A method, system and terminal for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement

Abstract

The present invention relates to the field of material extrusion additive manufacturing technology, and in particular to a method, system, and terminal for optimizing process parameters for additive manufacturing of density-adjustable materials based on online volume and mass measurement. The process parameter optimization method comprises the following steps: obtaining volume data of a printed part, mass data of printed material consumed, and a cross-sectional profile of a single-pass printing deposition path under different process parameters, thereby obtaining the relationship between different process parameters and actual printing density, and the relationship between the geometric dimensions of a single-pass cross-section and feed volume flow rate under different process parameters; then establishing an input-output model between the process parameters and foam density; and optimizing the process parameters by online measuring the volume and mass of the printed part and inputting them into the input-output model. By establishing the input-output model between the process parameters and foam density, and by online measuring the volume and mass of the printed part and inputting them into the input-output model, real-time monitoring and adjustment are achieved, ensuring the consistency and stability of printing quality, and optimizing the process parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of material extrusion additive manufacturing, and in particular to a method, system and terminal for optimizing process parameters of density-adjustable material additive manufacturing based on online measurement of volume and mass. Background Art

[0002] While material extrusion additive manufacturing (MEX AM) offers numerous advantages for manufacturing microporous foams, it still faces technical challenges in achieving precise control of material density and porosity. Conventional density measurement methods typically rely on invasive procedures such as the water displacement method, resulting in inaccurate measurements and requiring frequent manipulation. Furthermore, the highly coupled and nonlinear relationships between the effects of different process parameters (such as printing speed and temperature) on the density and properties of foam materials make it difficult to achieve precise control of the microstructure and macroscopic properties of foam materials quickly and efficiently using conventional methods.

[0003] Currently, some studies have attempted to control the density and porosity of microcellular foam materials during MEX AM by adjusting printing parameters. However, most of these methods rely on offline measurement and manual adjustment, and cannot achieve real-time monitoring and automatic adjustment, resulting in the following shortcomings:

[0004] 1. Inaccurate and cumbersome measurement: Traditional density measurement methods rely on invasive procedures such as the water displacement method, which are cumbersome, inefficient, and inaccurate.

[0005] 2. Difficulty in real-time monitoring and adjustment: Existing technologies cannot achieve real-time monitoring and adjustment of key parameters during the printing process (such as nozzle pressure and wire feed rate), resulting in unstable printing quality;

[0006] 3. Complex parameter window construction: Due to the highly coupled and nonlinear relationship between the effects of different process parameters on material density and performance, traditional methods have difficulty in quickly constructing parameter windows and exploring new material properties.

[0007] 4. High customization difficulty: Existing technologies make it difficult to achieve density customization in 3D space (including within and between layers), which limits the application of MEX AM in the manufacture of high-performance foam materials.

[0008] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0009] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method, system and terminal for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass, aiming to solve the problem that the density and porosity of microporous foam materials cannot be monitored and automatically adjusted in real time during the existing MEX AM process.

[0010] The technical solutions of the present invention are as follows:

[0011] A method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising the steps of:

[0012] The volume data and mass data of the printed parts and the single-pass cross-sectional profile are obtained under different process parameters. The relationship between different process parameters and actual printing density, and the relationship between the geometric dimensions of the single-pass cross-sectional dimensions and the feed volume flow rate under different process parameters are obtained.

[0013] Based on the relationship between the different process parameters and the actual printing density, combined with the single-channel cross-sectional profile and the analysis of the relationship between the single-channel cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the foam density is established;

[0014] The process parameters are optimized by measuring the volume and mass of the printed part online and inputting them into the input-output model.

[0015] The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass, wherein the process parameters include but are not limited to one or more of printing speed and printing temperature.

[0016] In the method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, the step of obtaining volume data and mass data of a printed part and a single-pass cross-sectional profile under different process parameters includes:

[0017] The width and layer height of the printed track are acquired using a line laser scanner to obtain the cross-sectional profile of a single track;

[0018] The encoder is used to obtain the wire feed length, and the corresponding mass is deduced based on the raw material density to obtain the volume data and mass data of the printed part.

[0019] The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, wherein the step of obtaining the wire feed length and mass using an encoder further includes:

[0020] Measuring the actual length and actual mass of the wire feed by using a first encoder;

[0021] The second encoder is used to measure the motor rotation angle and output the theoretical length and theoretical mass of the wire feed;

[0022] By comparing the actual length and the actual mass with the theoretical length and the theoretical mass, whether the motor has a wire slippage phenomenon is monitored.

