A crane accessory defect complementary multiplexing system and method

Abstract

The application relates to the technical field of crane accessory manufacturing, and particularly relates to a crane accessory defect complementary reuse system and method. The method comprises the following steps: performing multi-parameter fusion detection on unqualified accessories, establishing a digital defect model containing geometric deviation, material attribute and working condition associated offset; constructing a reusable accessory resource pool based on a safety access rule library screening model; intelligently matching a complementary combination scheme in the resource pool by using an optimization algorithm, with the minimum system cumulative error as the target; matching a fixed-point compensation process for a scheme that does not meet the error requirement and performing cost-benefit evaluation to generate a composite reuse scheme; and ensuring that the scheme meets the performance safety margin standard through virtual verification based on physical simulation. The application realizes accurate quantification, safe intelligent matching and full-process reliability verification of defective accessories, and significantly improves resource utilization and economy while ensuring reuse safety.

Description

Technical Field

[0001] This application relates to the field of crane parts manufacturing, and in particular to a crane parts defect-complementary reuse system and method. Background Technology

[0002] As core equipment for heavy-duty operations, cranes require components (such as transmission parts, welded structural parts, and counterweights) that can withstand complex working conditions including heavy outdoor loads, light indoor loads, and impact loads. This places stringent demands on structural strength, dimensional accuracy, and assembly compatibility. The manufacturing process for these components is complex (involving multiple steps such as welding, precision machining, and casting). During production, defects such as dimensional deviations, localized wear, uneven weight distribution, and uneven residual stress distribution can easily occur due to machining errors, welding deformation, and material stress release. Current technologies often handle unqualified crane components by directly scrapping them or repairing them individually for independent reuse. Defect combination and reuse technologies for some common parts only focus on basic dimensional complementarity, failing to consider the core characteristics of crane components—prioritizing heavy-duty safety, closely related working conditions, and modular assembly—and neglecting key design aspects such as the acquisition of working condition-related offsets and dynamic matching under safety threshold constraints.

[0003] However, the structural characteristics and usage requirements of crane components determine the clear feasibility and urgency of defect-complementary reuse: On the one hand, the core performance parameters of crane components (such as dimensional tolerances, weight range, and stress strength) all have clear safety thresholds, and the geometric deviation vectors and working condition-related offsets (such as stress deformation and residual stress) of different defective components can be offset in reverse to achieve overall compliance; on the other hand, the modular assembly characteristics of components make the combination and assembly of multiple defective components operationally feasible, and since crane components are often used in pairs or groups (such as end beams on both sides, symmetrically distributed traveling mechanisms), and safety margins are left in the design, this provides a theoretical possibility for using the positive deviation of one component to offset the negative deviation of another component. However, existing technologies have not fully utilized this characteristic and have significant drawbacks: First, single scrapping causes serious resource waste and increased manufacturing costs, while the independent repair mode does not consider repair as a combined service, which easily leads to repair costs exceeding reuse value; Second, the lack of a defect parameter acquisition system strongly correlated with crane working conditions, focusing only on dimensional deviations while ignoring key parameters such as stress deformation and residual stress, leads to safety hazards in the combined components under heavy load and vibration conditions. Currently, there is a lack of a systematic approach that can accurately quantify defects, intelligently match complementary solutions, and comprehensively consider safety and economy. This results in a large number of defective products with potential reuse value being wasted, and simple repairs often become meaningless due to excessive costs.

[0004] To address the aforementioned issues, and considering the characteristics of crane parts—heavy loads, multiple operating conditions, and high safety requirements—as well as the feasibility of defect-complementary reuse, this application proposes a method and system for defect-complementary reuse of crane parts. This method not only solves the resource waste problem of traditional processing methods but also ensures the adaptability and safety reliability of reused parts under operating conditions, filling the gap in the existing technology for defect reuse of crane-specific parts. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a system and method for complementary reuse of crane component defects.

[0006] Firstly, this application provides a method for complementary reuse of defects in crane components, employing the following technical solution:

[0007] A method for complementary reuse of defective crane components includes the following steps:

[0008] S1. Digital Defect Modeling: Multi-parameter fusion detection is performed on the crane parts that fail the inspection to generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vector, material property parameters, and working condition related offset parameters associated with the actual use scenario of the parts.

[0009] S2. Safety Access and Resource Pool Construction: Based on a predefined safety access rule base, the digital defect models of each crane component are screened, digital defect models with uncompensable defects are removed, and the remaining digital defect models are stored in the reusable component resource pool.

[0010] S3. Intelligent Complementary Matching: Based on the technical requirements of the target assembly, with the goal of minimizing the system cumulative error, an optimization algorithm is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool; the complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model;

[0011] S4. Process Compensation Decision and Generation: Determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, match the fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and perform a cost-benefit evaluation. If the matching is successful and the evaluation passes, generate a composite reuse scheme. If the matching fails or the evaluation fails, return to step S3 for recalculation. The fixed-point compensation process is used to correct the geometric deviation vector and / or working condition related offset parameters of the parts. The composite reuse scheme includes the unique identifier of each crane part, the fixed-point compensation process flow information, the updated digital defect model of the relevant crane parts after executing the fixed-point compensation process, and the overall predicted cumulative error value calculated based on the updated digital defect model.

