Multi-source information combined driven special environment additive repair overall planning method
By employing a holistic planning approach driven by multi-source information, the problem of low efficiency and fault tolerance in additive repair under special environments was solved, enabling rapid adaptation of repair materials and processes and improving repair efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-31
AI Technical Summary
In special environments, existing technologies cannot effectively take into account the impact of multi-source information on additive repair, resulting in low repair efficiency and low fault tolerance.
A holistic planning method driven by multi-source information is adopted, which forms additive repair engineering process documents for special environments through a total input module, a damaged part intrinsic data module, a repair material and process data module, a special environment multi-constraint condition module, a repairability evaluation module, and a scan reverse engineering and path planning module.
It improves the efficiency and fault tolerance of additive repair in special environments, and enables rapid matching of repair materials and adaptation of processes.
Smart Images
Figure CN121768547A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of additive repair and remanufacturing technology, and more specifically to a comprehensive planning method for additive repair in special environments driven by multi-source information. Background Technology
[0002] Special environment additive repair refers to the use of additive manufacturing technology to repair damaged parts in confined environments (such as small spaces, high heat, high humidity, surges, inaccessible sites, etc.) in order to restore their geometry, mechanical properties and service life.
[0003] Compared to additive repair in the laboratory, additive repair requires not only considering the intrinsic material properties of the damaged part to match repair materials and the geometric information of the damaged part for 3D reconstruction of the damaged area, but also multiple constraints such as space, time, energy, atmosphere, vibration, and unsteady-state conditions in the special environment. This necessitates evaluating the repairability of the damaged part and adapting the repair process. Currently, for additive repair in special environments with multi-source information, the focus is mainly on repair experience under ideal working conditions. This fails to comprehensively consider the impact of multiple constraints in the special environment on additive repair, resulting in low repair efficiency and low tolerance for errors.
[0004] Therefore, how to improve the efficiency and fault tolerance of additive repair in special environments is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0005] In view of this, the present invention provides a comprehensive planning method for additive repair in special environments driven by multi-source information, which improves the efficiency and fault tolerance of additive repair in special environments.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A multi-source information-driven overall planning method for additive repair in special environments includes a total input module, a damaged part intrinsic data module, a repair material and process data module, a special environment multi-constraint condition module, a repairability evaluation module, a scan-reverse engineering and path planning module, and a total output module. The total input module transmits the specifications, material properties, and post-repair performance requirements of the damaged part to the damaged part intrinsic data module. The damaged part intrinsic data module performs data matching based on the specifications and outputs the original 3D model data to the scan-reverse engineering and path planning module. The scan-reverse engineering and path planning module calculates the defect model data of the damaged part and outputs the path planning data to the output module. The system comprises the following modules: the intrinsic data module for damaged parts transmits material properties and post-repair performance requirements to the repair material and process data module for initial screening to obtain repair process parameters; the special environment multi-constraint information module transmits stored multi-constraint data to the repairability evaluation module to obtain repairability evaluation weight coefficients, which are then transmitted to the repair material and process data module. The repair material and process data module performs secondary optimization of the repair process parameters based on the repairability evaluation weight coefficients. The secondary optimized repair process parameters, along with the damaged part defect model and path planning data, are transmitted to the overall output module, forming an engineering process technology document for additive repair in special environments.
[0007] Preferably, the scanning and path planning module obtains the damaged part defect model data by performing Boolean operations on the original part model and the damaged part defect model, and then slices the damaged part defect model into layers to form path planning data.
[0008] Preferably, the repair material and process data module matches the corresponding metal-based intensive repair material based on the material properties of the damaged part and the performance requirements after repair, and initially screens repair process parameters that meet the performance requirements after repair.
[0009] Preferably, the repairability evaluation module uses fuzzy hierarchical analysis to evaluate the repairability of damaged parts under special environments, obtaining repairability evaluation weight coefficients. The specific calculation process for the relevant weights is as follows: First, determine the weighting coefficients under specific constraints. The calculation formula is as follows: ; In the formula, These are the weight values for specific constraints. l , M and N It is its triangular fuzzy number, and l Represents the minimum value among trigonometric numbers. m It is the median value of a trigonometric number. μ It is the maximum value among trigonometric numbers. Finally, the weight coefficients under specific constraints are normalized. .
