Method and system for improving metallurgical bonding strength of multi-material additive manufacturing interface in real time

Abstract

The invention relates to the technical field of additive manufacturing interface metallurgical bonding strength real-time improvement scheme design, in particular to a multi-material additive manufacturing interface metallurgical bonding strength real-time improvement method and system. The method comprises the following steps: synchronously capturing an interface thermal-mechanical coupling state through a multi-mode sensing system; calculating the solid solubility and the crack sensitivity coefficient based on a thermodynamic model; double-loop intelligent control is triggered, a feedforward module predicts an energy compensation strategy according to a material database and activates nano activated particles to intervene, and a feedback module adopts reinforcement learning to optimize energy parameters; and injecting functionalized nano-powder into the interface and accurately adjusting the laser power density to a critical value. The system comprises a multi-physical field sensing module, a real-time decision center and a microcell energy regulation and control unit. The method has the core advantages that a dynamic black box in the heterogeneous material interface metallurgy process is broken; the in-situ active regulation and control of the material compatibility are realized; precise adaptation of a micron-scale energy field is achieved. And the interface metallurgy quality and the member service reliability are obviously improved.

Description

Technical Field

[0001] This invention relates to the technical field of designing schemes for real-time improvement of metallurgical bonding strength at the interface of additive manufacturing, and specifically to a method and system for real-time improvement of metallurgical bonding strength at the interface of multi-material additive manufacturing. Background Art

[0002] Multimaterial additive manufacturing technology achieves integrated forming of complex components by depositing heterogeneous materials layer by layer, and has significant application value in aerospace, energy equipment and other fields. However, improving the metallurgical bonding strength at the interface of heterogeneous materials has long faced three technical bottlenecks:

[0003] First, the dynamic process is uncontrollable. Existing technologies rely on single infrared temperature monitoring or offline metallographic analysis, which cannot simultaneously capture the transient coupling effect of temperature field, stress field, and element diffusion field, resulting in a lag in the feedback of interfacial thermodynamic state. Typically, this manifests as the inability to suppress element segregation caused by molten pool thermodynamic oscillations in real time, with parameters only being passively adjusted after microcracks have initiated.

[0004] Secondly, there is a lack of control over material compatibility. During the high-energy beam melting of dissimilar metals, the superposition of lattice mismatch, differences in thermal expansion coefficients, and diffusion activation energy barriers leads to the formation of brittle intermetallic compounds at the interface. Conventional methods employ empirical process parameter windows (such as fixed laser power and scanning speed), lacking an online control mechanism based on the intrinsic thermodynamic properties of the materials.

[0005] Third, the energy field precision is insufficient. Although traditional heat sources can adjust their power, their energy distribution patterns are difficult to match the geometric characteristics of the micron-level interface transition region. Excessive energy input induces grain coarsening, while insufficient energy leads to incomplete fusion defects.

[0006] Although existing studies have attempted to introduce ultrasonic vibration or electromagnetic stirring, external field intervention often disrupts the stability of the molten pool, and the control logic is decoupled from the core manufacturing process, failing to form a closed-loop optimization. While some scholars have proposed machine learning prediction models in recent years, the lack of high spatiotemporal resolution sensor data has resulted in insufficient generalization and real-time performance. Therefore, there is an urgent need to develop a systematic solution that integrates multi-physics in-situ sensing, real-time materials genome adaptation, and precise micro-region energy modulation.

[0007] Therefore, existing technologies still need further development. Summary of the Invention

[0008] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a method and system for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces, so as to solve the problems existing in the prior art.

[0009] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a method for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing, comprising:

[0010] (a) The temperature field, elemental diffusion spectrum and stress wave signal of the deposition interface are simultaneously acquired through a multimodal sensing system;

[0011] (b) Dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on a thermodynamic coupling model;

[0012] (c) When the SSI or CSC exceeds the threshold, a feedforward-feedback dual-loop control is executed: the feedforward control calls the material compatibility database to predict the energy compensation value; the feedback control optimizes the energy input parameters through reinforcement learning.

[0013] (d) Inject activated nanopowder into the interface transition region and adjust the laser power density to the critical value. .

[0014] Specifically, in step (a):

[0015] The multimodal sensing system includes a multispectral CCD, an infrared thermal imager, and an acoustic emission detector.

[0016] Specifically, in step (b):

[0017]

[0018] in, Measured enthalpy of mixture; Enthalpy of mixture of an ideal solid solution; : Maximum rate of change of stress; E: Elastic modulus; Coefficient of thermal expansion.

[0019] Specifically, in step (d), the critical laser power density satisfy:

[0020]

[0021] in, Critical power density; Thermal conductivity constant; Interface temperature difference; : Intrinsic binding coefficient; V: Scan rate.

[0022] Specifically, α∈[0.15, 0.35], β∈[8, 12].

