An online monitoring method for intelligent processing of super-high-speed laser cladding

Abstract

The application discloses an online monitoring method for super-high-speed laser cladding intelligent processing, which comprises the following steps: setting process parameters in advance, preparing a cladding layer based on the process parameters, detecting the spatial coordinates of each point on the surface of a part in real time during the preparation process, wherein the spatial coordinates of each point are X, Y and Z coordinate values of each point on the surface of the part, the surface of the part comprises a part coating surface and a part base surface, calculating an online evaluation index of the quality of the cladding layer based on the spatial coordinates of the surface points of the part, determining a distribution threshold value of the evaluation index, and online regulating and controlling the quality of the cladding layer; if the online evaluation index is within the distribution threshold value range, the preparation of the cladding layer is continued, otherwise, the process parameters are adjusted.

Description

TECHNICAL FIELD

[0001] The present application relates to an online monitoring method for super-high-speed laser cladding intelligent processing, and belongs to the technical field of online monitoring of part surface manufacturing process. BACKGROUND

[0002] Super-high-speed laser cladding technology is a new type of efficient and low-cost surface treatment technology, which has been widely used in coal mine machinery, automobile industry, engineering machinery and other equipment manufacturing industries. As we all know, digitalization and intelligentization of manufacturing process can effectively improve manufacturing efficiency and reduce manufacturing cost. Therefore, intelligentization of super-high-speed laser cladding processing will be the development trend in the future. However, online monitoring of coating quality during processing is a key link to realize intelligent processing.

[0003] At present, around the online monitoring technology of coating quality during laser cladding process, emission spectrum, acoustic sensing, infrared camera and other online monitoring methods have been developed. The limitation of the emission spectrum method is that the information obtained is not intuitive, and the detection result depends on the effectiveness of the data processing and analysis algorithm. The acoustic sensing detection method is easily affected by external interference, such as environmental noise and cladding system electrical signal. The infrared camera method is mainly used in the process development of laser cladding, and cannot accurately evaluate the forming quality of the coating in the batch manufacturing process. In recent years, visual sensing technology has been increasingly applied to the analysis of laser cladding molten pool dynamics and cladding layer surface morphology. However, the molten pool solidification process is affected by many external factors, and its geometric shape characteristics cannot directly reflect the forming quality of the cladding layer after solidification. In addition, the deposition speed of super-high-speed laser cladding is very high, about 20-200 m / min, and the data acquisition accuracy of traditional CCD camera and CMOS camera is low under high-speed movement. Moreover, the running of the manufacturing site environment is easy to cause data loss, which cannot meet the needs of online monitoring of real-time surface state of coating during super-high-speed laser cladding processing. SUMMARY

[0004] In order to solve the problems of the prior art, the present application provides an online monitoring method for super-high-speed laser cladding intelligent processing, which solves the problems of single online monitoring means and low accuracy of real-time surface state of coating during super-high-speed laser cladding processing in the prior art.

[0005] In order to achieve the above-mentioned target, the present application adopts the following technical solution:

[0006] An online monitoring method for super-high-speed laser cladding intelligent processing, comprising the following steps:

[0007] Pre-setting process parameters;

[0008] Based on the process parameters, preparing a cladding layer;

[0009] In the preparation process, the spatial coordinates of each point on the surface of the part are detected in real time, the spatial coordinates of each point on the surface of the part are X, Y and Z coordinate values of each point on the surface of the part, and the surface of the part includes the surface of the coating of the part and the surface of the base body of the part;

[0010] Based on the spatial coordinates of the surface points of the part, the online evaluation indexes of the cladding layer quality are calculated, the evaluation indexes include the thickness H of the cladding layer and the surface feature parameters of the cladding layer, the surface feature parameters of the cladding layer include the height coordinate fluctuation ΔZ of the surface point of the cladding layer, the arithmetic average roughness S a , the skewness S sk and the aspect ratio S tr of the surface feature;

[0011] The distribution threshold of the evaluation indexes is determined, the distribution threshold of the evaluation indexes includes the distribution threshold ΔH' of the thickness of the cladding layer, the distribution threshold ΔZ' of the height coordinate fluctuation of the surface point of the cladding layer, the distribution threshold ΔS a ' of the arithmetic average roughness, the distribution threshold ΔS sk ' of the skewness and the distribution threshold ΔS tr ' of the aspect ratio of the surface feature;

[0012] Based on the distribution threshold of the evaluation indexes, the online regulation and control of the cladding layer quality is carried out: if the online evaluation indexes are within the range of the distribution threshold, the preparation of the cladding layer is continued, otherwise the process parameters are adjusted.