[0010] Preferably, the repair material and process data module specifically includes: First, considering the special environmental constraints, the actual performance values after repair are obtained. The calculation formula is as follows: ; In the formula: These are the post-repair performance values that can be obtained by performing repairs in a real-world field environment. It is the minimum value among the weighting coefficients. The performance values after the restoration of the laboratory environment in Module 2; determination Does it meet the requirements? If the required service performance is met, then the corresponding repair materials and process parameters are feasible.
[0011] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method for overall planning of additive repair in special environments driven by multi-source information. It can match repair materials and adapt processes for damaged parts based on multi-source information in special environments and quickly generate additive repair engineering process documents. Attached Figure Description
[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0013] Figure 1 This is a schematic diagram of the structure provided by the present invention. Detailed Implementation
[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0015] This invention discloses a multi-source information-driven overall planning method for additive repair in special environments, such as... Figure 1As shown, the system includes a total input module, a damaged part intrinsic data module, a repair material and process data module, a special environment multi-constraint condition module, a repairability evaluation module, a scan-based reverse engineering and path planning module, and a total output module. The total input module transmits the specifications, material properties, and post-repair performance requirements of the damaged part to the damaged part intrinsic data module. The damaged part intrinsic data module performs data matching based on the specifications and outputs the original 3D model data to the scan-based reverse engineering and path planning module. The scan-based reverse engineering and path planning module calculates the damaged part defect model data and generates path planning data, which is then output to the total output module. The damaged part intrinsic data module... The intrinsic data module transmits material properties and post-repair performance requirements to the repair material and process data module for initial screening to obtain repair process parameters. The special environment multi-constraint information module transmits stored multi-constraint data to the repairability evaluation module to obtain repairability evaluation weight coefficients, which are then transmitted to the repair material and process data module. The repair material and process data module performs secondary optimization of the repair process parameters based on the repairability evaluation weight coefficients. The secondary optimized repair process parameters, along with the damaged part defect model and path planning data, are transmitted to the overall output module, forming an engineering process technology document for additive repair in special environments. This is mainly achieved through the use of the damaged part intrinsic data module, repair material and process data module, special environment multi-constraint data module, and scan-based reverse engineering and path planning module. Interfaces between these modules facilitate the transmission of multi-source information, and the algorithms of each module combine to realize a comprehensive planning method for additive repair in special environments driven by multi-source information, rapidly forming an engineering process repair scheme.
[0016] Main input module: Used to input the specifications, material properties, and performance requirements of the damaged parts to be repaired.
[0017] Module 1: Intrinsic Data Information of Damaged Parts: Includes basic data information such as material properties, specifications, service performance, and three-dimensional data models of typical parts.
[0018] Module Two: Repair Materials and Process Data Information Module: Contains related data information on "Intensive Repair Materials - Repair Process - Post-Repair Performance".
[0019] Module 3 Special Environment Multi-Constraint Information Module: Contains multi-constraint data information for special environments, including space, environment, energy, and unsteady state conditions.
[0020] Module 4 Repairability Evaluation Module: Combining repair material and process data with data on multiple constraints in special environments, this module uses a weight matrix to autonomously recommend additive repair materials and processes for special environments.
[0021] Module 5: Reverse Sweep and Path Planning Module: Combining the original 3D modeling information data from the intrinsic data of the damaged part with the 3D modeling information data of the damaged part from the scan, Boolean operations are performed to obtain the defect model information data, and then slicing and path planning are carried out.
[0022] Overall Output Module: This module summarizes and outputs valid data from all modules. Based on the overall input and data from each module, it outputs a special environment additive repair engineering process document containing repair materials, repair processes, and repair paths.
[0023] Interface 1: The information transmission interface between the main input module and the intrinsic data information module of damaged parts in module 1, used to clarify the specifications and original three-dimensional model of the damaged parts.
[0024] Interface 2: The information transmission interface between Module 1 (Damaged Part Intrinsic Data Information Module) and Module 2 (Repair Material and Process Data Information Module). It is used to perform the initial screening of repair material and repair process information by combining the material properties of the damaged part and the performance requirements after repair (without considering multiple constraints in special environments).
[0025] Interface 3: The information transmission interface between Module 3 Special Environment Multi-Constraint Information Module and Module 4 Repairability Evaluation Module. It is used to evaluate the repairability of damaged parts under special environments by inputting special environment multi-constraint information data, and obtain the repairability evaluation weight coefficient.
[0026] Interface 4: The interface between Module 4 Repairability Evaluation Module and Module 2 Repair Material and Process Data Information Module. It is used to make secondary optimization recommendations on the initial selection information of repair materials and processes through repairability evaluation weight coefficients (considering multiple constraints in special environments).
