Universal laser path optimization method and module for metal 3D printing

Abstract

The invention discloses a universal laser path optimization method and module for metal 3D printing, and the method comprises the following steps: S1, carrying out the slicing and initial path planning of a three-dimensional model, and generating printing data containing vector coordinates, a scanning time sequence, laser power and a scanning speed; s2, the printing data and the equipment parameters are imported into a path heat balance optimization module, and a high forming risk area is evaluated based on a temperature field distribution model; s3, laser path secondary optimization is conducted on the high forming risk area through a preset optimization function, and a new laser scanning path vector data set is generated; s4, importing the optimized path vector data set into printer control software for processing; the laser parameters and the scanning path are dynamically adjusted, so that the local temperature gradient is reduced, the thermal stress concentration is reduced, and the defects of warping, collapse and the like are overcome; the optimization function based on the mathematical model can adapt to various complex structures and is suitable for efficient printing of macroscopic-mesoscopic integrated parts, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to additive manufacturing, and in particular to a laser path optimization method and module for general metal 3D printing. Background Art

[0002] The selective laser melting process slices the three-dimensional model and then uses a laser beam to scan and melt the metal powder layer by layer along a preset path to achieve layer-by-layer manufacturing of parts. Existing process software usually uses three general scanning strategies: strip, chessboard, and single vector in the path planning stage. However, for parts with mesoscopic structures such as thin walls, hollow structures, thin rods, porous structures, lattices, and macro-mesoscopic integrated structures, the above strategies have significant defects: due to the uneven temperature field, the local temperature gradient is too large, which causes thermal stress concentration, and then causes quality defects such as warping, collapse, and fracture. Although users can manually optimize the laser path for individual parts, this method lacks versatility and cannot be extended to other parts or scenarios. Summary of the Invention

[0003] The purpose of the present invention is to provide a universal laser path optimization method and module for metal 3D printing, which reduces thermal stress concentration during the forming process through thermal balance regulation, solves the quality defect problem when printing complex structural parts, and improves the universality of the process.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A general laser path optimization method for metal 3D printing, comprising the following steps:

[0006] S1, slicing the 3D model and initial path planning to generate printing data including vector coordinates, scanning timing, laser power and scanning speed;

[0007] S2, importing the printing data and equipment parameters into a path thermal balance optimization module, and evaluating high forming risk areas based on a temperature field distribution model;

[0008] S3, performing secondary optimization of the laser path in the high forming risk area using a preset optimization function to generate a new laser scanning path vector data set;

[0009] S4, importing the optimized path vector data set into the printer control software for processing.

[0010] In a preferred embodiment, the equipment parameters include but are not limited to the laser focus spot size, the number of lasers, the size of the laser scanning area, and the platform preheating temperature.

[0011] In a preferred solution, the optimization function is iteratively trained through process simulation and actual printing tests to achieve dynamic control of the temperature field distribution model; the specific optimization items of the optimization function include but are not limited to dynamic adjustment optimization of laser power, vector spacing optimization, scanning timing optimization and vector length optimization.

[0012] In a preferred embodiment, the iterative training includes reversely correcting the parameter weights of the temperature field distribution model based on the defect position of the formed part and the temperature field simulation results, until the optimized path makes the temperature gradient fluctuation of the target area less than a preset threshold.

[0013] In a preferred solution, the dynamic adjustment and optimization of laser power includes changing fixed power to variable power, and supports power adjustment within a stripe, between stripes, within a single layer, and in the height direction.

[0014] In a preferred solution, the vector spacing optimization includes dynamically adjusting the spacing between adjacent vectors according to the regional risk level and synchronously increasing or decreasing the number of vectors.

[0015] In a preferred solution, the scanning timing optimization includes adjusting the vector scanning order to avoid heat accumulation caused by continuous melting.

[0016] In a preferred solution, the vector length optimization includes merging short vectors to reduce temperature field fluctuations, or splitting long vectors into short vectors and performing jump scanning.

[0017] In addition, the present application also proposes a path heat balance optimization module for executing the method, the module comprising:

[0018] A data input unit, used for receiving printing data and device parameters;

[0019] Risk assessment unit with built-in temperature field distribution model to identify high forming risk areas based on finite element analysis or machine learning algorithms;

[0020] Optimization function execution unit, including power regulation module, spacing calculation module, timing planning module and vector processing module;

[0021] The data output unit generates NC code or CLI file containing the optimized vector data set.

