly; a third-state frame assembly generation module, used to input the second-state frame assembly into the auxiliary weld seam welding unit for welding to generate a third-state frame assembly; a fourth-state frame semi-finished product generation module, used to position and assemble the reinforcing rib accessories and the third-state frame assembly to generate a fourth-state frame semi-finished product; an optimization information generation module, used to optimize welding current parameters and welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information; and a semi-trailer frame finished product generation module, used to initialize the main weld seam welding unit according to the welding current parameter optimization information, initialize the auxiliary weld seam welding unit according to the welding speed parameter optimization information, and then perform welding processing on the fourth-state frame semi-finished product to generate a semi-trailer frame finished product.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of intelligent control of main and auxiliary weld seam partitions based on welding parameter optimization information and real-time adaptive adjustment of the welding process, the technical effects of improving welding forming accuracy, enhancing weld seam strength consistency and significantly extending the overall service life of the frame are achieved.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the welding process optimization method for a semi-trailer frame according to this application.

[0021] Figure 2 This is a schematic diagram of the welding process optimization system for a semi-trailer frame according to this application.

[0022] Explanation of reference numerals in the attached diagram: Module 1 for generating second-state chassis components, Module 2 for generating third-state chassis assemblies, Module 3 for generating fourth-state semi-finished chassis, Module 4 for generating optimization information, and Module 5 for generating finished semi-trailer chassis. Detailed Implementation

[0023] This application provides a method and system for optimizing the welding process of a semi-trailer frame. It solves the technical problem in existing technologies where the lack of parameter optimization and dynamic control mechanisms for different weld characteristics leads to the inability of key process parameters such as welding current, welding speed, and heat input to adaptively match according to actual stress distribution and heat conduction differences. This further affects the forming stability of the semi-trailer frame welding process, the uniformity of weld quality, and the long-term service reliability of the entire vehicle structure. The application achieves the technical goal of intelligent zoned control of main and auxiliary welds based on welding parameter optimization information and real-time adaptive adjustment of the welding process, thereby improving welding forming accuracy, enhancing weld strength consistency, and significantly extending the overall service life of the frame.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for optimizing the welding process of a semi-trailer frame, which is applied to a welding process optimization system for a semi-trailer frame, and specifically includes the following steps: S1: Input the first-state frame profile into the main weld welding unit for welding to generate the second-state frame assembly.

[0026] Furthermore, this application also includes: matching the main weld type, weld thickness parameter, and welding area parameter according to the first state frame profile; selecting the main weld welding control parameter based on the first cloud-based process library according to the main weld type, weld thickness parameter, and welding area parameter; and using the main weld welding control parameter to control the main weld welding unit to weld the first state frame profile to generate the second state frame assembly.

[0027] Furthermore, this application also includes: inputting the main weld type, weld thickness parameters, and welding area parameters into the first cloud-based process library to determine multiple main weld welding control schemes; and based on the multiple main weld welding control schemes, determining the main weld welding control parameters by screening multiple scheme support.

[0028] Furthermore, this application also includes: the multiple main weld seam welding control schemes include welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters; based on the process stability characteristics of the welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters, the reliability of the multiple main weld seam welding control schemes is evaluated, and the support level of the multiple schemes is generated.

[0029] Specifically, the first-state frame profile is the steel component of the semi-trailer frame. Based on the first-state frame profile, the welding characteristics are determined by matching the main weld type, weld thickness parameters, and welding area parameters. The main weld type refers to the weld form of the main load-bearing parts of the frame, such as butt welds and fillet welds; different weld types have different requirements for the welding process. The weld thickness parameter refers to the thickness of the metal layer at the weld, directly affecting the welding heat input and penetration control. The welding area parameter represents the total surface area to be welded; the larger the area, the longer the required heat energy and welding time.

[0030] Furthermore, the main weld type, weld thickness parameters, and welding area parameters are input into the first cloud-based process library to determine multiple main weld welding control schemes. Parameter data describing the characteristics of the welded workpiece are input into the cloud database to call upon historical experience and process models, generating several selectable welding schemes. The first cloud-based process library is a centrally managed welding data platform that stores welding process samples and control experience from different vehicle chassis models. Once the input parameters are transmitted to the cloud, multiple feasible main weld welding control schemes are generated based on data matching and algorithmic reasoning. Each scheme contains a complete set of parameter combinations.