[0012] S5. Virtual Verification and Decision: Perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme and generate corresponding simulation prediction data; if the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme; otherwise, return to step S3 to recalculate or terminate the process.

[0013] Preferably, in step S1, the working condition associated offset parameters include at least: the stress deformation offset for transmission components, the environmental load compensation offset for outdoor components, and the residual stress distribution offset for welded structure components.

[0014] Preferably, in step S2, the security access rule base includes security rejection rules to identify and exclude accessory models with the following uncompensable defects:

[0015] The material strength in the core load-bearing area is lower than the predetermined proportion of the design nominal value;

[0016] There are macroscopic or microscopic cracks whose length or depth exceeds a preset threshold;

[0017] The damage size in the critical load-bearing area exceeds the preset proportion of damage allowed in that area.

[0018] Preferably, in step S3, the optimization algorithm is based on a multi-dimensional fitness function for optimization. This function comprehensively evaluates at least two dimensions: geometric deviation complementarity and operating condition safety compliance. The evaluation of the operating condition safety compliance dimension incorporates the typical operating condition load spectrum of the target assembly. The optimization algorithm dynamically adjusts the calculation weights of the two dimensions, geometric deviation complementarity and operating condition safety compliance, according to the technical requirements of the target assembly and the typical operating condition load spectrum.

[0019] Preferably, the multi-dimensional fitness function also evaluates the correction economy dimension; the optimization algorithm dynamically allocates the calculation weights of the three dimensions of geometric deviation complementarity, working condition safety compliance and correction economy according to the safety criticality level of the components in the target assembly.

[0020] Preferably, in step S4, the cost-benefit assessment includes:

[0021] Estimate the cost of implementing the fixed-point compensation process and compare it with the current new manufacturing cost of the component;

[0022] Assess whether the expected increase in the number of effective complementary combinations that the component can participate in within the reusable component resource pool, after compensation, meets the preset standard.

[0023] Preferably, in step S4, the fixed-point compensation process adopts a precision dynamic adjustment strategy. Its correction target is not to restore to the design nominal value, but to precisely control and retain a controllable functional deviation that is in the same direction as the required complementary amount and whose absolute value is within the preset precision range, based on the complementary amount required to offset the cumulative error of the system in the matching calculation.

[0024] Preferably, in step S5, the performance safety margin standard requires that the virtual verification based on finite element analysis must cover the load simulation of the crane under rated operating conditions and at least one non-standard typical operating condition, including heavy load, impact load or specific environmental load conditions.

[0025] Preferably, after outputting a feasible reuse scheme in step S5, the following steps are also included:

[0026] S6. Dynamic parameter calibration: Based on the feasible reuse scheme, calculate the change in safety margin of the assembly formed according to the feasible reuse scheme compared with the standard new product assembly, and generate differentiated use guidance parameters accordingly. The differentiated use guidance parameters include the adjusted maximum working load or the enhanced maintenance and inspection cycle.

[0027] S7. Closed-loop learning optimization: Collect performance monitoring data of the reusable assembly deployed according to the feasible reuse scheme in actual operation, compare and analyze it with the simulation prediction data in step S5, and based on the analysis results, use parameter tuning algorithm to iteratively update the threshold in the security access rule base, the weight parameters of the optimization algorithm, and the verification indicators in the performance safety margin standard.

[0028] Secondly, this application provides a crane component defect complementation and reuse system, which adopts the following technical solution:

[0029] A crane component defect complementation and reuse system includes:

[0030] The defect digitization module is used to perform multi-parameter fusion detection on crane parts that fail inspection, and generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vectors, material property parameters, and working condition-related offset parameters associated with the actual use scenario of the parts.

[0031] The model filtering module is used to filter the digital defect models of various crane parts based on a predefined safety access rule base, remove digital defect models with uncompensable defects, and store the remaining digital defect models in the reusable parts resource pool.

[0032] The intelligent complementary matching module is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool based on the technical requirements of the target assembly and with the goal of minimizing the system cumulative error. The complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model.

[0033] The process compensation decision module is used to determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, it matches a fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and performs a cost-benefit evaluation. If the matching is successful and the evaluation is passed, a composite reuse scheme is generated. If the matching is unsuccessful or the evaluation is not passed, it returns to the intelligent complementary matching module for recalculation.

[0034] The simulation verification module is used to perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme and generate corresponding simulation prediction data. If the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme; otherwise, it returns to the intelligent complementary matching module to recalculate or terminates the process.

[0035] In summary, this application includes at least one of the following beneficial technical effects:

[0036] 1. This method accurately quantifies defects using a multi-dimensional digital model and achieves intelligent error cancellation and safe matching between parts through optimization algorithms. On this basis, virtual verification, parameter calibration, and closed-loop learning mechanisms are introduced to form a full-chain reliability guarantee covering prediction, verification, calibration, and optimization. At the same time, by leveraging cost-benefit assessment and dynamic process compensation decisions, the economic feasibility of reuse is significantly improved while ensuring safety. This systematically realizes the transformation of crane parts from defective waste to safe, economical, and reliable reuse, effectively improving the efficient and safe utilization of defective crane parts.