[0023] Specifically, in step (c), the energy compensation value ΔE satisfies:

[0024]

[0025] in, Activation energy correction factor; : Diffusion activation energy; Gas constant; Interface temperature;

[0026] Specifically, in step (c), the reinforcement learning adopts the TD3 framework, and the reward function is:

[0027]

[0028] in, : Melt pool oscillation frequency; Target frequency threshold; Real-time stress at the interface; Yield strength; , Weighting coefficient.

[0029] Specifically, in step (d): the activated nanopowder is a Ti / Ni composite particle with a particle size d∈[50,200]nm and a surface coated with a Y2O3 film.

[0030] Specifically, this also includes depositing at a rate greater than or equal to 10 4 A cooling rate of K / s is used to implement micro-zone rapid cooling.

[0031] According to a second aspect of the present invention, a system for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing is provided, comprising:

[0032] The acquisition module is used to synchronously acquire the temperature field, elemental diffusion spectrum, and stress wave signal of the deposition interface through a multimodal sensing system;

[0033] The control module is used to dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on a thermodynamic coupling model; it is used to execute feedforward-feedback dual-loop control when the SSI or CSC exceeds the threshold. The feedforward control calls the material compatibility database to predict the energy compensation value, and the feedback control optimizes the energy input parameters through reinforcement learning; it is used to inject activated nanopowder into the interface transition region and adjust the laser power density to the critical value. .

[0034] Beneficial effects:

[0035] This technology achieves a fundamental breakthrough in the metallurgical properties of multi-material interfaces through an innovative architecture:

[0036] First, the multi-physics collaborative sensing capability has been fundamentally improved. The coaxial integrated high-resolution sensing system enables synchronous dynamic capture of temperature distribution, stress state, and element diffusion behavior, completely overcoming the limitations of traditional single-signal monitoring. The solid solubility and crack sensitivity dual-parameter criteria generated by the thermodynamic coupling model establish a direct correlation between the interfacial metallurgical state and process control parameters, transforming the response mechanism from passive lag to real-time adaptive control.

[0037] Second, the material interaction behavior is made intelligent and controllable. Nano-activated powders precisely construct energy regulation sites at the interface of heterogeneous materials, effectively reducing the element diffusion barrier and promoting solid solution formation, thereby fundamentally inhibiting the precipitation of brittle phases. The reinforcement learning-driven dual-loop control core autonomously optimizes the energy input state, stabilizing the fluid dynamics of the molten pool within the optimal flow range, significantly improving the uniformity of element distribution and the metallurgical continuity of the interface.

[0038] Third, significant progress has been made in energy field matching accuracy. The laser critical power control model deeply integrates the intrinsic properties of materials with the instantaneous process state, and combined with a high-precision positioning jetting device, it achieves precise construction of the energy gradient at the micron scale interface. The accompanying micro-area quenching technology effectively eliminates intergranular segregation and refines the interface microstructure through ultrafast phase transformation control, achieving a breakthrough improvement in metallurgical quality while maintaining the stability of the molten pool core.

[0039] This solution systematically solves the bottleneck of interfacial bonding strength in multi-material additive manufacturing, providing revolutionary technical support for the manufacturing of complex heterogeneous components. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces provided in a specific embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of the system composition of the multi-material additive manufacturing interface metallurgical bonding strength real-time improvement system provided in a specific embodiment of the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0043] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0044] Please see Figure 1 This invention provides a method for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing, comprising:

[0045] (a) The temperature field, element diffusion spectrum and stress wave signal of the deposition interface are collected synchronously through a multimodal sensing system.

[0046] Specifically, in step (a): the multimodal sensing system includes a multispectral CCD with a spatial resolution ≤5μm and an infrared thermal imager with a sampling frequency ≥100kHz.

[0047] It should be further explained that, regarding the multimodal sensing system, the solution designed in this invention includes: employing a coaxially integrated multimodal sensing module.

[0048] Multispectral sensor: wavelength range 200-1000nm, spatial resolution 5μm (preferred value), based on the fact that 5μm can resolve the diffusion boundary layer of dissimilar materials (typical thickness 8μm-30μm).

[0049] High-speed infrared thermal imager: sampling frequency 200kHz (preferred value), based on Shannon's sampling theorem to satisfy the molten pool oscillation period (0.1ms);

[0050] Acoustic emission detector: resonant frequency 150kHz, positioning accuracy ±0.1mm.

[0051] Furthermore, regarding the execution module parameters, the present invention is designed as follows:

[0052] The fiber laser has a wavelength of 1070nm and a power density adjustment range of 50-500W / mm².

[0053] The piezoelectric nozzle has a positioning accuracy of ±3μm and a powder spraying pulse frequency of 5kHz (preferred value), based on the fact that a 5kHz pulse can match the oscillation period of the molten pool.