[0027] Interface 5: The interface between the module for intrinsic data information of damaged parts and the module for reverse engineering and path planning. It is used to combine the original 3D modeling data of the parts and the 3D modeling of the damaged parts to obtain the defect model of the damaged parts and to plan the repair path.
[0028] Interface 6: The interface between the repair materials and processes, repair path planning information data and the overall output module, used to integrate multi-source information data to realize additive repair engineering process documents for special environments.
[0029] The specific operation steps of this invention are as follows: Step 1: Construction of a platform for a comprehensive planning method for additive repair in special environments driven by multi-source information. Based on the implementation platform (PC, tablet, mobile phone, etc.), computer programming languages (C, C++, Java, Python, etc.) are used to assemble different modules and their respective interfaces, realizing the input, output, transmission, calculation, and storage of data for each module.
[0030] Step Two: Inputting Effective Information Data for Different Modules. Module One: Inputting data for the intrinsic data of damaged parts. Input information includes basic data such as material properties, specifications, service performance, and 3D data model. Module Two: Inputting data for repair materials and processes. Input repair materials (including intensive repair materials for iron-based, titanium-based, and aluminum-based repairs), repair processes (including specific repair process parameters under energy beam forms such as laser, electric arc, and plasma), and post-repair performance correlation data. Module Three: Inputting data for special environment multi-constraint conditions. Input multi-constraint data for special environments, including space, environment, energy, and unsteady-state conditions.
[0031] Step 3: Inputting information into the main input module. Enter the specifications, material properties, and post-repair performance requirements of the damaged part to be repaired.
[0032] Step 4: Acquisition of 3D Model and Repair Path Planning Information for Damaged Part. Using the specification and model information of the damaged part to be repaired from the total input information, data matching is performed in the intrinsic data information module for damaged parts (Module 1). After matching, the original 3D model data of the part to be repaired is transferred to the inverse surface analysis and path planning module (Module 5) via interface 5. In Module 5, Boolean operations are performed between the original part model and the damage model to obtain the defect model data of the damaged part. The defect model is then layered and sliced to form path planning data, which is transferred to the total output module via interface 5.
[0033] Step 5: Initial screening of repair materials and repair process information. Using Interface 2, the material properties of the damaged parts and the post-repair performance requirements from the total input information are transmitted to Module 2, the Repair Materials and Process Data Information Module. In Module 2, based on the material properties of the damaged parts (e.g., iron-based, titanium-based, copper-based, magnesium-based, aluminum-based), and combined with the post-repair performance requirements, corresponding integrated repair materials (such as iron-based, titanium-based, copper-based, magnesium-based, and aluminum-based materials) are matched to initially screen repair process parameters that meet the post-repair performance requirements.
[0034] Step Six: Repairability Evaluation of Damaged Parts Based on Multiple Constraints in Special Environments. Data on multiple constraints related to space, environment, energy, and unsteady states in the special environment from Module Three (Special Environment Multiple Constraints Information Module) are transmitted to the information transmission interface of Module Four (Repairability Evaluation Module) via Interface 3. Fuzzy hierarchical analysis is used to evaluate the repairability of damaged parts under special environments, yielding repairability evaluation weight coefficients.
[0035] The specific calculation process for the relevant weights is as follows: First, determine the weights under specific constraints. The calculation formula is as follows: ; In the formula: These are the weight values for specific constraints. l , M and N It is its triangular fuzzy number, and l represents the minimum value of a trigonometric number, and m is the median value of a trigonometric number. μ It is the maximum value among trigonometric numbers. Finally, the weights under specific constraints are normalized as follows: ; Step 7: Secondary optimization of repair materials and repair process information data. The repairability evaluation weight matrix under special conditions obtained in Step 6 is transmitted to Module 2, Repair Materials and Process Data Information Module, through Interface 4 to perform secondary optimization and recommendation of the initial selection information of repair materials and processes (considering multiple constraints under special conditions).
[0036] The specific optimization process is as follows: First, considering special environmental constraints, the actual performance values after repair are obtained, and the calculation formula is as follows:
[0037] In the formula, These are the post-repair performance values that can be obtained by performing repairs in a real-world field environment. It is the minimum value among the weighting coefficients. This represents the performance value of the laboratory environment after repair in Module 2.
[0038] determination Does it meet the requirements? The input for module one is the service performance requirements of the damaged part after repair. If these requirements are met, the corresponding repair materials and process parameters are feasible.