[0023] The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, wherein the geometric dimensions of the single-channel section include single-channel width, layer height, and cross-sectional area; the method for establishing the input-output model includes but is not limited to linear interpolation and genetic algorithm.

[0024] The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, wherein, after completing the step of optimizing the process parameters, the method further includes:

[0025] Based on the input-output model, process parameters are designed according to the density gradient, and the porosity is controlled by combining real-time regulation of process parameters to achieve additive manufacturing of gradient structures.

[0026] The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, wherein, for a print with unknown material properties, 3D point cloud and mass measurement are performed on the print.

[0027] A method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising the steps of:

[0028] The volume and mass data of the printed parts and the single-pass cross-sectional profile are obtained under different process parameters. The relationship between different process parameters and the actual printing volume, and the relationship between the geometric dimensions of the single-pass cross-sectional dimensions and the feed volume flow rate under different process parameters are obtained.

[0029] Based on the relationship between the different process parameters and the actual printing volume, combined with the single-pass cross-sectional profile and the analysis of the relationship between the single-pass cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the print volume is established;

[0030] The process parameters are optimized by measuring the volume and mass of the printed part online and inputting them into the input-output model.

[0031] A system for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising:

[0032] A data acquisition module, used to obtain volume data and mass data of the printed part and a single-channel cross-sectional profile;

[0033] A model building module, for establishing an input-output model between process parameters and foam density or an input-output model between process parameters and printed part volume;

[0034] The process parameter optimization module is used to optimize the process parameters according to the input-output model.

[0035] A terminal comprises a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, the program implements the steps of a method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement.

[0036] Beneficial effect: The present invention provides a method, system and terminal for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass includes the following steps: obtaining volume data of printed parts and mass data consumed by printed materials and cross-sectional profile of single-channel printing deposition path under different process parameters, and obtaining the relationship between different process parameters and actual printing density, and the relationship between single-channel cross-sectional geometric dimensions and feed volume flow rate under different process parameters; establishing an input-output model between process parameters and foam density based on the relationship between the different process parameters and actual printing density, combined with the cross-sectional profile of the single-channel printing deposition path and analysis of the relationship between the single-channel cross-sectional geometric dimensions and feed volume flow rate under different process parameters; and completing the optimization of process parameters by measuring the volume and mass of printed parts online and inputting them into the input-output model. The present invention performs real-time online measurement of the volume and mass of printed parts during the material extrusion additive manufacturing process. This non-contact measurement method can accurately and efficiently obtain key parameters in the printing process, such as the geometric dimensions of a single-channel cross-section and the volume flow rate, thereby realizing online monitoring of the density of the microporous foam material. At the same time, by systematically studying the influence of different process parameters on thermally expandable microspheres, an input-output model between the process parameters and the foam density is established. By measuring the volume and mass of the printed parts online and inputting them into the input-output model, real-time monitoring and adjustment are realized, ensuring the consistency and stability of the printing quality and completing the optimization of the process parameters. In addition, the model can be used to realize the density gradient design of structural components and the precise manufacturing of micro-gradient structures, thereby constructing the performance gradient of the printed parts and realizing reverse design and manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of a process parameter optimization method for additive manufacturing of density-adjustable materials based on online volume and mass measurement of the present invention;

[0038] Figure 2 This is a density measurement data diagram under different process parameters in Example 1;

[0039] Figure 3 This is a single-channel cross-sectional profile diagram under different process parameters in Example 1;

[0040] Figure 4 This is a relationship diagram of single-channel width, layer height, cross-sectional area, and volume flow rate under different process parameters in Example 1;

[0041] Figure 5 This is a nonlinear mapping relationship diagram between process parameters and density in Example 1;

[0042] Figure 6 Schematic diagram of the design and additive manufacturing of the density (porosity) gradient structure in Example 1;

[0043] Figure 7 This is a microscopic characteristic diagram of the density (porosity) gradient structure under different process parameters in Example 1. DETAILED DESCRIPTION

[0044] The present invention provides a method, system, and terminal for optimizing process parameters for additive manufacturing of density-adjustable materials based on online volume and mass measurement. To clarify the objectives, technical solutions, and effects of the present invention, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0045] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0046] Additive manufacturing technology, especially material extrusion additive manufacturing (MEX AM), has been widely used in recent years. This technology builds complex geometric structures and functional components by depositing molten thermoplastic materials layer by layer. Due to its advantages of high efficiency, low cost and low material waste, it is widely used in aerospace, automotive and medical fields. Microcellular foam materials are often used in packaging, cushioning, thermal management and structural applications due to their excellent shock absorption, heat insulation and cushioning properties. Traditional foam manufacturing methods include online foaming, extrusion foaming and injection molding foaming, but these methods have challenges in producing complex geometries and functional gradient materials.

