Method for applying ultrasonic treatment technology to weld joint fatigue improvement

Abstract

The invention discloses a method for applying an ultrasonic treatment technology to weld joint fatigue improvement, and belongs to the technical field of welding. Comprising the following steps of S1, weld information collection and analysis, S2, self-adaptive ultrasonic treatment, S3, treatment effect evaluation and feedback, and S4, post-treatment and quality detection. According to the method, through multi-dimensional welding seam information collection and intelligent analysis, proper processing parameters can be preset according to the characteristics of different welding seams, self-adaptive adjustment is carried out in the processing process according to real-time monitoring data, precise processing is achieved, and the welding seam fatigue improvement effect is improved; meanwhile, a real-time monitoring and feedback optimization mechanism can find problems occurring in the processing process in time, dynamic adjustment is carried out, the problems of local overheating, uneven processing and the like are avoided, and the processing stability and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of weld seam welding, and in particular to a method for improving weld seam fatigue by applying ultrasonic processing technology. Background Art

[0002] In modern industrial production, welded structures are widely used in numerous fields, including machinery manufacturing, aerospace, automobiles, and ships. However, as a critical component of welded structures, the fatigue performance of welds is often a key factor in determining the service life and safety of the entire structure. Residual stress is generated in welds during the welding process, and the geometric discontinuity and stress concentration at the welds significantly reduce the fatigue strength of the welds. Under alternating loads, fatigue cracks are prone to initiation and propagation in the welds, ultimately leading to structural failure, serious safety incidents, and economic losses.

[0003] Currently, the main methods for improving weld fatigue performance include mechanical grinding, hammering, and shot peening. While mechanical grinding can improve weld surface roughness, it is not very effective in eliminating residual stress within the weld. Hammering and shot peening can introduce compressive stress to a certain extent, but the process is difficult to precisely control, easily causing damage to the weld surface, and has limited effectiveness in treating complex weld shapes.

[0004] Ultrasonic treatment technology has achieved certain results in improving the fatigue performance of welds. It can eliminate residual stress, refine grains and improve surface quality. However, there are still some problems with existing ultrasonic treatment technology. On the one hand, the welds formed by different welding processes, materials and structures vary significantly in terms of residual stress distribution, grain size and surface condition, but the existing treatment methods often use fixed treatment parameters, which makes it difficult to accurately control different welds, resulting in uneven treatment effects. On the other hand, during the ultrasonic treatment process, due to the dynamic changes in the heat conduction, sound energy absorption and other characteristics of the weld material, problems such as local overheating and uneven treatment may occur, affecting the fatigue improvement effect of the weld and may even introduce new defects. Summary of the Invention

[0005] In response to the above-mentioned technical problems, the present invention overcomes the shortcomings of the existing technology and provides a method for applying ultrasonic processing technology to improve weld fatigue. Through multi-dimensional weld information collection and intelligent analysis, appropriate processing parameters can be pre-set according to the characteristics of different welds, and adaptive adjustments can be made according to real-time monitoring data during the processing process to achieve precise processing and improve the effect of improving weld fatigue.

[0006] The method of applying ultrasonic treatment technology to improve weld fatigue in this scheme includes the following steps: S1, weld information collection and analysis, S2, adaptive ultrasonic treatment, S3, treatment effect evaluation and feedback, S4, post-processing and quality inspection; Step S1 specifically includes the following steps: T1, multi-dimensional information collection, T2, establishment of weld feature database, T3, intelligent analysis and processing parameter presetting.

[0007] Furthermore, in step T1, a laser scanning device is used to obtain the three-dimensional geometric shape information of the weld, and ultrasonic non-destructive testing technology is used to detect the defect distribution inside the weld and the initial distribution of residual stress, while recording the welding process parameters and material information of the weld; in step T2, the weld information collected in step T1 is sorted and analyzed to establish a weld feature database; in step T3, a machine learning algorithm is used to study and analyze the weld feature database, and the initial parameters of the ultrasonic processing are predicted based on the characteristic information of the current weld.

[0008] Furthermore, step S2 includes the following steps: E1, building a real-time monitoring system, E2, adaptive parameter adjustment, and E3, dynamic path planning.

[0009] Furthermore, in step S2, multiple types of sensors are installed on the ultrasonic processing tool to monitor the temperature, stress changes and acoustic energy transfer in the weld processing area in real time, and the ultrasonic processing parameters are adjusted in real time using a fuzzy control algorithm; combined with the three-dimensional geometric shape information of the weld and real-time monitoring data, the moving path of the ultrasonic processing tool is dynamically adjusted using a path planning algorithm.

