Integrally-formed automobile front bumper bearing detection tool

Abstract

The invention relates to the technical field of automobile front bumper bearing detection, in particular to an integrally-formed automobile front bumper bearing detection tool which comprises a sensing unit, a signal conditioning unit, an algorithm processing unit, an association network unit, a judgment reasoning unit, a result presentation unit and a feedback control unit. According to the invention, intelligent acquisition and analysis of multi-dimensional data of the bumper are realized through cooperation of multiple units such as sensing and sensing, a dynamic association network is constructed to generate an abnormal positioning repair indication, risk prompt and process improvement scheme, and a high-precision intelligent quality control solution is provided by combining a feedback control self-learning closed loop with a result to present multi-modal interaction.

Description

Technical Field

[0001] The invention relates to the technical field of automobile front bumper load detection technology, in particular to an integrally formed automobile front bumper load detection tool. Background Art

[0002] In the field of automobile manufacturing, one-piece molded front bumpers are widely used due to their lightweight structure and balanced strength. Performance testing covers multi-dimensional indicators such as morphological accuracy, mechanical strength, thermal stability, vibration characteristics and material consistency, which directly affect the safety and reliability of the entire vehicle.

[0003] Currently, performance testing technologies for automotive front bumpers often rely on single methods such as static dimensional testing, single-point stress measurement, or manual visual inspection. These methods make it difficult to fully perceive and evaluate the bumper's multi-dimensional performance. Data processing in the testing system relies solely on simple filtering and threshold discrimination, which cannot effectively extract features under complex operating conditions. This results in delayed anomaly recognition and the inability to promptly detect potential risks. At the same time, there is a lack of dynamic correlation analysis of multi-dimensional factors such as structure, performance, and process, insufficient abnormal identification and optimization decision-making, and it cannot meet the high-precision and high-efficiency requirements in mass production. It also does not have a feedback control mechanism and lacks self-learning and optimization functions, making it difficult to improve detection accuracy and system adaptability. Summary of the Invention

[0004] The object of the present invention is to provide an integrally formed automobile front bumper load-bearing detection tooling for solving the problems mentioned in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: an integrally formed automobile front bumper load detection tool, comprising a sensing unit, a signal conditioning unit, an algorithm processing unit, an association network unit, a judgment and reasoning unit, a result presentation unit, and a feedback control unit; The sensing unit continuously acquires multi-directional data of the bumper and transmits it to the signal conditioning unit; The signal conditioning unit performs normalization processing on the multi-directional data to form a standardized data packet; The algorithm processing unit generates a comprehensive evaluation result of bumper performance based on the normalized data packet; The association network unit establishes a dynamic association network based on the comprehensive performance evaluation result; The judgment and reasoning unit integrates the dynamic association network and outputs abnormal marking instructions, quality risk prompts and improvement plans; The result presentation unit is used to present the evaluation results and reasoning content, and provide multi-device interactive operations; The feedback control unit receives the abnormal marking instruction from the judgment and reasoning unit, intelligently adjusts the detection equipment parameters, and transmits the adjusted operating data back to the signal conditioning unit.

[0006] Furthermore, the multi-directional data includes morphological parameter data, stress state data, thermal distribution data, vibration spectrum data, material characteristic data, internal detection data and surface quality data, and the normalized data packet generated by the signal conditioning unit contains multi-directional data after filtering, homogenization, singularity point detection and phase correction.

[0007] Furthermore, the comprehensive evaluation results of the bumper performance include a morphological deviation mark, a mechanical concentration mark, a strength loss mark, a service life risk mark, an internal damage mark, a surface defect mark, an insufficient impact performance mark, a reduced rigidity mark, an insufficient elastic recovery mark, an abnormal temperature adaptation mark, and an abnormal resonance characteristic mark.

[0008] Furthermore, the algorithm processing unit performs real-time and longitudinal analysis on the normalized data packets, specifically including: analyzing the morphological parameter data and generating a morphological deviation mark if an anomaly exists; and analyzing the stress state data and generating a mechanical concentration mark if an anomaly exists.

