A control method, storage medium, and system for welding additive manufacturing.

Abstract

This invention discloses a control method, storage medium, and system for welding additive manufacturing, relating to the field of welding additive manufacturing. The control method includes scanning the workpiece to be repaired and its in-service mating part, establishing a base model and a current morphology model respectively; acquiring the load and boundary conditions of the workpiece to be repaired; generating an initial digital repair body, and then performing virtual assembly and fit clearance analysis between it and the mating part model; based on the clearance analysis results, as well as the load and boundary conditions, performing stress topology optimization calculations on the initial digital repair body to generate a target digital repair body with optimal performance; finally, controlling the system to perform welding deposition. This invention breaks through the traditional repair mode that only restores geometry, realizing intelligent repair based on multi-source data fusion and performance-driven principles. It can significantly improve the fit performance and service life of the repaired components, effectively preventing premature failure.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of welding additive manufacturing, in particular to a welding additive manufacturing control method, a storage medium and a system. BACKGROUND

[0002] Additive repair (also known as remanufacturing repair) technology is an important part of advanced manufacturing and modern service industry. It restores the geometry and performance of damaged parts through laser cladding, plasma cladding, electric arc additive, etc. based on the principle of welding "additive" method, which is a key technology to realize efficient recycling of resources.

[0003] In the prior art, the method for determining the repaired workpiece pattern mainly depends on restoring its original geometry. The standard process is usually as follows: obtain the point cloud data of the workpiece to be repaired by a three-dimensional scanning device, compare it with the original computer-aided design model of the workpiece, and calculate the material defect area; then, generate a digital repair body for filling the area, which has the same shape as the original model; finally, based on the repair body, plan the path and deposit the material by the additive manufacturing equipment.

[0004] However, this "geometric restoration" type of repair method has two significant limitations:

[0005] (1) Ignoring the system matching relationship. The traditional repair method considers the workpiece to be repaired in isolation, ignoring its pairing relationship with the "mating part" in the equipment. In actual service process, the mating part may also have wear or deformation. For example, on a metallurgical rolling mill, the neck of the roller to be repaired may have a deformed inner hole of the bearing seat as its mating part; in the field of energy and power, the blade root of the turbine blade to be repaired may have a worn contact surface of the mortise groove of the blade disc as its mating part. If the workpiece is simply repaired to the original size, it may not match well with the deformed mating part, resulting in an increase in local contact stress, abnormal wear or overheating, and causing the repaired assembly to fail again in a short time.

[0006] (2) Lack of mechanical performance guidance. The traditional repair body generation only considers "geometric filling" and does not deeply analyze the mechanical performance of the repair body under real service load. The bonding interface between the repair body and the base material is often a mechanical weak link, which is prone to crack and expand under cyclic load, resulting in repair body peeling or breaking, and causing complete failure of the repair.

[0007] Therefore, there is an urgent need in the art for an additive repair control method that can go beyond simple geometric restoration and make intelligent decisions and optimizations from the system matching relationship and service performance level, in order to solve the industry pain points of short service life and low reliability of the repaired assembly. SUMMARY

[0008] The application aims to provide a welding additive manufacturing control method, a storage medium and a system, which aim to overcome the problems of poor assembly performance after repair, stress concentration in the repair area and short service life caused by only focusing on single geometric restoration in the existing additive repair technology.

[0009] To achieve the purpose, the application provides the following technical solutions.

[0010] A welding additive manufacturing control method comprises the following steps:

[0011] Step S1: three-dimensional scanning is performed on a workpiece to be repaired to establish a base model of the workpiece to be repaired; the base model is compared with an original design model of the workpiece to be repaired to determine a material defect area;

[0012] Step S2: three-dimensional scanning is performed on a counterpart of the workpiece to be repaired in service to establish a current topography model of the counterpart;

[0013] Step S3: the load and boundary conditions borne by the workpiece to be repaired in service are obtained;

[0014] Step S4: based on the original design model, an initial digital repair body covering the material defect area is generated;