[0047] However, existing density measurement technologies for additively manufactured parts have technical problems such as inaccurate measurement, difficulty in real-time monitoring, complex parameter window construction, and high difficulty in density customization.

[0048] Based on this, for materials with varying density, the principle of conservation of mass is used, such as Figure 1 As shown, the present invention provides a method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising the steps of:

[0049] Step S10: Obtaining volume data and mass data of the printed part and a single-pass cross-sectional profile under different process parameters, and obtaining the relationship between different process parameters and actual printing density, and the relationship between the geometric dimensions of the single-pass cross-sectional profile and the feed volume flow rate under different process parameters;

[0050] Step S20: Based on the relationship between the different process parameters and the actual printing density, combined with the single-channel cross-sectional profile and the analysis of the relationship between the single-channel cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the foam density is established;

[0051] Step S30: Optimizing the process parameters by measuring the volume and mass of the printed part online and inputting the measured values ​​into the input-output model.

[0052] In this embodiment, by performing real-time online measurement of the volume and mass of the printed part during the material extrusion additive manufacturing process, this non-contact measurement method can accurately obtain key parameters in the printing process, such as the single-channel cross-sectional geometric dimensions and volume flow rate, thereby realizing online monitoring of the density of the microporous foam material; at the same time, by systematically studying the influence of different process parameters on thermally expandable microspheres, an input-output model between process parameters and foam density is established, and by measuring the volume and mass of the printed part online and inputting them into the input-output model, real-time monitoring and adjustment are realized to ensure the consistency and stability of the printing quality, thereby completing the optimization of the process parameters.

[0053] Specifically, the density-adjustable material additive manufacturing process parameter optimization method based on online volume and mass measurement of the present invention obtains printing data in real time through non-contact, avoids the measurement inaccuracy problem caused by traditional invasive procedures, and improves the accuracy and efficiency of density measurement; and, the process parameter optimization method can monitor key parameters in real time during the printing process, and automatically adjust according to the measurement data to ensure the consistency and stability of the printing quality. In addition, the present invention systematically studies the influence of different process parameters on material density, quickly establishes an input-output model of process parameters and density, and facilitates the optimization of printing elaboration and exploration of new material properties. Moreover, using the established input-output model, customized design of density can be achieved in 3D space (including within and between layers), thereby improving the manufacturing capacity of high-performance foam materials. That is, the present invention realizes density control and optimization of microporous foam materials in the additive manufacturing process through real-time measurement and data analysis, providing new methods and ideas for the design and manufacture of high-performance materials.

[0054] In some embodiments, after step S30, step S40 is further included: based on the input-output model, process parameters are designed according to the density (porosity) gradient, and porosity is controlled by real-time control of the process parameters to achieve additive manufacturing of gradient structures. Using the input-output model, the corresponding parameters are output based on the gradient structure additive material to be printed, and the process parameters at each stage are defined to obtain the gradient structure additive material, thereby achieving reverse design and manufacturing.

[0055] In some embodiments, the process parameters include, but are not limited to, one or more of printing speed and printing temperature. By controlling the printing speed and temperature, the density of the printed part can be adjusted. By analyzing the relationship between process parameters and density, an input-output model between process parameters and foam density can be established. This model accurately describes how process parameters affect the density distribution of the final material. Using this model, density gradient design can be performed for structural components, ensuring desired density characteristics are achieved in different regions, thereby optimizing the overall performance and functionality of the material.

[0056] In some embodiments, in step S10, the step of obtaining volume data and mass data of the printed part and a single-pass cross-sectional profile under different process parameters includes:

[0057] Step S11: using a line laser scanner to obtain the width and layer height of the printing track to obtain a single track cross-sectional profile;

[0058] Step S12: Use the encoder to obtain the wire feed length, and deduce the corresponding mass in combination with the raw material density to obtain the volume data and mass data of the printed part.

