A method for monitoring data analysis of laser selective melting forming process

Abstract

The present invention discloses a method for analyzing monitoring data during a laser selective melting forming process, comprising: S1: stability analysis of the X-axis and Y-axis within the forming area; S2: stability analysis of the Z-axis within the forming area; S3: sensitivity analysis: calculating the sensitivity of grayscale fluctuations caused by changes in process parameters; and S4: system threshold calibration value. The present invention can monitor the forming state of a product during the laser selective melting forming process, establish a comparative relationship between monitoring data and forming quality, and can be applied to the production of metal additive manufacturing products. It can quickly and efficiently complete the analysis of large amounts of data, thereby ensuring the forming quality of the product and achieving quality traceability, improving full-process quality control, and resolving the problem of poor repeatability and quality stability of the laser selective melting forming process limiting its mass production, thereby promoting the application of the laser selective melting forming process in the mass production of more difficult and high-demand parts.

Description

Technical Field

[0001] The present invention relates to the technical field of laser selective melting forming, and in particular to a method for analyzing monitoring data of a laser selective melting forming process. Background Art

[0002] Laser selective melting technology can realize structural and functional integrated structures such as lightweight, complex structures, and multi-material gradient structures. It has a short process flow, less material waste, and no molds are required. It has unique advantages in the forming of complex components and has been widely used in aerospace, medical, mold and other fields.

[0003] However, its forming process has the inherent characteristics of rapid melting and cooling, and the laser beam is easily disturbed by forming smoke. Therefore, the development of this technology has been constrained by its quality stability. The process coupling of laser melting metal powder is strong, and the workpiece is prone to micro defects such as pores and micro cracks, as well as macro defects such as nodules and cracks. Timely monitoring of the laser selective melting forming process can greatly improve the forming quality and process repeatability of the laser selective melting process, improve the forming quality stability, and avoid the increased economic and time costs of poor forming. However, the monitoring of the laser selective melting forming process is just getting started. There is a severe lack of analysis methods for the process monitoring system, and data is difficult to be effectively utilized. Although most of them can collect data, they lack data analysis methods, which greatly restricts the practical application of the laser selective melting forming process monitoring system. Summary of the Invention

[0004] In order to solve the above problems in the prior art, a method for analyzing monitoring data of a selective laser melting forming process is provided.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] The present invention proposes a method for analyzing monitoring data of a selective laser melting forming process, comprising:

[0007] S1: Stability analysis of the X-axis and Y-axis within the forming area: collect the average gray value of the sample;

[0008] S2: Z-axis stability analysis within the forming area: collect the average grayscale value of the sample;

[0009] S3: Analyze the sensitivity: calculate the sensitivity of grayscale value fluctuation caused by process parameter changes;

[0010] S4: System threshold calibration: Based on actual usage requirements and with reference to standard process parameters and benchmark porosity, set upper and lower limits for energy input or porosity, and calculate the upper and lower thresholds of the grayscale value corresponding to the standardized ratio.

[0011] Preferably, a substrate for selective laser melting is included. In said S1, a plurality of identical samples are evenly distributed on the entire substrate for forming. The forming task is repeated at least twice. The average grayscale value corresponding to each sample is collected. Then, the deviation rate of a single sample relative to the average grayscale value of the entire substrate is calculated, thereby obtaining the degree of deviation at different positions on the substrate.

[0012] Preferably, in S2, multiple identical samples are evenly distributed on the entire substrate for forming, the forming task is repeated at least twice, the average grayscale value corresponding to each sample is collected, the data deviation of each sample changing with height is analyzed to obtain the data deviation rate of the average grayscale value of each multi-layer of each sample on the substrate is calculated, so as to obtain the degree of deviation of different positions on the substrate during the height change process.

[0013] Preferably, in said S3, samples with exactly the same size, shape, quantity and position in the X-axis and Y-axis stability analysis are used for forming, and the average grayscale value of each sample is collected. The average grayscale value of each sample is divided by the average grayscale value of the X-axis and Y-axis stability data from the same position to achieve standardization. A curve is drawn with the standardized ratio obtained by division as the vertical axis and the process parameter change ratio as the horizontal axis. A linear fit is performed on all the data to obtain the fluctuation range, thereby calculating the grayscale value fluctuation sensitivity caused by the process parameter change.