[0031] Furthermore, the welding control scheme for multiple main weld seams includes multiple sets of welding control parameters related to the welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency. These parameters are interrelated and collectively determine the quality and stability of the welding process. The welding torch positioning coordinates refer to the spatial position parameters of the welding torch in the welding path, used to determine the start, end, and trajectory of the weld seam, ensuring a uniform distribution of the weld pool. The wire feed speed refers to the speed at which the welding wire is fed into the weld pool during welding; too high a speed may lead to weld overgrowth, while too low a speed may result in an incomplete weld. The shielding gas flow rate refers to the volumetric flow rate of the inert gas or mixed gas supplied during welding, used to isolate the weld from air and prevent oxidation. The welding pulse frequency represents the number of pulses of the welding machine's output current per unit time, affecting the weld penetration, heat input, and weld formation characteristics.

[0032] Based on the process stability characteristics of welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters, reliability evaluations are conducted on multiple main weld seam welding control schemes, generating multiple scheme support scores. In other words, after obtaining multiple welding schemes with different parameter combinations, performance analysis and reliability assessments are performed. Process stability characteristics refer to the comprehensive performance of arc combustion stability, weld seam uniformity, spatter amount, and welding process repeatability during welding, reflecting whether the welding process possesses consistency and controllability. Reliability evaluation involves comparing and statistically analyzing welding data for each set of parameters under different operating conditions to calculate the comprehensive score of each scheme in terms of quality, efficiency, and energy consumption. Multiple scheme support scores refer to the probability values ​​of each scheme being optimal or near-optimal, reflecting the credibility of the scheme's successful application in actual welding. Higher support scores indicate a higher welding success rate for the scheme under similar operating conditions.

[0033] Furthermore, based on multiple main weld seam welding control schemes, the main weld seam welding control parameters are determined by screening multiple scheme support levels. A quantitative index is used to evaluate the feasibility and stability of each scheme. Multiple scheme support levels refer to a comprehensive evaluation index of the process stability, welding quality consistency, and equipment adaptability demonstrated by each scheme in historical applications; a higher value indicates a more reliable scheme. Screening and determining the main weld seam welding control parameters involves selecting the scheme with the highest support level from the candidate schemes as the final control parameters to guide the operation of the welding unit. Combining statistical weights with model prediction results ensures that the selected parameters maintain stable output under different production conditions.

[0034] Furthermore, the main weld seam welding control parameters are used to control the welding of the main weld seam welding unit on the first-state frame profile, generating the second-state frame assembly. The main weld seam welding unit refers to the automated work unit responsible for welding the main load-bearing structure of the frame, including a robotic arm, welding torch, sensors, and a control module. By inputting parameters such as the main weld seam type, weld seam thickness parameters, and welding area parameters obtained through screening, the welding trajectory and heat input can be controlled to achieve stable weld formation quality. After welding is completed, the first-state frame profile is transformed into the second-state frame assembly, that is, the main frame structure that has been welded but has not yet undergone auxiliary welding or assembly.

[0035] S2: Input the second state frame assembly into the auxiliary weld welding unit for welding to generate the third state frame assembly.

[0036] Furthermore, this application also includes: matching auxiliary weld length parameters and welding gap parameters according to the second state frame assembly; filtering auxiliary weld welding control parameters based on a second cloud-based process library according to the auxiliary weld length parameters and welding gap parameters; and using the auxiliary weld welding control parameters to control the auxiliary weld welding unit to weld the second state frame assembly to generate the third state frame assembly.

[0037] Specifically, based on the second-state frame assembly, the auxiliary weld length parameters and welding gap parameters are matched. This involves structural identification and parameter extraction of the formed second-state frame assembly to determine the geometric characteristics of the auxiliary welds. The auxiliary weld length parameter refers to the length of the weld used for auxiliary connections in different parts of the frame, and is directly related to the frame structural strength and the distribution of connection points. The welding gap parameter refers to the distance or gap between the workpieces to be welded, which affects the molten pool filling effect and welding stress distribution. Through parameter matching, the welding requirements of different parts of the frame can be understood, thus providing input for subsequent welding parameter selection.

