Gradient density metal implant additive manufacturing method

Abstract

The invention provides a gradient density metal implant additive manufacturing method, and relates to the technical field of selective laser melting additive manufacturing, according to the method, continuous gradient density distribution of a metal implant from an outer layer to an inner layer is achieved through multi-mode sensor fusion and a self-adaptive control algorithm, biomechanical characteristics of a skeleton are accurately simulated, and the manufacturing precision of the metal implant is improved. The step of designing a porous structure is omitted; cortical bone (high density) and cancellous bone (porous) structures of human bones can be accurately simulated, and the biomechanical suitability of the implant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of selective laser melting (SLM) additive manufacturing, and in particular to a method for additive manufacturing of gradient density metal implants. Background Art

[0002] In the field of orthopedic repair, the additive manufacturing technology of metal implants, especially the selective laser melting (SLM) technology, has great development potential. However, the current traditional SLM process faces many challenges in biocompatibility when manufacturing metal implants. Specifically, the traditional SLM process usually uses fixed laser power and scanning parameters, which makes the density of the implant single and cannot effectively simulate the gradient structure of human bones, that is, the high density of cortical bone and the porous characteristics of cancellous bone; it can only achieve homogeneous density of the implant or construct a limited gradient distribution through a preset porous model. This single density design not only limits the adaptability of the implant to the complex human bone structure, but may also lead to insufficient biomechanical adaptability: homogeneous metal implants are prone to stress shielding effects due to their higher elastic modulus than that of human bones, which in turn leads to other complications such as bone resorption.

[0003] In addition, existing technologies also have significant lags in density detection. Traditional offline detection methods, such as CT scanning and water drainage, cannot monitor density in real time during the manufacturing process, making it difficult to dynamically adjust process parameters and thus unable to promptly correct deviations in the manufacturing process, further exacerbating the uncertainty of implant quality.

[0004] Traditional design methods rely on complex processes such as topology optimization and parametric modeling, requiring repeated iterations to construct a limited gradient distribution. This process is not only time-consuming and labor-intensive, but also significantly prolongs the design-to-manufacturing cycle, typically increasing time costs by 30%-50%. This lengthy cycle is extremely disadvantageous for patients in urgent need of implants, while also increasing manufacturing costs and making it difficult to dynamically match the mechanical gradient properties of bone.

[0005] In view of this, this application is filed. Summary of the Invention

[0006] The present invention provides a method for additive manufacturing of gradient density metal implants, which can at least partially improve the above-mentioned problems.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for additive manufacturing of a gradient density metal implant, comprising:

[0009] The three-dimensional structure of the implant to be manufactured is designed based on the biomimetic gradient model and divided into an outer dense area, a transition gradient area, and an inner porous area;

[0010] Start the laser selective melting technology to lay powder layer by layer, obtain sensor data collected by the preset multi-modal sensor component, and calculate the density based on the sensor data;

[0011] Based on the preset control algorithm and density, the laser power and scanning speed of the outer dense area, transition gradient area and inner porous area are dynamically adjusted, and the actuator is driven to print to obtain a printed implant;

[0012] During the printing process, the implant is subjected to micro-CT scanning calibration processing to generate a calibration result, and the portion of the implant that does not meet the conditions is locally remelted according to the calibration result until the entire implant is calibrated.

[0013] In summary, the gradient density metal implant additive manufacturing method uses multimodal sensor fusion technology to monitor the density of the molten pool in real time, including a coaxial high-speed infrared thermal imager, a laser ultrasonic module and online microfocus X-ray imaging, combined with an adaptive fuzzy-PID collaborative control algorithm to dynamically adjust the laser power and scanning speed, thereby achieving a continuous gradient density distribution of the metal implant from the outer layer to the inner layer. The outer dense area (density ≥ 95%), the transition gradient area (70%-80%) and the inner porous area (60%-70%) are precisely matched to the biomechanical properties of human bones through a zoning control strategy to reduce the stress shielding effect. The system includes a perception module, a control module and an execution module, and triggers a micro-CT scan calibration deviation every 5 layers. This method breaks through the limitations of traditional static processes, improves the biocompatibility and defect controllability of implants, and is suitable for the manufacture of complex structures such as femurs and humeri. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 1 is a schematic flow chart of a method for additive manufacturing of a gradient density metal implant provided by an embodiment of the present invention;