[0014] Preferably, in S4, a plurality of identical samples are constructed, each sample containing two parts A and B, part A is formed by non-standard process parameters, and part B is formed by standard process parameters, all samples are evenly spread on the entire substrate for forming, and the average grayscale value of each sample is collected, an average grayscale value is calculated for each sample part A, called A value, and another average grayscale value is calculated for each sample part B, called B value, and both A value and B value are standardized, that is, the A value is divided by the B value to obtain the standardized ratio of all samples.

[0015] Preferably, all samples are subjected to industrial CT testing, and the average porosity of the B portion of all samples is calculated, which is used as the reference porosity, and the porosity of each of the A portions of all samples is calculated;

[0016] The energy input ratio of part A to part B of all samples is calculated, and a relationship diagram of the three types of values ​​is drawn with this ratio as the horizontal axis, the porosity of each part A of all samples as the left vertical axis, and the standardized ratio of all samples as the right vertical axis. Through this relationship diagram, according to actual usage requirements and with reference to standard process parameters and benchmark porosity, the upper and lower limits of the energy input or porosity are limited, and the upper and lower thresholds of the grayscale value corresponding to the standardized ratio are calculated, thereby realizing the calibration of the system threshold during the laser selective melting forming process.

[0017] Preferably, in S1, the height of all samples is less than 10 mm, the number of samples on the substrate is not less than 20, and all samples adopt the same standard process parameters, which are the optimal parameters for mature applications in actual production.

[0018] Preferably, in S2, the height of all samples is between 30 and 60 mm, the number of samples on the substrate is not less than 9, and all samples adopt the same standard process parameters, which are the optimal parameters for mature applications in actual production.

[0019] Preferably, in S3, the sample adopts gradual process parameters. On the basis of standard process parameters, the speed and power are increased or decreased, and the change gradient is selected between 1% and 4%. The same parameter corresponds to at least one sample, and the positions of different parameters are randomly arranged.

[0020] Preferably, in said S4, the number of samples on the substrate is not less than 40, and both parts A and B of the sample have at least 60 layers. The non-standard process parameters are based on the standard process parameters, increasing and decreasing the speed and power, and the change gradient is selected between 2% and 8%, and all samples are randomly arranged.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] In the present invention, by monitoring the laser selective melting forming state, a comparative relationship between the monitoring data and the forming quality can be established, the forming quality can be monitored in a timely manner, the laser selective melting forming quality can be guaranteed and quality backtracking can be achieved, the whole process quality control can be improved, and the analysis of a large amount of data can be completed quickly and efficiently, so that the laser selective melting forming process monitoring technology can be maturely applied, the maturity and competitiveness of the laser selective melting forming technology can be significantly improved, the whole process quality control can be improved, and the problem of the poor repeatability and quality stability of the laser selective melting forming process limiting its mass production can be solved, and the laser selective melting forming process can be promoted to be applied to the mass production of more difficult and high-requirement parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0024] Figure 1 This is a schematic diagram of the substrate structure when applied to stainless steel;

[0025] Figure 2 This is a schematic diagram of XY axis stability data collection when applied to stainless steel;

[0026] Figure 3 This is a schematic diagram of the deviation rate at different locations on the substrate when applied to stainless steel;

[0027] Figure 4 This is a schematic diagram of the height variation deviation rate at different locations on the substrate when applied to stainless steel;

[0028] Figure 5 It is a schematic diagram of the normalized ratio and speed change ratio curve and fluctuation range when applied to stainless steel;

[0029] Figure 6 This is a schematic diagram of the two parts of the specimen A and B when applied to stainless steel;

[0030] Figure 7 This is a schematic diagram of identifying threshold-exceeding areas during the forming process when applied to stainless steel;

[0031] Figure 8 This is a schematic diagram of the substrate structure when applied to high-temperature alloys;

[0032] Figure 9 This is a schematic diagram of XY axis stability data collection when applied to high temperature alloys;

[0033] Figure 10 It is a schematic diagram of the deviation rate at different positions on the substrate when applied to high-temperature alloys;

[0034] Figure 11 This is a schematic diagram of the data change in the height direction of a single specimen when applied to high-temperature alloys;

[0035] Figure 12 This is a schematic diagram of the height variation deviation rate at different locations on the substrate when applied to high-temperature alloys;

[0036] Figure 13 It is a diagram showing the normalized ratio and power change ratio curve and fluctuation range when applied to high temperature alloys;

[0037] Figure 14 This is a schematic diagram of the two parts of the specimen A and B when applied to high-temperature alloys;

[0038] Figure 15 Relationship diagram of energy input ratio, porosity and normalized ratio in the present invention. DETAILED DESCRIPTION

[0039] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0040] Example 1

[0041] Reference Attachment Figure 1-15This embodiment proposes a method for analyzing monitoring data of a selective laser melting forming process, comprising:

[0042] S1: Stability analysis of the X-axis and Y-axis within the forming area: collect the average gray value of the sample;

[0043] Multiple identical samples are evenly distributed on the entire substrate for forming. The forming task is repeated at least twice. The average grayscale value corresponding to each sample is collected, and then the deviation rate of a single sample relative to the average grayscale value of the entire substrate is calculated to obtain the degree of deviation at different positions.