[0054] (b) Dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on the thermodynamic coupling model.

[0055] Specifically, in step (b):

[0056]

[0057] in, Measured enthalpy of mixing (J / mol); Enthalpy of mixing of an ideal solid solution (J / mol); : Maximum stress change rate (MPa / s), extracted by the second derivative of acoustic emission signal; E: Elastic modulus (GPa), 200GPa for IN718 alloy and 110GPa for TC4 alloy. The coefficient of thermal expansion is 14.5 × 10⁻⁶ for IN718. -6 K -1 TC4 is 8.6 × 10 -6 K -1 .

[0058] Preferred value description: CSC danger threshold 0.3, based on fatigue test of 316L / IN625 combination (crack rate steep increase point CSC=0.32±0.05).

[0059] (c) When the SSI or CSC exceeds the threshold, a feedforward-feedback dual-loop control is executed: the feedforward control calls the material compatibility database to predict the energy compensation value; the feedback control optimizes the energy input parameters through reinforcement learning.

[0060] Specifically, in step (c), the energy compensation value ΔE satisfies:

[0061]

[0062] in, Activation energy correction factor (J / mm3); : Diffusion activation energy (kJ / mol); Gas constant (8.314 J·mol) -1 ·K -1 ); Interface temperature (K).

[0063] It should be further noted that the method includes:

[0064] 1. When SSI < 0.85, call the Q value from the material database;

[0065] 2. After calculating ΔE, the generated laser power increment ΔP = ΔE / (v·d²spot) (where v is the scanning speed, d...) spot (where is the diameter of the light spot).

[0066] Specifically, in step (c), the reinforcement learning adopts the TD3 framework, and the reward function is:

[0067]

[0068] in:

[0069] The oscillation frequency of the molten pool was obtained through time-domain analysis using an infrared thermal imager.

[0070] Target frequency threshold, fixed at 12.5kHz (based on the fact that Marangoni convection velocity reaches 0.8m / s at this frequency, which can suppress stomata).

[0071] Real-time interface stress is inverted using acoustic emission signals;

[0072] Yield strength is an inherent property of a material.

[0073] Weighting coefficients, taken as 0.6 and 0.4 respectively (based on the Pareto optimal solution from finite element simulation).

[0074] It should be further explained that, regarding the feedback control reinforcement learning algorithm, this invention designs the framework parameters for TD3:

[0075] State space: (Temperature / Stress / Molten Pool Frequency / Temperature Gradient / Enthalpy of Mixture);

[0076] Action space: (Laser power increment / powder spraying frequency increment).

[0077] It should be further explained that, regarding the implementation of training, the scheme designed in this invention includes:

[0078] 1. Collect 100,000 sets of data using the IN718 / TC4 combination. data;

[0079] 2. Offline pre-training: Learning rate policy network 1×10 -4 Value network 3×10 -4 ;

[0080] 3. Online fine-tuning: The network is updated every 5mm of deposition, with priority given to the replay buffer weight α=0.6;

[0081] Parameter basis: α=0.6 can improve learning efficiency by 300% in high-risk situations (CSC>0.25).

[0082] (d) Inject activated nanopowder into the interface transition region and adjust the laser power density to the critical value. .

[0083] Specifically, in step (d), the critical laser power density satisfy:

[0084]

[0085] in:

[0086] Critical laser power density, the final output control value;

[0087] Thermal conductivity constant, preferred value (Based on thermal equilibrium point simulated by Thermal-FSI);

[0088] Interface temperature difference ;

[0089] Intrinsic binding coefficient, preferred value (Based on penetration depth) (powder layer thickness);

[0090] Scanning speed.

[0091] Specifically, α∈[0.15, 0.35], β∈[8, 12].

[0092] Specifically, in step (d): the activated nanopowder is a Ti / Ni composite particle with a particle size d ∈ [50, 200] nm and a surface coating The film (thickness δ∈[20,50]nm).

[0093] It should be further explained that, regarding powder spraying control, the solution designed in this invention includes:

[0094] Powder: Ti-65wt%Ni (based on the combination of elements with the lowest diffusion barrier), particle size ;

[0095] Coating layer: Thickness 30nm (Based on: can reduce interfacial tension by 25%)

[0096] Powder application rate: SSI) .

[0097] Specifically, this also includes cooling at a rate ≥10 after deposition. 4 K / s implements micro-zone rapid cooling.

[0098] It should be further explained that, regarding the rapid cooling treatment, the solution designed in this invention includes:

[0099] Cooling medium: liquid nitrogen atomized stream (purity > 99.999%);

[0100] Jet pressure 0.5 MPa, cooling rate 3.2 × 10⁻⁶ 4 K / s (Based on achieving an interface grain size of 1.1 μm).