[0038] Based on the auxiliary weld length and welding gap parameters, and using a second cloud-based process library, welding control parameters for the auxiliary weld are screened. The extracted geometric feature data of the auxiliary weld is input into a cloud database for auxiliary welding process management. By comparing and analyzing historical welding data with model recommendations, the most suitable combination of welding parameters is selected. The second cloud-based process library is a process knowledge platform related to auxiliary welds, containing a large number of welding condition samples, parameter settings, and records linking welding quality results. Auxiliary weld welding control parameters include welding current, welding speed, shielding gas flow rate, and welding sequence, which collectively determine the weld metal formation quality and residual stress distribution. The screening process uses algorithms to analyze the stability and consistency of historical welding results, selecting the parameter set with the highest suitability.

[0039] The auxiliary weld seam welding unit is used to weld the second-state frame components using auxiliary weld seam welding control parameters, generating the third-state frame assembly. Specifically, the selected welding parameters are sent to the auxiliary weld seam welding unit, which automatically executes the welding operation, thus transforming the second-state frame components into the third-state frame assembly. The auxiliary weld seam welding unit is an automated or semi-automated welding device used to weld auxiliary connection parts of the frame. It can precisely control the welding path, welding torch angle, and heat input based on input parameters. The third-state frame assembly refers to the frame structure formed after both the main weld seam and auxiliary weld seam are completed.

[0040] S3: Position and assemble the reinforcing rib accessories and the third-state frame assembly to generate the fourth-state frame semi-finished product.

[0041] Specifically, the reinforcing ribs and the third-state frame assembly are positioned and assembled to generate a fourth-state frame semi-finished product. This involves precisely aligning the reinforcing ribs, used to enhance structural strength, with the frame body using a rational assembly and positioning method, based on the third-state frame assembly. Reinforcing ribs are structural components used to improve the local stiffness and overall deformation resistance of the frame. They are made of high-strength steel or aluminum alloy, and their shape varies depending on the stress points. The third-state frame assembly is a complete frame assembly with auxiliary structures, completed through main and auxiliary weld processes, possessing high structural integrity. Positioning assembly refers to accurately placing the reinforcing ribs in designated positions using assembly fixtures or visual measurement systems, ensuring assembly accuracy and a reasonable distribution of structural stress. Generating a fourth-state frame semi-finished product signifies that after the assembly process, the frame has entered an intermediate stage of structural forming. Although not all welding or surface treatment is yet complete, its overall rigidity, strength, and geometry are close to the final product.

[0042] S4: Optimize welding current parameters and welding speed parameters, and generate optimized welding current parameter information and optimized welding speed parameter information.

[0043] Furthermore, this application also includes: performing quality prediction based on welding current parameters and welding speed parameters to obtain a pass probability; when the pass probability is less than or equal to a probability threshold, optimizing the welding current parameters and the welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information.

[0044] Furthermore, this application also includes: when the welding current parameter is input into the pass probability evaluation model, activating the current influence evaluation layer and outputting the current adaptation probability; when the welding speed parameter is input into the pass probability evaluation model, activating the speed influence evaluation layer and outputting the speed adaptation probability; and determining the pass probability through the current adaptation probability and the speed adaptation probability.

[0045] Furthermore, this application also includes: setting multiple current parameter variable ranges and multiple speed parameter variable ranges based on the welding current parameters and the welding speed parameters; performing variable analysis based on the multiple current parameter variable ranges and multiple speed parameter variable ranges, and generating a k-th pass probability based on the pass probability evaluation model; if the k-th pass probability is greater than the probability threshold, setting the k-th welding current parameter as the welding current parameter optimization information, and setting the k-th welding speed parameter as the welding speed parameter optimization information.

[0046] Furthermore, this application also includes: when the kth pass probability is less than or equal to the probability threshold, determining whether the kth pass probability is less than the (k-1)th pass probability; if it is not less than, adding the (k-1)th welding current parameter and the (k-1)th welding speed parameter to the elimination dataset, continuing to iterate based on the kth welding current parameter and the kth welding speed parameter, and outputting the optimal solution when a preset number of iterations is met, which is set as the welding current parameter optimization information and the welding speed parameter optimization information.