[0039] Step 8: The main output module generates engineering process documents for additive repair in special environments. The damaged part defect model data and path planning data obtained in Step 4, and the repair material and repair process data considering multiple constraints in special environments obtained in Step 7 are transferred to the main output module through interface 6. The main output module generates engineering process documents for additive repair in special environments. Specific implementation examples: Step 1: Construction of a comprehensive planning platform for additive repair in special environments driven by multi-source information. The embodiment uses C language to assemble the programming languages for different modules and their respective interfaces, realizing the input, output, transmission, calculation, and storage of data for each module.
[0041] Step 2: Inputting valid information data from different modules.
[0042] Module 1: Input of intrinsic data information for damaged parts. Input information includes basic data such as material properties, specifications, service performance, and 3D data model.
[0043] Module Two: Data Input for Repair Materials and Processes. Input the repair materials (including integrated repair materials for iron-based, titanium-based, and aluminum-based repairs), the repair process (including specific repair process parameters for energy beam methods such as laser, electric arc, and plasma), and post-repair performance correlation data. Information is summarized in Table 1 below.
[0044] Table 1 Summary of Input Information for Module 2
[0045] Module 3: Input of Data for Special Environment Multi-Constraint Information. Input data on multiple constraints related to the special environment, including spatial, environmental, energy, and unsteady-state conditions. Typical constraints for typical special environments can be pre-entered and saved in Module 3; field-specific constraints are input based on field measurement results. A summary of the typical special constraints that need to be input is shown in Table 2.
[0046] Table 2 Summary of Typical Special Environmental Constraint Information Input
[0047] Step 3: Inputting information into the main input module. Input the specifications, material properties, and post-repair performance requirements of the damaged part to be repaired. The main input module serves as the boundary condition for data transfer and calculation between modules. In this example, the input is a 17CrNiMo6H gear part, with iron-based material properties, and the post-repair performance requirements are a hardness of not less than 40 HRC and a yield strength of not less than 700 MPa.
[0048] Step 4: Obtaining the 3D model of the damaged part and repair path planning information. Using the specification and model information of the damaged part to be repaired from the total input information, data matching is performed in Module 1, the intrinsic data information module of the damaged part. After matching, the 17CrNiMo6H part to be repaired...
[0049] The original 3D model data of the gear is transmitted to the scanning and path planning module 5 through interface 4. In module 5, the damage model data of the damaged part is calculated to form path planning data. The path planning result is 5 layers with a layer thickness of 1mm. The relevant data information is transmitted to the main output module through interface 5.
[0050] Step 5: Initial Screening of Repair Material and Process Information. Using Interface 1, the material properties of the damaged part and the post-repair performance requirements are transmitted from the total input information to Module 2, the Repair Material and Process Data Information Module. In Module 2, integrated repair materials are matched, and repair process parameters that meet the post-repair performance requirements are initially screened. The process parameters initially selected in Module 2 to meet the post-repair performance requirements of the 17CrNiMo6H gear are: laser repair process, high-strength martensitic iron-based powder as the repair material, and scanning speed of 10mm / s with a power of 1.5KW, 8mm / s with a power of 1.2KW, and 12mm / s with a power of 1.7KW. The performance after repair with different process parameters can reach hardness of 400HRC and yield strength of 710MPa, hardness of 420HRC and yield strength of 750MPa, and hardness of 430HRC and yield strength of 800MPa, respectively.
[0051] Step Six: Repairability Evaluation of Damaged Parts Based on Multiple Constraints in Special Environments. Data on multiple constraints related to space, environment, energy, and unsteady states in the special environment from Module Three (Special Environment Multiple Constraints Information Module) is transmitted to the information transmission interface of Module Four (Repairability Evaluation Module) via Interface 2. Repairability evaluation of the damaged parts under special environments is performed, and repairability evaluation weight coefficients are obtained. Example 1: The 17CrNiMo6H gear needs to be repaired in a high humidity and high heat environment (75% relative humidity, 40℃). Simultaneously, the site can only support the continuous and stable use of a 1.5KW laser. Based on the above information, a repairability evaluation is performed, with a weight evaluation coefficient of 0.92. The non-weight evaluation coefficient is 1 for energy density not exceeding 1.5KW and 0 for energy density exceeding 1.5KW.