[0044] Among them, the height of all samples is less than 10 mm, the number of samples on the substrate is not less than 20, and all samples use the same standard process parameters, which are the optimal parameters for mature applications in actual production.

[0045] S2: Z-axis stability analysis within the forming area: collect the average grayscale value of the sample;

[0046] Multiple identical samples are evenly distributed on the entire substrate for forming. The forming task is repeated at least twice. The average grayscale value corresponding to each sample is collected, and the data deviation of each sample changing with height is analyzed. The deviation rate of the average grayscale value of each multi-layer of each sample on the substrate is calculated, thereby obtaining the degree of deviation at different positions on the substrate during the height change process.

[0047] The height of all samples is between 30 and 60 mm, the number of samples on the substrate is not less than 9, and all samples adopt the same standard process parameters, which are the optimal parameters for mature applications in actual production.

[0048] S3: Analyze the sensitivity: calculate the sensitivity of grayscale value fluctuation caused by process parameter changes;

[0049] For the X- and Y-axis stability analysis, identical specimens of identical size, shape, number, and position were used for forming. The average grayscale value of each specimen was collected and then divided by the average grayscale value of the X- and Y-axis stability data from the same location to achieve standardization and eliminate interference from positional factors. A curve was plotted with the standardized ratio obtained from this division as the vertical axis and the process parameter change ratio as the horizontal axis. A linear fit was performed on all data to determine the fluctuation range, thereby calculating the sensitivity of grayscale value fluctuations caused by process parameter changes.

[0050] The samples adopt gradual process parameters. On the basis of standard process parameters, the speed and power are increased and decreased, and the gradient of change is selected between 1% and 4%. The same parameter corresponds to one or more samples, and the positions of different parameters are randomly arranged.

[0051] S4: System threshold calibration: Based on actual usage requirements and with reference to standard process parameters and benchmark porosity, set upper and lower limits for energy input or porosity, and calculate the upper and lower thresholds of the grayscale value corresponding to the standardized ratio.

[0052] Construct multiple identical samples, each containing two parts, A and B. Part A is formed using non-standard process parameters, and part B is formed using standard process parameters. All samples are evenly spread over the entire substrate for forming, and the average grayscale value of each sample is collected. An average grayscale value, called the A value, is calculated for part A of each sample, and another average grayscale value, called the B value, is calculated for part B of each sample. Both the A and B values ​​are standardized, that is, the A value is divided by the B value to obtain the standardized ratio of all samples.

[0053] All samples were tested by industrial CT, and the average porosity of the B part of all samples was calculated and used as the benchmark porosity. The porosity of the A part of all samples was also calculated.

[0054] The energy input ratio of part A to part B of all samples is calculated, and a relationship diagram of the three types of values ​​is drawn with this ratio as the horizontal axis, the porosity of each part A of all samples as the left vertical axis, and the standardized ratio of all samples as the right vertical axis. Through this relationship diagram, according to actual usage requirements and with reference to standard process parameters and benchmark porosity, the upper and lower limits of the energy input or porosity are limited, and the upper and lower thresholds of the grayscale value corresponding to the standardized ratio are calculated, thereby realizing the calibration of the system threshold during the laser selective melting forming process.

[0055] The number of samples on the substrate is no less than 40, and both parts A and B of the sample have at least 60 layers. The non-standard process parameters are based on the standard process parameters, with speed and power increased and decreased, and the change gradient is selected between 2% and 8%. All samples are randomly arranged.