[0022] In a preferred embodiment, the temperature field distribution model is a finite element model based on a heat conduction equation or a neural network model trained based on historical printing data, and the input parameters include material thermal conductivity, laser energy density, and scanning path geometric characteristics.

[0023] Due to the application of the above technical solution, the beneficial effects of this application compared with the prior art are:

[0024] This application provides a general laser path optimization method and module for metal 3D printing. By dynamically adjusting laser parameters and scanning paths, local temperature gradients are reduced, thermal stress concentration is reduced, and defects such as warping and collapse are significantly improved. The optimization function based on the mathematical model can adapt to a variety of complex structures, avoiding manual case-by-case optimization, and is suitable for the efficient printing of macro-mesoscopic integrated parts. While ensuring the forming quality, the printing time is reduced through timing and power optimization, thereby improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 This is a flow chart of a general laser path optimization method for metal 3D printing according to the present invention;

[0027] Figure 2 This is a schematic diagram of the existing scanning strategy;

[0028] Figure 3 A schematic diagram showing a comparison of the dynamic adjustment of laser power according to the present invention;

[0029] Figure 4 Schematic diagram of a path heat balance optimization module of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In this application, terms such as "upper," "lower," "left," "right," "front," "back," "top," "bottom," "inner," "outer," "center," "vertical," "horizontal," "transverse," and "longitudinal" indicate positions or locations based on the positions or locations shown in the accompanying drawings. These terms are primarily intended to better describe the present invention and its embodiments and are not intended to limit the devices, elements, or components indicated to having a specific orientation, or to being constructed or operated in a specific orientation.

[0033] Furthermore, some of the above terms may be used to express other meanings besides indicating a position or location. For example, the term "on" may also be used to indicate a dependency or connection in certain circumstances. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0034] Furthermore, the terms "installed," "disposed," "provided with," "connected," "connected," and "socketed" should be interpreted broadly. For example, they can refer to fixed connections, removable connections, or integral structures; mechanical connections or electrical connections; direct connections or indirect connections through an intermediary; or internal communication between two devices, elements, or components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0035] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] Example 1

[0037] See Figure 1-3 , the present application provides a general laser path optimization method for metal 3D printing, comprising the following steps:

[0038] S1, slicing the 3D model and initial path planning to generate printing data including vector coordinates, scanning timing, laser power and scanning speed;

[0039] S2, importing the printing data and equipment parameters into a path thermal balance optimization module, and evaluating high forming risk areas based on a temperature field distribution model;

[0040] The equipment parameters include but are not limited to the laser focus spot size, the number of lasers, the laser scanning area size, and the platform preheating temperature;

[0041] S3, performing secondary optimization of the laser path in the high forming risk area using a preset optimization function to generate a new laser scanning path vector data set;

[0042] The optimization function dynamically controls the temperature field distribution model through iterative training of process simulation and actual printing tests. The iterative training includes reversely correcting the parameter weights of the temperature field distribution model based on the defect location of the formed part and the temperature field simulation results, until the optimized path causes the temperature gradient fluctuation in the target area to be less than a preset threshold.

[0043] Specific optimization items of the optimization function include but are not limited to laser power dynamic adjustment optimization, vector spacing optimization, scanning timing optimization and vector length optimization;

[0044] The dynamic adjustment and optimization of laser power includes changing fixed power to variable power, and supports power adjustment within a stripe, between stripes, within a single layer, and in the height direction;

[0045] The vector spacing optimization includes dynamically adjusting the spacing between adjacent vectors according to the regional risk level and synchronously increasing or decreasing the number of vectors;

[0046] The scanning timing optimization includes adjusting the vector scanning order to avoid heat accumulation caused by continuous melting;

[0047] The vector length optimization includes merging short vectors to reduce temperature field fluctuations, or splitting long vectors into short vectors and performing jump scanning.

[0048] S4, importing the optimized path vector data set into the printer control software for processing.

[0049] Example 2

[0050] A detailed introduction to a general laser path optimization method for metal 3D printing is provided, which includes the following steps:

[0051] S1, slicing and initial path planning

[0052] The three-dimensional model is sliced to generate layer-by-layer two-dimensional contour data, and initial path planning is performed through traditional process software to obtain printing data including vector coordinates, scanning timing, laser power (such as 100W-500W), and scanning speed (such as 5000mm / s-15000mm / s).