[0009] Furthermore, the material characteristic data is analyzed and the consistency is evaluated using a random algorithm. When the strength does not meet the standard or the fluctuation exceeds the range, a strength loss mark is generated; the life span is predicted and a service life risk mark is generated when the design requirements are not met; the vibration spectrum data is analyzed, the frequency characteristics and attenuation ratio are extracted, and a resonance characteristic abnormality mark is generated when the characteristic frequency deviates from the design value or the attenuation ratio exceeds the range.

[0010] Furthermore, by analyzing the thermal distribution data, a temperature adaptation anomaly mark is generated if the local temperature rise or temperature gradient exceeds the set range; by analyzing the internal detection data, an internal damage mark is generated if the detected damage size or damage density exceeds the set range; by analyzing the surface quality data, a surface defect mark is generated if the defect area or depth exceeds the set range.

[0011] Furthermore, the association network unit establishes a dynamic association network according to the following steps: entity extraction: extracting bumper codes, abnormality types, location information, and degree levels from the comprehensive performance evaluation results; attribute construction: bumper nodes contain product batches and material types, abnormality nodes contain types and sizes, performance nodes contain measured values ​​and standard benchmarks, and regional nodes contain spatial locations and functional definitions; connection creation: establishing subordinate connections, influence levels, and association strength relationships; dynamic evolution: updating node connections in real time through a graphic database, updating characteristics or creating connections when new results are imported, and outputting the entire network to the judgment and reasoning unit.

[0012] Furthermore, the judgment and reasoning unit combines with the dynamic association network to generate abnormal marking instructions, quality risk prompts and improvement plans: when internal damage is identified, a positioning instruction containing position information is generated, and when surface defects are identified, a repair instruction containing range coordinates is generated; when mechanical concentration is identified, a risk prompt is generated, and when strength loss is identified, a secondary risk prompt is generated; when morphological deviation is identified, an improvement plan containing process parameters is generated; the abnormal marking instruction is transmitted to the feedback control unit, and the quality risk prompt and improvement plan are sent to the result presentation unit along with the evaluation results.

[0013] Furthermore, the result presentation unit supports multi-dimensional display and multi-platform interaction operations, specifically including: multi-dimensional display and multi-platform interaction.

[0014] Furthermore, the feedback control unit realizes closed-loop monitoring and adaptive optimization according to the instruction parsing and execution, status monitoring, and feedback output process: parsing abnormal marking instructions and directing re-inspection of detection tooling; collecting secondary detection data to generate a verification data set; encapsulating verification results and parameter adjustment records, and inputting them into the algorithm processing unit after processing for model training or evaluation rule optimization to form a detection closed loop.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes the intelligent collection and analysis of multi-dimensional data through the coordinated operation of sensor perception, signal conditioning, algorithm processing, association network, judgment and reasoning, result presentation and feedback control unit. By identifying multiple types of abnormal identification and building multi-dimensional connections of structure, performance and process through dynamic association network, the judgment and reasoning unit can generate positioning and repair instructions with coordinates, quantitative risk prompts and process improvement plans for different abnormalities. The feedback control unit can also form a self-learning closed loop by intelligently adjusting the parameters of the detection equipment and returning the secondary detection data. Combined with the multi-dimensional visualization display and multi-platform interaction function of the result presentation unit, it provides a high-precision, intelligent and systematic solution for quality control in mass production scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 The system frame of the present invention Figure 1 ; Figure 2 The system frame of the present invention Figure 2 . DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example 1: Figure 1 As shown, the one-piece automobile front bumper load-bearing detection tooling includes a sensing unit, a signal conditioning unit, an algorithm processing unit, a correlation network unit, a judgment and reasoning unit, a result presentation unit, and a feedback control unit that are directly or indirectly connected to each other; The sensor perception unit continuously obtains multi-directional data of the bumper and transmits it to the signal conditioning unit; The signal conditioning unit performs denoising, normalization, and feature extraction on the multi-directional data to form a standardized data packet; The algorithm processing unit generates comprehensive evaluation results of bumper performance based on standardized data packets and with the help of the collaborative operation of intelligent computing mechanisms; The association network unit establishes a dynamic association network including multi-dimensional connections of structure, performance, quality and process based on the comprehensive performance evaluation results; The judgment and reasoning unit integrates the dynamic association network to output abnormal marking instructions, quality risk warnings and improvement plans; The result presentation unit uses a visual display screen as a carrier to display the evaluation results and reasoning content, and provides multi-device interactive operations; The feedback control unit receives the abnormal marking instruction from the judgment and reasoning unit, intelligently adjusts the parameters of the detection equipment, and returns the adjusted operating data to the signal conditioning unit.

