Method for self-adaptive precision machining of small-curvature structure

Abstract

The invention provides a method for adaptively and precisely machining a small-curvature structure, which comprises the following specific steps of: placing the small-curvature structure to be machined on a platform, and controlling the platform to walk according to a machining track by a servo driver; in combination with platform acceleration waveform monitoring and visual morphology detection, determining a platform acceleration fluctuation standard deviation, a maximum position deviation of a processing track and a geometric shape similarity percentage of a preset path and an actual processing path; judging whether a target value is met or not, and if not, dynamically adjusting at least one of a smoothing instruction filtering coefficient, a maximum adaptive gain and an adaptive gain scale factor in built-in parameters of the servo driver according to a comparison result; reprocessing by utilizing the adjusted built-in parameters of the servo driver until a target value is met; and when the target value is met, a small-curvature structure with a smooth contour is obtained. The method has the advantages of good process stability, adaptive correction of servo parameters, high efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the field of laser processing, and particularly to a method for adaptively and precisely processing small-curvature structures. Background Art

[0002] Quartz devices are currently widely used in fields such as aerospace, inertial navigation systems, precision instruments and meters, and high-end semiconductor equipment. Their core advantages stem from the unique properties of quartz glass materials. For example, high purity and low impurities ensure the stable operation of the devices in extreme environments, reducing performance degradation caused by material contamination; excellent optical and thermal stability meets the stringent requirements of high-precision lithography, laser processing and other processes for material light transmittance and thermal expansion coefficient; high insulation can effectively reduce external electromagnetic interference during use. However, quartz glass is a typical hard and brittle material, and it is extremely easy to cause microcracks or chipping due to stress concentration during the processing. Therefore, it is difficult to process small-curvature structures with high quality and high precision on hard and brittle materials.

[0003] Currently, the processing of small-curvature structures on hard and brittle materials mainly relies on the following three methods: mechanical grinding method, chemical etching method, laser modification cutting-chemical etching method; the existing defects are described separately below:

[0004] First, for the mechanical grinding method, this method grinds small-curvature structures through a grinding wheel, and needs to adjust the tool path with the help of a grinding wheel shaper. However, there are often problems such as difficulty in ensuring dimensional consistency, low processing efficiency, and easy formation of micro-damage on the material edge.

[0005] Secondly, for the chemical etching method, this method uses hydrofluoric acid solution to selectively etch the pre-masked small-curvature structure area. However, there are often problems such as long process time and difficulty in controlling the smoothness of the transition of complex geometries.

[0006] Finally, for the laser modification cutting-chemical etching method, this method uses an ultrafast laser (such as picosecond laser or femtosecond laser) to penetrate the entire quartz glass to form a modified area, and combines wet etching technology to selectively etch and form. It has the advantages of non-contact processing, high precision, and high repeatability, and is suitable for processing small-curvature structures. However, there are still certain technical bottlenecks in this method at present. For example, the trajectory deviation (such as servo lag, mechanical return clearance) of the laser processing platform during X / Y axis linkage will be amplified as the radius of the small-curvature structure decreases, resulting in the modified line deviating from the preset path, the connection is not smooth, and "steps" or breakpoints are likely to appear in the transition area. And this problem will further make it difficult for the smoothness and consistency of the quartz hollowed-out small-curvature structure obtained after selective etching to meet the preset indicators.

[0007] Therefore, there is an urgent need for a method to correct the motion trajectory of the laser processing platform and improve the multi-axis linkage precision, so as to improve the processing precision of small-curvature structures. Summary of the Invention

[0008] In view of the deficiencies in the existing technology, the present invention provides a method for adaptively machining small curvature structures. During the laser precision machining process, by combining servo waveform monitoring and visual topography detection, the standard deviation of the platform acceleration fluctuation is calculated , the maximum position deviation of the machining trajectory , and the percentage C of the geometric shape similarity between the preset path and the actual machining path; according to the data obtained from the detection and calculation, the servo drive parameters are dynamically optimized through a feedback control algorithm; based on the optimized servo parameters, multiple rounds of closed-loop iterative machining and detection are carried out until the parameter indicators of the machining effect meet the target values, and finally a small curvature structure with high smoothness is obtained.

[0009] The present invention achieves the above technical objectives through the following technical means.