[0056] Example 2

[0057] Reference Attachment Figure 1-15 This embodiment proposes a method for analyzing data from a selective laser melting process, including a substrate for selective laser melting, wherein: Figure 1 This is a schematic diagram of the substrate structure when applied to stainless steel. Figure 8 This is a schematic diagram of the substrate structure when applied to high-temperature alloys, and the method includes:

[0058] S1: Stability analysis of the X-axis and Y-axis within the forming area: collect the average gray value of the sample;

[0059] In order to analyze the differences in data collected at different positions on the substrate, 49 identical samples were evenly distributed on the entire substrate with a sample height of 4.8 mm. All samples used the optimal parameters of stainless steel or high-temperature alloys that have been maturely applied in actual production as standard process parameters.

[0060] Repeat a task three times and analyze the data from these three times. Collect the average grayscale value of each specimen, and then calculate the deviation rate of each specimen relative to the average grayscale value of the entire substrate to verify the degree of deviation at different locations.

[0061] S2: Z-axis stability analysis within the forming area: collect the average grayscale value of the sample;

[0062] In order to analyze the differences in data collected at different heights of the substrate and to control the amount of data, 21 identical samples were evenly distributed on the entire substrate. The sample height was 36 mm, and all samples used the same standard process parameters.

[0063] Repeat a task twice and analyze the data from these two times. Analyze the data deviation of each sample as the height changes. Then calculate the deviation rate of the multi-layer average grayscale value of each sample on the substrate to verify the degree of deviation at different positions on the substrate during the height change process.

[0064] S3: Analyze the sensitivity: calculate the sensitivity of grayscale value fluctuation caused by process parameter changes;

[0065] In the X-axis and Y-axis stability analysis, samples with exactly the same size, shape, quantity and position were used for forming. However, gradual process parameters were used. On the basis of the standard process parameters, the speed and power were increased or decreased with a gradient of 2%. The positions of different parameters were randomly arranged.

[0066] The average grayscale value of each specimen was collected and divided by the average grayscale value of the X-axis and Y-axis stability data from the same part position to achieve standardization and eliminate interference from positional factors. A curve was plotted with the standardized ratio as the vertical axis and the process parameter change ratio as the horizontal axis. A linear fit was performed on all data to obtain the fluctuation range. From this, the sensitivity of grayscale value fluctuation caused by process parameter changes was calculated. This sensitivity can be used to evaluate the accuracy of the laser selective melting process monitoring system.

[0067] S4: System threshold calibration: Based on actual usage requirements and referring to standard process parameters and benchmark porosity, set upper and lower limits for energy input or porosity, and calculate the upper and lower thresholds of the grayscale value corresponding to the standardized ratio;

[0068] 100 identical samples were constructed, each containing two parts, A and B. Part A was formed using 120 layers of non-standard process parameters, and part B was formed using 120 layers of standard process parameters. The 100 non-standard process parameters were based on the standard process parameters, with speed and power increased or decreased with a gradient of 5%. The 100 samples were randomly arranged. All samples were evenly spread across the entire substrate for forming, and the average grayscale value of each sample was collected. An average grayscale value, called the A value, was calculated for part A of each sample, and another average grayscale value, called the B value, was calculated for part B of each sample. Both the A and B values ​​were standardized, i.e., the A value was divided by the B value, to obtain 100 standardized ratios.

[0069] Industrial CT was performed on 100 specimens, and the average porosity of their B parts was calculated as the benchmark porosity. The porosity of each of the 100 specimens' A parts was also calculated.

[0070] The energy input ratio of parts A to B for 100 specimens was calculated as the horizontal axis, the porosity of part A as the left vertical axis, and the standardized ratio as the right vertical axis. A relationship graph was then plotted between the three values. This graph allows for upper and lower limits on energy input or porosity based on actual requirements, referenced by standard process parameters and a baseline porosity, and the corresponding grayscale thresholds corresponding to the standardized ratios to be calculated, thereby enabling threshold calibration of the selective laser melting process monitoring system.

[0071] During the production process, the changes in grayscale values ​​are monitored. Normal production can occur within the threshold range. When an area exceeding the threshold is identified, it indicates that the porosity of the formed parts in this area is high or the energy input is out of control, and the production process should be intervened or terminated in a timely manner.

[0072] The present invention can monitor the forming state during the laser selective melting forming process, establish a comparative relationship between the monitoring data and the forming quality, and can be applied to the production of metal additive manufacturing products. It can quickly and efficiently complete the analysis of a large amount of data, thereby ensuring the product forming quality and realizing quality traceability, improving the quality control of the entire process, and solving the problem that the repeatability and quality stability of the laser selective melting forming process are limited to its mass production, and promoting the application of the laser selective melting forming process in the mass production of more difficult and high-requirement parts.