[0047] Specifically, when welding current parameters are input into the pass / fail probability assessment model, the current influence assessment layer is activated, outputting the current fit probability. This means that parameters representing the magnitude and fluctuation characteristics of the welding current are input into a statistical or neural network model used to judge weld quality. The model contains multiple structural layers for analyzing the influence of different factors, with the current influence assessment layer specifically analyzing the impact of current changes on quality indicators such as weld formation, penetration depth, and weld width. Welding current parameters include indicators such as average current, peak current, and current waveform stability, which directly determine the metal melting rate and weld metal fluidity. The current fit probability represents the degree of matching between the current current parameters and the optimal current distribution in historical qualified weld samples.

[0048] When welding speed parameters are input into the pass / fail probability assessment model, the speed influence assessment layer is activated, outputting the speed adaptation probability. That is, when welding speed-related parameters are input into the same model, the speed influence assessment layer within the model is activated to determine the impact of welding speed variations on weld continuity and heat input uniformity. Welding speed parameters include the welding torch movement speed, speed fluctuation amplitude, and the trend of heat input rate changes. If the welding speed is too fast, incomplete penetration may occur in the weld; if it is too slow, weld beads or overheating deformation may result. The speed adaptation probability is used to quantify the feasibility and stability of the welding process under the current speed conditions.

[0049] By using current matching probability and speed matching probability, and through methods such as weighted summation, product, or fuzzy inference, the pass probability is determined to characterize the overall quality controllability of the welding process parameter combination. A higher pass probability indicates a more reasonable match between the current and speed, resulting in a more stable welding result.

[0050] Furthermore, based on the welding current and welding speed parameters, multiple variable ranges for the current parameter and multiple variable ranges for the speed parameter were established. That is, to further study the influence of different parameter combinations on welding quality, the current and speed were divided into several continuously varying intervals to form multiple sets of candidate parameters. The variable ranges were set by discretizing the ranges of current and speed. For example, the current was divided into 5 intervals from 160 A to 200 A, and the speed was divided into 5 intervals from 15 cm / min to 25 cm / min, thus forming 25 different parameter combinations for subsequent analysis.

[0051] Based on multiple current and velocity parameter ranges, variable analysis is performed using a pass / fail probability assessment model to generate the k-th pass / fail probability. This involves sequentially inputting different combinations of current and velocity into the model for calculation, analyzing the performance of each parameter group in terms of welding stability, weld formation quality, and weld penetration uniformity, and outputting the corresponding pass / fail probability. Variable analysis calculates and evaluates the impact of different variable combinations one by one in a multi-dimensional parameter space to determine which parameters are most conducive to obtaining high-quality welds. The k-th pass / fail probability represents the probability that the welding result meets the quality standard under the k-th combination of current and velocity.

[0052] If the probability of passing the k-th parameter is greater than the probability threshold, the k-th welding current parameter is set as the welding current parameter optimization information, and the k-th welding speed parameter is set as the welding speed parameter optimization information. That is, when the calculated probability of passing the k-th set of parameters exceeds the set passing standard threshold, this set of parameters is automatically identified as the optimal solution, and the current and speed values ​​are recorded as optimization information for subsequent welding process adjustments. The probability threshold is the boundary value for judging the quality of parameters; parameter combinations exceeding this value are considered to be able to stably achieve high-quality welding. The welding current parameter optimization information and welding speed parameter optimization information refer to the optimal current and speed settings that have been selected.

[0053] Furthermore, when the k-th pass probability is less than or equal to the probability threshold, it is determined whether the k-th pass probability is less than the (k-1)-th pass probability. That is, if the pass probability calculated from the current k-th set of parameters fails to meet the preset pass standard, it is further compared with the performance of the previous set of parameters to determine whether the welding effect of the current scheme is worse than the previous one. The k-th pass probability represents the predicted welding pass rate under the k-th combination of welding current and welding speed, while the (k-1)-th pass probability represents the predicted result under the previous combination. Through the adjacent comparison mechanism, the correspondence between the direction of parameter change and the change in welding quality can be identified, thereby determining whether the optimization process is progressing in the correct direction.