[0019] The multi-directional data includes morphological parameter data, stress state data, thermal distribution data, vibration spectrum data and material characteristic data; the standardized data packet generated by the signal conditioning unit contains various types of data after filtering, homogenization, singular point detection and phase correction.

[0020] The comprehensive evaluation results of bumper performance include morphological deviation mark, mechanical concentration mark, strength loss mark, service life risk mark, internal damage mark, surface defect mark, insufficient impact performance mark, reduced rigidity mark, insufficient elastic recovery mark, abnormal temperature adaptation mark and abnormal resonance characteristic mark.

[0021] The algorithm processing unit performs real-time and longitudinal analysis on the standardized data packets, including: morphological parameter data analysis: real-time acquisition of bumper point cloud data, accurate comparison with the ideal geometric model using adaptive iterative closest point registration technology based on functional area difference weights, calculation of shape variation index, and assessment of local maximum variation based on bumper functional area division; When the variation index is greater than 0.5mm or the variation in the key functional area is greater than 0.3mm, a morphological deviation mark is generated; stress state data analysis: the strain distribution on the bumper surface under standard load conditions is obtained, and the global stress field is constructed using finite element reconstruction technology to identify high stress points. When the local stress value is greater than 85% of the design threshold or the stress gradient is greater than 10MPa / mm, a mechanical concentration mark is generated; Material characteristic data analysis: Standardized hardness, elastic modulus, and density index. In this invention, hardness, elastic modulus, and density index respectively refer to important physical performance parameters of the bumper material. A material characteristic evaluation model is constructed using the random forest algorithm to assess material consistency. When the material strength of a local area is less than 90% of the design standard or the strength fluctuation of adjacent areas is greater than 15%, a strength loss mark is generated. The service life of the bumper is predicted through the cyclic load accumulation algorithm and damage theory. When the predicted service life is less than 95% of the design requirement, a service life risk mark is generated. Vibration spectrum data analysis: The response characteristics of the bumper under dynamic loading conditions are obtained. The frequency characteristics and attenuation ratio are extracted through modal analysis. When the characteristic frequency deviates from the design value by ±5% or the attenuation ratio is less than 0.05, a resonance characteristic abnormality mark is generated.

[0022] Thermal distribution data analysis: Thermal imaging technology is used to obtain changes in the bumper's temperature distribution during loading. Heat conduction analysis methods are used to evaluate the evolution of thermal stress. When a local temperature rise is greater than 5°C / min or a temperature gradient is greater than 2°C / cm, a temperature adaptation anomaly indicator is generated. Internal detection data analysis: Phased array ultrasound is used to scan the internal structure of the bumper. A deep residual network that fuses multimodal ultrasonic signal features is used to accurately identify and classify internal damage to the bumper. An internal damage mark is generated when the damage size is greater than 1mm or the damage density is greater than 3 / 100cm². Surface quality data analysis: High-definition images are collected from the bumper surface, and surface defects are identified through deep learning target detection. A surface defect mark is generated when the defect area is greater than 5mm² or the depth is greater than 0.3mm.