[0015] Figure 2 This is a simplified flowchart of the method for additive manufacturing of gradient density metal implants provided by an embodiment of the present invention;

[0016] Figure 3 This is a diagram of the overall process framework of the gradient density metal implant additive manufacturing method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] refer to Figures 1 to 3 As shown, the first embodiment of the present invention discloses a method for additive manufacturing of a gradient density metal implant, which can be performed by a gradient density metal implant additive manufacturing device (hereinafter referred to as a manufacturing device), and in particular, by one or more processors in the manufacturing device to implement the following method:

[0019] S1, designing the three-dimensional structure of the implant to be manufactured based on the bionic gradient model, and dividing it into an outer dense area, a transition gradient area, and an inner porous area;

[0020] Specifically, step S1 includes: importing a biomimetic gradient model, using a standard STL format file to design a three-dimensional structure of the implant to be manufactured through a preset CAD software, and dividing it according to preset biomechanical performance parameters to obtain an outer dense area, a transition gradient area, and an inner porous area;

[0021] The biomechanical performance parameters include the laser power and scanning speed of each partition.

[0022] In this embodiment, a standard STL format 3D model file is imported based on the design requirements of the implant to be manufactured. This model is designed using professional CAD software and accurately reflects the implant's external shape and internal structure (e.g., femoral or humeral prostheses). During the design phase, the implant is divided into three main regions: an outer dense zone, a transition gradient zone, and an inner porous zone. This zoning design is based on biomechanical parameters, aiming to make the implant structure more similar to the characteristics of natural human bone. Specifically, the outer dense zone has a higher density, providing sufficient strength and wear resistance; the transition gradient zone has a gradually decreasing density, acting as a buffer and transition; and the inner porous zone has a lower density, facilitating bone tissue growth and integration. To achieve this zoning structure, laser power and scanning speed parameters are preset for each zone. For example, the outer dense zone has higher laser power and a slower scanning speed to ensure sufficient material melting and form a dense structure; while the inner porous zone uses lower laser power and a higher scanning speed to create a porous structure.

[0023] S2: Start the laser selective melting technology to spread powder layer by layer, obtain sensor data collected by the preset multi-modal sensor component, and calculate the density based on the sensor data;

[0024] Specifically, step S2 includes: obtaining the molten pool temperature field, ultrasonic surface wave velocity, and microscopic porosity collected by a preset multimodal sensor assembly;

[0025] The molten pool cooling rate dT / dt is determined according to the molten pool temperature field, and the density is inversely calculated based on the molten pool cooling rate dT / dt. The formula is: ρ∝1 / (dT / dt), where ρ is the density, dT is the temperature change in the molten pool temperature field, and dt is the time change;

[0026] The local density ρ′ is calculated based on the ultrasonic surface wave velocity v, and the formula is: Among them, K is the material coefficient and E is the elastic modulus of the material;

[0027] Microporosity is used to verify the accuracy of density and local density.

[0028] Preferably, the multimodal sensor assembly includes a coaxial high-speed infrared thermal imager integrated in the coaxial optical path of the laser printing head, a laser ultrasonic detection head installed on the side of the working platform, and an online microfocus X-ray source arranged below the working platform, wherein the coaxial high-speed infrared thermal imager is configured to capture the molten pool temperature field in real time, the laser ultrasonic detection head is configured to emit high-frequency laser pulses to excite ultrasonic waves, and the online microfocus X-ray source is configured to capture the microscopic porosity of the molten pool solidification process.

[0029] In this example, Selective Laser Melting (SLM) technology is activated to perform a layer-by-layer powder application. This process, a core step in additive manufacturing, melts metal powder layer by layer by precisely controlling the movement and energy output of the laser beam to form the desired implant structure. Simultaneously, a pre-configured multimodal sensor assembly operates, collecting various data related to the melt pool in real time. This data is used for subsequent density calculations and quality control.

