3D printing parameter adjusting method and device, electronic equipment and storage medium
By recognizing the extruded material image of the 3D printing equipment and adjusting the neural network model, the printing parameters are automatically adjusted, solving the problem of low efficiency in traditional manual adjustment and improving printing quality and production efficiency.
Patent Information
- Application Number
- CN202511207559.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-27
- Publication Date
- 2025-11-28
AI Technical Summary
Existing 3D printing equipment often experiences extrusion damage during the printing process due to material properties, equipment precision, or environmental factors, resulting in low printing quality and production efficiency. Traditional manual parameter adjustment is also inefficient.
By acquiring images of materials extruded by 3D printing equipment, image recognition is performed to extract attribute information of damaged areas, and a neural network model is used to adjust printing parameters based on the attribute information to achieve automatic parameter adjustment.
It improved the efficiency of adjusting printing parameters, enhanced the quality of printed products, reduced material and time waste, and increased production efficiency.
Smart Images
Figure CN121018948A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of 3D printing, and in particular to a 3D printing parameter adjustment method and device, electronic equipment and a storage medium. BACKGROUND
[0002] With the rapid development of 3D printing technology, direct writing type 3D printing equipment is increasingly widely used in manufacturing, medical treatment, construction and other fields. In the printing process of the 3D printing equipment, due to the influence of material properties, equipment precision or environmental factors, the phenomenon of extrusion damage often occurs, such as material fracture, uneven extrusion or surface defects, which seriously affects the quality and production efficiency of the printed product.
[0003] The main means to solve the problem of extrusion damage is to adjust the printing parameters of the 3D printing equipment. The traditional printing parameter adjustment process is that the operator observes the printing process by naked eye and manually adjusts the printing parameters after finding the damage. The manual adjustment of the printing parameters has the problem of low efficiency. SUMMARY
[0004] The present application provides a 3D printing parameter adjustment method, device, electronic equipment and storage medium to solve the technical problem of low efficiency of adjusting the printing parameters of the 3D printing equipment in the prior art.
[0005] The present application provides a 3D printing parameter adjustment method, comprising: obtaining an image of extruded material of a 3D printing equipment and printing parameters of the 3D printing equipment; performing image recognition on the image of the extruded material to obtain attribute information of a damaged part in the extruded material; inputting the attribute information of the damaged part and the printing parameters into a neural network model to obtain an adjustment amount of the printing parameters output by the neural network model; adjusting the printing parameters according to the adjustment amount of the printing parameters.
[0006] According to the 3D printing parameter adjustment method provided by the present application, the image recognition on the image of the extruded material is performed to obtain attribute information of a damaged part in the extruded material, comprising: extracting a feature vector of the extruded material image; classifying the feature vector to obtain the attribute information of the damaged part.
[0007] According to the 3D printing parameter adjustment method provided by the present application, the attribute information of the damaged part includes at least one of a damage type, a damage position and a damage severity.
[0008] According to the 3D printing parameter adjustment method provided by the application, the attribute information of the damaged part includes a damage severity; After the image recognition on the image of the extrusion material is performed to obtain the attribute information of the damaged part in the extrusion material, the method further includes: If the damage severity is higher than a preset threshold, an alarm signal is sent.
[0009] According to the 3D printing parameter adjustment method provided by the application, before the attribute information of the damaged part and the printing parameter are input into the neural network model to obtain the adjustment amount of the printing parameter output by the neural network model, the method further includes: Training data is obtained, and the training data includes an attribute information sample of the damaged part, a sample of the printing parameter and an adjustment amount sample of the printing parameter; The neural network model is trained using the training data.
[0010] According to the 3D printing parameter adjustment method provided by the application, the adjustment of the printing parameter according to the adjustment amount of the printing parameter includes: A control signal for adjusting the printing parameter is generated according to the adjustment amount of the printing parameter; The control signal is sent to an execution unit of the 3D printing device.
[0011] According to the 3D printing parameter adjustment method provided by the application, the method further includes: At least one of the printing parameter before adjustment, the attribute information of the damaged part, the printing parameter after adjustment and the image of the extrusion material after adjustment is displayed graphically.
[0012] The application further provides a 3D printing parameter adjustment device, which includes: An acquisition module is configured to acquire an image of extrusion material of a 3D printing device and a printing parameter of the 3D printing device; An identification module is configured to perform image recognition on the image of the extrusion material to obtain attribute information of a damaged part in the extrusion material; A determination module is configured to input the attribute information of the damaged part and the printing parameter into a neural network model to obtain an adjustment amount of the printing parameter output by the neural network model; An adjustment module is configured to adjust the printing parameter according to the adjustment amount of the printing parameter.