[0023] Example 2: Figure 2 As shown, the association network unit establishes a dynamic association network according to the following steps: Entity extraction: extracting bumper codes, anomaly types, location information, and degree levels from the comprehensive performance evaluation results; Attribute construction: Bumper nodes include product batch and material type; abnormality nodes include type and size; performance nodes include measured values ​​and standard benchmarks; and area nodes include spatial location and functional definition. Connection creation: establishing subordinate connections, degree of influence, and strength of association relationships; Dynamic evolution: Node connections are updated in real time through a graph database, features are updated or connections are created when new results are imported, and the entire network is output to the judgment and reasoning unit; The judgment and reasoning unit combines with the dynamic association network to generate instructions and solutions: Abnormal marking instructions: When marking internal damage, the precise location of the damage is obtained based on the associated network, and positioning instructions are generated, including spatial coordinates, inspection direction, and recommended inspection tools. When marking surface defects, repair instructions are generated, including defect range coordinates and recommended treatment processes. Quality risk warning: When marking concentrated mechanical properties, a warning including risk level, impact range, and failure probability is generated based on historical abnormal data of similar types. When marking strength loss, a secondary risk warning including loss area range, strength difference value, and performance impact assessment is generated. Improvement plan: When morphological deviation is identified, an improvement plan containing mold adjustment parameters, injection pressure correction values, and cooling time optimization values ​​is generated based on process parameter correlation data. The generated abnormal marking instruction is transmitted to the feedback control unit, and the quality risk prompt and improvement plan are sent to the result presentation unit along with the evaluation results; The result presentation unit supports multi-dimensional display and multi-platform interactive operations, including: Multi-dimensional display: The bumper shape and abnormal distribution locations are intuitively displayed through a 3D virtual model, stress distribution and temperature field distribution are represented by a color scale diagram, vibration response characteristics are displayed through a spectrum diagram, and comprehensive inspection reports and optimization implementation effect reports are automatically generated; Multi-platform interaction: Supports viewing complete detection information on industrial control terminals, receiving real-time risk push notifications on mobile devices, and dynamically displaying batch statistics on large screens. Operators are allowed to manually mark suspicious areas through the interactive interface, which are then updated to the associated network after confirmation by the algorithm processing unit.

[0024] The feedback control unit implements closed-loop monitoring and adaptive optimization according to the following process: Instruction parsing and execution: Analyze the location information, detection equipment, and detection parameters in the abnormal marking instructions, and command the detection tooling to conduct accurate re-inspection through the industrial control bus; Condition monitoring: Collect secondary inspection data, including abnormal precise dimensions, peripheral stress state, and material property indicators, and generate verification data sets according to the standardized process of the signal conditioning unit; Feedback output: The package verification results and the detection equipment parameter adjustment records are processed by the signal conditioning unit and input into the algorithm processing unit for subsequent model training or evaluation rule optimization, forming a self-learning and self-optimizing detection closed loop.

[0025] In combination with Example 1 and Example 2, it can be seen that the present invention realizes the intelligent collection and analysis of multi-dimensional data through the coordinated operation of sensor perception, signal conditioning, algorithm processing, association network, judgment and reasoning, result presentation and feedback control unit. By identifying multiple types of abnormal identification and building multi-dimensional connections of structure, performance and process through dynamic association network, the judgment and reasoning unit can generate coordinate positioning repair instructions, quantitative risk warnings and process improvement plans for different abnormalities, and form a self-learning closed loop through the intelligent adjustment of detection equipment parameters and the feedback of secondary detection data by the feedback control unit. Combined with the multi-dimensional visualization display and multi-platform interaction function of the result presentation unit, a high-precision, intelligent and systematic solution is provided for quality control in mass production scenarios.

[0026] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The one-piece automobile front bumper load-bearing test tool is characterized by: It includes sensing unit, signal conditioning unit, algorithm processing unit, association network unit, judgment and reasoning unit, result presentation unit and feedback control unit. The sensing unit continuously acquires multi-directional data of the bumper and transmits it to the signal conditioning unit; The signal conditioning unit performs normalization processing on the multi-directional data to form a standardized data packet; The algorithm processing unit generates a comprehensive evaluation result of bumper performance; The association network unit establishes a dynamic association network; The judgment and reasoning unit outputs abnormal marking instructions, quality risk prompts and improvement plans; The result presentation unit is used to present the evaluation results and reasoning content, and provide multi-device interactive operations; The feedback control unit intelligently adjusts the detection equipment parameters and transmits the adjusted operating data back to the signal conditioning unit.