[0052] Step 7: Secondary optimization of repair materials and repair process information data. The repairability evaluation weight matrix obtained in Step 6 under special conditions is transmitted to Module 2 (Repair Materials and Process Data Information Module) through Interface 3 to perform secondary optimization and recommendation of the initial selection information of repair materials and processes (considering multiple constraints under special conditions). Combining the weighted evaluation and yes / no evaluation results, the repair materials and processes for additive repair under special conditions in the embodiment can be realized as follows: the repair material is high-strength martensitic iron-based powder, the scanning speed is 8mm / s, and the power is 1.2KW.
[0053] Step 8: The main output module generates engineering process documents for additive repair in special environments. The damaged part defect model data and path planning data obtained in Step 4, and the repair material and repair process data considering multiple constraints in special environments obtained in Step 7 are transferred to the main output module through interface 5. The main output module generates engineering process documents for additive repair in special environments.
[0054] This invention provides a multi-source information-driven overall planning method for additive repair in special environments. It can match repair materials and adapt processes for damaged parts based on multi-source information in special environments, and quickly generate additive repair engineering process documents. Based on intrinsic data modules of damaged parts, repair material and process data modules, multi-constraint data modules for special environments, and inverse scanning and path planning modules, the method achieves multi-source information transmission through interfaces between modules. Combining the algorithms of each module, it realizes a multi-source information-driven overall planning method for additive repair in special environments, quickly generating engineering process repair schemes. Depending on the implementation terminal (PC, tablet, mobile phone, etc.), different computer programming languages (C, C++, Java, Python, etc.) can be used to assemble the modules and their respective interfaces, realizing the input, output, transmission, calculation, and storage of data from each module.
[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant parts can be found in the method section.
[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A comprehensive planning method for additive repair in special environments driven by multi-source information, characterized in that, It includes a total input module, a damaged part intrinsic data module, a repair material and process data module, a special environment multi-constraint condition module, a repairability evaluation module, a scan reverse calculation and path planning module, and a total output module. The total input module transmits the specifications, material properties, and post-repair performance requirements of the damaged part to the damaged part intrinsic data module. The intrinsic data module of the damaged part performs data matching based on the specifications and outputs the original three-dimensional model data to the scanning inverse calculation and path planning module. The scanning inverse calculation and path planning module calculates the defect model data of the damaged part and outputs the path planning data to the total output module. The intrinsic data module of the damaged part transmits the material properties and post-repair performance requirements information to the repair material and process data module for initial screening and to obtain repair process parameters. The special environment multi-constraint information module transmits the stored multi-constraint data to the repairability evaluation module to obtain the repairability evaluation weight coefficient, and then transmits it to the repair material and process data module. The repair material and process data module performs secondary optimization on the repair process parameters based on the repairability evaluation weight coefficient. The secondary optimized repair process parameters, along with the damaged part defect model and path planning data, are transmitted to the total output module, where an engineering process technology document for additive repair in special environments is formed.
2. The overall planning method for additive repair in special environments driven by multi-source information as described in claim 1, characterized in that, The scanning and path planning module obtains the damaged part defect model data by performing Boolean operations on the original part model and the damaged part defect model, and then slices the damaged part defect model into layers to form path planning data.
3. The overall planning method for additive repair in special environments driven by multi-source information as described in claim 1, characterized in that, The repair material and process data module matches the corresponding metal-based intensive repair materials based on the material properties of the damaged part and the performance requirements after repair, and initially screens repair process parameters that meet the performance requirements after repair.
4. The overall planning method for additive repair in special environments driven by multi-source information as described in claim 1, characterized in that, The repairability evaluation module uses fuzzy hierarchical analysis to evaluate the repairability of damaged parts under special environments, obtaining repairability evaluation weight coefficients. The specific calculation process for the relevant weights is as follows: First, determine the weighting coefficients under specific constraints. The calculation formula is as follows: ； In the formula, These are the weight values for specific constraints. l , M and N It is its triangular fuzzy number, and l Represents the minimum value among trigonometric numbers. m It is the median value of a trigonometric number. μ It is the maximum value among trigonometric numbers. Finally, the weight coefficients under specific constraints are normalized. .
5. The overall planning method for additive repair in special environments driven by multi-source information as described in claim 4, characterized in that, The repair material and process data module specifically includes: First, considering the special environmental constraints, the actual performance values after repair are obtained. The calculation formula is as follows: ； In the formula: These are the post-repair performance values that can be obtained by performing repairs in a real-world field environment. It is the minimum value among the weighting coefficients. The performance values after the restoration of the laboratory environment in Module 2; determination Does it meet the requirements? If the required service performance is met, then the corresponding repair materials and process parameters are feasible.