[0037] 2. The digital defect model established in this application not only reflects the static geometric deviation of the parts, but more importantly, it captures their dynamic response characteristics under actual working loads, providing a comprehensive and reliable data foundation for subsequent accurate complementary matching and safety assessment, and overcoming the shortcomings of existing technologies that only focus on dimensions and ignore the correlation of working conditions.

[0038] 3. Intelligent complementary matching is achieved by using an optimization algorithm based on a multi-dimensional fitness function. This algorithm can actively search for the component combination that minimizes the cumulative error of the system and can make precise fine-tuning through fixed-point compensation process. This allows the overall accuracy of an assembly made up of multiple defective components to meet or even exceed the technical requirements. Attached Figure Description

[0039] Figure 1 This is a block diagram of a method for complementary reuse of defects in crane components according to an embodiment of this application;

[0040] Figure 2 This is a system block diagram of a crane accessory defect complementation and reuse system according to an embodiment of this application.

[0041] Explanation of reference numerals in the attached diagram: 1. Defect digitization module; 2. Model selection module; 3. Intelligent complementary matching module; 4. Process compensation decision module; 5. Simulation verification module; 6. Parameter calibration module; 7. Closed-loop optimization module. Detailed Implementation

[0042] The following is in conjunction with the appendix Figure 1 and Figure 2 This application will be described in further detail.

[0043] This application discloses a method for complementary reuse of defective crane components, applicable to defect reuse scenarios for various core components such as crane transmission parts, welded structural parts, and outdoor counterweights. It effectively solves the resource waste problem caused by the direct scrapping of traditional defective components, while ensuring the safety and reliability of the reused assembly. (Refer to...) Figure 1 A method for complementary reuse of defective crane parts includes the following steps:

[0044] S1. Digital Defect Modeling: Multi-parameter fusion detection is performed on the crane parts that fail the inspection to generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vector, material property parameters, and working condition related offset parameters associated with the actual use scenario of the parts.

[0045] S2. Safety Access and Resource Pool Construction: Based on a predefined safety access rule base, the digital defect models of each crane component are screened, digital defect models with uncompensable defects are removed, and the remaining digital defect models are stored in the reusable component resource pool.

[0046] S3. Intelligent Complementary Matching: Based on the technical requirements of the target assembly, with the goal of minimizing the system cumulative error, an optimization algorithm is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool; the complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model;

[0047] S4. Process Compensation Decision and Generation: Determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, match the fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and perform a cost-benefit evaluation. If the matching is successful and the evaluation passes, generate a composite reuse scheme. If the matching fails or the evaluation fails, return to step S3 for recalculation. The fixed-point compensation process is used to correct the geometric deviation vector and / or working condition related offset parameters of the parts. The composite reuse scheme includes the unique identifier of each crane part, the fixed-point compensation process flow information, the updated digital defect model of the relevant crane parts after executing the fixed-point compensation process, and the overall predicted cumulative error value calculated based on the updated digital defect model.

[0048] S5. Virtual Verification and Decision-Making: Perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme, and generate corresponding simulation prediction data (including stress cloud diagrams, deformation data, fatigue life, etc.). If the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme; otherwise, return to step S3 to recalculate or terminate the process. It should be noted that the maximum number of recalculations can be set according to actual needs, such as 3 times. If the standard is still not met, the process will be terminated.

[0049] S6. Dynamic parameter calibration: Based on the feasible reuse scheme, calculate the change in safety margin of the assembly formed according to the feasible reuse scheme compared with the standard new product assembly, and generate differentiated use guidance parameters accordingly. The differentiated use guidance parameters include the adjusted maximum working load or the enhanced maintenance and inspection cycle.

[0050] S7. Closed-Loop Learning Optimization: Collect performance monitoring data of reusable assemblies deployed according to feasible reuse schemes during actual operation, compare and analyze this data with the simulation prediction data from step S5, and based on the analysis results, use parameter tuning algorithms to iteratively update the thresholds in the safety access rule base, the weight parameters of the optimization algorithm, and the verification indicators in the performance safety margin standard. Through the above steps, defects are accurately quantified using a multi-dimensional digital model, and intelligent error cancellation and safety matching between parts are achieved through optimization algorithms. On this basis, virtual verification, parameter calibration, and closed-loop learning mechanisms are introduced to form a full-chain reliability guarantee covering prediction, verification, calibration, and optimization. At the same time, by leveraging cost-benefit assessment and dynamic process compensation decisions, the economic feasibility of reuse is significantly improved while ensuring safety, thereby systematically realizing the transformation of crane parts from defective waste to safe, economical, and reliable reuse, achieving the effect of efficient and safe utilization of defective crane parts.

[0051] The aforementioned multi-parameter fusion detection is a comprehensive detection method combining geometric dimension detection, mechanical property detection, and working condition-related characteristic detection. In this embodiment, the following equipment and processes are specifically employed: Geometric dimension detection uses high-precision 3D scanning equipment (such as a laser scanner or structured light scanner), with a preferred scanning accuracy of ±0.02mm or higher, to collect point cloud data of key functional surfaces of the components (such as hole diameter, shaft diameter, and flatness). The point cloud data is compared with the original design model (such as STEP format), and a quantified geometric deviation vector is calculated and generated. Material property parameters are tested using a material testing machine to detect key mechanical properties (such as tensile strength and yield strength), and a hardness tester is used to test surface hardness to ensure that the data meets the material standards for crane components. Working condition-related performance is sampled, with appropriate testing methods used for different types of components.