[0010] A method for adaptively machining small curvature structures, the specific steps are as follows:

[0011] According to the external shape drawing of the small curvature structure to be machined, the machining trajectory is obtained; the small curvature structure to be machined is placed on the platform, and the servo drive controls the platform to move along the machining trajectory;

[0012] Determine the target value of the standard deviation of the platform acceleration fluctuation, the target value of the maximum position deviation of the machining trajectory, and the target value of the percentage of the geometric shape similarity between the preset path and the actual machining path;

[0013] During laser machining, when a machining trajectory is completed, by combining servo waveform monitoring, visual topography detection, and an acceleration sensor, determine the standard deviation of the platform acceleration fluctuation , the maximum position deviation of the machining trajectory , and the percentage C of the geometric shape similarity between the preset path and the actual machining path;

[0014] Judge whether the standard deviation of the platform acceleration fluctuation , the maximum position deviation of the machining trajectory and the percentage C of the geometric shape similarity meet the target values. If not, at least one of the smoothing instruction filter coefficient, the maximum adaptive gain, and the adaptive gain proportional factor in the built-in parameters of the servo drive is dynamically adjusted according to the comparison result; using the adjusted built-in parameters of the servo drive, re-machine until the target values are met;

[0015] When the target values are met, a small curvature structure with a smooth contour is obtained.

[0016] Furthermore, the built-in parameters of the servo drive are dynamically adjusted respectively according to the judgment conditions of the maximum position deviation, the judgment conditions of the percentage of the geometric shape similarity, and the judgment conditions of the acceleration fluctuation standard deviation.

[0017] Further, according to the judgment condition of the maximum position deviation, at least one of the smoothing command filter coefficient, the maximum adaptive gain, and the adaptive gain scaling factor in the built-in parameters of the servo drive is dynamically adjusted. Specifically:

[0018] Let the target value of the maximum position deviation be A2;

[0019] When > 2A2, then adjust the smoothing command filter coefficient. The adjusted smoothing command filter coefficient is denoted as S new , S new = min(S + 20%×K, ), where S is the current smoothing command filter coefficient, is the current maximum adaptive gain; K is the current adaptive gain scaling factor; the adjusted smoothing command filter coefficient S new satisfies the condition that S new [20%, 95%];

[0020] When , then the adjusted adaptive gain scaling factor is denoted as K new , K new = 1.1K, the adjusted smoothing command filter coefficient S new = S + 5%; the adjusted smoothing command filter coefficient S new satisfies the condition that S new [20%, 95%], and the adjusted adaptive gain scaling factor K new satisfies the condition that K new [0.5, 1.8].

[0021] Further, according to the judgment condition of the geometric shape similarity percentage, at least one of the smoothing command filter coefficient, the maximum adaptive gain, and the adaptive gain scaling factor in the built-in parameters of the servo drive is dynamically adjusted. Specifically:

[0022] Let the target value of the geometric shape similarity percentage be A3;

[0023] When C < A3 - 5%, then the adjusted smoothing command filter coefficient S new = S + 15%; the adjusted maximum adaptive gain is denoted as , = max( ×0.9, 100%); the adjusted smoothing command filter coefficient S new satisfies the condition that S new [20%, 95%], and the adjusted maximum adaptive gain The condition to be satisfied is [80%, 200%;

[0024] When then the adjusted adaptive gain ratio factor K new = 1.1K, and the adjusted smoothing command filtering coefficient S new = S - 5%; the adjusted smoothing command filtering coefficient S new The condition to be satisfied is S new [30%, 95%], and the adjusted adaptive gain ratio factor K new The condition to be satisfied is K new [0.5, 1.8].

[0025] Furthermore, according to the judgment condition of the acceleration fluctuation standard deviation, at least one of the smoothing command filtering coefficient, the maximum adaptive gain, and the adaptive gain ratio factor in the built-in parameters of the servo driver is dynamically adjusted. Specifically:

[0026] Let the target value of the acceleration fluctuation standard deviation be A1,

[0027] When > 1.6A1, then the adjusted maximum adaptive gain is denoted as = × 0.7, the adjusted adaptive gain ratio factor K new = 0.8K, and the adjusted smoothing command filtering coefficient S new = S + 10%; the adjusted smoothing command filtering coefficient S new The condition to be satisfied is S new [30%, 95%], and the adjusted adaptive gain ratio factor K new The condition to be satisfied is K new [0.6, 1.8]; the adjusted maximum adaptive gain The condition to be satisfied is [80%, 200%;

[0028] When then the adjusted smoothing command filtering coefficient S new = S + 4%, and the adjusted maximum adaptive gain is denoted as = min( × 1.05, 150%), the adjusted smoothing command filtering coefficient S new The condition to be satisfied is S new [30%, 95%], and the adjusted maximum adaptive gain The satisfaction condition is [80%, 200%].