[0013] The application further provides an electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the 3D printing parameter adjustment method according to any one of the above when executing the program.
[0014] The application further provides a non-transitory computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the 3D printing parameter adjustment method according to any one of the above.
[0015] The 3D printing parameter adjustment method, device, electronic equipment and storage medium provided by the application perform image recognition on the image of the extrusion material of the 3D printing device, obtain the attribute of the damaged part of the extrusion material, input the attribute of the damaged part and the printing parameter into a neural network model, obtain the adjustment amount of the printing parameter output by the neural network model, and adjust the printing parameter according to the adjustment amount of the printing parameter, so that the automatic adjustment of the printing parameter of the 3D printing device is realized, the manual adjustment of the printing parameter is not needed, the adjustment efficiency of the printing parameter is improved, and the printing quality of the printing product is improved, and the waste of printing materials and time is reduced. BRIEF DESCRIPTION OF DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.
[0017] Figure 1 is a flowchart of the 3D printing parameter adjustment method provided by the application.
[0018] Figure 2 is one of the schematic diagrams of the extrusion material image provided by the application.
[0019] Figure 3 is another schematic diagram of the extrusion material image provided by the application.
[0020] Figure 4 is a structural schematic diagram of the 3D printing parameter adjustment device provided by the application.
[0021] Figure 5 is a structural schematic diagram of the electronic equipment provided by the application. DETAILED DESCRIPTION
[0022] In order to make the purpose, technical solutions and advantages of the application more clear, the technical solutions in the application will be described clearly and completely in the following with reference to the drawings in the application. Obviously, the described embodiments are some embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.
[0023] The 3D printing parameter adjustment method provided by the present application will be described below. Figures 1-5 The 3D printing parameter adjustment method, device, electronic equipment and storage medium provided by the present application are described.
[0024] As shown in Figure 1 The 3D printing parameter adjustment method provided by the present application can include steps S1, S2, S3 and S4.
[0025] Step S1, obtaining an image of the extrusion material of the 3D printing device and the printing parameters of the 3D printing device.
[0026] The image of the extrusion material of the 3D printing device can be as shown in Figure 2 As can be seen, the extrusion material has many damages. The image of the extrusion material can be obtained by real-time shooting the image at the extrusion head of the 3D printing device through a high-resolution camera. The sampling frequency of the camera can be 10 frames per second or 30 frames per second to ensure real-time monitoring of changes in the printing process. The resolution of the camera should not be less than 1920x1080 pixels to ensure that the details of the extrusion material can be clearly captured.
[0027] The present application can provide uniform lighting conditions for the camera through the light source to ensure image quality. The light source can use LED lights, and the adjustable range of brightness can be 100-1000 lumens to adapt to different printing environments; the color temperature of the light source can be 5000K to ensure the authenticity of the image color.
[0028] The image collected by the camera needs to be transmitted to the 3D printing parameter adjustment device in real time, and the image can be transmitted through a gigabit Ethernet interface, and the transmission rate can be not less than 1Gbps to ensure real-time transmission of the image; the image can be compressed before transmission to reduce data transmission volume.
[0029] The printing parameters can include at least one of the extrusion pressure, the printing speed and the extrusion speed, such as the extrusion pressure, the printing speed and the extrusion speed. The printing parameters can be collected in real time through a high-precision sensor, and the sampling frequency of the sensor can be 100 times per second or 150 times per second to ensure the real-time and accuracy of the data. The output of the sensor is a digital signal, which is convenient for subsequent processing.
[0030] The present application can store the printing parameters through a data storage unit for subsequent analysis and optimization. The data storage unit can use a solid state disk, and the storage capacity can be not less than 1TB to ensure that a large amount of historical data can be stored. The data storage unit can support a fast retrieval function to quickly access historical data.
[0031] Step S2, image recognition is performed on the image of the extrusion material to obtain attribute information of the damaged part in the extrusion material.
[0032] After the image of the extruded material is acquired, the image can be denoised, enhanced and cropped to improve the image quality. Then, a deep learning-based image recognition algorithm can be used to detect whether the extruded material is damaged and the attribute information of the damaged part. The detection accuracy of the image recognition algorithm is not less than 95%, so as to ensure that the extrusion damage can be accurately identified.