[0054] If the parameters are not less than the minimum value, the (k-1)th welding current parameter and the (k-1)th welding speed parameter are added to the elimination dataset. Iteration continues based on the k-th welding current parameter and the k-th welding speed parameter. When a preset number of iterations is met, the optimal solution is output and set as the optimization information for the welding current parameter and the welding speed parameter. That is, if the k-th pass probability is not lower than the (k-1)-th pass probability, it indicates that parameter adjustments have improved welding quality to some extent. In this case, the (k-1)-th group of parameters with poor performance is added to the elimination dataset to avoid reusing undesirable parameter combinations in the future. The elimination dataset is a storage set used to record parameters judged as non-optimal or suboptimal, used to accelerate the model's convergence process. Subsequently, the next round of iterations continues with the k-th group of parameters as a new starting point. By continuously adjusting the small range of changes in current and speed, the optimal parameters are gradually approached. When the preset number of iterations or convergence conditions are reached, the current optimal parameters are output as the optimization information for the welding current parameter and the welding speed parameter, i.e., the best set values ​​obtained in this round of optimization.

[0055] S5: After initializing the main weld seam welding unit according to the welding current parameter optimization information and the auxiliary weld seam welding unit according to the welding speed parameter optimization information, the semi-finished frame in the fourth state is welded to generate the finished semi-trailer frame.

[0056] Specifically, the main weld unit is initialized based on optimized welding current parameters. This means that after optimization calculations, the optimal setting value of the welding current is obtained, enabling the main weld to achieve the best penetration, formation, and metal structure stability during the welding process. The process of initializing the main weld unit involves inputting the optimized current parameters into the control system, ensuring that the equipment is in an optimal energy output state before operation, thereby ensuring the stability and consistency of subsequent welding quality.

[0057] The auxiliary weld seam welding unit is initialized based on the optimized welding speed parameters. This means that after configuring the main weld seam parameters, the auxiliary weld seam welding unit is initialized according to the optimized welding speed settings. Initializing the auxiliary weld seam welding unit involves adjusting the motion trajectory, speed curve, and energy output rhythm to ensure a smooth weld transition and good weld surface quality.

[0058] The fourth-state semi-finished frame is welded to produce the finished semi-trailer frame. This involves initializing the parameters of the main and auxiliary weld seams, then performing the final welding operation on the fourth-state semi-finished frame. Welding refers to the welding and cooling process of all weld seams on the frame under the control of preset welding paths and process parameters, resulting in a semi-trailer frame with continuous overall stress and minimal welding defects. The finished product stage signifies the completion of the entire welding process, and the frame then enters the quality inspection, painting, and assembly stages.

[0059] In summary, the welding process optimization method for a semi-trailer frame provided in this application has the following technical effects: by achieving the technical goal of intelligent control of main and auxiliary weld seam partitions based on welding parameter optimization information and real-time adaptive adjustment of the welding process, the technical effects of improving welding forming accuracy, enhancing weld strength consistency, and significantly extending the overall service life of the frame are achieved.

[0060] Example 2: Based on the same inventive concept as the welding process optimization method for a semi-trailer frame described in the foregoing examples, this application also provides a welding process optimization system for a semi-trailer frame. Please refer to the appendix. Figure 2 The system includes: a second-state frame assembly generation module 1, used to input the first-state frame profile into the main weld seam welding unit for welding to generate a second-state frame assembly; a third-state frame assembly generation module 2, used to input the second-state frame assembly into the auxiliary weld seam welding unit for welding to generate a third-state frame assembly; a fourth-state frame semi-finished product generation module 3, used to position and assemble the reinforcing rib accessories and the third-state frame assembly to generate a fourth-state frame semi-finished product; an optimization information generation module 4, used to optimize the welding current parameters and welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information; and a semi-trailer frame finished product generation module 5, used to initialize the main weld seam welding unit according to the welding current parameter optimization information, initialize the auxiliary weld seam welding unit according to the welding speed parameter optimization information, and then perform welding processing on the fourth-state frame semi-finished product to generate a semi-trailer frame finished product.

[0061] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: predict the quality based on welding current parameters and welding speed parameters to obtain the pass probability; when the pass probability is less than or equal to a probability threshold, optimize the welding current parameters and the welding speed parameters to generate welding current parameter optimization information and welding speed parameter optimization information.

[0062] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: match the main weld type, weld thickness parameter, and welding area parameter according to the frame profile in the first state; filter the main weld welding control parameters based on the main weld type, weld thickness parameter, and welding area parameter, using a first cloud-based process library; and use the main weld welding control parameters to control the main weld welding unit to weld the frame profile in the first state to generate the frame assembly in the second state.

[0063] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: input the main weld type, weld thickness parameters, and welding area parameters into the first cloud-based process library to determine multiple main weld welding control schemes; and based on the multiple main weld welding control schemes, determine the main weld welding control parameters by screening multiple scheme support.