[0013] Further, the calculation expression of the height coordinate value Z of each point on the surface of the part is:

[0014]

[0015] In the formula, K1 is the imaging object distance, L is the displacement of the imaging light spot, β is the included angle between the optical axis of the imaging lens and the photosensitive surface, K2 is the imaging image distance, and α is the included angle between the optical axis of the imaging lens and the laser.

[0016] Further, the X coordinate value of each point on the surface of the part is:

[0017] (N-1)*(T / n)

[0018] In the formula, N is the arrangement position of the point on the line profile, n is the number of all points on the line profile, and T is the length of the rectangular light spot. Further, the Y coordinate value of each point on the surface of the part is:

[0019] Y=(M-1)*V1*(1 / f)

[0020] In the formula, M is the arrangement position of the point on the line profile, V1 is the moving speed of any point on the surface of the part in the process of the ultra-high-speed laser cladding, and f is the sampling frequency of the online detection equipment.

[0021] Further, the expression of the aforementioned evaluation index cladding layer thickness H is:

[0022] H = Z caver -Z saver

[0023] Z caver =∑Z ci / n1, i = 1, 2, 3, 4…, n1

[0024] Z saver =∑Z sj / n2, j = 1, 2, 3, 4…, n2

[0025] In the formula, Z caver is the average value of all point Z coordinates on the part coating surface profile line, n1 is the number of test points on the part coating surface profile, Z ci is the height coordinate value of the i-th test point on the part coating surface profile, Z saver is the average value of all point Z coordinates on the part substrate surface profile line, n2 is the number of test points on the substrate surface profile, and Zsj is the height coordinate value of the j-th test point on the substrate surface profile.

[0026] Further, the aforementioned evaluation index cladding layer surface morphology characteristic parameter includes cladding layer surface point height coordinate fluctuation ΔZ, and the calculation formula of ΔZ is:

[0027] ΔZ = Z cmax -Z cmmin

[0028] In the formula, Z cmax is the maximum value of the cladding layer surface point height coordinate, and Z cmin is the minimum value of the cladding layer surface point height coordinate.

[0029] Further, the aforementioned step of determining the distribution threshold of the evaluation index includes:

[0030] In combination with the characteristics of the ultra-high-speed laser cladding technology, the characteristics of the cladding material, and the performance of the substrate material to be cladded, key processing parameters are selected, and an orthogonal test scheme is designed.

[0031] The optimal preparation process parameter values of the cladding layer are formulated;

[0032] Using a single-factor test method, the above-mentioned key process parameters are used as influencing factors, and a single-factor test scheme is designed;

[0033] Based on the single-factor test scheme, the cladding layer is prepared;

[0034] The surface morphology of the cladding layer in the preparation process is detected and the characteristic parameters are evaluated, and the evaluation results of the cladding layer characteristic parameters under different process parameters are obtained.

[0035] By using big data analysis technology, and combining with the macro-morphology characteristics of the cladding layer, the cladding layer thickness distribution threshold ΔH', the cladding layer surface point height coordinate fluctuation distribution threshold ΔZ', the arithmetic average roughness distribution threshold ΔS a ′, the skewness distribution threshold ΔS sk ′ and the surface property aspect ratio distribution threshold ΔS tr ′ are obtained.

[0036] Further, the step of online regulation of the cladding layer quality based on the cladding layer thickness distribution threshold ΔH' comprises:

[0037] setting the design thickness value H1 of the cladding layer and the thickness allowable distribution threshold ΔH';

[0038] obtaining the actual thickness H of the cladding layer in real time, and calculating ΔH = |H-H1|;

[0039] If ΔH exceeds ΔH', the system will issue an alarm prompt, analyze the abnormal process parameters, and automatically adjust the process parameters to the set value.