[0052] For transmission components, the working load can be simulated on a dynamic load test bench to measure their stress deformation.

[0053] For welded structural components, residual stress detection equipment (such as X-ray diffractometer or ultrasonic stress meter) is used to detect the residual stress distribution in key areas.

[0054] For outdoor accessories, environmental compensation quantities such as wind pressure center offset can be calculated based on their three-dimensional morphology and wind load model.

[0055] As an optional implementation, the digital defect model in step S1 above can be represented in a computer system as a data structure with specific fields, a record in a database, or a JSON / XML format data file. This model must contain at least the following fields:

[0056] ;

[0057] The operating condition associated offset parameters include at least the following:

[0058] The amount of stress-induced deformation offset for transmission components (such as gears and drive shafts) (for example, under 1.2 times the rated load, the bending deformation of the drive shaft is approximately 0.05-0.1 mm).

[0059] Environmental load compensation offset for outdoor accessories (such as counterweights and outrigger pads) (for example, wind load offset of approximately 0.03 mm at a simulated wind speed of 8 m / s, and thermal expansion and contraction offset of approximately 0.06 mm at a low temperature of -20℃).

[0060] The residual stress distribution offset for welded structural components (such as outrigger welds and vehicle frames) (for example, the residual stress in the weld area is about 15-25 MPa, and in the non-weld area it is about 5-10 MPa).

[0061] The range of values ​​for the above offset parameters is determined based on crane design specifications (such as GB / T3811), finite element simulation analysis, and historical test data, reflecting the performance response characteristics of the components under typical working conditions.

[0062] Thus, the established digital defect model not only reflects the static geometric deviation of the parts, but more importantly, it captures their dynamic response characteristics under actual working loads, providing a comprehensive and reliable data foundation for subsequent accurate complementary matching and safety assessment, overcoming the shortcomings of existing technologies that only focus on dimensions and ignore the correlation of working conditions.

[0063] In step S2 above, the safety access rule base is a structured set of rules built based on crane component failure mode analysis (FMEA), stored in a relational database, and maintained by managers based on industry standards and engineering experience. This rule base includes safety rejection rules used to identify and exclude component models with the following uncompensable defects:

[0064] The preset percentage of the core load-bearing area material strength being lower than the design nominal value: The preset percentage can be set to 90% (industry standard recommended value). For example, if the design nominal tensile strength of a certain leg weld is 600MPa, and the detected value is ≤540MPa, it is determined to be uncompensable.

[0065] There are macroscopic or microscopic cracks with a length or depth exceeding a preset threshold: the preset threshold for macroscopic crack length can be set to ≥0.5mm (e.g., cracks in the weld of the support leg), and the preset threshold for microscopic crack depth can be set to ≥0.3mm (e.g., microscopic cracks on the surface of the drive shaft).

[0066] The damage size in the critical load-bearing area exceeds a preset percentage of the allowable damage for that area: the preset percentage can be set to 8%, for example, the load-bearing surface area of ​​the outrigger pad is 0.2m². 2 The maximum permissible damage area is 0.016m². 2 (160cm) 2 If the threshold is exceeded, it is deemed uncompensable. The aforementioned preset ratios and thresholds are determined by management personnel based on crane design specifications, failure mode analysis, historical quality data of the enterprise, and safety margin requirements, and can be dynamically calibrated through subsequent closed-loop learning optimization modules.

[0067] The screening process is as follows: The model screening module calls the rule engine, reads the fields of the digital defect model, compares them one by one with the thresholds in the safety access rule base, and automatically marks and removes uncompensable defect models. The screened models are categorized by component type and defect dimension and stored in a reusable component resource pool, which supports efficient indexing and querying. The aforementioned thresholds are determined comprehensively based on the "Crane Design Code" (GB / T 3811-2008), the company's historical failure data, and safety margin requirements, and can be dynamically calibrated through a closed-loop optimization module. Through the automatic screening of this rule base, components with inherent safety risks such as insufficient material strength, cracks, and excessive damage can be efficiently and consistently excluded from the resource pool. This is equivalent to setting up a safety firewall for the entire reuse system, ensuring that all subsequent operations are carried out within the safety baseline, fundamentally eliminating the risk of unqualified components being mixed into the assembly.

[0068] In step S3 above, the optimization algorithm can be one or more intelligent optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). This embodiment uses the PSO algorithm as an example, and its volume parameters can be set as follows: population size 80 (searching 80 combinations each time), number of iterations 40, dynamic adjustment of inertia weight (0.8 in the initial iteration, 0.4 in the later iteration), and learning factors c1=c2=2.0.

[0069] The optimization algorithm is based on a multi-dimensional fitness function, which comprehensively evaluates at least three dimensions: geometric deviation complementarity, operating condition safety compliance, and corrected economic efficiency. In this embodiment, the multi-dimensional function expression is: F = w1 × C + w2 × S + w3 × E (where F is the fitness value, C is the geometric deviation complementarity, S is the operating condition safety compliance, E is the corrected economic efficiency, and w1 + w2 + w3 = 1). The calculation methods for each dimension are as follows:

[0070] Geometric deviation complementarity C: C = 1 - (residual cumulative error after assembly / maximum allowable deviation of the target assembly), for example, if the maximum allowable deviation of the target is 0.3mm and the total deviation of a certain assembly is 0.15mm, then C = 0.5;

[0071] Safety compliance under working conditions S: Evaluate the typical working condition load spectrum of the target assembly (such as including heavy load, light load, and impact load), S=1-(maximum equivalent stress under the combined working condition / allowable stress of the material). For example, if the allowable stress of the material is 300MPa and the maximum stress under the combined heavy load condition is 240MPa, then S=0.2.