[0030] The multimodal sensor assembly includes a coaxial high-speed infrared thermal imager integrated into the coaxial optical path of the laser printhead, a laser ultrasonic inspection head mounted on the side of the work platform, and an online microfocus X-ray source located below the work platform. The coaxial high-speed infrared thermal imager captures the melt pool's temperature field in real time at a sampling frequency of 1000 Hz. By analyzing the melt pool cooling rate (with an accuracy of ±1 K / s), the density can be inferred. Specifically, density is inversely proportional to the melt pool cooling rate; this means that slower melt pool cooling rates result in higher density, while faster cooling rates result in lower density. For example, a cooling rate of 0.5 K / s corresponds to a density of 95%, while increasing the rate to 2.0 K / s results in a density drop of 60%. This temperature field-based density calculation method rapidly and in real time reflects the melt pool's solidification state, eliminates interference from high-light sources, and ensures the stability of dynamic monitoring of high-temperature melt pools, providing a crucial basis for subsequent process adjustments.

[0031] Simultaneously, a laser ultrasonic testing head with a wave velocity measurement accuracy of ±0.5% emits high-frequency laser pulses to excite 10MHz ultrasonic waves, measuring the ultrasonic surface wave velocity within a range of 3000-6000 m / s. Local density is calculated based on the wave velocity. This formula links ultrasonic wave velocity to density, leveraging the differences in ultrasonic propagation speed in materials of varying densities to accurately calculate local density. This device complements infrared data, forming a synergistic mechanism to enhance the reliability of deep-level density monitoring. This method can penetrate deep into the material, detecting microstructural changes within the melt pool, thereby providing more comprehensive information for density assessment.

[0032] The online microfocus X-ray source is used to capture the microscopic porosity during the solidification process of the molten pool to verify the accuracy of the data. X-ray imaging technology can observe the microstructure inside the molten pool at a high resolution (for example, 10 microns), detect possible pores or other defects (such as identifiable molten pool porosity (threshold ≤ 5%)), to assist in verifying the accuracy of infrared and ultrasonic data. It is activated every 10 layers of the critical transition layer or when an abnormal signal occurs. By analyzing the microscopic porosity, the accuracy of the density and local density calculated by the molten pool temperature field and ultrasonic surface wave velocity can be verified, thereby reducing system energy consumption. This step is crucial because it ensures the reliability of the calculation results, thereby providing accurate data support for subsequent process optimization.

[0033] S3, based on the preset control algorithm and density, dynamically adjusts the laser power and scanning speed of the outer dense area, transition gradient area, and inner porous area, and drives the actuator to print to obtain a printed implant;

[0034] Specifically, step S3 includes: calculating a density deviation Δρ based on the density and a preset target density, determining a temperature gradient ΔT based on a temperature difference between adjacent regions, and calculating the density deviation Δρ and the temperature gradient ΔT based on a fuzzy logic control algorithm, and adjusting the laser power and scanning speed of the outer dense area, wherein the formula is: Δρ / ΔT;

[0035] An embedded AI controller runs an adaptive fuzzy-PID collaborative algorithm to dynamically adjust the laser power and scanning speed in the transition gradient region through integral and differential terms.

[0036] A model predictive control algorithm is used to scroll the optimization window with a preset time value to predict the porosity change and adjust the laser power and scanning speed of the inner porous area. Its objective function is to minimize the combined deviation of density and elastic modulus.

[0037] Preferably, the dynamic adjustment rule of laser power is: when the density deviation exceeds 2%, the power adjustment is triggered, and the adjustment range is ±10%. When the density deviation exceeds 5%, local remelting is triggered, and the remelting area is 1-3mm. 2 .

[0038] Preferably, the elastic modulus of the outer dense zone is 30 GPa, the elastic modulus of the transition gradient zone is in the range of 10-15 GPa, the elastic modulus of the inner porous zone is 5 GPa, and the gradient continuity R 2 >0.98.