[0033] The attribute information of the damaged part can include at least one of the damage type, the damage position and the damage severity, or include the damage type, the damage position and the damage severity. The damage type can include discontinuity, shape abnormality or surface defect of the extruded material, the discontinuity of the extruded material can specifically be material fracture, the shape abnormality can specifically be uneven extrusion, and the surface defect can specifically be surface bubbles; the damage severity can be divided into slight, moderate and severe.
[0034] Specifically, the acquired image can be compared with a standard damaged image, and if the matching degree is high enough, it means that the extruded material is damaged, the damage type and damage position of the standard damaged image are known, and the damage severity of the damaged part is further identified according to the image of the damaged part.
[0035] In step S3, the attribute information of the damaged part and the printing parameters are input into the neural network model to obtain the adjustment amount of the printing parameters output by the neural network model.
[0036] The neural network model is a trained model and has the ability to infer the adjustment amount of the printing parameters according to the input attribute information of the damaged part and the printing parameters. The neural network model can be a convolutional neural network model. The inference speed of the neural network model can be not less than 100 times per second to ensure real-time generation of the adjustment amount.
[0037] If the printing parameters include the extrusion pressure, the printing speed and the extrusion speed, the adjustment amount of the printing parameters should include the adjustment amount of the extrusion pressure, the adjustment amount of the printing speed and the adjustment amount of the extrusion speed.
[0038] In step S4, the printing parameters are adjusted according to the adjustment amount of the printing parameters.
[0039] The PID (Proportional-Integral-Derivative) control algorithm can be used to adjust the printing parameters, and the adjustment accuracy can be ±0.1% to ensure accurate control of the printing parameters.
[0040] It can be understood that after the printing parameters are adjusted according to the adjustment amount of the printing parameters, the extruded material will not be damaged when the 3D printing device performs 3D printing based on the adjusted printing parameters. The image of the extruded material after the 3D printing device of the present application adjusts the printing parameters is as follows: Figure 3As shown, it can be seen that the extruded material is substantially free of damage.
[0041] From the above, the 3D printing parameter adjustment method of the present application performs image recognition on the image of the extruded material of the 3D printing equipment, obtains the attribute of the damaged part of the extruded material, inputs the attribute of the damaged part and the printing parameter into the neural network model, obtains the adjustment amount of the printing parameter output by the neural network model, and adjusts the printing parameter according to the adjustment amount of the printing parameter, thereby realizing automatic adjustment of the printing parameter of the 3D printing equipment, without the need for manual adjustment of the printing parameter, and improving the adjustment efficiency of the printing parameter. And it is beneficial to improve the printing quality of the printed product and reduce the waste of printing materials and time.
[0042] In one embodiment, before step S3, the 3D printing parameter adjustment method can further include: obtaining training data, the training data including attribute information samples of damaged parts, samples of printing parameters, and adjustment amount samples of printing parameters; training the neural network model using the training data.
[0043] The training data can include 10,000-15,000 groups of samples to ensure the generalization ability of the model. Each group of samples includes attribute information samples of damaged parts, samples of printing parameters, and adjustment amount samples of printing parameters. GPU acceleration can be used during training, and the training speed can be not less than 1000 times / sec to improve the training efficiency.
[0044] During training, first, forward propagation is performed, the attribute information samples of the damaged parts and the samples of the printing parameters are input into the neural network model, and the adjustment amount prediction value output by the neural network model is obtained; then the loss between the adjustment amount prediction value and the adjustment amount sample is calculated through a loss function; then backward propagation is performed to calculate the gradient of the loss with respect to each parameter (such as the convolution kernel weight); finally, the parameters are adjusted according to the gradient to reduce the loss. The training is completed when the maximum number of iterations is reached or the loss meets the convergence condition.
[0045] In one embodiment, step S2 of the present application can further include: extracting a feature vector of the extruded material image; classifying the feature vector to obtain attribute information of the damaged part.
[0046] A convolutional neural network can be used to automatically extract the feature vector in the image, and the feature vector is a multi-dimensional vector including shape, texture, and color features. A convolutional neural network with a classification accuracy of not less than 98% can be used to classify the feature vector to determine whether the extruded material is damaged and output the attribute information of the damaged part.
[0047] In this way, the purpose of determining the damage attribute according to the extrusion material image can be achieved by pre-extracting the image feature vector and classifying.
[0048] In one embodiment, if the attribute information of the damage part includes the damage severity, after step S2, the 3D printing parameter adjustment method can further include: If the damage severity is higher than the preset threshold, an alarm signal is sent.