[0064] Furthermore, the welding process optimization system for a semi-trailer frame is also used for: the welding control schemes for the multiple main welds include welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters; based on the process stability characteristics of the welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameters, the system evaluates the reliability of the multiple main welds welding control schemes and generates the support degree of the multiple schemes.

[0065] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: match auxiliary weld length parameters and welding gap parameters according to the second state frame assembly; filter auxiliary weld welding control parameters based on a second cloud-based process library according to the auxiliary weld length parameters and welding gap parameters; and use the auxiliary weld welding control parameters to control the auxiliary weld welding unit to weld the second state frame assembly to generate the third state frame assembly.

[0066] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: activate the current influence evaluation layer and output the current adaptation probability when the welding current parameter is input into the pass probability evaluation model; activate the speed influence evaluation layer and output the speed adaptation probability when the welding speed parameter is input into the pass probability evaluation model; and determine the pass probability through the current adaptation probability and the speed adaptation probability.

[0067] Furthermore, the welding process optimization system for a semi-trailer frame is also used for: setting multiple current parameter variable ranges and multiple speed parameter variable ranges based on the welding current parameters and the welding speed parameters; performing variable analysis based on the pass probability evaluation model according to the multiple current parameter variable ranges and multiple speed parameter variable ranges to generate the k-th pass probability; if the k-th pass probability is greater than the probability threshold, setting the k-th welding current parameter as the welding current parameter optimization information and the k-th welding speed parameter as the welding speed parameter optimization information.

[0068] Furthermore, the welding process optimization system for a semi-trailer frame is also used to: when the k-th pass probability is less than or equal to the probability threshold, determine whether the k-th pass probability is less than the (k-1)-th pass probability; if it is not less than, add the (k-1)-th welding current parameter and the (k-1)-th welding speed parameter to the elimination dataset, continue iterating based on the k-th welding current parameter and the k-th welding speed parameter, and output the optimal solution when the preset number of iterations is met, setting it as the welding current parameter optimization information and the welding speed parameter optimization information.

[0069] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The welding process optimization method and specific examples of a semi-trailer frame in the foregoing embodiment one are also applicable to the welding process optimization system of a semi-trailer frame in this embodiment. Through the foregoing detailed description of the welding process optimization method of a semi-trailer frame, those skilled in the art can clearly understand the welding process optimization system of a semi-trailer frame in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the welding process of a semi-trailer frame, characterized in that, A welding production line for semi-trailer frames, the welding production line being equipped with a main weld seam welding unit and an auxiliary weld seam welding unit, including: The first-state frame profile is input into the main weld welding unit for welding to generate the second-state frame assembly. The second-state frame assembly is input into the auxiliary weld welding unit for welding to generate the third-state frame assembly. Position and assemble the reinforcing ribs and the third-state frame assembly to generate a fourth-state frame semi-finished product. The welding current parameters and welding speed parameters are optimized to generate optimized welding current parameter information and optimized welding speed parameter information. After initializing the main weld seam welding unit according to the welding current parameter optimization information and the auxiliary weld seam welding unit according to the welding speed parameter optimization information, the semi-finished frame in the fourth state is welded to generate the finished semi-trailer frame.

2. The method for optimizing the welding process of a semi-trailer frame as described in claim 1, characterized in that, The method includes optimizing welding current parameters and welding speed parameters to generate optimized welding current parameter information and optimized welding speed parameter information. Quality prediction is performed based on welding current and welding speed parameters to obtain the probability of passing the test. When the pass probability is less than or equal to the probability threshold, the welding current parameter and the welding speed parameter are optimized to generate welding current parameter optimization information and welding speed parameter optimization information.

3. The method for optimizing the welding process of a semi-trailer frame as described in claim 1, characterized in that, The method involves inputting the first-state frame profile into the main weld welding unit for welding to generate the second-state frame assembly, the method comprising: Based on the frame profile in the first state, match the main weld type, weld thickness parameters, and welding area parameters; Based on the main weld type, weld thickness parameters, and welding area parameters, the main weld welding control parameters are selected using the first cloud-based process library. The main weld seam welding control parameters are used to control the main weld seam welding unit to weld the first state frame profile, thereby generating the second state frame assembly.