[0015] Step S5: the initial digital repair body is combined with the base model to obtain a workpiece model with the initial digital repair body; the workpiece model is virtually assembled with the current topography model to perform a fit clearance analysis;

[0016] Step S6: based on the results of the fit clearance analysis and the load and boundary conditions, stress topology optimization calculation is performed on the initial digital repair body to generate a target digital repair body; wherein the stress topology optimization calculation takes balanced stress distribution of the target digital repair body and a combined interface of the target digital repair body and the workpiece to be repaired as one of the optimization objectives;

[0017] Step S7: based on the target digital repair body, additive manufacturing path planning is generated;

[0018] Step S8: a welding additive manufacturing system is controlled to deposit welding material in the material defect area of the workpiece to be repaired according to the path planning to form an actual repair body.

[0019] Further, the step S3 specifically comprises the following sub-steps:

[0020] Step S31: a reference static load of the workpiece to be repaired is obtained;

[0021] Step S32: Obtain the historical operation data of the workpiece to be repaired in the normal service period, and construct a dynamic load spectrum reflecting the actual working condition;

[0022] Step S33: Establish a simplified simulation model of the process by the process parameters of the equipment served by the workpiece to be repaired, and perform numerical calculation through the simplified simulation model to back-propagate the external load of the workpiece to be repaired;

[0023] Step S34: Cross-verify and data-fuse the reference static load, dynamic load spectrum and external load obtained in steps S31 to S33 to construct a high-fidelity comprehensive load model; at the same time, according to the mechanical structure of the equipment, the boundary conditions of the workpiece to be repaired are clearly defined in the simplified simulation model.

[0024] Further, the stress topology optimization calculation in step S6 includes:

[0025] Step S61: Perform finite element analysis to identify the stress concentration area of the initial digital repair body and the combination interface thereof with the workpiece to be repaired;

[0026] Step S62: On the premise of ensuring no interference with the mating part, iteratively calculate and adjust the shape of the initial digital repair body with the optimization objective of minimizing the peak stress and uniformizing the stress distribution.

[0027] Further, in step S62, the optimization objective of minimizing the peak stress and uniformizing the stress distribution includes: minimizing the contact stress between the target digital repair body and the current topography model; minimizing the peak stress of the target digital repair body itself and the stress concentration at the combination interface with the workpiece to be repaired.

[0028] Further, the gap analysis in step S5 is used to calculate the gap distribution between the initial digital repair body and the current topography model, and the gap distribution is used as a constraint condition for the stress topology optimization calculation in step S6 to ensure that the optimized target digital repair body forms a conformal fit with the mating part.

[0029] Further, in step S6, the surface shape of the generated target digital repair body is constructed to form a conformal contact with the current topography model of the mating part to achieve the minimization and uniformization of the contact stress.

[0030] Further, after step S8, it further includes:

[0031] Step S9: Perform three-dimensional scanning on the repaired workpiece to obtain a repaired workpiece model;

[0032] Step S10: comparing the above repaired workpiece model with the above target digital restoration, verifying the repair accuracy; and / or virtually assembling and simulating the above repaired workpiece model and the current topography model of the counterpart, verifying the fitting performance.

[0033] A welding additive manufacturing system for performing the control method as described above, comprising:

[0034] A three-dimensional scanning device for acquiring three-dimensional scanning data of the workpiece to be repaired and the counterpart;

[0035] A data processing and control unit for processing the three-dimensional scanning data to establish a model; for performing virtual assembly analysis and stress topology optimization calculation to generate a target digital restoration; for generating an additive manufacturing path planning; for accessing or receiving a database containing the above workpiece service load and boundary conditions or user input; a welding additive manufacturing execution mechanism for depositing welding material under the control of the above data processing and control unit.

[0036] Further, the welding additive manufacturing execution mechanism comprises a multi-axis robot arm, and a laser cladding head, a plasma cladding head or an electric arc additive welding head mounted on the multi-axis robot arm.

[0037] A computer readable storage medium having a computer program stored thereon, when the computer program is executed by a processor, the control method as described above is realized.