[0039] In this embodiment, the current density is calculated by using data such as the molten pool temperature field, ultrasonic surface wave velocity, and micro-porosity obtained by the multimodal sensor assembly. Subsequently, the calculated density is compared with the preset target density to obtain the density deviation (allowable ±5%). At the same time, the temperature gradient (threshold ±50K) is determined by analyzing the temperature difference between adjacent areas. Based on these two key parameters, the system uses a fuzzy logic control algorithm for calculation to achieve rapid response, and then adjusts the laser power and scanning speed of the outer dense area to ensure that the density of the outer dense area can quickly and accurately meet the design requirements. For example: for example, when Δρ = +3% and ΔT>10K, the power is reduced by 10% (such as 400W→360W). The introduction of the fuzzy logic control algorithm enables the system to flexibly cope with complex manufacturing environments and quickly respond to various changes, thereby ensuring the high density and high strength of the outer dense area, laying a solid foundation for the overall performance of the implant.

[0040] For the transition gradient zone, the system uses an embedded AI controller to run an adaptive fuzzy-PID collaborative algorithm with a control accuracy of ±2%. The algorithm dynamically adjusts the laser power and scanning speed through integral and differential terms to achieve precise control of the density of the transition gradient zone. This collaborative algorithm gives full play to the flexibility of fuzzy logic control and the accuracy of PID control. It can effectively reduce error accumulation while ensuring a smooth transition of the density in the transition gradient zone, and ensure the continuity and uniformity of the gradient change. This process not only improves the manufacturing accuracy of the transition gradient zone, but also provides an important guarantee for the biomechanical properties of the implant, enabling it to better simulate the natural gradient characteristics of human bones. Among them, the proportional coefficient K of the adaptive fuzzy-PID collaborative algorithm is p The value range is 0.8-1.2, the integral coefficient K i The value range is 0.01-0.05, and the differential coefficient K d The value range of is 0.05-0.2. Specifically, in this embodiment, the parameter configuration is the proportional coefficient K p =1.0, integral coefficient K i =0.03, differential coefficient K d =0.1.

[0041] During the manufacturing process of the inner porous area, the system adopts the model predictive control (MPC) algorithm. The algorithm uses a 5ms rolling optimization window to predict the change in porosity, and adjusts the laser power and scanning speed accordingly to balance the porosity and mechanical properties. Its objective function is to minimize the combined deviation of density and elastic modulus by optimizing the control strategy. This predictive control method can predict potential changes in the manufacturing process in advance, thereby achieving precise regulation of the porosity and mechanical properties of the inner porous area. In this way, the inner porous area can not only form a porous structure that meets the design requirements, but also effectively promote the growth and integration of bone tissue, thereby improving the biocompatibility and long-term stability of the implant.

[0042] The outer dense zone is defined as the shell between 0 and 2 mm from the surface, with a target density of no less than 95%. The laser power range is 350-400W, the scanning speed is 400-500mm / s, and the elastic modulus is 30GPa. The transition gradient zone is located in the middle layer between 2 and 5 mm from the surface, with a target density of 70% to 80%. The laser power range is 250-300W, the scanning speed is 600-800mm / s, and the elastic modulus is between 10 and 15GPa. The inner porous zone is the core area, located more than 5 mm from the surface, with a target density of 60% to 70%. The laser power range is 150-200W, the scanning speed is 1000-1200mm / s, and the elastic modulus is 5GPa. The sizes of the three zones can be adjusted according to actual requirements, and the power adjustment response time does not exceed 50μs.

[0043] In addition, in order to further improve the stability and reliability of the manufacturing process, this method also sets a dynamic adjustment rule for the laser power. When the density deviation exceeds 2%, the system will automatically trigger power adjustment with an adjustment range of ±10%. This rule ensures that during the manufacturing process, once a density deviation is detected, the system can respond quickly and adjust the laser power in time to control the deviation within a reasonable range. When the density deviation exceeds 5%, the system will trigger a local remelting operation, the remelting area is 1-3mm2, and the remelting power is reduced by 20%. In actual use, the outer layer responds before the inner layer to ensure surface density. The introduction of local remelting provides an effective remedial measure for the manufacturing process. When a large deviation is found, the density of the local area can be readjusted by remelting to ensure that the quality of the final product meets strict standards. In this embodiment, a laser power modulator with a power adjustment range of 100-400W and an adjustment accuracy of ±5W is used.