[0053] S2 Risk Area Assessment

[0054] The printing data and equipment parameters (including laser focus spot size 50μm-100μm, number of lasers, scanning area size, platform preheating temperature 50℃-400℃, etc.) are imported into the path thermal balance optimization module, and high forming risk areas (such as thin-walled areas with temperature gradient >50℃ / mm) are identified based on the temperature field distribution model (such as finite element model or neural network model).

[0055] S3 laser path secondary optimization

[0056] Targeted adjustments are made to high-forming risk areas through preset optimization functions. Specific optimization items include:

[0057] Dynamic laser power adjustment: Fixed power is changed to variable power, supporting power gradient adjustment within a stripe (adjacent vector power difference ≤ 10%), between stripes, within a single layer, and in the height direction to avoid local overheating.

[0058] Vector spacing optimization: Dynamically adjust the hatching distance (HD) between adjacent vectors based on the risk level. Reduce HD by 10%-30% and increase the number of vectors in risky areas, and increase HD by 10%-20% in low-risk areas to reduce scanning time.

[0059] Scan timing optimization: Use jump scanning (short vector interval ≥ 3 vectors) or "Z"-shaped timing (cooling time between adjacent scans in thin-walled areas ≥ 0.5 seconds) to avoid heat accumulation caused by continuous melting.

[0060] Vector length optimization: Merge short vectors <2 mm (length ≤ 20 mm after merging, corners > 45°) to reduce temperature fluctuations, or split long vectors >50 mm into 2-5 segments (delay laser shutdown for 50-100 ms at each segment) to improve melt pool stability.

[0061] The optimization function is trained through reverse iteration of process simulation (such as ANSYS thermal conduction analysis) and actual printing tests, and the model parameter weights are corrected based on the defect location and temperature field simulation results until the temperature gradient fluctuation in the target area is less than the preset threshold (such as 10°C / mm).

[0062] S4 path data output and printing

[0063] Import the optimized path vector dataset (including NC code or CLI file) into the printer control software to drive the device to complete the processing.

[0064] Example 3

[0065] See Figure 4 In addition, the present application also proposes a path heat balance optimization module for executing the method, the module comprising:

[0066] Data input unit, used to receive printing data (layer thickness, vector coordinates, laser parameters) and equipment parameters (spot diameter, preheating temperature, etc.);

[0067] The risk assessment unit has a built-in temperature field distribution model (a finite element model based on the heat conduction equation or a neural network model trained with historical data). It inputs parameters such as material thermal conductivity and laser energy density, and identifies high-forming risk areas based on finite element analysis or machine learning algorithms.

[0068] Optimized function execution unit, including power regulation module (dynamic power algorithm), spacing calculation module (HD adaptive adjustment), timing planning module (scan order rearrangement) and vector processing module (merge / split strategy), supporting user-defined parameters (power accuracy ±1W, timing offset ±0.1ms);

[0069] The data output unit generates NC code or CLI files containing optimized vector data sets, which are compatible with mainstream SLM equipment control software.

[0070] The temperature field distribution model is a finite element model based on the heat conduction equation or a neural network model trained based on historical printing data, and the input parameters include material thermal conductivity, laser energy density and scanning path geometric characteristics.

[0071] Example 4

[0072] Take the printing of a titanium alloy thin-walled part (wall thickness 0.5mm, including 30μm thin rod structure) as an example:

[0073] S1, initial planning: slice thickness 50 μm, generate an initial path containing 2000 short vectors (average length 3 mm), fixed power 200 W, HD = 100 μm.

[0074] S2, risk assessment: The module identifies the thin rod area with a temperature gradient of 80°C / mm (>threshold 50°C / mm) and marks it as a high-risk area.

[0075] S3, secondary optimization:

[0076] Power adjustment: The power in the thin rod area gradually changes from 200W to 180W (the power difference between adjacent vectors is 5%) to reduce local heat input.

[0077] Spacing adjustment: HD is reduced from 100μm to 80μm, and 500 vectors are added to improve melt pool continuity.

[0078] Timing adjustment: Use a jump scan with an interval of 5 vectors to ensure that the cooling time between each scan in the thin rod area is ≥ 0.8 seconds.

[0079] Vector processing: Merge short vectors less than 2mm into 5-10mm segments, and reserve a 45° transition zone at the corners.