[0038] Compared with the prior art, the present application has the following beneficial effects:

[0039] Firstly, the present application performs stress topology optimization calculation on the initial digital restoration based on the results of the fitting gap analysis and the load and boundary conditions to generate a target digital restoration. The generated target digital restoration not only fills the defect, but also intelligently optimizes the shape, so as to simultaneously satisfy the best fitting relationship with the counterpart and the best mechanical performance.

[0040] Secondly, in the present application, the load in step S3 is obtained through multi-source data fusion, specifically including: acquiring reference static load, dynamic load spectrum and external load inversely deduced through process parameters, and cross verifying and fusing these data to construct a high-fidelity comprehensive load model. This multi-source data fusion method ensures the authenticity and reliability of the load model, provides key input for subsequent stress topology optimization, and thus realizes performance-driven intelligent repair.

[0041] Thirdly, the present application optimizes the contact stress through conformal fitting and eliminates internal stress concentration through topology optimization, thereby improving the durability and reliability of the repaired workpiece under real working conditions from two dimensions.

[0042] Fourth, the present invention deeply integrates digital twins, multiphysics simulation and optimization algorithms into the additive repair process, enabling the welding additive manufacturing system to have intelligent capabilities of perception, analysis, decision-making and execution. Attached Figure Description

[0043] Figure 1 This is a flowchart of the control method in this invention.

[0044] Figure 2 This is a block diagram of the control system in this invention. Detailed Implementation

[0045] Specific embodiments of the present invention will now be described with reference to the accompanying drawings. Many details are described below to provide a comprehensive understanding of the invention; however, those skilled in the art will be able to implement the invention without these details.

[0046] like Figure 1 and Figure 2 As shown, a control method for welding additive manufacturing includes the following steps:

[0047] Step S1: Perform a 3D scan of the workpiece to be repaired to establish a base model of the workpiece; compare the base model with the original design model of the workpiece to be repaired to determine the material defect area.

[0048] Specifically, a 3D scanning device is used to perform a full-range scan of the disassembled workpiece to be repaired (such as a rolling mill roll) to obtain point cloud data of its current state. The point cloud data is then encapsulated into a triangular facet model, i.e., a base model, through a data processing and control unit. This base model is then aligned and compared with the original design model of the workpiece to be repaired obtained from the manufacturer to determine the material defect areas (such as uniform wear or localized pits), and the geometric parameters, such as the volume and depth of the material defect areas, are accurately calculated, generating accurate geometric input for the subsequent repair.

[0049] As a preferred method, the coordinate alignment process employs a best-fit algorithm, prioritizing the matching of undamaged reference areas (such as the end face of the roller body) on the workpiece to be repaired, to ensure the accuracy of the comparison.

[0050] Step S2: Perform a three-dimensional scan on the mating parts that the workpiece to be repaired was paired with during service, and establish the current morphological model of the mating parts.

[0051] Specifically, a 3D scanning device is used to perform a 3D scan of the mating component (such as a bearing housing) that the workpiece to be repaired is paired with during service. Through a data processing and control unit, a current morphological model of the mating component is established based on the 3D scan data. This step breaks through the limitations of traditional on-the-fly repair, expanding the repair target from the isolated workpiece to be repaired to the entire mating pair. Obtaining the current morphology of the mating component is the foundation for achieving adaptive repair.

[0052] Step S3: Obtain the loads and boundary conditions that the workpiece to be repaired has experienced during its service.

[0053] In one specific embodiment, step S3 specifically includes the following sub-steps:

[0054] Step S31: Obtain the reference static load of the workpiece to be repaired. Specifically, retrieve the rated load parameters of the workpiece to be repaired as specified in the design phase from the equipment design data or technical manual stored in the database. This serves as the reference static load, providing a theoretical reference value and initial direction for load analysis.

[0055] Taking the workpiece to be repaired as an example of the rolling mill rolls, its rated load parameters include, but are not limited to, rolling force, torque, bending moment, etc.