[0029] Furthermore, when two of the judgment conditions of the maximum position deviation, the geometric shape similarity percentage, and the standard deviation of acceleration fluctuation are satisfied simultaneously, the built-in parameters of the servo drive are adjusted according to the following priority order. The priority order is:

[0030] Judgment condition of maximum position deviation > Judgment condition of geometric shape similarity > Judgment condition of standard deviation of acceleration fluctuation.

[0031] Furthermore, after adjusting the built-in parameters of the servo drive according to the judgment condition of the maximum position deviation, if C < A3 - 5% simultaneously, first increase the adaptive gain ratio factor after adjusting the judgment condition of the maximum position deviation by 5%, and then adjust the built-in parameters of the servo drive according to the judgment condition of the geometric shape similarity percentage.

[0032] Furthermore, after adjusting the built-in parameters of the servo drive according to the judgment condition of the geometric shape similarity, if simultaneously < 1.2A1, first increase the adaptive gain ratio factor after adjusting the judgment condition of the geometric shape similarity by 8%, and then adjust the built-in parameters of the servo drive according to the judgment condition of the standard deviation of acceleration fluctuation.

[0033] Furthermore, after adjusting the built-in parameters of the servo drive according to the judgment condition of the standard deviation of acceleration fluctuation, if simultaneously 2A2, limit the platform acceleration to 90% of the set value.

[0034] The beneficial effects of the present invention are as follows:

[0035] 1. The method for adaptively processing a small-curvature structure according to the present invention combines servo drive platform acceleration waveform monitoring and visual morphology detection during the laser precision machining process, calculates the standard deviation of platform acceleration fluctuation , the maximum position deviation of the machining trajectory , and the geometric shape similarity percentage C between the preset path and the actual machining path; according to the detected and calculated data, dynamically optimize the parameters of the servo drive through a feedback control algorithm; perform multiple rounds of closed-loop iterative machining and detection based on the optimized servo parameters until the parameter indicators of the machining effect meet the target values, and finally obtain a small-curvature structure with high smoothness. The small-curvature structure processed by the machining method of the present invention has high geometric accuracy and good process stability.

[0036] 2. The method for adaptively machining small-curvature structures according to the present invention can be applied to the machining of small-curvature structures of various hard and brittle materials such as borosilicate glass, aluminosilicate glass, quartz glass, sapphire, etc., and is compatible with a variety of lasers that can be used for machining hard and brittle materials, with a wide range of applications.

[0037] 3. The method for adaptively machining small-curvature structures according to the present invention greatly reduces the number of manual reworks due to the adaptive parameter correction, and has a high machining efficiency; moreover, after the parameter correction, iterative machining and quality closed-loop verification can be automatically performed, and the operation is simple.

[0038] 4. The method for adaptively machining small-curvature structures according to the present invention breaks through the traditional single feedback mode, fuses the servo waveform signal and the visual topography data, and realizes multi-dimensional parameter adaptive compensation based on the feedback control algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. The following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, it is obvious that other drawings can also be obtained according to these drawings.

[0040] Figure 1 It is a flowchart of the method for adaptively machining small-curvature structures according to the present invention.

[0041] Figure 2 It is a schematic diagram of the ultrafast shaping laser processing platform according to the present invention.

[0042] Figure 3 It is a preset trajectory diagram of the small-curvature structure to be machined according to the present invention.

[0043] Figure 4 It is an effect diagram of the modification of the trajectory line of the small-curvature structure characterized under the microscope before the adaptive optimization of the servo parameters according to the present invention.

[0044] Figure 5 It is an effect diagram of the modification of the trajectory line of the small-curvature structure characterized under the microscope after the adaptive optimization of the servo parameters according to the present invention.

[0045] Figure 6 It is an etching effect diagram of the engraved small-curvature structure obtained under the microscope after the chemically etched quartz glass processed by the modification before the parameter optimization according to the present invention.

[0046] Figure 7 It is an etching effect diagram of the engraved small-curvature structure obtained under the microscope after the chemically etched quartz glass processed by the modification after the parameter optimization according to the present invention.

[0047] In the figure:

[0048] 1 - Ultra-fast picosecond laser system; 2 - Beam expander; 3 - Reflecting mirror; 4 - Axicon lens; 5 - Plano-convex lens; 6 - Microscope objective; 7 - Sample to be processed; 8 - High-precision displacement platform; 9 - CMOS camera and LED light source; 10 - Computer control system. Specific embodiments

[0049] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, and should not be construed as limiting the present invention.