[0072] Corrected economic efficiency E: E = 1 - (estimated compensation cost / new component manufacturing cost), if no compensation is required, then E = 1.

[0073] The optimization algorithm dynamically adjusts the calculation weights of geometric deviation complementarity and operating condition safety compliance based on the technical requirements and typical load spectrum of the target assembly: the larger the load and the higher the safety risk, the higher the weight of operating condition safety compliance; simultaneously, it dynamically allocates calculation weights across three dimensions based on the safety criticality level of the components in the target assembly, as illustrated in this embodiment:

[0074] Safety-critical components (such as outrigger welds and steering knuckles): w1=0.35, w2=0.55, w3=0.1;

[0075] Non-critical safety components (such as guard plates and decorative parts): w1=0.55, w2=0.35, w3=0.1.

[0076] The specific process of intelligent complementary matching is as follows: 1. Initialization: Randomly select 2-3 similar parts from the resource pool to form an initial combination population; 2. Fitness calculation: Calculate the fitness value of each combination according to the above function; 3. Iterative update: Retain the best solution and eliminate the worst solution until the iteration converges; 4. Output results: The combination with the best fitness value is taken as the complementary combination scheme. The overall prediction cumulative error value is calculated by superimposing the geometric deviation vector of each part with the working condition related offset. For example, the prediction cumulative error value of a certain outrigger combination scheme is 0.18mm (≤ allowable range 0.3mm).

[0077] This dynamic weighting and multi-dimensional optimization means that the matching algorithm does not mechanically pursue perfect geometric fit, but intelligently balances accuracy, safety, and economy based on the importance of components (safety criticality level) and actual working conditions (typical load spectrum). The result is a complementary combination scheme that not only theoretically minimizes error, but also offers the highest safety margin and optimal overall cost under the expected real-world working environment.

[0078] In step S4 above, the process compensation knowledge base stores the mapping relationship of "defect type → compensation process → process parameters", as shown in the following example:

[0079] ;

[0080] It should be noted that the specific mapping relationships for process compensation are existing technologies in this industry and will not be elaborated here. These mapping relationships can be updated as technology continues to develop. Specific process parameters can be dynamically adjusted according to the material of the parts (such as Q355B, ZG310-570) and the degree of defects. This embodiment is only an illustrative example and does not limit the specific value range of the process parameters.

[0081] Secondly, in step S4, the cost-benefit assessment includes two core criteria, both of which must be met to pass:

[0082] The estimated cost of performing the fixed-point compensation process is compared with the current new manufacturing cost of the part. The specific comparison threshold is set by the management personnel according to actual needs. In this embodiment, the estimated cost of performing the fixed-point compensation process is ≤ 30% of the current new manufacturing cost of the part. The compensation cost is calculated based on process parameters, material consumption and equipment depreciation. For example, the new manufacturing cost of a certain transmission gear is 1200 yuan, and the laser cladding compensation cost is 300 yuan.

[0083] The assessment evaluates whether the expected increase in the number of effective complementary combinations that the component can participate in within the reusable component resource pool after compensation meets the preset standard. This expected increase is set by the administrator; for example, in this embodiment, it is set to 20%. For instance, before compensation, it could only participate in one combination, but after compensation, it can participate in three (expected increase ≥ 200%, meeting the requirement).

[0084] The fixed-point compensation process employs a dynamic precision adjustment strategy. Its correction target is not to restore the design nominal value, but rather to precisely control and retain a controllable functional deviation within a preset precision range (e.g., ≤0.2mm) based on the complementary amount required to offset the accumulated system error in the matching calculation. This deviation is in the same direction as the required complementary amount, and its absolute value differs from the absolute value of the required complementary amount from a predetermined value. For example, if the target accumulated system error to be offset is +0.15mm (radial), and the original geometric deviation of a component is +0.2mm (radial), after correction by laser cladding, a controllable functional deviation of -0.04mm (radial) is retained. The difference is then 0.11mm (≤0.2mm), effectively offsetting the system error. This dynamic precision adjustment strategy changes the traditional mindset of restoring to the nominal value, transforming it into precisely manufacturing controllable deviations to serve system complementarity. This significantly reduces the stringent requirements for the repair precision of individual components, thereby broadening the range of repairable components and significantly reducing the difficulty and cost of the compensation process. It greatly expands the range of economically reusable defective components, improving the economic feasibility and engineering practicality of the entire reuse method.

[0085] As mentioned earlier, step S5, virtual verification and decision-making, is the core link and final checkpoint for ensuring the security and reliability of the reuse scheme. To illustrate how to implement this critical verification, the performance safety margin standards and typical verification procedures on which it is based are described in detail below.