[0056] Step S32: Obtain dynamic service data of the workpiece to be repaired. Specifically, extract historical operating data of the workpiece during its normal service cycle from the production line monitoring system related to the workpiece. This historical operating data constitutes a dynamic load spectrum reflecting the actual working conditions.

[0057] Taking the workpiece to be repaired as a rolling mill roll as an example, the vibration sensor data installed on the bearing housing or equipment body is analyzed to identify its characteristic vibration frequency and amplitude, thereby reflecting the dynamic load components; the temperature sensor data is analyzed to obtain the thermal cycling characteristics of the workpiece during service, and the influence of the temperature field on material properties and thermal stress is considered.

[0058] Step S33: Infer external loads from process parameters. Specifically, based on the specific process parameter records of the equipment used by the workpiece to be repaired (such as the strength, thickness, and reduction of the rolled plate), a simplified simulation model of the process is established in finite element analysis software. Numerical calculations are performed using this simplified simulation model to infer the quantifiable external loads (including load magnitude, direction, and period of change) experienced by the workpiece at a specific process instant. Examples include: the total rolling force and its distribution calculated using a rolling force model; the torque converted from drive power and rotational speed; and the support reaction force determined by structural equilibrium relationships.

[0059] This method transforms macroscopic process parameters into specific external forces acting on components, providing accurate boundary inputs for subsequent finite element analysis. These inversely derived external loads complement and validate the baseline static loads and dynamic load spectra.

[0060] Step S34: Data Fusion and Boundary Condition Definition. Specifically, the baseline static load, dynamic load spectrum, and external load obtained in steps S31 to S33 are cross-validated and fused to construct a high-fidelity comprehensive load model. Simultaneously, based on the mechanical structure of the equipment, the boundary conditions of the workpiece to be repaired are clearly defined in the simplified simulation model. For example, the contact relationship between the roll necks at both ends of the roll and the bearing housing is simplified to a fixed hinge or elastic support.

[0061] As can be seen, in this specific embodiment, the load mentioned in step S3 is obtained through multi-source data fusion, specifically including: acquiring the reference static load, dynamic load spectrum and external load derived from process parameters, and cross-validating and fusing these data to construct a high-fidelity comprehensive load model.

[0062] Understandably, depending on the actual operating conditions and data availability, the load obtained in step S3 can be determined based on one or more combinations of the aforementioned reference static load, dynamic load spectrum, and external load. For example, when data sources are limited, analysis can primarily rely on the reference static load and the external load derived from process parameters; for vibration-sensitive components, the focus can be on the analysis of the dynamic load spectrum. The core principle is that the obtained load information can be used to reflect the key mechanical conditions of the workpiece during service, thereby driving the subsequent stress topology optimization process.

[0063] Step S4: Based on the original design model, generate an initial digital restoration that covers the material defect area.

[0064] Specifically, in CAD or dedicated repair software, based on the ideal contour of the workpiece to be repaired in the original design model, a preliminary three-dimensional model with a regular shape that completely fills the material defect area determined in step S1 is generated through Boolean operations or surface extension. This model, known as the "initial digital repair body," provides a geometric starting point and benchmark for subsequent optimization.

[0065] Step S5: Combine the initial digital repair body with the base model to obtain a workpiece model with the initial digital repair body; virtually assemble the workpiece model with the current morphology model and perform a fit clearance analysis.

[0066] Specifically, in the simulation software, the initial digital repair body generated in step S4 is combined with the defective area of ​​the base model obtained in step S1 to form a complete, repaired workpiece model. Subsequently, the workpiece model is virtually assembled with the current morphology model of the mating part obtained in step S2 in the simulation software.

[0067] This step simulates the fit between the workpiece to be repaired and the mating parts in the simulation software in advance. The fit clearance analysis can quantitatively calculate whether there are problems such as interference fit (resulting in assembly difficulties) or excessive local clearance (resulting in uneven load-bearing) between the initial digital repair body and the mating parts, providing a clear direction for improvement in subsequent optimization.