[0050] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "axial", "radial", "vertical", "horizontal", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0051] In the present invention, unless otherwise clearly defined and limited, the terms "mounted", "connected", "connected to", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] As Figure 1 shown, the method for adaptively processing a small-curvature structure according to the present invention comprises the following specific steps:

[0053] S01: Construct a 4f beam reduction system for Bessel beams based on the thickness of the sample of the transparent brittle material, ensuring that the focal depth of the focused beam is greater than the glass thickness; the beam reduction system consists of a plano-convex lens and a 10X microscope objective lens, which is used to control the focal depth and increase the energy density. Use a computer control system to control the optical modulator to adjust the number of Bursts output (i.e., the number of sub-pulses within the pulse envelope), the light output frequency, the pulse energy, and the spatial interval of pulse emission; at the same time, set the z-axis position so that the focal depth of the Bessel beam penetrates the upper and lower surfaces of the sample; according to the contour map of the small curvature structure to be processed, set the platform movement speed and acceleration parameters based on the preset trajectory, and then the computer control system controls the multi-axis linkage based on the preset path to obtain the processing trajectory of the small curvature structure. The small curvature is below a curvature radius of 1 mm.

[0054] S02: Use an acceleration sensor to collect the acceleration fluctuations of the platform where the workpiece to be processed is placed. By collecting the acceleration sensor signals in real time, monitor the acceleration fluctuations of the platform during the processing stage through an integrated controller, and calculate the standard deviation of the acceleration fluctuations to ensure the stability of the multi-axis linkage platform movement; at the same time, by integrating an ultra-clear camera and a coaxial illumination light source, take a microscopic image (resolution 1 µm / pixel) of the modified line of the small curvature structure after the processing is completed, extract the processing contour based on the edge detection algorithm, compare the maximum position deviation with the preset theoretical path, and calculate the geometric shape similarity, that is, the geometric shape percentage, based on the shape context matching algorithm. The processing platform is driven by a servo driver to move according to the processing trajectory of the small curvature structure. The shape context matching algorithm and the edge detection algorithm are both existing algorithms.

[0055] S03: Use the standard deviation of acceleration fluctuations, the maximum position deviation, and the geometric shape similarity percentage to dynamically adjust the built-in parameters of the servo driver (smooth command filter coefficient, maximum adaptive gain, adaptive gain proportional factor) to correct the trajectory deviation during the actual processing; after covering the original parameters with the corrected servo control parameters, perform a new round of "processing - detection - analysis - compensation" full closed-loop control process until the standard deviation of acceleration fluctuations, the maximum position deviation of the processing trajectory, and the geometric shape similarity percentage meet the predetermined requirements, as follows:

[0056] Let the target value of the standard deviation of acceleration fluctuations be A1, the target value of the maximum position deviation be A2, and the target value of the geometric shape similarity percentage be A3; the calculated standard deviation of acceleration fluctuations is denoted as , the calculated maximum position deviation , and the calculated geometric shape similarity percentage is denoted as C;

[0057] When > 2A2, then adjust the smooth command filter coefficient, and the adjusted smooth command filter coefficient is denoted as S new , S new= min(S + 20%×K, ), where S is the current smoothing instruction filtering coefficient, is the current maximum adaptive gain; K is the current adaptive gain ratio factor; the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new [20%, 95%];

[0058] When holds, the adjusted adaptive gain ratio factor is denoted as K new , K new = 1.1K, and the adjusted smoothing instruction filtering coefficient S new = S + 5%; the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new [20%, 95%], and the adjusted adaptive gain ratio factor K new satisfies the condition that K new [0.5, 1.8];

[0059] When C < A3 - 5%, the adjusted smoothing instruction filtering coefficient S new = S + 15%; the adjusted maximum adaptive gain is denoted as , = max( ×0.9, 100%); the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new [20%, 95%], and the adjusted maximum adaptive gain satisfies the condition that [80%, 200%];

[0060] When holds, the adjusted adaptive gain ratio factor K new = 1.1K, and the adjusted smoothing instruction filtering coefficient S new = S - 5%; the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new [30%, 95%], and the adjusted adaptive gain ratio factor K new satisfies the condition that K new [0.5, 1.8];