[0086] According to the aforementioned performance safety margin standard, virtual verification based on finite element analysis must cover load simulation of the crane under rated operating conditions and at least one non-standard typical operating condition. The non-standard typical operating conditions include heavy loads, impact loads, or specific environmental loads. Taking finite element analysis software as an example, the specific process is as follows:

[0087] 1. Model Import: Convert the digital defect model of the combined scheme into STEP format and import it into the simulation software;

[0088] 2. Mesh generation: Tetrahedral meshes are used, with a size of 2-5mm. In critical areas (such as welds and load-bearing surfaces), the mesh is densified to 1mm.

[0089] 3. Boundary condition settings: Apply fixed constraints (simulating the fixed state of the assembly) and loads (set according to the load spectrum of the working condition).

[0090] 4. Calculation and solution: The static analysis and fatigue analysis modules are used to calculate stress distribution, deformation and fatigue life;

[0091] 5. Results Output: Generates simulation prediction data such as stress cloud diagrams, deformation curves, and fatigue life reports.

[0092] The performance safety margin standard requires that virtual verification must cover load simulation of the crane under rated operating conditions and at least one non-standard typical operating condition. As a specific example, the performance safety margin standard can be set with the following quantitative indicators:

[0093] Rated operating conditions (e.g., 100t rated load): maximum equivalent stress of combined components ≤240MPa (≤80% of the material's allowable stress of 300MPa), no motion interference, estimated fatigue life ≥12000 cycles (≥1.2 times the design requirement of 10000 cycles).

[0094] Non-standard typical working conditions (heavy load 120t): maximum equivalent stress ≤270MPa (≤90% of allowable stress), fatigue life ≥8000 cycles;

[0095] Non-standard typical working condition (impact load 150t): instantaneous maximum stress ≤300MPa (≤allowable stress), no plastic deformation.

[0096] In step S6 above, the safety margin is calculated as follows: Safety margin = 1 - (the maximum equivalent stress predicted by the reuse scheme under rated conditions in virtual verification / allowable stress of the material), (1 - 240MPa / 300MPa). For example, if the safety margin of a standard new assembly under rated load is 30%, and the safety margin of a reused assembly is 25%, then the usage parameters need to be adjusted accordingly. Differentiated usage guidance parameters can be generated by the parameter calibration module based on the calculation tool, as shown in the following example:

[0097] The maximum working load has been adjusted from 100t to 90t (≤90% of the standard new product) to avoid overload and safety risks.

[0098] Enhanced maintenance and inspection cycle: shortened from the standard 6 months to 4.5 months (a 25% reduction), with a focus on inspecting wear and cracks in component connections and stress concentration areas.

[0099] The differentiated usage guidance parameters are linked to feasible reuse solutions, such as generating a "Reusable Parts User Manual" that clarifies usage limitations and maintenance requirements, and is delivered to the construction unit along with the solution.

[0100] In step S7 above, actual operating data is collected through sensors deployed on the assembly (such as stress sensors and vibration sensors). More than 10 sets of valid data are collected each quarter, including actual working load, running time, maximum stress value, vibration amplitude, etc., and transmitted to the data server via TCP / IP protocol. Comparative analysis can be performed using data analysis tools to calculate the deviation between the simulation prediction data and the actual data (such as stress deviation, life deviation). If the deviation exceeds a preset range (such as ±10%), optimization iteration is triggered.

[0101] Based on the deviations identified in the analysis, the parameter tuning algorithm can employ gradient descent (GD) and regression analysis algorithms to iteratively update the core system parameters. Taking gradient descent as an example, a specific optimization example is as follows:

[0102] If the maximum stress in actual operation (260MPa) is higher than the simulation prediction value (240MPa), the deviation is +8.3%: the preset ratio of material strength in the core load-bearing area in the safety access rule base is increased from 90% to 92%, the working condition safety compliance weight of safety key components in the optimization algorithm is increased from 55% to 58%, and the maximum equivalent stress threshold of rated working condition in the performance safety margin standard is adjusted to ≤230MPa;

[0103] If the actual proportion of compensation cost (35%) is higher than the estimated proportion (30%): adjust the weight of the economic dimension from 10% to 15%, and update the cost estimation model of the corresponding process in the process compensation knowledge base.

[0104] Virtual verification and differentiated parameter calibration constitute the factory quality inspection and user manual for the reuse solution, ensuring that each solution undergoes rigorous operating condition simulation testing before application and clearly defining its safe usage boundaries. Closed-loop learning optimization endows the system with self-evolution capabilities, continuously calibrating the model and optimizing rules based on actual operating data. This allows the system's matching accuracy, safety prediction capabilities, and economic evaluation to continuously improve over time, forming a virtuous cycle.

[0105] In summary, the crane component defect complementarity and reuse method provided in this application, through the closed-loop coordination of seven steps S1 to S7, achieves the following systematic technical effects:

[0106] 1. Advanced Data Foundation: By integrating a digital defect model (S1) that incorporates geometric, material, and operational condition offsets, the limitations of existing technologies that only focus on dimensional tolerances are overcome, enabling a precise digital twin of the performance of defective components.

[0107] 2. Intelligent decision-making process: By introducing a multi-dimensional, dynamic weighted intelligent matching algorithm (S3) and cost-benefit driven compensation decision (S4), the transformation from experience-based matching to global optimization is realized, intelligently balancing safety, accuracy and economy.