[0010] Furthermore, in step S3, the residual stress elimination rate, grain refinement rate and surface roughness improvement degree are set as real-time evaluation indicators; the real-time evaluation results are compared with the preset processing targets, and secondary processing or optimization of the processing path is performed according to the comparison results.

[0011] Furthermore, in step S4, after the ultrasonic treatment is completed, the weld is post-processed to further stabilize the weld structure and residual stress state; at the same time, a variety of detection methods are used to perform comprehensive quality inspection on the weld.

[0012] Furthermore, the weld feature database contains geometric features, internal defect features, residual stress features of different types of welds and corresponding welding process and material information; the initial parameters of the ultrasonic treatment include ultrasonic frequency, amplitude, treatment time and applied pressure.

[0013] The beneficial effects of the present invention are: (1) Through multi-dimensional weld information collection and intelligent analysis, the present invention can pre-set appropriate processing parameters according to the characteristics of different welds, and make adaptive adjustments according to real-time monitoring data during the processing process, thereby achieving precise processing and improving the effect of weld fatigue improvement; (2) The real-time monitoring and feedback optimization mechanism of the present invention can promptly detect problems that occur during the treatment process and make dynamic adjustments to avoid problems such as local overheating and uneven treatment, thereby improving the stability and reliability of the treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is a flow chart of the method for applying ultrasonic processing technology to improve weld fatigue of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be further described below with reference to the accompanying drawings.

[0016] according to Figure 1 As shown: The present invention provides a method for applying ultrasonic processing technology to improve weld fatigue: comprising the following steps: S1, weld information collection and analysis, S2, adaptive ultrasonic processing, S3, processing effect evaluation and feedback, S4, post-processing and quality inspection.

[0017] Step S1 includes the following steps: T1, multi-dimensional information collection, T2, establishment of weld feature database, T3, intelligent analysis and processing parameter presetting.

[0018] In step T1, a laser scanning device is used to obtain the three-dimensional geometric shape information of the weld, including the length, width, height and surface undulation of the weld; ultrasonic non-destructive testing technology is used to detect the defect distribution inside the weld and the initial distribution of residual stress, and the welding process parameters (such as welding current, voltage, welding speed) and material information (such as material type and composition) of the weld are recorded at the same time; in step T2, the weld information collected in step T1 is sorted and analyzed to establish a weld feature database, which contains the geometric characteristics, internal defect characteristics, residual stress characteristics and corresponding welding process and material information of different types of welds; in step T3, a machine learning algorithm is used to learn and analyze the weld feature database, and based on the characteristic information of the current weld, the most suitable initial ultrasonic processing parameters are predicted, including ultrasonic frequency, amplitude, processing time and applied pressure.

[0019] Step S2 includes the following steps: E1, building a real-time monitoring system, E2, adaptive parameter adjustment, and E3, dynamic path planning.

[0020] In step S2, multiple sensors, including temperature, stress, and acoustic energy sensors, are installed on the ultrasonic treatment tool to monitor the temperature, stress changes, and acoustic energy transfer in the weld treatment area in real time. The data collected by the sensors is transmitted to the control system via a wireless transmission module. Based on the real-time monitoring data and preset treatment objectives (such as residual stress relief rate and grain refinement), the control system uses a fuzzy control algorithm to adjust the ultrasonic treatment parameters in real time. For example, if the temperature in the treatment area is detected to be too high, the control system automatically reduces the amplitude or increases the treatment speed. If the residual stress relief effect is poor, the ultrasonic frequency and applied pressure are appropriately increased. A path planning algorithm, combined with the three-dimensional geometry of the weld and real-time monitoring data, dynamically adjusts the ultrasonic treatment tool's movement path to ensure that the treatment tool evenly covers the entire weld area and avoids blind spots or over-treatment areas.

[0021] In step S3, residual stress relief rate, grain refinement rate, and surface roughness improvement are set as real-time evaluation indicators. These indicators are calculated in real time by analyzing and processing real-time monitoring data. The real-time evaluation results are compared with preset treatment targets. If the evaluation indicators do not meet the target values, the control system further adjusts the treatment parameters based on the deviation, performs secondary processing, or optimizes the treatment path.