4. The method for optimizing the welding process of a semi-trailer frame as described in claim 3, characterized in that, Based on the main weld type, weld thickness parameters, and welding area parameters, and using a first cloud-based process library, the method for selecting main weld welding control parameters includes: Input the main weld type, weld thickness parameters and welding area parameters into the first cloud-based process library to determine multiple main weld welding control schemes; Based on the multiple main weld seam welding control schemes, the main weld seam welding control parameters are determined by screening the support of multiple schemes.

5. The method for optimizing the welding process of a semi-trailer frame as described in claim 4, characterized in that, The multiple main weld seam welding control schemes include welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under multiple sets of welding control parameter associations; Based on the process stability characteristics of welding torch positioning coordinates, wire feed speed, shielding gas flow rate, and welding pulse frequency under the association of multiple sets of welding control parameters, the reliability of the multiple main weld seam welding control schemes is evaluated, and the support degree of the multiple schemes is generated.

6. The method for optimizing the welding process of a semi-trailer frame as described in claim 1, characterized in that, The second-state frame assembly is input into the auxiliary weld welding unit for welding to generate the third-state frame assembly. The method includes: Based on the second state frame assembly, match the auxiliary weld length parameters and welding gap parameters; Based on the auxiliary weld length parameter and welding gap parameter, and using the second cloud-based process library, the auxiliary weld welding control parameters are selected. The auxiliary weld seam welding control parameters are used to control the auxiliary weld seam welding unit to weld the second state frame assembly, thereby generating the third state frame assembly.

7. The method for optimizing the welding process of a semi-trailer frame as described in claim 2, characterized in that, The method for predicting quality and obtaining the probability of pass / fail based on welding current and welding speed parameters includes: When the welding current parameter is input into the pass probability evaluation model, the current influence evaluation layer is activated, and the current adaptation probability is output. When the welding speed parameter is input into the pass probability evaluation model, the speed influence evaluation layer is activated, and the speed adaptation probability is output. The pass / fail probability is determined by the current adaptation probability and the speed adaptation probability.

8. The method for optimizing the welding process of a semi-trailer frame as described in claim 7, characterized in that, When the pass probability is less than or equal to a probability threshold, the welding current parameter and the welding speed parameter are optimized to generate welding current parameter optimization information and welding speed parameter optimization information. The method includes: Based on the welding current parameters and the welding speed parameters, multiple current parameter variable ranges and multiple speed parameter variable ranges are set; Based on the multiple current parameter variable ranges and multiple speed parameter variable ranges, variable analysis is performed using the qualification probability evaluation model to generate the kth qualification probability. If the kth pass probability is greater than the probability threshold, the kth welding current parameter is set as the welding current parameter optimization information, and the kth welding speed parameter is set as the welding speed parameter optimization information.

9. The method for optimizing the welding process of a semi-trailer frame as described in claim 8, characterized in that, If the probability of the kth pass is greater than the probability threshold, the method further includes: If the probability of passing the kth pass is less than or equal to the probability threshold, determine whether the probability of passing the kth pass is less than the probability of passing the (k-1)th pass. If it is not less than, add the (k-1)th welding current parameter and the (k-1)th welding speed parameter to the elimination dataset, and continue iterating based on the kth welding current parameter and the kth welding speed parameter. When the preset number of iterations is met, output the optimal solution, which is set as the welding current parameter optimization information and the welding speed parameter optimization information.

10. A welding process optimization system for a semi-trailer frame, characterized in that, The steps for implementing the welding process optimization method for a semi-trailer frame according to any one of claims 1 to 9 include: The second-state frame assembly generation module is used to input the first-state frame profile into the main weld welding unit for welding to generate the second-state frame assembly. The third-state frame assembly generation module is used to input the second-state frame components into the auxiliary weld welding unit for welding, thereby generating the third-state frame assembly. The fourth-state frame semi-finished product generation module is used to position and assemble the reinforcing rib accessories and the third-state frame assembly to generate the fourth-state frame semi-finished product. The optimization information generation module is used to optimize welding current parameters and welding speed parameters, and generate optimization information for welding current parameters and welding speed parameters. The semi-trailer frame finished product generation module is used to initialize the main weld seam welding unit according to the welding current parameter optimization information, initialize the auxiliary weld seam welding unit according to the welding speed parameter optimization information, and then perform welding processing on the fourth state frame semi-finished product to generate the semi-trailer frame finished product.

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