[0059] In this embodiment, a single-track cross-sectional profile is obtained by measuring the width and layer height of the printing track using a line laser scanner, and the relationship between the geometric dimensions of the single-track cross-sectional profile and the feed volume flow rate under different process parameters can be obtained through the single-track cross-sectional profile; and the encoder can be used to obtain the wire feed length and mass, and the length can be used to calculate the volume data of the wire. In addition, the microcontroller can also be used to calculate the speed and time of the wire feed.

[0060] Specifically, during the printing process, a line laser scanner and encoder operate simultaneously, acquiring real-time mass and volume data for the printed part. This real-time data is then transmitted to a computer, where software processes the data and calculates the actual print density. Furthermore, based on the measured data under different process parameters, the relationship between these parameters and the actual print density, the relationship between these parameters and the cross-sectional profile of a single pass, and the relationship between the geometric dimensions of a single pass and the feed volume flow rate under different process parameters are determined. Based on this information, an input-output model is established between the process parameters and foam density. This model is used to optimize printing parameters, achieving density gradient design and printing structural components with specific density gradients.

[0061] In some embodiments, the line laser scanner can also be replaced by a laser rangefinder or an ultrasonic measuring instrument; the laser rangefinder has the characteristics of high precision and fast response, and is suitable for real-time monitoring of geometric changes during the printing process; the ultrasonic measuring instrument can obtain material thickness and geometric shape in real time during the printing process, and is suitable for non-contact measurement.

[0062] In some embodiments, in step S20, an input-output model between process parameters and foam density is established by performing data processing and process parameter optimization using a machine learning algorithm; that is, by learning and analyzing historical data and real-time data, the machine learning system can automatically adjust printing parameters to further improve printing quality and efficiency.

[0063] Furthermore, an intelligent control system was developed to achieve comprehensive monitoring and optimization of the printing process by integrating multiple sensors and data processing algorithms. The system can dynamically adjust process parameters to cope with different materials and printing conditions.

[0064] In some embodiments, in step S12, the step of obtaining the wire feed length and mass using an encoder further includes:

[0065] Step S121: using a first encoder to measure the actual length and actual mass of the wire feed;

[0066] Step S122: using a second encoder to measure the motor rotation angle and output the theoretical length and theoretical mass of the wire feed;

[0067] Step S123: monitoring whether the motor has wire slippage by comparing the actual length and the actual mass with the theoretical length and the theoretical mass.

[0068] In some embodiments, the geometric dimensions of the single-lane cross section include, but are not limited to, single-lane width, layer height, and cross-sectional area.

[0069] In some embodiments, methods for establishing the input-output model include but are not limited to linear interpolation and genetic algorithms.

[0070] For example, linear interpolation is used to perform preliminary processing and parameter estimation on volume data and mass data to help determine the basic trends of density and performance; then, genetic algorithms are used to simulate natural selection and evolution processes to optimize the combination of process parameters to ensure that the best foam density and performance configuration is found. In addition, other statistical and machine learning methods, such as regression analysis and neural networks, are used to further improve the accuracy and robustness of the model. Through these models, we can deeply analyze the influence of different process parameters (such as temperature, pressure, injection speed, etc.) on foam material properties (such as density gradient, strength, toughness, etc.), and ultimately provide a scientific basis and data support for optimizing production processes and material design.

[0071] In some implementations, for a print with unknown material properties, a 3D point cloud and quality measurement are performed on the print.

[0072] In this embodiment, when the material properties of the printed part are unknown, multiple sets of printing speeds and temperatures are selected, test samples are printed, and data is recorded through 3D point cloud and mass measurement and encoders; based on the experimental data, methods including but not limited to linear interpolation and genetic algorithms are used to establish a relationship model between process parameters and density (microstructure) and performance, and analyze the influence of different process parameters on material properties.

[0073] In some implementations, simulation technology can be used in place of the actual data acquisition process in step S10; computer simulation technology can be used to study the relationship between material process parameters, density, and performance. By establishing a numerical model of the material, the impact of different process parameters on material density and performance can be simulated in a virtual environment, reducing the cost and time of actual experiments. Alternatively, multi-material printing technology can be used to replace the actual data acquisition process for a single material in step S10. This technology combines different materials to create functionally graded structures. Multi-material printing technology allows for simultaneous printing of multiple materials, enabling more complex functional design and optimization.