[0044] Preferably, the driving execution component includes a high-precision galvanometer system and a powder spreading mechanism, wherein the high-precision galvanometer system is configured as a scanning bionic structure, and its path planning adopts spiral scanning, and the powder spreading mechanism is driven by a scraper of a servo motor.

[0045] In this embodiment, to achieve precise gradient density distribution and high-quality implant manufacturing, high-precision actuators, including a high-precision galvanometer system and a powder spreading mechanism, are employed. Firstly, the high-precision galvanometer system is the core component for precise laser scanning. Its scanning speed is adjustable from 400-1200 mm / s, adapting to speed variations in the gradient zone. Its positioning accuracy is ±0.05 mm, ensuring the precision of complex biomimetic structures. The galvanometer system controls the laser beam's path using high-speed, high-precision mirrors, enabling layer-by-layer melting of metal powder with exceptionally high precision. The galvanometer system's path planning utilizes a spiral scanning method to reduce thermal stress. Spiral scanning is a highly efficient scanning path planning method that evenly distributes laser energy, reduces thermal stress concentration, and thus minimizes the possibility of thermal deformation. This scanning method is particularly suitable for manufacturing complex biomimetic structures, such as the outer dense region, transition gradient region, and inner porous region of an implant. Through spiral scanning, the laser beam continuously and evenly coats each layer of powder, ensuring a more stable melting and solidification process, thereby improving the density and mechanical properties of the implant.

[0046] In addition, the powder spreading mechanism is also an indispensable part of the manufacturing process. Its powder spreading thickness range is 20-50μm, and the powder spreading accuracy is ±5μm. It is suitable for pure tantalum powder (particle size 15-53μm, oxygen content ≤0.1%). The powder spreading mechanism consists of a scraper driven by a servo motor. Its function is to spread the metal powder evenly on the work platform, providing a basis for laser melting of each layer. The precise control of the servo motor ensures the uniformity and consistency of the powder spreading thickness, which is crucial for the precise control of gradient density. During the manufacturing process, the powder spreading mechanism spreads the powder on the work platform according to the preset parameters, and then the galvanometer system controls the laser beam for melting. By precisely controlling the powder spreading thickness and the laser scanning path, the system can achieve a continuous gradient density distribution from the outer dense area to the inner porous area. In addition, it can also dynamically adjust the powder spreading scraper pressure (0.1 to 0.5N) according to the layer thickness deviation.

[0047] S4, during the printing process, performing micro-CT scanning calibration processing on the implant to generate a calibration result, and locally remelting the part of the implant that does not meet the conditions according to the calibration result until the entire implant is calibrated.

[0048] Specifically, step S4 includes: during the printing process, after a preset number of layers are printed, automatically triggering micro-CT scanning calibration to generate a three-dimensional density distribution map;

[0049] Compare the three-dimensional density distribution map and the preset gradient model. When the local deviation between the two exceeds 3%, mark the defective area and perform local remelting.

[0050] In this embodiment, during the printing process, after the preset number of layers are printed, the system will automatically trigger the micro-CT scanning calibration, which is 5 layers here. As a high-precision non-destructive testing technology, micro-CT scanning can generate a three-dimensional density distribution map of the implant with an extremely high resolution (for example, 10 microns) in 30 seconds or less. This three-dimensional density distribution map can intuitively reflect the microstructure and density distribution inside the implant, providing detailed data support for subsequent calibration. Through micro-CT scanning, the system can promptly detect potential defects or deviations during the manufacturing process, thereby preventing these problems from further expanding in subsequent printing.

[0051] After generating the three-dimensional density distribution map, the system will compare it with the preset gradient model. The preset gradient model is pre-set according to the design requirements and biomechanical performance parameters of the implant, and includes the ideal density distribution of the outer dense area, the transition gradient area, and the inner porous area. By comparing the actual three-dimensional density distribution map with the preset gradient model, the system can accurately calculate the local deviation. When the local deviation exceeds 3%, the system will automatically mark the defective area (1 to 3mm2). This threshold is set based on the biomechanical performance requirements and clinical application standards of the implant to ensure that the final product meets high quality requirements.