[0049] If the damage severity is higher than the preset threshold, the damage degree of the extrusion material is too serious, which may not be a problem of the printing parameter, and timely alarm is needed to prompt the operator to intervene manually. The alarm mode can be an audible and visual alarm, and the alarm sound can be not less than 80 decibels to ensure that the operator can timely perceive. At the same time, the operator can also be notified remotely by short message.
[0050] In one embodiment, step S5 of the present application can further include: generating a control signal for adjusting the printing parameter according to the adjustment amount of the printing parameter; sending the control signal to an execution unit of the 3D printing device.
[0051] The execution unit is used to adjust the printing parameter of the 3D printing device, and the execution unit includes a stepper motor and a servo motor. After the control signal is sent to the execution unit, the execution unit can adjust the printing parameter according to the adjustment amount. The response time of the execution unit is not more than 10 ms to ensure rapid adjustment of the printing parameter. The adjustment accuracy of the execution unit can be ±0.1% to ensure accurate control of the printing parameter.
[0052] In this way, the purpose of adjusting the printing parameter according to the adjustment amount can be achieved by sending the control signal corresponding to the adjustment amount to the execution unit.
[0053] In one embodiment, the 3D printing parameter adjustment method of the present application can further include: graphically displaying at least one of the printing parameter before adjustment, the attribute information of the damage part, the printing parameter after adjustment, and the extrusion material image after adjustment of the printing parameter.
[0054] Graphical display of data helps the operator to monitor the printing process in real time. The present application can graphically display the printing parameter before adjustment, the attribute information of the damage part, the printing parameter after adjustment, and the extrusion material image after adjustment. High-resolution display screens can be used to display various data, and the resolution of the display screen can be not less than 1920x1080 pixels to ensure clear display effect. The displayed data can be updated in real time to enable the operator to monitor the printing process in real time.
[0055] The application can also record the print parameters before adjustment, the attribute information of the damaged part, the adjusted print parameters and the image of the extruded material in each printing process for subsequent analysis and optimization. A database management system can be used to store the data, and the database supports fast retrieval function to quickly access historical data. The storage capacity of the database can be no less than 1TB to ensure that a large amount of historical data can be stored.
[0056] After the 3D printing parameter adjustment method of the application is actually applied, it is found that the qualified rate of the 3D printing equipment printing finished product is increased by 20%, the material waste is reduced by 15%, and the production efficiency is increased by 10%.
[0057] As shown in Figure 4 The application also provides a 3D printing parameter adjustment device, which comprises: An acquisition module is configured to acquire an image of an extruded material of a 3D printing equipment and print parameters of the 3D printing equipment; An identification module is configured to perform image recognition on the image of the extruded material to obtain attribute information of a damaged part in the extruded material; A determination module is configured to input the attribute information of the damaged part and the print parameters into a neural network model to obtain an adjustment amount of the print parameters output by the neural network model; An adjustment module is configured to adjust the print parameters according to the adjustment amount of the print parameters.
[0058] In one embodiment, the identification module is further configured to: extract a feature vector of the image of the extruded material; classify the feature vector to obtain the attribute information of the damaged part.
[0059] According to the 3D printing parameter adjustment method provided by the application, the attribute information of the damaged part comprises at least one of a damage type, a damage position and a damage severity.
[0060] In one embodiment, the attribute information of the damaged part comprises the damage severity. The 3D printing parameter adjustment device can further comprise: An alarm module is configured to send an alarm signal if the damage severity is higher than a preset threshold.
[0061] In one embodiment, the 3D printing parameter adjustment device can further comprise: A training module is configured to acquire training data, wherein the training data comprises attribute information samples of the damaged part, samples of the print parameters and adjustment amount samples of the print parameters; and the training module is configured to train the neural network model using the training data.
[0062] In one embodiment, the adjustment module is further configured to: generate a control signal for adjusting the printing parameter according to the adjustment amount of the printing parameter; send the control signal to an execution unit of the 3D printing device.
[0063] In one embodiment, the 3D printing parameter adjustment device can further include: a display module configured to display at least one of the printing parameter before adjustment, the attribute information of the damaged part, the printing parameter after adjustment, and the extrusion material image after adjusting the printing parameter.
[0064] It should be noted that the 3D printing parameter adjustment device provided by the present application can execute the 3D printing parameter adjustment method of any of the above embodiments when in operation, and thus the present embodiment will not be described in detail.