[0040] Further, the step of online regulation of the cladding layer quality based on the cladding layer surface point height coordinate fluctuation distribution threshold ΔZ' comprises:

[0041] setting the threshold ΔZ' of the height coordinate fluctuation range of the cladding layer,

[0042] When ΔZ exceeds ΔZ', the system will issue an alarm prompt, analyze the abnormal process parameters, and automatically adjust the process parameters to the set value.

[0043] Further, the step of online regulation of the cladding layer quality based on the arithmetic average roughness distribution threshold ΔS a ′, the skewness distribution threshold ΔS sk ′ and the surface property aspect ratio distribution threshold ΔS tr ′ comprises:

[0044] respectively setting the set values S a1 , S sk , S tr of the arithmetic average roughness, the skewness and the surface property aspect ratio;

[0045] obtaining the actual values S a , S sk , S tr of the arithmetic average roughness, the skewness and the surface property aspect ratio of the cladding layer in real time, and calculating ΔS a = |S a -S a1 |, ΔSsk = |S sk - S sk |, ΔS tr = |S tr - S tr1 |;

[0046] When ΔS a is within ΔS a ', ΔS sk is within ΔS sk ', ΔS tr is within ΔS tr ', it is indicated that the process parameters and the processing system in the processing process are in a normal state, otherwise the system will issue an alarm prompt, analyze the abnormal process parameters, and automatically adjust the process parameters to the set value.

[0047] The beneficial effects achieved by the present application are:

[0048] (1) The present application designs an online monitoring method for cladding layer forming quality, which is based on the surface feature of the cladding layer and has high online detection accuracy, solving the technical problems of poor online detection accuracy and unreliable results of the prior art.

[0049] (2) The present application designs an online detection method for the spatial coordinates of each point on the surface of the cladding layer, with a sampling frequency distribution range of 1 kHz to 16 kHz, which can effectively ensure the detection accuracy of the surface point coordinates under high-speed moving conditions.

[0050] (3) The online evaluation and control method for cladding layer quality developed by the present application can realize multi-dimensional evaluation of cladding layer thickness, surface point height coordinate fluctuation range and cladding layer surface feature parameters, which is beneficial to improving the comprehensiveness of the evaluation results.

[0051] (4) The present application designs an online control method for the super-high-speed laser cladding layer on the surface of the part, which covers the evaluation feature parameter threshold determination method and the automatic control method for the cladding process parameters, and can realize automatic adjustment of the cladding process parameters. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is the online monitoring method flowchart of the present application for super-high-speed laser cladding intelligent processing;

[0053] Figure 2 is the part surface point coordinate detection principle schematic diagram of the present application;

[0054] Figure 3 is the online evaluation method for the cladding layer thickness of the present application;

[0055] Figure 4It is the surface morphology characteristic parameter online evaluation method of the cladding layer of the application;

[0056] Figure 5 It is the surface morphology equivalent forming method of the cladding layer of the application;

[0057] Figure 6 It is the surface morphology characteristic influence factor analysis of the cladding layer of the application;

[0058] Figure 7 It is the flow chart of the cladding layer forming quality evaluation index threshold value determination of the application;

[0059] Figure 8 It is the macro-morphology of the super-high-speed laser cladding layer of the application;

[0060] Figure 9 It is the characteristic parameter variation law of the super-high-speed laser cladding layer of the application with laser power. DETAILED DESCRIPTION

[0061] The technical solutions of the application will be described in detail below by means of the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments and the specific features in the embodiments are detailed descriptions of the technical solutions of the application, rather than limitations of the technical solutions of the application. In the case of no conflict, the technical features in the embodiments and the technical features in the embodiments can be combined with each other.

[0062] The embodiment discloses an online monitoring method for super-high-speed laser cladding intelligent processing, as shown in Figure 1 , comprising the following steps:

[0063] STEP 1: Pre-set process parameters.

[0064] STEP 2: Start the equipment and prepare the cladding layer.