[0052] For the marked defective areas, the system will initiate a local remelting procedure (with a 20% reduction in power). Local remelting is an effective remedial measure that remelts and solidifies the defective areas by adjusting the laser power and scanning path. This local processing method not only corrects deviations but also avoids remanufacturing the entire implant, thereby significantly improving manufacturing efficiency and material utilization. During the local remelting process, the system will dynamically adjust the laser power and scanning speed according to the specific conditions of the defective area to ensure that the density of the remelted area meets the design requirements.

[0053] This micro-CT scan calibration and local remelting mechanism runs through the entire printing process. After each preset number of layers are printed, the above calibration and remelting steps are repeated until the entire implant is calibrated. This closed-loop calibration mechanism not only ensures that the gradient density distribution of the implant meets the design requirements, but also significantly improves the manufacturing accuracy and quality stability of the implant. By real-time monitoring and timely correction of deviations in the manufacturing process, the system can effectively reduce the defect rate and improve the biomechanical properties and biofitness of the implant. In addition, this calibration mechanism also has significant clinical significance. By precisely controlling the gradient density distribution of the implant, the natural structure of the human bone can be better simulated, thereby reducing the stress shielding effect and reducing the risk of complications such as bone resorption. At the same time, by reducing manufacturing defects, the long-term stability and reliability of the implant are improved, providing patients with safer and more effective treatment options.

[0054] Preferably, the method further comprises: introducing connected pores in the inner porous region by short-time power pulses to obtain controllable pores, wherein the porosity of the outer layer is ≤1%; and the controllable pores of the inner layer are 3-5%.

[0055] In this embodiment, during the manufacturing process, when printing into the inner porous area, connected pores with a pore size of 50 to 200 μm and a porosity of 3%-5% are actively introduced by means of short-term power pulses. Specifically, the system will reduce the laser power for a short period of time (for example, by 20% within 0.5 ms) in a specific printing stage / specific area (such as the inner bone integration area), thereby forming pores of predetermined size and distribution in the inner porous area. The introduction of such pores is achieved by precisely controlling the time and power of the laser pulse to ensure that the size and distribution of the pores meet the design requirements. For example, the size of the pores can be controlled between 50-200 microns and the porosity can be controlled at 3%-5%. This controllable pore design can not only promote the attachment and growth of bone cells, but also improve the integration of the implant with bone tissue.

[0056] In this embodiment, the final implant is suitable for the following parts: femur, humerus. The key parameters of the gradient density metal implant additive manufacturing method include the target density of each partition, laser power, scanning speed and control algorithm. The outer layer adopts fuzzy logic control (response time ≤ 50μm), the transition zone relies on PID regulation (accuracy ± 5%), and the inner layer balances the porosity through MPC. MicroCT calibration is performed every 5 layers to ensure gradient continuity (R 2 The expected results are: 1. Gradient precision: density of 95±5% in the outer layer and 75±5% in the inner layer, with gradient continuity R2 > 0.98; 2. Mechanical adaptability: elastic modulus gradually decreases from 30GPa in the outer layer to 5GPa in the inner layer, matching the biomechanical properties of human bone; 3. Efficiency improvement: design-manufacturing cycle shortened by 40%, with defect rate reduced to below 0.1%.

[0057] Tantalum alloy hip implants were used as an example for experimental verification. The outer layer was tested at a power of 380W and a speed of 450mm / s, the transition zone at a power of 280W and a speed of 700mm / s, and the inner layer at a power of 180W and a speed of 1100mm / s. The final test results showed that the density gradient was 95.2±0.5% in the outer layer, 78.3±1.2% in the transition zone, and 65.7±1.8% in the inner layer (gradient continuity R 2 =0.99), with elastic moduli of 28 GPa, 12 GPa, and 4.8 GPa, respectively. The matching error with human bone was less than 5%, and the defect rate was only 0.08% (compared to 1.2% with traditional processes). In tests of tantalum alloy spinal fusion cages, the inner layer porosity reached 4.2%, the pore size was 150 ± 50 μm, and the bone cell attachment rate increased by 50%. This indicates that the gradient density metal implant additive manufacturing method has achieved the desired effect.