[0061] When > 1.6A1, the adjusted maximum adaptive gain is denoted as = ×0.7, the adjusted adaptive gain ratio factor K new = 0.8K, the adjusted smoothing command filter coefficient S new = S + 10%; the adjusted smoothing command filter coefficient S new The condition for satisfaction is S new [30%, 95%], the adjusted adaptive gain ratio factor K new The condition for satisfaction is K new [0.6, 1.8]; the adjusted maximum adaptive gain The condition for satisfaction is [80%, 200%];

[0062] When then, the adjusted smoothing command filter coefficient S new = S + 4%, the adjusted maximum adaptive gain is denoted as = min( ×1.05, 150%), the adjusted smoothing command filter coefficient S new The condition for satisfaction is S new [30%, 95%], the adjusted maximum adaptive gain The condition for satisfaction is [80%, 200%];

[0063] If the smoothing command filter coefficient, the maximum adaptive gain, and the adaptive gain ratio factor exceed the boundary limits when calculated separately, then the parameter values that exceed the boundary limits are corrected to the corresponding boundary values that are exceeded; assume the adjusted maximum adaptive gain is 75%, exceeding the minimum boundary of 80%, then the adjusted maximum adaptive gain is changed to 80%.

[0064] When multiple error conditions are triggered simultaneously, the parameter correction is performed in the priority order of maximum position deviation > geometric shape similarity > standard deviation of acceleration fluctuation; that is, first adjust the built-in parameters of the servo drive according to the judgment condition of the maximum position deviation, then superimpose and adjust the built-in parameters of the servo drive according to the judgment condition of the geometric shape similarity, and finally superimpose and adjust the built-in parameters of the servo drive according to the judgment condition of the standard deviation of acceleration fluctuation.

[0065] The rules for linked adjustment are also designed, specifically as follows:

[0066] After adjusting the built-in parameters of the servo drive according to the judgment condition of the maximum position deviation, if C < A3 - 5% at the same time, then the adjusted adaptive gain ratio factor K new= 1.05K, where K is the adaptive gain ratio factor adjusted according to the judgment condition of the maximum position deviation;

[0067] After adjusting the built-in parameters of the servo driver according to the judgment condition of geometric shape similarity, if at the same time < 1.2A1, then the adjusted adaptive gain ratio factor K new = 1.08K, where K is the adaptive gain ratio factor adjusted according to the judgment condition of geometric shape similarity;

[0068] After adjusting the built-in parameters of the servo driver according to the judgment condition of the standard deviation of acceleration fluctuation, if at the same time 2 A2, then limit the platform acceleration to 90% of the set value.

[0069] S04: After the closed-loop iterative processing is completed, process the small curvature structure of the target sample based on the finally optimized servo drive parameters; for the laser precision processing method of direct ablation and gasification, it is necessary to perform path filling according to actual needs on the basis of the preset processing trajectory, or repeat the processing times multiple times; for the processing method of laser modification supplemented by wet etching, the modified sample needs to be placed in a chemical etching solution for selective etching to finally achieve the processing of a small curvature structure with high smoothness.

[0070] Example 1

[0071] Process the small curvature structure of quartz by the method of picosecond laser modification combined with chemical etching, which specifically includes the following steps:

[0072] S01: The laser wavelength of the picosecond laser system is 1064 nm, the pulse width is 10 ps, the repetition frequency is set to 100 kHz, the number of bursts is adjusted to 3 by an optical modulator, the time interval between the light outputs of each burst is 25 ns, and at the same time, the single pulse energy is set to 250 µJ, the laser processing mode is the PSO mode, and the pulse step interval is 2.5 µm; after the Gaussian beam is expanded by an expander, the beam waist radius is 3 mm, then the propagation direction is changed by a reflector, and then it is shaped into a Bessel beam by a cone lens with a cone bottom angle of 5°, and finally the beam radius is reduced by a 4f beam reduction system to improve the energy density, and the modification effect on quartz glass can be achieved; the 4f system consists of a plano-convex lens with a focal length of 50 mm and a microscopic objective lens with a focal length of 9 mm. After calculation, the spot diameter of the Bessel beam is 1.75 µm and the non-diffraction length is 2.46 mm, which meets the modification requirements of the sample to be processed; the CMOS camera (Basler ace 2) and the LED light source (wavelength 630 nm) are coaxial with the Bessel optical system and are used for taking modification morphology pictures. The processing optical path is as Figure 2 shown.