[0065] STEP 3: During the preparation process, the spatial coordinates of each point on the surface of the part are detected in real time, and the point spatial coordinates are the X, Y and Z coordinate values of each point on the surface of the part;

[0066] As shown in Figure 2 , it is the principle of detecting the coordinates of each point on the surface of the cladded part (including the surface of the part coating and the surface of the part base), the sensing head emits a parallel laser beam to project on the surface of the part, the direction of the laser beam emission is parallel to the normal direction of the surface of the measured region of the part, after the laser irradiation on the surface of the part, a diffuse reflection is formed, and a diffuse reflection laser beam is generated. The diffuse reflection laser beam is imaged on the high-precision CMOS camera after passing through the imaging lens. At the same time, in order to improve the online detection efficiency and range during the measurement process, the sensing head emits a parallel laser beam to form a rectangular spot on the surface of the part, and the rectangular spot is diffusely reflected on the CMOS camera to form a 2D test profile. The meanings of the parameters in the figure are as follows:

[0067] Z - height of each point on the surface of the part;

[0068] L - displacement of the imaging spot;

[0069] a - angle between the optical axis of the imaging lens and the laser;

[0070] b - angle between the optical axis of the imaging lens and the photosensitive surface;

[0071] K1 - object distance of the imaging;

[0072] K2 - image distance of the imaging;

[0073] O - optical center of the imaging lens;

[0074] N - position of the laser spot on the surface of the part;

[0075] N' - position of the imaging spot on the CMOS camera;

[0076] As can be seen from the geometric relationship in the figure, △MON and △M'ON' are similar, and according to the theorem of similar triangles, we can get

[0077]

[0078] Further, according to the relationship of trigonometric functions, we can get

[0079]

[0080]

[0081]

[0082]

[0083] By listing the above relationships, we can analyze and get

[0084]

[0085] Further analysis can get the relationship between the height Z of each point on the surface of the part and the displacement L of the imaging spot:

[0086]

[0087] In the formula, the parameters K1, K2, a, b are constants in the actual application of the online detection equipment, and the displacement L of the imaging spot can be analyzed by the detection equipment, so the height coordinate Z of each point on the surface of the part can be calculated.

[0088] The length of the rectangular light spot in the X direction is inversely proportional to the sampling frequency f of the online detection device, and the line profile is fitted by n points. The sampling frequency distribution range can reach 1 kHz-16 kHz. Assuming that the sampling frequency is f and the length of the rectangular light spot is T, the distance between the adjacent two points is T / n. Taking one end of the profile as the starting point, i.e., the X coordinate of the point is marked as 0, the X coordinate value of the Nth point can be calculated as: (N-1)*(T / n).

[0089] The online monitoring system analysis software can fit a series of 2D test profiles into a three-dimensional surface profile. The distance Y' between the adjacent two 2D test profiles in the Y direction is: Y' = V1*(1 / f),

[0090] wherein V1 is the moving speed of any point on the surface coating of the part in the super-high-speed laser cladding process.

[0091] Thus, the Y coordinate value of the point on the Mth line profile can be calculated as:

[0092] Y = (M-1)*Y' = (M-1)*V1*(1 / f).

[0093] Therefore, the online monitoring system analysis software can calculate the X, Y and Z coordinate values of each point on the surface of the part. In addition, by adjusting the irradiation position of the rectangular light spot on the surface of the part, the coordinate values of each point on the surface of the part substrate and the coating can be detected online.

[0094] STEP 4: Based on the spatial coordinates of the points on the surface of the part, the online evaluation index of the cladding layer quality is calculated, and the evaluation index includes the cladding layer thickness and the surface feature parameters of the cladding layer;

[0095] 4.1) Online evaluation index of cladding layer thickness

[0096] As described above, in the online monitoring of the cladding layer quality in the super-high-speed laser cladding process, the parallel laser beams emitted by the sensing head of the online monitoring system irradiate on the surface of the part substrate and the surface of the coating at the same time. As shown in FIG. 1, the 2D test profiles detected by the online monitoring system include the surface profile of the cladding layer and the surface profile of the part substrate, respectively. Assuming that the width of the rectangular light spot irradiating on the surface of the part substrate in the X direction is t, the online monitoring system evaluation software automatically extracts the Zci coordinate values of the 2D test profile points on the surface of the part coating (T-t range), and analyzes and calculates the average value Zcaver of the Z coordinates of all points in the range: Figure 3

[0097] Z caver =∑Z ci / n1, i = 1, 2, 3, 4…, n1

[0098] n1 is the number of test points on the profile of the surface of the part coating, and Z​ci Z is the height coordinate value of the i-th test point on the surface profile of the part coating.