[0080] S4, printing verification: After optimization, no fracture occurred in the thin rod area of the part, the thin-wall warpage was reduced from 0.3mm to 0.05mm, and the forming accuracy was improved by 83%.

[0081] Temperature field balancing: Through dynamic power, spacing, and timing adjustments, the temperature gradient in high-risk areas is reduced by 40%-60%, thermal stress concentration is significantly improved, and the incidence of defects such as warping and collapse is reduced by more than 75%.

[0082] Versatility and intelligence: The optimization function based on iterative training can automatically adapt to different part structures (such as complex lattices in aerospace and porous structures in medical implants) without the need for manual case-by-case adjustments, improving process adaptation efficiency by 300%.

[0083] Excellent quality and efficiency: While ensuring the forming accuracy of the mesostructure, the printing time of typical parts is shortened by 15%-25% through long vector splitting and increasing the spacing between non-critical areas, significantly improving production efficiency.

[0084] This application provides a general laser path optimization method and module for metal 3D printing. By dynamically adjusting laser parameters and scanning paths, local temperature gradients are reduced, thermal stress concentration is reduced, and defects such as warping and collapse are significantly improved. The optimization function based on the mathematical model can adapt to a variety of complex structures, avoiding manual case-by-case optimization, and is suitable for the efficient printing of macro-mesoscopic integrated parts. While ensuring the forming quality, the printing time is reduced through timing and power optimization, thereby improving production efficiency.

[0085] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A general laser path optimization method for metal 3D printing, characterized in that: The following steps are involved: S1, slicing the 3D model and initial path planning to generate printing data including vector coordinates, scanning timing, laser power and scanning speed; S2, importing the printing data and equipment parameters into a path thermal balance optimization module, and evaluating high forming risk areas based on a temperature field distribution model; S3, performing secondary optimization of the laser path in the high forming risk area using a preset optimization function to generate a new laser scanning path vector data set; S4, importing the optimized path vector data set into the printer control software for processing.

2. The laser path optimization method for general metal 3D printing according to claim 1, characterized in that: The equipment parameters include but are not limited to the laser focus spot size, the number of lasers, the size of the laser scanning area, and the platform preheating temperature.

3. The laser path optimization method for general metal 3D printing according to claim 1, characterized in that: The optimization function is iteratively trained through process simulation and actual printing tests to achieve dynamic control of the temperature field distribution model; the specific optimization items of the optimization function include but are not limited to dynamic adjustment optimization of laser power, vector spacing optimization, scanning timing optimization and vector length optimization.

4. The laser path optimization method for general metal 3D printing according to claim 3, characterized in that: The iterative training includes reversely correcting the parameter weights of the temperature field distribution model based on the defect position of the formed part and the temperature field simulation results, until the optimized path makes the temperature gradient fluctuation of the target area less than a preset threshold.

5. The laser path optimization method for general metal 3D printing according to claim 3, characterized in that: The dynamic adjustment and optimization of laser power includes changing fixed power to variable power, and supports power adjustment within a stripe, between stripes, within a single layer, and in the height direction.

6. The laser path optimization method for general metal 3D printing according to claim 3, characterized in that: The vector spacing optimization includes dynamically adjusting the spacing between adjacent vectors according to the regional risk level and synchronously increasing or decreasing the number of vectors.

7. The laser path optimization method for universal metal 3D printing according to claim 3, characterized in that: The scanning timing optimization includes adjusting the vector scanning sequence to avoid heat accumulation caused by continuous melting.

8. The laser path optimization method for universal metal 3D printing according to claim 3, characterized in that: The vector length optimization includes merging short vectors to reduce temperature field fluctuations, or splitting long vectors into short vectors and performing jump scanning.

9. A path heat balance optimization module, characterized in that: Used to execute the method according to any one of claims 1 to 8, the module comprises: A data input unit, used for receiving printing data and device parameters; Risk assessment unit with built-in temperature field distribution model to identify high forming risk areas based on finite element analysis or machine learning algorithms; Optimization function execution unit, including power regulation module, spacing calculation module, timing planning module and vector processing module; The data output unit generates NC code or CLI file containing the optimized vector data set.

10. The path heat balance optimization module according to claim 9, characterized in that: The temperature field distribution model is a finite element model based on the heat conduction equation or a neural network model trained based on historical printing data, and the input parameters include material thermal conductivity, laser energy density and scanning path geometric characteristics.

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