[0074] In addition, for materials with constant density, the principle of volume conservation is adopted. The present invention also provides a method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising the following steps:

[0075] Step S100: Obtaining volume data and mass data of the printed part and a single-pass cross-sectional profile under different process parameters, and obtaining the relationship between the different process parameters and the actual printing volume, and the relationship between the geometric dimensions of the single-pass cross-sectional profile and the feed volume flow rate under different process parameters;

[0076] Step S200: Based on the relationship between the different process parameters and the actual printing volume, combined with the single-pass cross-sectional profile and the analysis of the relationship between the single-pass cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the printed part volume is established;

[0077] Step S300: Optimizing process parameters by measuring the volume and mass of the printed part online and inputting the measured values ​​into the input-output model.

[0078] In this embodiment, for materials with constant density, a relationship model between process parameters (such as printing speed, path width, layer height, cross-sectional area and volume flow) and the volume of the printed part is established. The printing parameters are optimized based on the measured print volume data to ensure precise control of the material volume under the premise of constant density. During the printing process, the printing parameters are adjusted according to real-time measurement data to ensure the volume consistency and stability of the printed part.

[0079] For materials with unknown properties, through systematic experimental research and data analysis, we gain an in-depth understanding of the process parameter-density-performance relationship of unknown materials, providing a scientific basis for the application and development of new materials. By utilizing established models and online measurement technology, we monitor and adjust process parameters in real time during the printing process to ensure that the density and performance of the material meet the expected requirements. Furthermore, by precisely controlling process parameters, we improve the quality and consistency of printed parts, enhance the mechanical properties and functional characteristics of the material, and meet the requirements of various applications.

[0080] In summary, compared with the prior art, the method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement of the present invention has the following advantages:

[0081] 1. Non-contact online density measurement:

[0082] The existing technology mainly adopts invasive procedures and drainage methods to measure density. The present invention uses a line laser scanner and an encoder to achieve non-contact online density measurement, which significantly improves the accuracy and efficiency of measurement.

[0083] 2. Real-time monitoring and automatic adjustment:

[0084] Traditional methods make it difficult to monitor and adjust key parameters in real time during the printing process. This invention obtains the geometric and volumetric data of the printed part in real time and automatically adjusts the printing parameters based on the measurement results, ensuring the consistency and stability of the printing quality.

[0085] 3. Research methods for various material properties

[0086] The present invention is not only applicable to materials whose density changes with process parameters (mass conservation), but also to materials whose density remains unchanged (volume conservation). Corresponding process parameter and density / volume relationship models are established respectively, covering a wider range of material types.

[0087] 4. Density Gradient Design and Optimization

[0088] By establishing a model of the relationship between process parameters and density / performance, the present invention achieves customized design of density within 3D space (including within and between layers), and can print structural components with specific density gradients.

[0089] 5. Performance optimization and adaptive adjustment

[0090] During the printing process, the present invention can achieve precise control of the mechanical properties and functional characteristics of the material through real-time measurement of data and adaptive adjustment of process parameters, thereby improving the quality and consistency of the printed parts.

[0091] Of course, the non-contact measurement of the present invention can also be performed using infrared measurement, optical scanning, etc.; the present invention can also use contact measurement systems to measure data. Although non-contact measurement has many advantages, in some cases, contact measurement systems such as robotic arms or probes can also be used to obtain geometric data of printed parts. These systems can provide higher accuracy and stability under certain conditions. Alternatively, optical interferometers can be used for high-precision surface topography measurement. Optical interferometers can provide nanometer-level measurement accuracy and are suitable for detailed analysis of surface smoothness and microstructure.

[0092] In addition, the present invention also provides a density-adjustable material additive manufacturing process parameter optimization system based on online volume and mass measurement, comprising:

[0093] A data acquisition module, used to obtain volume data and mass data of the printed part and a single-channel cross-sectional profile;

[0094] A model building module, for establishing an input-output model between process parameters and foam density or an input-output model between process parameters and printed part volume;

[0095] The process parameter optimization module is used to optimize the process parameters according to the input-output model.

[0096] In addition, the present invention also provides a terminal, which includes a memory, a processor, and a program stored in the memory and runnable on the processor. When the program is executed by the processor, the steps of the method for optimizing the process parameters of density-adjustable material additive manufacturing based on online measurement of volume and mass are implemented.