[0099] Using the same analysis method, the average value Zsaver of the height coordinates of all test points on the profile line within the range of the substrate surface profile t can be calculated and analyzed:

[0100] Z saver sj / n2, j = 1, 2, 3, 4..., n2

[0101] n2 is the number of test points on the profile of the substrate surface, Z sj is the height coordinate value of the j-th test point on the profile of the substrate surface. Based on the above analysis results, the thickness H of the cladding layer on the entire 2D test profile is further analyzed:

[0102] H = Z caver - Z saver

[0103] 4.2) On-line evaluation index of surface morphology characteristic parameters of cladding layer

[0104] The present application proposes a method for on-line evaluation of surface morphology characteristic parameters of cladding layer combining 2D test profile and three-dimensional surface profile. On the one hand, by comparing the fluctuation range of the height coordinates of the coating surface points of the 2D test profile, it is determined whether the profile surface is abnormal, which has the advantages of small data processing amount and fast response speed. On the other hand, by comparing and analyzing the morphology characteristic parameters of the three-dimensional surface profile, it is further determined whether there is an abnormality in the forming process, which has the advantages of reliable and accurate evaluation results.

[0105] (a) On-line evaluation method of fluctuation range of height coordinates of coating surface points of 2D test profile

[0106] As shown in Figure 4 , the on-line monitoring system evaluation software reads the height coordinates Z ci of each point of the coating surface of the cladding layer in the 2D test profile in real time, then automatically analyzes the maximum height coordinate Z cmax and the minimum height coordinate Z cmin of the coating surface of the cladding layer in the 2D test profile, and finally analyzes and calculates the fluctuation range ΔZ of the height coordinates of the coating surface:

[0107] ΔZ = Z cmax - Z cmin

[0108] (b) On-line evaluation method of morphology characteristic parameters of three-dimensional surface profile

[0109] The present application proposes a new equivalent forming method for surface morphology of ultra-high-speed laser cladding layer, as Figure 5 ​​As shown, the 2D test profile of the cladding layer 2D in XOZ plane is taken as the initial profile, which is fitted by several surface points of the cladding layer, each of which moves along a respective moving path to finally form the three-dimensional surface profile of the cladding layer. Therefore, it can be seen that the three-dimensional surface profile characteristics of the cladding layer are mainly determined by the initial line profile and the moving path.

[0110] As shown in Figure 6 , the characteristics of the initial profile are mainly determined by the height difference characteristics and the height distribution characteristics of each point in the 2D test profile, therefore, the present application proposes to use the arithmetic average roughness Sa and the skewness Ssk in the three-dimensional roughness parameters to represent the above two characteristics respectively. At the same time, since the ultra-high-speed laser cladding process is an automatic processing process, the forming of the cladding layer has strong directionality, so the aspect ratio Str of the surface characteristics of the three-dimensional roughness parameters is used to represent the directionality of the moving path. The calculation algorithms of the above three parameters are as follows.

[0111]

[0112]

[0113]

[0114] wherein,

[0115] A is the selected area, and Z(x, y) is the height coordinate of each point in the area A.

[0116] STEP 5: Determine the distribution threshold of the evaluation index, and perform online control on the quality of the cladding layer. If the online evaluation index is within the distribution threshold range, jump to STEP 2 to continue the preparation of the cladding layer, otherwise jump to STEP 1 to adjust the process parameters.

[0117] 5.1) Determine the distribution threshold of the evaluation index

[0118] The process of determining the distribution threshold of the evaluation index of the forming quality of the cladding layer in the ultra-high-speed laser cladding process is shown in Figure 7 .

[0119] (a) According to the characteristics of the ultra-high-speed laser cladding technology, the characteristics of the cladding material and the performance of the substrate material to be cladded, etc., select the key process parameters as the influencing factors, design an orthogonal test scheme. At the same time, taking the key performance index type of the cladding layer as the evaluation index, the optimal preparation process parameter value of the cladding layer is obtained by optimization.