[0108] 3. Systematized security verification: Through mandatory multi-condition virtual verification and differentiated parameter calibration, a dual security guarantee is built from virtual testing to usage guidance, ensuring that the security and reliability of the reused solution is no less than, or even manageable as, that of a new product.

[0109] 4. System capability evolution: Through closed-loop learning optimization (S7) based on actual service data, the system's matching rules, verification standards and cost models can continuously self-calibrate and improve, thus possessing the ability to continuously evolve.

[0110] This application also discloses a crane component defect complementation and reuse system. (Refer to...) Figure 2 A crane parts defect complementation and reuse system includes the following interconnected modules:

[0111] The defect digitization module 1 is used to perform multi-parameter fusion detection on crane parts that fail the inspection, and generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vector, material property parameters, and working condition related offset parameters associated with the actual use scenario of the parts.

[0112] Model filtering module 2 is used to filter the digital defect models of each crane accessory based on a predefined safety access rule base, remove digital defect models with uncompensable defects, and store the remaining digital defect models in the reusable accessory resource pool.

[0113] The intelligent complementary matching module 3 is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool according to the technical requirements of the target assembly and with the goal of minimizing the system cumulative error. The complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model.

[0114] The process compensation decision module 4 is used to determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, it matches the fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and performs a cost-benefit evaluation. If the matching is successful and the evaluation is passed, a composite reuse scheme is generated. If the matching is unsuccessful or the evaluation is not passed, it returns to the intelligent complementary matching module for recalculation.

[0115] The simulation verification module 5 is used to perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme and generate corresponding simulation prediction data. If the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme; otherwise, it returns to the intelligent complementary matching module to recalculate or terminates the process.

[0116] The parameter calibration module 6 is used to calculate the change in safety margin of the assembly assembled according to the feasible reuse scheme compared with the standard new product assembly, and generate differentiated use guidance parameters accordingly. The differentiated use guidance parameters include the adjusted maximum working load or the enhanced maintenance and inspection cycle.

[0117] The closed-loop optimization module 7 is used to collect performance monitoring data of reusable assemblies deployed according to feasible reuse schemes during actual operation, compare and analyze this data with the simulation prediction data from step S5, and based on the analysis results, iteratively update the thresholds in the safety access rule base, the weight parameters of the optimization algorithm, and the verification indicators in the performance safety margin standard using a parameter tuning algorithm. This system accurately quantifies defects using a multi-dimensional digital model and achieves intelligent error cancellation and safe matching between parts through optimization algorithms. On this basis, virtual verification, parameter calibration, and closed-loop learning mechanisms are introduced to form a full-chain reliability guarantee covering prediction, verification, calibration, and optimization. Simultaneously, by leveraging cost-benefit assessment and dynamic process compensation decisions, the economic feasibility of reuse is significantly improved while ensuring safety. This systematically realizes the transformation of crane parts from defective discarding to safe, economical, and reliable reuse, achieving the effect of efficient and safe utilization of defective crane parts.

[0118] This application also discloses a computer-readable storage medium that stores a computer program that can be loaded by a processor and executed as described above. The computer-readable storage medium includes, for example, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0119] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. A method for complementary reuse of defective crane components, characterized in that, Includes the following steps: S1. Digital Defect Modeling: Multi-parameter fusion detection is performed on the crane parts that fail the inspection to generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vector, material property parameters, and working condition related offset parameters associated with the actual use scenario of the parts. S2. Safety Access and Resource Pool Construction: Based on a predefined safety access rule base, the digital defect models of each crane component are screened, digital defect models with uncompensable defects are removed, and the remaining digital defect models are stored in the reusable component resource pool. S3. Intelligent Complementary Matching: Based on the technical requirements of the target assembly, with the goal of minimizing the system cumulative error, an optimization algorithm is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool; the complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model; S4. Process Compensation Decision and Generation: Determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, match the fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and perform a cost-benefit evaluation. If the matching is successful and the evaluation passes, generate a composite reuse scheme. If the matching fails or the evaluation fails, return to step S3 for recalculation. The fixed-point compensation process is used to correct the geometric deviation vector and / or working condition related offset parameters of the parts. The composite reuse scheme includes the unique identifier of each crane part, the fixed-point compensation process flow information, the updated digital defect model of the relevant crane parts after executing the fixed-point compensation process, and the overall predicted cumulative error value calculated based on the updated digital defect model. S5. Virtual Verification and Decision-Making: Perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme, and generate corresponding simulation prediction data; if the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme. Otherwise, return to step S3 to recalculate or terminate the process; In step S1, the working condition associated offset parameters include at least: the stress deformation offset for transmission components, the environmental load compensation offset for outdoor components, and the residual stress distribution offset for welded structure components. In step S3, the optimization algorithm is based on a multi-dimensional fitness function for optimization. This function comprehensively evaluates at least two dimensions: geometric deviation complementarity and working condition safety compliance. The evaluation of the working condition safety compliance dimension incorporates the typical working condition load spectrum of the target assembly. The optimization algorithm dynamically adjusts the calculation weights of the two dimensions, geometric deviation complementarity and working condition safety compliance, according to the technical requirements of the target assembly and the typical working condition load spectrum. The multi-dimensional fitness function also evaluates the corrected economic dimension; the optimization algorithm dynamically allocates the calculation weights of the three dimensions of geometric deviation complementarity, working condition safety compliance and corrected economic based on the safety criticality level of the components in the target assembly. In step S4, the fixed-point compensation process adopts a precision dynamic adjustment strategy. Its correction target is not to restore to the design nominal value, but to precisely control and retain a controllable functional deviation that is in the same direction as the required complementary amount and whose absolute value is within the preset precision range, based on the complementary amount required to offset the cumulative error of the system in the matching calculation.