[0022] In step S4, after the ultrasonic treatment is completed, the weld is subjected to appropriate post-treatment, including low-temperature tempering treatment, to further stabilize the weld structure and residual stress state; at the same time, a variety of detection methods are used to conduct comprehensive quality inspection of the weld, including residual stress detection, microstructure analysis and fatigue performance testing, to verify whether the treatment effect meets expectations.

[0023] Taking the improvement of weld fatigue of an aircraft engine blade weldment as an example, the specific steps are as follows: High-precision laser scanning equipment is used to perform three-dimensional scanning of the blade weld to obtain detailed geometric information. At the same time, ultrasonic non-destructive testing technology is used to detect the distribution of defects and the initial state of residual stress within the weld.

[0024] The collected information is input into a weld feature database and analyzed and compared using a machine learning algorithm. Based on the characteristics of the blade weld, the initial ultrasonic treatment parameters are pre-set: a frequency of 30 kHz, an amplitude of 20 μm, a treatment time of 10 minutes, and an applied pressure of 30 N. Temperature, stress, and acoustic energy sensors are installed on the ultrasonic impact gun to monitor the temperature, stress, and acoustic energy transfer in the weld treatment area in real time. The data collected by the sensors is transmitted to the control system in real time via a wireless transmission module.

[0025] During the treatment process, when the temperature sensor detects that the temperature in the treatment area is approaching a preset safety threshold, the control system automatically reduces the amplitude by 5μm and increases the treatment speed by 10% using a fuzzy control algorithm. Simultaneously, based on the residual stress changes reported by the stress sensor, the ultrasonic frequency and applied pressure are adjusted to ensure the desired residual stress elimination effect.

[0026] Combining the weld's 3D geometry with real-time monitoring data, a path planning algorithm dynamically adjusts the ultrasonic impact gun's trajectory to ensure uniform coverage of the entire weld area. The control system calculates, in real time, evaluation metrics such as residual stress relief rate, grain refinement, and surface roughness improvement. If the residual stress relief rate falls short of the preset target, the control system automatically extends the treatment time by two minutes and appropriately increases the ultrasonic frequency and applied pressure.

[0027] After the secondary treatment, the treatment effect is evaluated again until all evaluation indicators reach a satisfactory level.

[0028] After ultrasonic treatment, the blade welds were subjected to low-temperature tempering at 200°C for two hours to stabilize the weld structure and residual stress. Residual stress in the welds was measured using a blind hole method, and the weld microstructure was observed using a metallographic microscope. Fatigue specimens were then prepared for fatigue performance testing.

[0029] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for improving weld fatigue using ultrasonic processing technology, characterized in that: The following steps are involved: S1. Weld information collection and analysis, S2. Adaptive ultrasonic processing, S3. Processing effect evaluation and feedback, S4. Post-processing and quality inspection; Step S1 specifically includes the following steps: T1, multi-dimensional information collection, T2, establishment of weld feature database, T3, intelligent analysis and processing parameter presetting.

2. The method according to claim 1, characterized in that In step T1, a laser scanning device is used to obtain the three-dimensional geometric shape information of the weld, and ultrasonic non-destructive testing technology is used to detect the defect distribution and initial distribution of residual stress inside the weld. At the same time, the welding process parameters and material information of the weld are recorded. In step T2, the weld information collected in step T1 is sorted and analyzed to establish a weld feature database. In step T3, a machine learning algorithm is used to study and analyze the weld feature database, and the initial ultrasonic processing parameters are predicted based on the characteristic information of the current weld.

3. The method according to claim 1, characterized in that Step S2 includes the following steps: E1, building a real-time monitoring system, E2, adaptive parameter adjustment, and E3, dynamic path planning.

4. The method according to claim 3, characterized in that In step S2, multiple types of sensors are installed on the ultrasonic processing tool to monitor the temperature, stress changes and acoustic energy transfer in the weld processing area in real time, and the ultrasonic processing parameters are adjusted in real time using a fuzzy control algorithm; the moving path of the ultrasonic processing tool is dynamically adjusted using a path planning algorithm based on the three-dimensional geometric shape information of the weld and real-time monitoring data.

5. The method according to claim 1, characterized in that In step S3, the residual stress relief rate, grain refinement rate and surface roughness improvement degree are set as real-time evaluation indicators; the real-time evaluation results are compared with the preset processing targets, and secondary processing or optimization of the processing path is performed according to the comparison results.

6. The method according to claim 1, characterized in that In step S4, after the ultrasonic treatment is completed, the weld is post-processed to further stabilize the weld structure and residual stress state; at the same time, a variety of detection methods are used to perform comprehensive quality inspection on the weld.

Images