[0068] Step S6: Based on the results of the fit clearance analysis and the load and boundary conditions, perform stress topology optimization calculation on the initial digital repair body to generate a target digital repair body; wherein, the stress topology optimization calculation takes balancing the stress distribution of the target digital repair body and its interface with the workpiece to be repaired as one of the optimization objectives.

[0069] Specifically, the workpiece model with the initial digital repair body, the current morphology model, and the comprehensive load model and boundary conditions obtained in step S34 are imported into finite element analysis and topology optimization software (such as Altair OptiStruct, ANSYS Topology Optimization, etc.).

[0070] The optimization objectives are set as follows: minimize the contact stress between the target digital repair body and the current morphology model to ensure uniform contact stress distribution; minimize the peak stress of the target digital repair body itself, as well as the stress concentration at the interface between the target digital repair body and the workpiece to be repaired, to prevent fatigue cracks from initiating there.

[0071] Set constraints: Use the gap distribution calculated in step S5 as constraints to ensure that the optimized target digital repair body can form a conformal fit with the bearing.

[0072] Then, the finite element analysis and topology optimization software continuously adjusts the material layout of the initial repair body through iterative calculations while satisfying the constraints, and finally obtains a mechanically optimal target digital repair body.

[0073] This step integrates geometric fit optimization with structural mechanics optimization, generating a non-intuitive, optimal target digital repair that cannot be obtained by traditional methods.

[0074] Step S7: Generate an additive manufacturing path plan based on the target digital repair body.

[0075] Specifically, the optimized target digital repair body is sliced ​​and layered, and the motion trajectory (such as G-code) of the welding additive manufacturing actuator of the welding additive manufacturing system is generated. At the same time, process parameters (such as laser power, powder feed rate, scanning speed, etc. for laser cladding) are set for each layer path to ensure the quality of solder deposition.

[0076] Step S8: Control the welding additive manufacturing system to deposit welding material in the material defect area of ​​the workpiece to be repaired according to the path planning, so as to form an actual repair body.

[0077] Specifically, the welding additive manufacturing actuator of the control welding additive manufacturing system strictly follows the path planning generated in step S7 to deposit solder that matches the base material layer by layer in the defect area of ​​the workpiece to be repaired, and finally forms an actual repair body that is consistent with the shape of the target digital repair body.

[0078] Step S9: Perform a 3D scan on the repaired workpiece to obtain a model of the repaired workpiece.

[0079] Step S10: Compare the actual repair body in the repair workpiece model with the target digital repair body to verify the repair accuracy (such as whether the dimensional accuracy is within the tolerance range).

[0080] Preferably, in step S10, the repair workpiece model and the current morphology model of the mating part are further subjected to virtual assembly simulation to verify the fitting performance.

[0081] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method described above.

[0082] like Figure 1 and Figure 2 As shown, the present invention also discloses a welding additive manufacturing system for performing the control method described above, the system comprising:

[0083] 3D scanning equipment is used to acquire 3D scanning data of the workpiece to be repaired and its mating parts;

[0084] The data processing and control unit is used to process 3D scanning data and build models; to perform virtual assembly analysis and stress topology optimization calculations to generate target digital repair bodies; to generate additive manufacturing path planning; and to access or receive databases or user inputs containing the service loads and boundary conditions of the workpiece. The welding additive manufacturing execution mechanism is used to complete the deposition of welding materials under the control of the data processing and control unit.

[0085] Preferably, the welding additive manufacturing execution mechanism includes, but is not limited to, a multi-axis robotic arm and a laser cladding head mounted on the multi-axis robotic arm. Of course, in practical use, those skilled in the art can replace the laser cladding head with a plasma cladding head or an arc additive welding head, etc., as needed, without making too many restrictions here.