[0073] AsFigure 2 As shown in the figure, the picosecond laser processing device includes an ultrafast picosecond laser system 1, a beam expander 2, a reflector 3, a cone lens 4, a plano-convex lens 5, a 10× microscope objective 6, a CMOS camera, and an LED light source 9. The sample to be processed 7 is placed at the center of the high-precision displacement platform 8, and an acceleration sensor is placed inside the platform. The acquisition module is connected to the accelerometer and the computer control system 10. After the ultrafast laser system generates Gaussian light, the output power of the laser is controlled by an internal attenuation sheet. The computer control system is used to control the Q-switch in the laser system to adjust the repetition frequency, and an optical modulator is used to control the number of pulses in the Burst mode. The spot radius is magnified by the beam expander, and the beam is shaped into a Bessel beam after passing through the cone lens. The adjusted laser beam is focused on the sample located on the high-precision translation stage through the plano-convex lens and the microscope objective.

[0074] The transparent brittle material selected for the experiment is quartz glass (purity ≥ 99.99%). The thickness of the glass sample is 1 mm, and the refractive index is 1.51. The glass sample is fixed on the high-precision translation stage. The processing path is generated according to the shape of the small-curvature structure to be modified. The morphology of the small-curvature structure to be detected consists of a main arc and two arc transition lines, and the corresponding curvature radii are 270 µm, 680 µm, and 1060 µm respectively. The three arcs are centered at different positions and are designed to achieve smooth transition. The preset trajectory diagram is as shown in the appendix. Figure 3 The speed of the multi-axis linkage platform is initially set to 10 mm / s, and the acceleration is 20 mm / s². Then, trial processing is carried out on the laser processing platform according to the preset path. The Bessel beam penetrates the upper and lower surfaces of the glass to form a continuous modified area. During this process, the area directly affected by the Bessel light changes from the original silicon-oxygen six-membered ring structure to silicon-oxygen three-membered ring and four-membered ring structures.

[0075] S02: During the period from the start to the end of processing, the signal of the MEMS accelerometer (Kistler 8704B) placed at the key position of the platform is continuously collected through the integrated DAQ module (sampling rate: 10 KHz) to detect the acceleration fluctuation during the sample processing in the start and stop stages. The data is transmitted to the computer control system to calculate the standard deviation of the acceleration fluctuation in the first round of processing = 0.064g, where g represents the acceleration due to gravity, and 1g ≈ 9.8 ; At the same time, after processing is completed, the LED light source remains on. The CMOS camera (resolution 1 µm / pixel) will automatically perform visual morphology detection of the modified trajectory. After obtaining the microscopic image of the small-curvature structure, the Canny edge detection algorithm is used to extract the contour of the modified line, and the maximum position deviation is calculated to be 7.2 µm. Based on the obtained actual path and the preset path, the shape context matching algorithm is used to calculate the similarity percentage C of the geometric shapes = 87.1%.

[0076] S03: Target value of the standard deviation of acceleration fluctuation 0.05g, target value of the maximum position deviation 3µm, target value of the percentage of geometric shape similarity 95%; For the convenience of calculation, let the target A1 of the standard deviation of acceleration fluctuation 0.05g, target value A2 of the maximum position deviation 3µm, target value A3 of the percentage of geometric shape similarity 95%;

[0077] Utilize the standard deviation of acceleration fluctuation, the maximum position deviation, and the percentage of geometric shape similarity to dynamically adjust the built-in parameters of the servo drive (smooth command filter coefficient, maximum adaptive gain, adaptive gain scale factor), so as to correct the trajectory deviation in the actual machining process;

[0078] In the embodiment, none of the standard deviation of acceleration fluctuation, the maximum position deviation, and the percentage of geometric shape similarity reaches the target. Due to triggering multiple error conditions, parameter correction is performed in the priority order of maximum position deviation > geometric shape similarity > standard deviation of acceleration fluctuation, as shown in Table 1;

[0079] The initial smooth command filter coefficient S = 50%, the initial adaptive gain scale factor K = 0.8, and the initial maximum adaptive gain of the servo drive of the platform = 120%;

[0080] In the first-round optimization process:

[0081] Due to the maximum position deviation 7.2µm > 6µm, the adjusted smooth command filter coefficient = min(50% + 20%×0.8, ) = 66%, the adjusted smooth command filter coefficient S new Satisfies the condition that S new [20%, 95%]; After adjusting the built-in parameters of the servo drive according to the judgment condition of the maximum position deviation, since at the same time C = 87.1% < 90%, the adjusted adaptive gain scale factor K new = 1.05K = 0.8×105% = 0.84;

[0082] Since the percentage of geometric shape similarity C = 87.1% < 90%, the adjusted smooth command filter coefficient S new = S + 15% = 66% + 15% = 81%; The adjusted maximum adaptive gain = max( ×0.9, 100%)= max( ×0.9, 100%)=108%;

[0083] Due to the standard deviation of acceleration fluctuations of 0.05g ≤ 0.08g, then the adjusted smoothing command filter coefficient S new =S+4%= 81%+ 4%=85%, the maximum adaptive gain after adjustment is recorded as = min( ×1.05, 150%)=min(108%×1.05, 150%)=113.4%, so the result of this round of servo parameter optimization is: S new =85%, K new =0.84, =113.4%, the servo parameters are within the boundary limits.