2. The method for complementary reuse of crane parts according to claim 1, characterized in that, In step S2, the security access rule base includes security rejection rules to identify and exclude accessory models with the following uncompensable defects: The material strength in the core load-bearing area is lower than the predetermined proportion of the design nominal value; There are macroscopic or microscopic cracks whose length or depth exceeds a preset threshold; The damage size in the critical load-bearing area exceeds the preset proportion of damage allowed in that area.

3. The method for complementary reuse of crane parts according to claim 1, characterized in that, In step S4, the cost-benefit assessment includes: Estimate the cost of implementing the fixed-point compensation process and compare it with the current new manufacturing cost of the component; Assess whether the expected increase in the number of effective complementary combinations that the component can participate in within the reusable component resource pool, after compensation, meets the preset standard.

4. The method for complementary reuse of crane parts according to claim 1, characterized in that, In step S5, the performance safety margin standard requires that the virtual verification based on finite element analysis must cover the load simulation of the crane under rated operating conditions and at least one non-standard typical operating condition, including heavy load, impact load or specific environmental load conditions.

5. A method for complementary reuse of crane parts according to claim 1, characterized in that, After step S5 outputs a feasible reuse scheme, the following steps are also included: S6. Dynamic parameter calibration: Based on the feasible reuse scheme, calculate the change in safety margin of the assembly formed according to the feasible reuse scheme compared with the standard new product assembly, and generate differentiated use guidance parameters accordingly. The differentiated use guidance parameters include the adjusted maximum working load or the enhanced maintenance and inspection cycle. S7. Closed-loop learning optimization: Collect performance monitoring data of the reusable assembly deployed according to the feasible reuse scheme in actual operation, compare and analyze it with the simulation prediction data in step S5, and based on the analysis results, use parameter tuning algorithm to iteratively update the threshold in the security access rule base, the weight parameters of the optimization algorithm, and the verification indicators in the performance safety margin standard.

6. A crane component defect complementation and reuse system, characterized in that, include: The defect digitization module is used to perform multi-parameter fusion detection on crane parts that fail inspection, and generate a digital defect model of the crane parts. The digital defect model includes geometric deviation vectors, material property parameters, and working condition-related offset parameters associated with the actual use scenario of the parts. The model filtering module is used to filter the digital defect models of various crane parts based on a predefined safety access rule base, remove digital defect models with uncompensable defects, and store the remaining digital defect models in the reusable parts resource pool. The intelligent complementary matching module is used to search and output a complementary combination scheme consisting of multiple crane parts in the reusable parts resource pool based on the technical requirements of the target assembly and with the goal of minimizing the system cumulative error. The complementary combination scheme includes the unique identifier of each crane part, a digital defect model, and the overall predicted cumulative error value of the scheme calculated based on the digital defect model. The process compensation decision module is used to determine whether the predicted cumulative error value of the complementary combination scheme exceeds the allowable range of the technical requirements of the target assembly. If it does, it matches a fixed-point compensation process from the preset process compensation knowledge base based on the complementary combination scheme and performs a cost-benefit evaluation. If the matching is successful and the evaluation is passed, a composite reuse scheme is generated. If the matching is unsuccessful or the evaluation is not passed, it returns to the intelligent complementary matching module for recalculation. The simulation verification module is used to perform physical simulation-based performance verification on the complementary combination scheme or composite reuse scheme and generate corresponding simulation prediction data; if the verification result meets the preset performance safety margin standard, the output is a feasible reuse scheme. Otherwise, return to the intelligent complementary matching module to recalculate or terminate the process; The working condition-related offset parameters include at least: the stress deformation offset for transmission components, the environmental load compensation offset for outdoor components, and the residual stress distribution offset for welded structural components. The optimization algorithm is based on a multi-dimensional fitness function for optimization. This function comprehensively evaluates at least two dimensions: geometric deviation complementarity and working condition safety compliance. The evaluation of the working condition safety compliance dimension incorporates the typical working condition load spectrum of the target assembly. The optimization algorithm dynamically adjusts the calculation weights of the two dimensions, geometric deviation complementarity and working condition safety compliance, according to the technical requirements of the target assembly and the typical working condition load spectrum. The multi-dimensional fitness function also evaluates the corrected economic dimension; the optimization algorithm dynamically allocates the calculation weights of the three dimensions of geometric deviation complementarity, working condition safety compliance and corrected economic based on the safety criticality level of the components in the target assembly. The fixed-point compensation process adopts a precision dynamic adjustment strategy. Its correction target is not to restore to the design nominal value, but to precisely control and retain a controllable functional deviation that is in the same direction as the required complementary amount and whose absolute value is within the preset precision range, based on the complementary amount required to offset the cumulative error of the system in the matching calculation.

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