[0097] In some embodiments, the memory may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Furthermore, the memory may also include both an internal storage unit of the terminal and an external storage device. The memory is used to store application software and various types of data installed on the terminal, such as program codes of the installation terminal, etc. The memory may also be used to temporarily store data that has been output or is to be output. In one embodiment, a density-adjustable material additive manufacturing process parameter optimization program based on online volume and mass measurement is stored on the memory, and the process parameter optimization program can be executed by a processor, thereby realizing the density-adjustable material additive manufacturing process parameter optimization method based on online volume and mass measurement of the present invention.

[0098] In some embodiments, the processor can be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory, such as executing the density-adjustable material additive manufacturing process parameter optimization method based on online volume and mass measurement.

[0099] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium stores a density-adjustable material additive manufacturing process parameter optimization program based on online volume and mass measurement, and when the density-adjustable material additive manufacturing process parameter optimization program based on online volume and mass measurement is executed by a processor, the steps of the density-adjustable material additive manufacturing process parameter optimization method based on online volume and mass measurement are implemented.

[0100] The present invention will be described in detail with reference to the following examples. It should also be understood that the following examples are only intended to further illustrate the present invention and are not to be construed as limiting the scope of protection of the present invention. Any non-essential improvements and adjustments made by those skilled in the art based on the above disclosure of the present invention fall within the scope of protection of the present invention.

[0101] Example 1

[0102] This embodiment provides a method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, comprising the following steps:

[0103] Step 1: Use the line laser scanner and encoder to work simultaneously to obtain the volume and quality data of the printed part in real time under different process parameters. The real-time data is transmitted to the computer, and the data is processed by software to calculate the actual printing density.

[0104] Step 2: Based on the measurement data under different process parameters, the following Figure 2 (The solid line represents the density obtained by online measurement based on laser scanning + encoder, and the dotted line represents the density obtained by traditional drainage method. The unmarked data points of drainage method indicate that the measurement failed and no corresponding data was obtained) The density measurement data (process parameters - density) under different process parameters are shown, as well as Figure 3 The single-pass cross-sectional profiles (process parameters-cross-sectional profiles) under different process parameters shown in FIG. Figure 4 The relationship between single channel width, layer height, cross-sectional area and volume flow rate under different process parameters is shown (process parameters-cross-sectional geometric dimensions). Figure 2 The original material density measured using the drainage method ( Figure 2 The dotted line in the figure is the drainage method), the error between the two measurement methods is analyzed, and the track density under different process conditions is determined based on the online measurement results.

[0105] based on Figure 2 The experimental data obtained in Figure 3 Single-pass cross-sectional profile under different process parameters, and analysis Figure 4 The relationship between single-pass width, layer height, cross-sectional area and volume flow rate under different process parameters is shown in the figure. Linear interpolation and genetic algorithm are used to establish the input-output model between temperature, printing speed and foam density. The nonlinear mapping relationship between process parameters and density is shown in the figure below. Figure 5 shown.

[0106] Furthermore, the model is used to reverse design density gradient structural parts. Figure 5 The model results of input process parameters and output density (microstructure caused by the degree of foaming of expanded microspheres) are combined with the actual control limitations of process parameters during printing to select appropriate process parameters to produce foam samples with uneven density (density gradient within and between 3D printing layers) and properties (microstructure), such as Figure 6 shown; among them, Figure 6 (a) and (b) are the design of gradient structures and the gradient process parameters of additive manufacturing: temperature and speed; (c) is a gradient structural part; (d)-(g) are parts with precisely controlled density.

[0107] And, show as Figure 7 The microscopic features of the density gradient structure under different process parameters are shown. Through these steps, the printing process can be precisely controlled and optimized, and the overall performance and consistency of the material can be improved.