[0084] Table 1

[0085]

[0086] S04: The servo drive of the platform is based on S new =85%, K new =0.84, =113.4% The next round of improved line processing, effect detection, analysis and servo parameter re-correction is carried out until the optimized processing trajectory parameters reach the predetermined indicators; after four rounds of iteration, the acceleration fluctuation standard deviation is obtained. =0.045g, maximum position deviation 2.7µm, geometric shape similarity percentage C=95.4%, the corresponding servo drive parameters are S=90%, K=1.4640, =107.16%. The comparison of the trajectory effect of small curvature structure modification before and after servo parameter adaptive optimization is shown in the figure below. Figure 4 and Figure 5 shown.

[0087] The quartz glass without adaptive parameter correction and the quartz glass after servo parameter correction were ultrasonically cleaned in anhydrous ethanol for 10 minutes to remove impurities on the glass surface. After drying in the drying oven, they are respectively placed in a hydrofluoric acid etching solution, and a hollow small curvature structure of the quartz pendulum piece is obtained based on the selective etching characteristics of the modified area. The concentration of the etching solution is 15%, the initial temperature is set at 30 °C, the etching time is 15 min, and ultrasonic oscillation is introduced during the etching to assist the etching, accelerating the etching efficiency and etching uniformity. After the etching is completed, the sample is first placed in clarified lime water to neutralize the residual hydrofluoric acid on the surface, and then ultrasonically cleaned and dried with absolute ethanol, and the processing of the small curvature structure of the quartz pendulum piece can be completed. Among them, the comparison diagram of the etching effect of the small curvature structure before and after the adaptive optimization of the servo parameters is as Figure 6 and Figure 7 shown.

[0088] It should be understood that although this specification is described according to each embodiment, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0089] The series of detailed descriptions listed above are only specific descriptions of the feasible embodiments of the present invention, and they are not used to limit the protection scope of the present invention. Any equivalent embodiments or changes made without departing from the technical spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptively precision machining a small curvature structure, characterized in that, The specific steps are as follows: Based on the external contour diagram of the small curvature structure to be processed, obtain the processing trajectory; place the small curvature structure to be processed on the platform, and the servo driver controls the platform to move along the processing trajectory; Determine the target value of the standard deviation of the platform acceleration fluctuation, the target value of the maximum position deviation of the processing trajectory, and the target value of the geometric shape similarity percentage between the preset path and the actual processing path; After completing a machining trajectory in laser processing, the standard deviation of the platform acceleration fluctuation is determined by combining acceleration waveform monitoring and visual topography detection , the maximum position deviation of the machining trajectory , and the percentage C of the geometric shape similarity between the preset path and the actual machining path; Judge the standard deviation of platform acceleration fluctuation and the maximum position deviation of the machining trajectory and whether the geometric shape similarity percentage C meets the target value. If not, at least one of the smoothing instruction filter coefficient, maximum adaptive gain, and adaptive gain ratio factor in the built-in parameters of the servo driver is dynamically adjusted according to the comparison result; Use the adjusted built-in parameters of the servo driver to reprocess until the target values are met; When the target values are met, obtain a small curvature structure with a smooth contour.

2. The method for adaptively and precisely machining a small curvature structure according to claim 1, characterized in that, Dynamically adjust the built-in parameters of the servo driver according to the judgment conditions of the maximum position deviation, the geometric shape similarity percentage, and the standard deviation of the acceleration fluctuation, respectively; 3. The method for adaptively and precisely machining a small-curvature structure according to claim 2, wherein According to the judgment condition of the maximum position deviation, dynamically adjust at least one of the smoothing instruction filter coefficient, the maximum adaptive gain, and the adaptive gain scaling factor in the built-in parameters of the servo driver. Specifically: Let the target value of the maximum position deviation be A2; When > 2 A2, the smoothing instruction filter coefficient is adjusted, and the adjusted smoothing instruction filter coefficient is denoted as S new , S new = min(S + 20%×K, ), where S is the current smoothing instruction filter coefficient, is the current maximum adaptive gain; K is the current adaptive gain ratio factor; the adjusted smoothing instruction filter coefficient S new satisfies the condition that S new ∈ [20%, 95%]; When the adjusted adaptive gain scaling factor is denoted as K new , K new = 1.1K, and the adjusted smoothing command filtering coefficient S new = S + 5%; the adjusted smoothing command filtering coefficient S new satisfies the condition that S new is in the range of [20%, 95%], and the adjusted adaptive gain scaling factor K new satisfies the condition that K new is in the range of [0.5, 1.8].