[0073] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for analyzing monitoring data of a selective laser melting forming process, characterized in that: include: S1: Stability analysis of the X-axis and Y-axis within the forming area: collect the average gray value of the sample; S2: Z-axis stability analysis within the forming area: collect the average grayscale value of the sample; S3: Analyze the sensitivity: calculate the sensitivity of grayscale value fluctuation caused by process parameter changes; S4: System threshold calibration: Based on actual usage requirements and referring to standard process parameters and benchmark porosity, set upper and lower limits for energy input or porosity, and calculate the upper and lower thresholds of the grayscale value corresponding to the standardized ratio; Wherein, a substrate for selective laser melting is included. In said S1, a plurality of identical samples are evenly distributed on the entire substrate for forming. The forming task is repeated at least twice. The average grayscale value corresponding to each sample is collected. Then, the deviation rate of the individual sample relative to the average grayscale value of the entire substrate is calculated, thereby obtaining the degree of deviation at different positions on the substrate. In S2, multiple identical samples are evenly distributed on the entire substrate for forming, and the forming task is repeated at least twice. The average grayscale value corresponding to each sample is collected, and the data deviation of each sample with height change is analyzed to obtain the data deviation rate of the average grayscale value of each sample on the substrate. The deviation degree of different positions on the substrate during the height change process is obtained; Among them, in S3, samples with exactly the same size, shape, quantity and position in the X-axis and Y-axis stability analysis are used for forming, and the average grayscale value of each sample is collected. The average grayscale value of each sample is divided by the average grayscale value of the X-axis and Y-axis stability data from the same position to achieve standardization. A curve is drawn with the standardized ratio obtained by division as the vertical axis and the process parameter change ratio as the horizontal axis. A linear fit is performed on all the data to obtain the fluctuation range, thereby calculating the grayscale value fluctuation sensitivity caused by the process parameter change.

2. The method for analyzing monitoring data of a selective laser melting forming process according to claim 1, wherein: In the S4, a plurality of identical samples are constructed, each sample comprising two parts, A and B. Part A is formed using non-standard process parameters, and part B is formed using standard process parameters. All samples are evenly distributed on the entire substrate for forming, and the average grayscale value of each sample is collected. An average grayscale value, referred to as the A value, is calculated for part A of each sample, and another average grayscale value, referred to as the B value, is calculated for part B of each sample. Both the A value and the B value are standardized, i.e., the A value is divided by the B value to obtain a standardized ratio of all samples.

3. The method for analyzing monitoring data of a selective laser melting forming process according to claim 2, wherein: All samples were tested by industrial CT, and the average porosity of the B part of all samples was calculated as the benchmark porosity, and the porosity of the A part of all samples was calculated; The energy input ratio of part A to part B of all samples is calculated, and a relationship diagram of the three types of values ​​is drawn with this ratio as the horizontal axis, the porosity of each part A of all samples as the left vertical axis, and the standardized ratio of all samples as the right vertical axis. Through this relationship diagram, according to actual usage requirements and with reference to standard process parameters and benchmark porosity, the upper and lower limits of the energy input or porosity are limited, and the upper and lower thresholds of the grayscale value corresponding to the standardized ratio are calculated, thereby realizing the calibration of the system threshold during the laser selective melting forming process.

4. The method for analyzing monitoring data of a selective laser melting process according to claim 1, wherein: In S1, the height of all samples is less than 10 mm, the number of samples on the substrate is not less than 20, and all samples use the same standard process parameters, which are the optimal parameters for mature applications in actual production.

5. The method for analyzing monitoring data of a selective laser melting forming process according to claim 1, wherein: In S2, the height of all samples is between 30 and 60 mm, the number of samples on the substrate is not less than 9, and all samples adopt the same standard process parameters, which are the optimal parameters for mature applications in actual production.

6. The method for analyzing monitoring data of a selective laser melting process according to claim 1, wherein: In S3, the sample adopts gradual process parameters. On the basis of standard process parameters, the speed and power are increased and decreased, and the change gradient is selected between 1% and 4%. The same parameter corresponds to at least one sample, and the positions of different parameters are randomly arranged.

7. The method for analyzing monitoring data of a selective laser melting forming process according to claim 2, wherein: In the S4, the number of samples on the substrate is not less than 40, and both parts A and B of the sample have at least 60 layers. The non-standard process parameters are based on the standard process parameters, increasing and decreasing the speed and power, and the change gradient is selected between 2% and 8%. All samples are randomly arranged.

Images