[0101] Furthermore, the implementation results are shown in Table 1:

[0102] Table 1 Implementation Results

[0103] index Traditional methods This plan Enhancement mechanism Interface strength 412MPa 586MPa <![CDATA[Y2O3 reduces the diffusion activation energy by 30%]]> Porosity 3.7% 0.9% 12.5kHz optimized melt flow Crack sensitivity index 0.41 0.18 Micro-area quenching suppresses intergranular segregation

[0104] It is understandable that:

[0105] Feedforward-feedback dual-loop control reduces the response delay to 50μs (compared to 450μs for traditional PID).

[0106] P c Formula control reduces the width of the heat-affected zone at the interface to 0.8 mm (compared to 2.3 mm using traditional methods);

[0107] Nanopowder spraying increases the diffusion coefficient to 2.7 × 10⁻⁶. -8 m² / s (matrix 1.2×10⁻⁶) -8 m 2 / s).

[0108] Please see Figure 2 The present invention provides another embodiment, which provides a real-time system for improving the metallurgical bonding strength of multi-material additive manufacturing interfaces. The system includes:

[0109] The acquisition module 100 is used to synchronously acquire the temperature field, elemental diffusion spectrum and stress wave signal of the deposition interface through a multimodal sensing system;

[0110] Control module 200 is used to dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on a thermodynamic coupling model; it is used to execute feedforward-feedback dual-loop control when SSI or CSC exceeds a threshold, with feedforward control calling the material compatibility database to predict the energy compensation value and feedback control optimizing the energy input parameters through reinforcement learning; it is used to inject activated nanopowder into the interface transition region and adjust the laser power density to a critical value. .

[0111] Specifically, the control module 200 includes:

[0112] Processing module: The calculation unit that configures the thermodynamic coupling model;

[0113] Control module: a dual-loop architecture of feedforward controller and reinforcement learning feedback optimizer;

[0114] Execution module: piezoelectric nanopowder nozzle and dynamically tuned laser;

[0115] Interactive module: A communication interface for real-time connection to the material compatibility database.

[0116] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising:

[0117] The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the method for real-time improvement of the metallurgical bonding strength of the multi-material additive manufacturing interface. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0118] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0119] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0120] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0121] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing, characterized in that... include: (a) The temperature field, elemental diffusion spectrum and stress wave signal of the deposition interface are simultaneously acquired through a multimodal sensing system; (b) Dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on a thermodynamic coupling model; (c) When the SSI or CSC exceeds the threshold, a feedforward-feedback dual-loop control is executed: the feedforward control calls the material compatibility database to predict the energy compensation value; the feedback control optimizes the energy input parameters through reinforcement learning. (d) Inject activated nanopowder into the interface transition region and adjust the laser power density to the critical value. .

2. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that, In step (a): The multimodal sensing system includes a multispectral CCD, an infrared thermal imager, and an acoustic emission detector.

3. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that, In step (b): in, Measured enthalpy of mixture; Enthalpy of mixture of an ideal solid solution; : Maximum rate of change of stress; E: Elastic modulus; Coefficient of thermal expansion.

4. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that, In step (d), the critical laser power density satisfy: in, Critical power density; Thermal conductivity constant; Interface temperature difference; : Intrinsic binding coefficient; V: Scan rate.

5. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 4, characterized in that: α∈[0.15，0.35]，β∈[8，12]。 6. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that, In step (c), the energy compensation value ΔE satisfies: in, Activation energy correction factor; : Diffusion activation energy; Gas constant; Interface temperature.

7. The method for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing according to claim 1, characterized in that, In step (c), the reinforcement learning adopts the TD3 framework, and the reward function is: in, : Melt pool oscillation frequency; Target frequency threshold; Real-time stress at the interface; Yield strength; , Weighting coefficient.

8. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that, In step (d): the activated nanopowder is a Ti / Ni composite particle with a particle size d∈[50,200]nm and a Y2O3 film coated on the surface.

9. The method for real-time improvement of the metallurgical bonding strength of multi-material additive manufacturing interfaces according to claim 1, characterized in that: This also includes post-deposition with a concentration greater than or equal to 10 4 A cooling rate of K / s is used to implement micro-zone rapid cooling.

10. A system for real-time improvement of the metallurgical bonding strength of interfaces in multi-material additive manufacturing, characterized in that, include: The acquisition module is used to synchronously acquire the temperature field, elemental diffusion spectrum, and stress wave signal of the deposition interface through a multimodal sensing system; The control module is used to dynamically calculate the interface solid solubility index (SSI) and crack sensitivity coefficient (CSC) based on a thermodynamic coupling model; it is used to execute feedforward-feedback dual-loop control when the SSI or CSC exceeds the threshold. The feedforward control calls the material compatibility database to predict the energy compensation value, and the feedback control optimizes the energy input parameters through reinforcement learning; it is used to inject activated nanopowder into the interface transition region and adjust the laser power density to the critical value. .

Images