[0120] (b) Using single factor test method, the above key process parameters are used as influencing factors, and each influencing factor is designed as several different levels, and finally a single factor test scheme is formed. Based on the above scheme, the cladding layer is prepared.

[0121] (c) Using each set of process parameters to repeatedly prepare cladding layers, the number of repetitions is not less than ten times. The surface morphology of all cladding layers is detected and the characteristic parameters are evaluated, and the evaluation results of the characteristic parameters of the cladding layers under different process parameters are obtained.

[0122] (d) Using big data analysis technology, and combining with the macro-morphology characteristics of the cladding layer, the distribution range of the threshold value of different characteristic parameters is analyzed and obtained.

[0123] 5.2) Online regulation method of cladding layer quality

[0124] (a) Using neural network analysis method, a mathematical model of the influence law of the key process parameters of the processing process on the evaluation indexes of the forming quality of the cladding layer such as cladding layer thickness H, cladding layer surface height coordinate fluctuation range ΔZ, cladding layer surface three-dimensional morphology characteristic parameters S a , S sk , S tr is established, and then the change law of the cladding process parameters of the processing process can be obtained according to the actual online detection results of each evaluation index.

[0125] (b) Online regulation method of cladding layer thickness H: Before the ultra-high-speed laser cladding, according to the design requirements of the cladding layer process, the design thickness value H1 of the cladding layer and the thickness allowable distribution threshold ΔH' are set in the system analysis software. At the same time, the actual thickness H of the cladding layer is analyzed in real time by the online monitoring system analysis software, and is compared and judged with the set thickness value H1. When the cladding layer thickness distribution value ΔH exceeds the allowable threshold ΔH', the system will give an alarm prompt, and according to the established mathematical model, the analysis software can give the type of the potential abnormal process parameters.

[0126] (c) Online regulation method of each point height coordinate fluctuation range ΔZ: the threshold value ΔZ' of the height coordinate fluctuation range of the cladding layer can be set in the online monitoring system evaluation software, when the actual fluctuation range ΔZ does not exceed the threshold value ΔZ', it is considered that the forming quality of the cladding layer meets the requirements, that is, the ultra-high-speed laser cladding process does not appear abnormal. On the contrary, when the actual fluctuation range ΔZ exceeds the threshold value ΔZ', it is considered that the forming quality of the cladding layer does not meet the requirements, that is, the ultra-high-speed laser cladding process appears abnormal, and the system will give an alarm. Similarly, according to the established mathematical model, the analysis software can give the type of the potential abnormal process parameters.

[0127] (d) Online regulation method of surface three-dimensional topography characteristic parameters of cladding layer: the online monitoring system can detect the surface topography characteristic parameters S a , S sk , S tr of the super-high-speed laser cladding layer, and compare them with the set values S a1 , S sk1 , S tr1 of the topography characteristic parameters in real time. Meanwhile, the actual fluctuation values ΔS a , ΔS sk , ΔS tr of the three parameters are compared with the distribution thresholds ΔS a ', ΔS sk ', ΔS tr ' in real time. If the above evaluation indexes are within the distribution threshold range, it indicates that the process parameters and the processing system are in normal state. Otherwise, if the online evaluation results of the above evaluation indexes exceed the distribution threshold, it is determined that the processing process is abnormal, and the online monitoring system will give an alarm and the type of the process parameter that may have been abnormal.

[0128] (e) The online monitoring system compares the actual values and the set values of the process parameters that may be abnormal according to the type of the process parameter that may have been abnormal, so as to quickly determine the process parameter that causes the abnormality and automatically adjust the process parameter to the set value, so as to maintain the stability of the processing system.

[0129] Based on the above method, JG-11 martensitic stainless steel cladding material is used to prepare a cladding layer by super-high-speed laser cladding process, and the optimal cladding layer preparation process is obtained by orthogonal test scheme optimization. The laser power is 4900 W, the deposition speed is 11 m / min, the powder feeding amount is 40 g / min, and the overlap rate is 70%. Under the optimal process parameters, the average thickness H of the cladding layer is 0.53 mm, the average fluctuation range ΔZ of the height coordinates of each point on the surface of the cladding layer is 60 μm, and the average detection values of S a , S sk , S tr are 10.6 μm, 0.21 and 0.18 respectively. The above values are the set values H1, ΔZ', S a1 , S sk1 , S tr1 .