[0065] Figure 5 is a structural schematic diagram of an electronic device provided by the present application, as Figure 5 shown, the electronic device can include a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory complete mutual communication through the communications bus. The processor can invoke a logical instruction in the memory to execute a 3D printing parameter adjustment method, which includes: obtaining an image of an extrusion material of a 3D printing device and a printing parameter of the 3D printing device; performing image recognition on the image of the extrusion material to obtain attribute information of a damaged part in the extrusion material; inputting the attribute information of the damaged part and the printing parameter into a neural network model to obtain an adjustment amount of the printing parameter output by the neural network model; and adjusting the printing parameter according to the adjustment amount of the printing parameter.
[0066] In addition, the logical instruction in the memory described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.
[0067] In another aspect, the present application also provides a computer program product, which comprises a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the 3D printing parameter adjustment method provided by any of the above embodiments, the method comprising: obtaining an image of extruded material of a 3D printing device and a printing parameter of the 3D printing device; performing image recognition on the image of the extruded material to obtain attribute information of a damaged part in the extruded material; inputting the attribute information of the damaged part and the printing parameter into a neural network model to obtain an adjustment amount of the printing parameter output by the neural network model; and adjusting the printing parameter according to the adjustment amount of the printing parameter.
[0068] In yet another aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement a 3D printing parameter adjustment method provided by any of the above embodiments, the method comprising: obtaining an image of extruded material of a 3D printing device and a printing parameter of the 3D printing device; performing image recognition on the image of the extruded material to obtain attribute information of a damaged part in the extruded material; inputting the attribute information of the damaged part and the printing parameter into a neural network model to obtain an adjustment amount of the printing parameter output by the neural network model; and adjusting the printing parameter according to the adjustment amount of the printing parameter.
[0069] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.
[0070] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus necessary general hardware platforms, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.
[0071] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for adjusting 3D printing parameters, characterized in that, include: Acquire images of the material extruded by the 3D printing equipment and the printing parameters of the 3D printing equipment; Image recognition is performed on the image of the extruded material to obtain the attribute information of the damaged parts in the extruded material; The attribute information of the damaged part and the printing parameters are input into the neural network model to obtain the adjustment amount of the printing parameters output by the neural network model; The printing parameters are adjusted according to the adjustment amount of the printing parameters.
2. The 3D printing parameter adjustment method according to claim 1, characterized in that, The step of performing image recognition on the image of the extruded material to obtain attribute information of the damaged parts in the extruded material includes: Extract the feature vector of the extruded material image; The feature vectors are classified to obtain the attribute information of the damaged parts.
3. The 3D printing parameter adjustment method according to claim 1, characterized in that, The attribute information of the damaged part includes at least one of the following: damage type, damage location, and damage severity.
4. The 3D printing parameter adjustment method according to claim 1, characterized in that, The attribute information of the damaged part includes the severity of the damage; After performing image recognition on the image of the extruded material to obtain the attribute information of the damaged parts in the extruded material, the method further includes: If the severity of the damage exceeds a preset threshold, an alarm signal will be issued.
5. The 3D printing parameter adjustment method according to claim 1, characterized in that, Before inputting the attribute information of the damaged part and the printing parameters into the neural network model to obtain the adjustment amount of the printing parameters output by the neural network model, the method further includes: Acquire training data, which includes attribute information samples of the damaged area, samples of printing parameters, and samples of adjustment amounts of the printing parameters; The neural network model is trained using the training data.
6. The 3D printing parameter adjustment method according to claim 1, characterized in that, The step of adjusting the printing parameters according to the adjustment amount of the printing parameters includes: A control signal for adjusting the printing parameters is generated based on the adjustment amount of the printing parameters; The control signal is sent to the execution unit of the 3D printing equipment.
7. The 3D printing parameter adjustment method according to claim 1, characterized in that, Also includes: The system graphically displays at least one of the following: the printing parameters before adjustment, the attribute information of the damaged area, the printing parameters after adjustment, and the image of the extruded material after adjustment.
8. A 3D printing parameter adjustment device, characterized in that, include: The acquisition module is used to acquire images of the material extruded by the 3D printing equipment and the printing parameters of the 3D printing equipment; The recognition module is used to perform image recognition on the image of the extruded material to obtain the attribute information of the damaged parts in the extruded material; The determination module is used to input the attribute information of the damaged part and the printing parameters into the neural network model to obtain the adjustment amount of the printing parameters output by the neural network model; The adjustment module is used to adjust the printing parameters according to the adjustment amount of the printing parameters.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the 3D printing parameter adjustment method as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the 3D printing parameter adjustment method as described in any one of claims 1 to 7.