[0058] In summary, this method achieves a continuous gradient density distribution from a dense outer layer to a porous inner layer through multimodal sensor fusion, adaptive control algorithm, and closed-loop calibration mechanism, accurately simulates the biomechanical properties of human bones, and significantly improves the biofitness and bone integration performance of the implant.

[0059] During the manufacturing process, a standard STL 3D model file is first imported. Professional CAD software is then used to design the implant's 3D structure, which is divided into an outer dense zone, a transition gradient zone, and an inner porous zone. This zoning design, based on biomechanical performance parameters, ensures that the implant's structure closely matches the body's natural bone structure. The high density of the outer dense zone provides sufficient strength and wear resistance, while the density of the transition gradient zone gradually decreases, acting as a buffer. The inner porous zone, with its controlled porosity (3%-5%), promotes bone growth and integration.

[0060] To achieve a precise gradient density distribution, a multimodal sensor fusion technology was employed, including a coaxial high-speed infrared thermal imager, a laser ultrasonic inspection head, and an online microfocus X-ray source. These sensors collected real-time data on the melt pool's temperature field, ultrasonic surface velocity, and microporosity. Fuzzy logic, PID, and model predictive control (MPC) algorithms were then used to dynamically adjust the laser power and scanning speed. This adaptive control strategy not only improved the flexibility and precision of the manufacturing process but also significantly reduced defect rates, improving the overall quality of the implant.

[0061] In addition, a micro-CT scanning calibration mechanism has been introduced. During the printing process, after each preset number of layers is completed, the system automatically triggers a micro-CT scan to generate a three-dimensional density distribution map and compare it with the preset gradient model. If the local deviation exceeds 3%, the system will mark the defective area and perform local remelting until the entire implant is calibrated. This closed-loop calibration mechanism ensures that the gradient density distribution of the implant meets the design requirements, further improving the biomechanical properties and biocompatibility of the implant. It is particularly worth mentioning that this method introduces connected pores in the inner porous area through short-term power pulses, thereby realizing the design of controllable porosity. The controllable porosity of the inner layer is 3%-5%, and the pore size is controlled between 50-200 microns, which provides a good growth environment for bone cells and significantly improves the bone integration performance of the implant. At the same time, the porosity of the outer layer is strictly controlled within 1%, ensuring the high strength and wear resistance of the implant.

[0062] In summary, the proposed additive manufacturing method for gradient-density metal implants achieves a continuous gradient density distribution from a dense outer layer to a porous inner layer through multimodal sensor fusion, adaptive control algorithms, and a closed-loop calibration mechanism, significantly improving the biomechanical properties and biocompatibility of the implants. This innovative manufacturing method not only improves the quality and performance of implants, but also shortens the design-to-manufacturing cycle and reduces manufacturing costs, possessing significant clinical application value and broad development prospects.

[0063] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for additive manufacturing of gradient density metal implants, characterized in that: include: The three-dimensional structure of the implant to be manufactured is designed based on the biomimetic gradient model and divided into an outer dense area, a transition gradient area, and an inner porous area; Start the laser selective melting technology to lay powder layer by layer, obtain sensor data collected by the preset multi-modal sensor component, and calculate the density based on the sensor data; Based on the preset control algorithm and density, the laser power and scanning speed of the outer dense area, transition gradient area and inner porous area are dynamically adjusted, and the actuator is driven to print to obtain a printed implant; During the printing process, the implant is subjected to micro-CT scanning calibration processing to generate a calibration result, and the portion of the implant that does not meet the conditions is locally remelted according to the calibration result until the entire implant is calibrated.

2. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: The three-dimensional structure of the implant to be manufactured is designed based on the bionic gradient model and divided into an outer dense area, a transition gradient area, and an inner porous area. Specifically: Import the biomimetic gradient model, use the standard STL format file to design the three-dimensional structure of the implant to be manufactured through the preset CAD software, and divide it according to the preset biomechanical performance parameters to obtain the outer dense area, transition gradient area and inner porous area; The biomechanical performance parameters include the laser power and scanning speed of each partition.

3. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: Obtain the sensor data collected by the preset multimodal sensor component and calculate the density based on the sensor data, specifically: Obtain the molten pool temperature field, ultrasonic surface wave velocity and micro porosity collected by the preset multi-modal sensor assembly; The molten pool cooling rate dT / dt is determined according to the molten pool temperature field, and the density is inversely calculated based on the molten pool cooling rate dT / dt. The formula is: ρ∝1 / (dT / dt), where ρ is the density, dT is the temperature change in the molten pool temperature field, and dt is the time change; The local density ρ′ is calculated based on the ultrasonic surface wave velocity v, and the formula is: Among them, K is the material coefficient and E is the elastic modulus of the material; Microporosity is used to verify the accuracy of density and local density.

4. The method for additive manufacturing of gradient density metal implants according to claim 3, characterized in that: The elastic modulus of the outer dense zone is 30 GPa, the elastic modulus of the transition gradient zone is in the range of 10-15 GPa, the elastic modulus of the inner porous zone is 5 GPa, and the gradient continuity R 2 >0.

98.

5. The method for additive manufacturing of gradient density metal implants according to claim 3, characterized in that: The multimodal sensor assembly includes a coaxial high-speed infrared thermal imager integrated in the coaxial optical path of the laser printing head, a laser ultrasonic detection head installed on the side of the working platform, and an online microfocus X-ray source arranged below the working platform, wherein the coaxial high-speed infrared thermal imager is configured to capture the molten pool temperature field in real time, the laser ultrasonic detection head is configured to emit high-frequency laser pulses to excite ultrasonic waves, and the online microfocus X-ray source is configured to capture the microscopic porosity of the molten pool solidification process.

6. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: Based on the preset control algorithm and density, the laser power and scanning speed of the outer dense area, transition gradient area and inner porous area are dynamically adjusted as follows: The density deviation Δρ is calculated based on the density and the preset target density, the temperature gradient ΔT is determined based on the temperature difference between adjacent areas, and the density deviation Δρ and the temperature gradient ΔT are calculated based on the fuzzy logic control algorithm to adjust the laser power and scanning speed of the outer dense area. The formula is: Δρ / ΔT; An embedded AI controller runs an adaptive fuzzy-PID collaborative algorithm to dynamically adjust the laser power and scanning speed in the transition gradient region through integral and differential terms. A model predictive control algorithm is used to scroll the optimization window with a preset time value to predict the porosity change and adjust the laser power and scanning speed of the inner porous area. Its objective function is to minimize the combined deviation of density and elastic modulus.

7. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: The dynamic adjustment rule of laser power is: when the density deviation exceeds 2%, power adjustment is triggered, and the adjustment range is ±10%. When the density deviation exceeds 5%, local remelting is triggered, and the remelting area is 1-3mm2.

8. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: The driving execution component includes a high-precision galvanometer system and a powder spreading mechanism, wherein the high-precision galvanometer system is configured as a scanning bionic structure, and its path planning adopts spiral scanning, and the powder spreading mechanism is driven by a scraper of a servo motor.

9. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: During the printing process, the implant is subjected to micro-CT scanning calibration to generate a calibration result. Based on the calibration result, the implant that does not meet the requirements is partially remelted until the entire implant is calibrated. Specifically, During the printing process, after the preset number of layers are printed, the micro-CT scan calibration is automatically triggered to generate a three-dimensional density distribution map; Compare the three-dimensional density distribution map and the preset gradient model. When the local deviation between the two exceeds 3%, mark the defective area and perform local remelting.

10. The method for additive manufacturing of gradient density metal implants according to claim 1, characterized in that: Also includes: By introducing interconnected pores in the inner porous area through short-time power pulses, controllable pores are obtained, wherein the porosity of the outer layer is ≤1%; the controllable pores of the inner layer are 3-5%.

Images