[0130] To analyze the distribution range of the evaluation index threshold value, taking the laser cladding process parameters as the influencing factors, based on the optimal cladding process parameters, a single-factor test scheme as shown in Table 1 was designed. In the ultra-high-speed laser cladding process, the laser power fluctuates from 3000 W to 5500 W, while the deposition speed, powder feeding amount and overlap rate parameters remain unchanged. Based on the single-factor test scheme shown in Table 1, ten samples were prepared for each process, the cladding layer forming quality during the preparation process was monitored online, and the average value of the analyzed characteristic parameters was obtained. As shown in Figure 8 , it is the macroscopic morphology of the ultra-high-speed laser cladding layer prepared by different process parameters. The characteristic parameters of the cladding layer under different parameters were evaluated by using the three-dimensional surface profile morphology characteristic parameter online evaluation method developed by the present application, as shown in Figure 9 , it is the variation law of different characteristic parameters with laser power.

[0131] Combined with the macroscopic morphology characteristics and the detection results of the distribution parameters of the cladding layer, using big data analysis technology, the threshold value distribution ranges ΔH', ΔZ', ΔS a , ΔS sk , ΔS tr of the evaluation parameter fluctuation values ΔH, ΔS a , ΔS sk , ΔS tr were obtained as shown in Table 2. At the same time, using neural network analysis method, the mathematical model of the influence law of laser power process parameter on each characteristic parameter was established. Based on the above analysis results, when the laser power process parameter fluctuates in the actual cladding process, the online detection system analysis software can detect the average thickness H of the cladding layer, the fluctuation range ΔZ of the height coordinate of each point on the cladding layer surface, and the average detection value of the three-dimensional morphology characteristic parameters S a , S sk , S tr in real time. Using the actual detection results of the indexes H, S a , S sk , S tr minus the set values H1, S a1 , S sk1 , S tr1 , and taking the absolute value as the actual fluctuation value ΔH, ΔS a , ΔS sk , ΔS tr of each index, and compared with the threshold values ΔH', ΔS a ', ΔS sk ', ΔS trComparing, the index ΔZ is compared with ΔZ' directly. If abnormality occurs, an alarm will be sent. At the same time, based on the influence mathematical model, the online monitoring system automatically compares the fluctuation value between the actual laser power output value and the set value, and automatically adjusts, so as to ensure that the cladding system automatically recovers to a stable state.

[0132] By using the same test method, the influence law and mathematical model of other any process parameters on the morphology characteristic parameters of the cladding layer can be analyzed, and then the comprehensive detection and accurate regulation of the coating quality in the super-high-speed laser cladding process are realized.

[0133] Table 1 Single factor test scheme

[0134]

[0135] Table 2 Distribution threshold value of evaluation parameters

[0136]

[0137] The above only describes the preferred embodiments of the present application, and it should be noted that, for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should be considered as the protection scope of the present application.