2. The integrally formed automobile front bumper load-bearing detection tool according to claim 1, characterized in that: The multi-directional data includes morphological parameter data, stress state data, thermal distribution data, vibration spectrum data, material characteristic data, internal detection data and surface quality data. The normalized data packet generated by the signal conditioning unit contains the multi-directional data after filtering, homogenization, singularity point detection and phase correction.

3. The integrally formed automobile front bumper load-bearing detection tool according to claim 1, characterized in that: The comprehensive evaluation results of the bumper performance include a morphological deviation mark, a mechanical concentration mark, a strength loss mark, a service life risk mark, an internal damage mark, a surface defect mark, an insufficient impact performance mark, a reduced rigidity mark, an insufficient elastic recovery mark, an abnormal temperature adaptation mark, and an abnormal resonance characteristic mark.

4. The integrally formed automobile front bumper load-bearing detection tool according to claim 1, characterized in that: The algorithm processing unit performs real-time and longitudinal analysis on the normalized data packets, specifically including: analyzing the morphological parameter data and generating a morphological deviation mark if an anomaly exists; and analyzing the stress state data and generating a mechanical concentration mark if an anomaly exists.

5. The integrally formed automobile front bumper load-bearing detection tool according to claim 4, characterized in that: Analyze material characteristic data, evaluate consistency using random algorithms, and generate a strength loss indicator when the strength does not meet the standard or the fluctuation exceeds the range; predict lifespan, and generate a service life risk indicator when it does not meet the design requirements; analyze vibration spectrum data, extract frequency characteristics and attenuation ratio, and generate a resonance characteristic abnormality indicator when the characteristic frequency deviates from the design value or the attenuation ratio exceeds the range.

6. The integrally formed automobile front bumper load-bearing detection tool according to claim 4, characterized in that: By analyzing the thermal distribution data, a temperature adaptation anomaly mark is generated if the local temperature rise or temperature gradient exceeds the set range; by analyzing the internal detection data, an internal damage mark is generated if the detected damage size or damage density exceeds the set range; by analyzing the surface quality data, a surface defect mark is generated if the defect area or depth exceeds the set range.

7. The integrally formed automobile front bumper load-bearing detection tool according to claim 1, characterized in that: The association network unit establishes a dynamic association network according to the following steps: Entity extraction: extracting bumper codes, anomaly types, location information, and degree levels from the comprehensive performance evaluation results; property Construction: Bumper nodes include product batch and material type, abnormal nodes include type and size, performance nodes include measured values ​​and standard benchmarks, and regional nodes include spatial location and functional definition; Connection creation: Establish subordinate connections, influence levels, and association strength relationships; Dynamic evolution: Update node connections in real time through the graphic database, update features or create connections when new results are imported, and output the entire network to the judgment and reasoning unit.

8. The integrally formed automobile front bumper load-bearing detection tool according to claim 7, characterized in that: The judgment and reasoning unit generates abnormal marking instructions, quality risk warnings and improvement plans in combination with the dynamic association network: when internal damage is identified, a positioning instruction containing position information is generated; when surface defects are identified, a repair instruction containing range coordinates is generated; when mechanical concentration is identified, a risk warning is generated; when strength loss is identified, a secondary risk warning is generated; Generate an improvement plan including process parameters when morphological deviation is identified; The abnormal marking instruction is transmitted to the feedback control unit, and the quality risk prompt and improvement plan are sent to the result presentation unit along with the evaluation results.

9. The integrally formed automobile front bumper load-bearing detection tool according to claim 8, characterized in that: The result presentation unit supports multi-dimensional display and multi-platform interaction operations, specifically including: multi-dimensional display and multi-platform interaction.

10. The integrally formed automobile front bumper load-bearing detection tool according to claim 8, characterized in that: The feedback control unit implements closed-loop monitoring and adaptive optimization according to the instruction parsing and execution, status monitoring, and feedback output process: parsing abnormal marking instructions and directing re-inspection of detection tooling; collecting secondary detection data to generate a verification data set; encapsulating verification results and parameter adjustment records, and after processing, inputting them into the algorithm processing unit for model training or evaluation rule optimization to form a detection closed loop.

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