[0108] In summary, the present invention provides a method, system and terminal for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online measurement of volume and mass includes the following steps: obtaining volume data and mass data of the printed part and the single-channel cross-sectional profile under different process parameters, and obtaining the relationship between different process parameters and actual printing density, and the relationship between the single-channel cross-sectional geometric dimensions and the feed volume flow rate under different process parameters; establishing an input-output model between process parameters and foam density based on the relationship between the different process parameters and actual printing density, combined with the single-channel cross-sectional profile and analyzing the relationship between the single-channel cross-sectional geometric dimensions and the feed volume flow rate under different process parameters; and completing the optimization of process parameters by measuring the volume and mass of the printed part online and inputting them into the input-output model. The present invention performs real-time online measurement of the volume and mass of printed parts during the material extrusion additive manufacturing process. This non-contact measurement method can accurately obtain key parameters in the printing process, such as the geometric dimensions of a single-channel cross-section and the volume flow rate, thereby realizing online monitoring of the density of the microporous foam material. At the same time, by systematically studying the influence of different process parameters on thermally expandable microspheres, an input-output model between the process parameters and the foam density is established. By measuring the volume and mass of the printed parts online and inputting them into the input-output model, real-time monitoring and adjustment are realized, ensuring the consistency and stability of the printing quality and completing the optimization of the process parameters. In addition, the model can be used to realize the density gradient design of structural components.

[0109] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, characterized in that: Including steps: The volume data and mass data of the printed parts and the single-pass cross-sectional profile are obtained under different process parameters. The relationship between different process parameters and actual printing density, and the relationship between the geometric dimensions of the single-pass cross-sectional dimensions and the feed volume flow rate under different process parameters are obtained. Based on the relationship between the different process parameters and the actual printing density, combined with the single-channel cross-sectional profile and the analysis of the relationship between the single-channel cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the foam density is established; The process parameters are optimized by measuring the volume and mass of the printed part online and inputting them into the input-output model.

2. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement according to claim 1, characterized in that: The process parameters include one or more of printing speed and printing temperature.

3. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement according to claim 1, characterized in that: The step of obtaining volume data and mass data of a printed part and a single-pass cross-sectional profile under different process parameters includes: The width and layer height of the printed track are acquired using a line laser scanner to obtain the cross-sectional profile of a single track; The encoder is used to obtain the wire feed length, and the corresponding mass is deduced based on the raw material density to obtain the volume data and mass data of the printed part.

4. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement according to claim 3, characterized in that: The step of obtaining the wire feeding length and mass by using an encoder further includes: Measuring the actual length and actual mass of the wire feed by using a first encoder; The second encoder is used to measure the motor rotation angle and output the theoretical length and theoretical mass of the wire feed; By comparing the actual length and the actual mass with the theoretical length and the theoretical mass, whether the motor has a wire slippage phenomenon is monitored.

5. The method for optimizing process parameters of density-adjustable material additive manufacturing based on online volume and mass measurement according to claim 1, characterized in that: The geometric dimensions of the single-track cross section include single-track width, layer height, and cross-sectional area; the method for establishing the input-output model includes linear interpolation and genetic algorithm.

6. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement according to claim 1, characterized in that: After the step of optimizing the process parameters is completed, the method further includes: Based on the input-output model, process parameters are designed according to the density gradient, and the porosity is controlled by combining real-time regulation of process parameters to achieve additive manufacturing of gradient structures.

7. The method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement according to claim 1, characterized in that: For prints with unknown material properties, 3D point cloud and quality measurement are performed on the prints.

8. A method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement, characterized in that: Including steps: The volume and mass data of the printed parts and the single-pass cross-sectional profile are obtained under different process parameters. The relationship between different process parameters and the actual printing volume, and the relationship between the geometric dimensions of the single-pass cross-sectional dimensions and the feed volume flow rate under different process parameters are obtained. Based on the relationship between the different process parameters and the actual printing volume, combined with the single-pass cross-sectional profile and the analysis of the relationship between the single-pass cross-sectional geometric dimensions and the feed volume flow rate under different process parameters, an input-output model between the process parameters and the print volume is established; The process parameters are optimized by measuring the volume and mass of the printed part online and inputting them into the input-output model.

9. A density-adjustable material additive manufacturing process parameter optimization system based on online volume and mass measurement, characterized in that: include: A data acquisition module, used to obtain volume data and mass data of the printed part and a single-channel cross-sectional profile; A model building module, for establishing an input-output model between process parameters and foam density or an input-output model between process parameters and printed part volume; The process parameter optimization module is used to optimize the process parameters according to the input-output model.

10. A terminal, characterized in that: The terminal includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, the steps of the method for optimizing process parameters of additive manufacturing of density-adjustable materials based on online volume and mass measurement as described in any one of claims 1 to 8 are implemented.

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