Claims

1. An online monitoring method for intelligent processing of ultra-high-speed laser cladding, characterized in that, The method comprises the following steps: pre-setting process parameters; based on the process parameters, preparing the cladding layer; during the preparation process, detecting the spatial coordinates of each point on the surface of the part in real time, the spatial coordinates of each point on the surface of the part being the X, Y and Z coordinate values of each point on the surface of the part, the surface of the part including the surface of the coating of the part and the surface of the base body of the part; the calculation expression of the height coordinate value Z of each point on the surface of the part is: ； wherein is the imaging object distance, is the imaging spot displacement, is the angle between the imaging lens optical axis and the photosensitive surface, is the imaging image distance, is the angle between the imaging lens optical axis and the laser; Based on the part surface point space coordinates, a cladding layer quality online evaluation index is calculated, the evaluation index including a cladding layer thickness and a cladding layer surface topography characteristic parameter, an expression of the evaluation index cladding layer thickness is: ； ； ； wherein Zmean is the average value of the Z coordinates of all points on the profile line of the part coating surface, N is the number of test points on the profile of the part coating surface, Zj is the height coordinate value of the i-th test point on the profile of the part coating surface, Zmean is the average value of the Z coordinates of all points on the profile line of the part substrate surface, N is the number of test points on the profile of the substrate surface, Zj is the height coordinate value of the j-th test point on the profile of the substrate surface. The surface topography characteristic parameters of the cladding layer include surface point height coordinate fluctuation of the cladding layer , arithmetic average roughness , skewness , and surface property aspect ratio ; Cladding layer coating surface point height coordinate fluctuation The calculation formula is: ； In the formula, is the maximum value of the point height coordinate of the surface of the cladding layer, is the minimum value of the point height coordinate of the surface of the cladding layer. the arithmetic average roughness , skewness , and the surface property aspect ratio , the calculation formula is: ； ； ； wherein , , , ; A is the selected region, h is the height coordinate of each point in region A; determining a distribution threshold of the evaluation index, the distribution threshold of the evaluation index including a cladding layer thickness distribution threshold , a cladding layer surface point height coordinate fluctuation distribution threshold , an arithmetic average roughness distribution threshold , a skewness distribution threshold , and a surface property aspect ratio distribution threshold ; based on the distribution threshold of the evaluation index, online regulating and controlling the quality of the cladding layer: if the online evaluation index is within the range of the distribution threshold, the preparation of the cladding layer is continued, otherwise the process parameters are adjusted.

2. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, characterized in that, the X coordinate value of each point on the surface of the part is: ； in the formula, N is the arrangement position of the point on the line profile, n is the number of all points on the line profile, and T is the length of the rectangular light spot.

3. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, characterized in that, the Y coordinate value of each point on the surface of the part is: ； in the formula, M is the arrangement position of the point on the line profile, V1 is the moving speed of any point on the surface of the part in the process of the ultra-high-speed laser cladding, and f is the sampling frequency of the online detection equipment.

4. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, characterized in that, the step of determining the distribution threshold of the evaluation index comprises: combining the characteristics of the ultra-high-speed laser cladding technology, the characteristics of the cladding material and the performance of the base body material to be cladded, selecting key process parameters, designing an orthogonal test scheme; formulating the optimal process parameter values for preparing the cladding layer; using the single-factor test method, taking the above key process parameters as the influencing factors, designing a single-factor test scheme; based on the single-factor test scheme, preparing the cladding layer; detecting the surface morphology of the cladding layer during the preparation process and evaluating the characteristic parameters to obtain the evaluation results of the characteristic parameters of the cladding layer under different process parameters; The threshold values of the thickness distribution, the point height coordinate fluctuation distribution, the arithmetic average roughness distribution, the skewness distribution and the aspect ratio distribution of the surface properties of the cladding layer are obtained by using big data analysis technology and in combination with the macroscopic morphological characteristics of the cladding layer. and the aspect ratio distribution of the surface properties​​​​ 5. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, characterized in that, the threshold value based on the cladding layer thickness distribution the step of online regulating the cladding layer quality comprises Setting a design thickness value for a cladding layer and a thickness allowance distribution threshold ; Real-time analysis of the actual thickness of the cladding layer , calculation ; If exceeds , the system will issue an alarm prompt, analyze the abnormal process parameters, and automatically adjust the process parameters to the set value.

6. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, wherein The threshold of the fluctuation distribution of the point height coordinate of the surface of the cladding layer The step of online regulating the quality of the cladding layer comprises: Setting a threshold for a cladding layer height coordinate fluctuation range , When time, the system will issue an alarm prompt, analysis of the process parameters occur abnormal, and the process parameters automatically adjusted to the set value.

7. The online monitoring method for intelligent processing of ultra-high-speed laser cladding according to claim 1, characterized in that, the arithmetic mean roughness distribution threshold , skewness distribution threshold and surface property aspect ratio distribution threshold the step of online regulating the quality of the cladding layer comprises: Setting values of arithmetic average roughness, skewness, and surface aspect ratio are set respectively ; Real-time analysis obtains actual values of arithmetic average roughness, skewness and aspect ratio of surface properties of the cladding layer , calculate , , ; When the following conditions are met simultaneously In , In , In , the processing process parameters and the processing system are in a normal state, otherwise the system will issue an alarm prompt, analyze the abnormal process parameters, and automatically adjust the process parameters to the set value.

Images