4. The method for adaptively and precisely machining a small-curvature structure according to claim 2, wherein According to the judgment condition of the geometric shape similarity percentage, dynamically adjust at least one of the smoothing instruction filter coefficient, the maximum adaptive gain, and the adaptive gain scaling factor in the built-in parameters of the servo driver. Specifically: Let the target value of the geometric shape similarity percentage be A3; When C < A3 - 5%, the adjusted smoothing instruction filtering coefficient S new = S + 15%; the adjusted maximum adaptive gain is denoted as , = max( × 0.9, 100%); the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new ∈ [20%, 95%], and the adjusted maximum adaptive gain satisfies the condition that ∈ [80%, 200%]; When then the adjusted adaptive gain scaling factor K new = 1.1K, and the adjusted smoothing command filtering coefficient S new = S - 5%; the adjusted smoothing command filtering coefficient S new satisfies the condition that S new is in the range of [30%, 95%], and the adjusted adaptive gain scaling factor K new satisfies the condition that K new is in the range of [0.5, 1.8].

5. The method for adaptively and precisely machining a small-curvature structure according to claim 2, wherein According to the judgment condition of the standard deviation of the acceleration fluctuation, dynamically adjust at least one of the smoothing instruction filter coefficient, the maximum adaptive gain, and the adaptive gain scaling factor in the built-in parameters of the servo driver. Specifically: Let the target value of the standard deviation of the acceleration fluctuation be A1, When > 1.6A1, the adjusted maximum adaptive gain is denoted as = × 0.7, the adjusted adaptive gain scaling factor K new = 0.8K, the adjusted smoothing command filtering coefficient S new = S + 10%; the adjusted smoothing command filtering coefficient S new The condition to be satisfied is S new [30%, 95%], the adjusted adaptive gain scaling factor K new The condition to be satisfied is K new [0.6, 1.8]; the adjusted maximum adaptive gain The condition to be satisfied is [80%, 200%]; When then the adjusted smoothing instruction filtering coefficient S new = S + 4%, and the adjusted maximum adaptive gain is denoted as = min( × 1.05, 150%), and the adjusted smoothing instruction filtering coefficient S new satisfies the condition that S new ∈ [30%, 95%], and the adjusted maximum adaptive gain satisfies the condition that ∈ [80%, 200%].

6. The method for adaptively and precisely machining a small-curvature structure according to claim 2, wherein When two of the judgment conditions of the maximum position deviation, the geometric shape similarity percentage, and the standard deviation of the acceleration fluctuation are simultaneously satisfied, adjust the built-in parameters of the servo driver according to the following priority order. The priority order is: The judgment condition of the maximum position deviation > the judgment condition of the geometric shape similarity > the judgment condition of the standard deviation of the acceleration fluctuation.

7. The method for adaptively and precisely machining a small-curvature structure according to claim 6, wherein After adjusting the built-in parameters of the servo driver according to the judgment condition of the maximum position deviation, if C < A3 - 5% at the same time, first increase the adaptive gain scaling factor adjusted by the judgment condition of the maximum position deviation by 5%, and then adjust the built-in parameters of the servo driver according to the judgment condition of the geometric shape similarity percentage.

8. The method for adaptively and precisely machining a small-curvature structure according to claim 6, wherein After adjusting the built-in parameters of the servo drive according to the judgment condition of geometric shape similarity, if at the same time < 1.2A1, first increase the adaptive gain proportional factor after adjusting the judgment condition of geometric shape similarity by 8%, and then adjust the built-in parameters of the servo drive according to the judgment condition of the standard deviation of acceleration fluctuation.

9. The method for adaptively and precisely machining a small-curvature structure according to claim 6, wherein After adjusting the built-in parameters of the servo driver according to the judgment condition of the standard deviation of acceleration fluctuation, if at the same time When it is 2 A2, the platform acceleration is limited to 90% of the set value.

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