[0086] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A control method of a welding additive manufacturing, characterized by, The method comprises the following steps: Step S1: performing three-dimensional scanning on the workpiece to be repaired to establish a base model of the workpiece to be repaired; comparing the base model with an original design model of the workpiece to be repaired to determine a material defect area; Step S2: performing three-dimensional scanning on a counterpart of the workpiece to be repaired in service to establish a current topography model of the counterpart; Step S3: obtaining a load and boundary condition borne by the workpiece to be repaired in service; Step S4: generating an initial digital repair body covering the material defect area based on the original design model; Step S5: combining the initial digital repair body with the base model to obtain a workpiece model with the initial digital repair body; virtually assembling the workpiece model with the current topography model to perform a fit clearance analysis; Step S6: performing stress topology optimization calculation on the initial digital repair body based on a result of the fit clearance analysis and the load and boundary condition to generate a target digital repair body; wherein the stress topology optimization calculation takes balanced stress distribution of the target digital repair body and a combination interface thereof with the workpiece to be repaired as one of the optimization objectives; The stress topology optimization calculation comprises: Step S61: performing finite element analysis to identify a stress concentration area of the initial digital repair body and the combination interface thereof with the workpiece to be repaired; Step S62: taking minimization of peak stress and uniformization of stress distribution as the optimization objectives, iteratively calculating and adjusting the shape of the initial digital repair body under the premise of ensuring no interference with the counterpart; the optimization objectives simultaneously include: minimizing contact stress between the target digital repair body and the current topography model; minimizing peak stress of the target digital repair body itself and stress concentration thereof at the combination interface with the workpiece to be repaired; The fit clearance analysis in the step S5 is used to calculate gap distribution between the initial digital repair body and the current topography model, and the gap distribution is taken as a constraint condition of the stress topology optimization calculation in the step S6 to ensure that the optimized target digital repair body forms conformal fit with the counterpart; Step S7: generating additive manufacturing path planning based on the target digital repair body; Step S8: controlling a welding additive manufacturing system to deposit welding material in the material defect area of the workpiece to be repaired according to the path planning to form an actual repair body.

2. A control method of a welding additive manufacturing according to claim 1, characterized in that, In the step S6, the surface shape of the generated target digital repair body is configured to form conformal contact with the current topography model of the counterpart to realize minimization and uniformization of contact stress.

3. The control method of a welding additive manufacturing according to claim 1, wherein, The step S3 specifically comprises the following sub-steps: Step S31: obtaining a reference static load of the workpiece to be repaired; Step S32: obtaining historical operation data of the workpiece to be repaired in a normal service period to constitute a dynamic load spectrum reflecting actual working conditions; Step S33: establishing a simplified simulation model of a process through process parameters of a device served by the workpiece to be repaired, and backstepping external load of the workpiece to be repaired through numerical calculation of the simplified simulation model; Step S34: cross-validation and data fusion of the reference static load, dynamic load spectrum and external load obtained in steps S31-S33 to construct a high-fidelity comprehensive load model; at the same time, according to the mechanical structure of the equipment, the boundary conditions of the repaired workpiece are defined in the simplified simulation model.

4. The method of controlling a welding additive manufacturing according to claim 1, wherein, The step S8 further comprises: Step S9: three-dimensional scanning of the repaired workpiece to obtain a repaired workpiece model; Step S10: comparing the repaired workpiece model with the target digital restoration to verify the repair accuracy; and virtually assembling and simulating the repaired workpiece model and the current topography model of the mating part to verify the fitting performance.

5. A computer readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the control method of any one of claims 1-4 is implemented.

6. A welding additive manufacturing system for performing the control method of any one of claims 1-4, characterized by Comprise: a three-dimensional scanning device for obtaining three-dimensional scanning data of the workpiece to be repaired and the mating part; a data processing and control unit for processing the three-dimensional scanning data and establishing a model; for performing virtual assembly analysis and stress topology optimization calculation to generate a target digital restoration; for generating an additive manufacturing path planning; for accessing or receiving a database containing workpiece service load and boundary conditions or user input; a welding additive manufacturing execution mechanism for depositing welding material under the control of the data processing and control unit.

7. A welding additive manufacturing system as defined in claim 6, wherein, The welding additive manufacturing execution mechanism comprises a multi-axis robot arm, and a laser cladding head, a plasma cladding head or an electric arc additive welding head mounted on the multi-axis robot arm.

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