Gear surface quality evaluation method based on multi-source information fusion

Abstract

The present application relates to the technical field of signal processing, and provides a gear surface quality evaluation method based on multi-source information fusion, which comprises the following steps: performing a hobbing cutter test to obtain a vibration signal of a hobbing cutter spindle; performing time domain index calculation on the vibration signal of the hobbing cutter spindle to obtain a time domain evaluation index X arv (i) corresponding to the machining process of the hobbing cutter spindle; sampling and detecting the batch of parts to obtain a plurality of detection indexes and construct a part surface quality evaluation function Y t (i) that fuses the plurality of detection indexes; further constructing a hobbing machining gear surface quality evaluation function R that fuses the vibration signal time domain evaluation index X arv (i) and the plurality of detection indexes; adjusting the weight ratio of the plurality of indexes in the hobbing machining gear surface quality evaluation function R so that the final hobbing machining gear surface quality evaluation function R can better reflect the part machining surface quality; and evaluating the surface quality of the entire batch of parts by using the hobbing machining gear surface quality evaluation function R.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and mainly to a method for evaluating the surface quality of gears based on multi-source information fusion. Background Technology

[0002] Gears are among the most widely used parts in mechanical engineering, and the various industrial products derived from them are used in almost every aspect of human life. Gear hobbing is the most important manufacturing method and a fundamental technical capability in the machinery manufacturing industry. During gear hobbing, tool wear is inevitable, and the wear phenomenon is quite complex. Existing general monitoring models are almost inadequate to meet the requirements. Therefore, in actual processing, tool replacement and sharpening operations are often performed by workers based on their actual observation of the surface quality of the parts and their own experience.

[0003] A review of existing technologies reveals that the common approach is to establish a gear tooth surface simulation model. This model can be used to quantitatively evaluate the impact of shaft system errors of the hob and gear on the surface quality of the machined gear, providing a theoretical basis for tracing the source of gear machining errors. For example, Chinese patent CN108006193A discloses a method for modeling an ideal gear surface based on hobbing simulation. First, a gear tooth surface simulation model is established, then a hob model is established, and then the gear coordinate system is transformed to the same coordinate system as the hob to simulate the meshing model of the hob and gear. The hobbing cutting thickness is calculated by finding the point on the gear tooth surface that is closest to the hob along its normal direction, and finally, the gear tooth surface model can be obtained. However, in actual production lines, workers' assessment of the surface quality of parts is mostly based on visual inspection and experience. This cannot achieve the required level for high-precision processes or workpieces, requiring extensive adjustments to subsequent processes to achieve higher precision. To date, almost all studies have involved sampling and inspecting finished parts, with continuous updates to inspection techniques to achieve higher evaluation levels. However, they have not addressed potential fluctuations that may occur during the parts manufacturing process, and the sampling levels in enterprises are generally simple, making it difficult to guarantee the density of samples. If a small batch of parts develops surface quality problems, a large number of parts within that time period need to be inspected, consuming significant manpower and resources. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the surface quality of gears by fusing multi-source information. By establishing an evaluation function that integrates the above indicators and by repeatedly adjusting the weight ratio of various indicators in the evaluation function, the final evaluation function can better reflect the surface quality of the machined parts.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a method for evaluating gear surface quality based on multi-source information fusion, the method being:

[0007] (1) Conduct hobbing test to obtain vibration signal of hobbing machine spindle;

[0008] (2) Calculate the time-domain index of the vibration signal of the hob spindle to obtain the time-domain evaluation index X of the hob spindle vibration signal corresponding to its machining process. arv (i);

[0009] (3) Sampling inspection was carried out on the batch of parts to obtain multiple inspection indicators, and a part surface quality evaluation function Y that integrates multiple inspection indicators was constructed. t (i);

[0010] (4) Further construct the time-domain evaluation index X of the fused vibration signal arv (i) and R, a surface quality evaluation function for hobbing gears with multiple detection indicators;

[0011] (5) Adjust the weight ratio of various indicators in the hobbing gear surface quality evaluation function R so that the final hobbing gear surface quality evaluation function R can better reflect the surface quality of the part.

[0012] (6) Use the surface quality evaluation function R of gear hobbing to evaluate the surface quality of the entire batch of parts.

[0013] Furthermore, the specific calculation process in step (2) is as follows:

[0014] From the hob spindle vibration signal throughout the entire machining cycle, a 5-second segment is extracted from the stable phase of the signal. The rectified average value in the time-domain index is selected as the time-domain evaluation index X of the hob spindle vibration signal. arv (i), the calculation process is as follows:

[0015]

[0016] Where A is the number of sampling points in the extracted hob spindle vibration signal sequence;

[0017] x k It is the amplitude of the kth sampling point in the sequence of the cutter spindle vibration signals;

[0018] X arv (i) is the i-th value in the time-domain index, i = 1, 2, 3, ...;

[0019] Furthermore, in step (3), the surface quality evaluation function Y of the part... t (i) The construction process is as follows:

[0020] (31) Sample and inspect parts on the processing production line to obtain multiple inspection indicators, including helix deviation F. αTooth profile deviation f fα ;

[0021] (32) Establish the surface quality evaluation function Y of the part based on the detection index. t (i):

[0022] (33) The weights of each parameter in the surface quality evaluation function of the part are determined by the expert evaluation statistical method.

[0023] Furthermore, the surface quality evaluation function Y of the part t The expression for (i) is:

[0024] Y t (i)=μ1*F α +μ2*f fα +μ3*f β ;

[0025] Where μ1 + μ2 + μ3 = 1;

[0026] μ1 is the total profile deviation F α The indicator weight values;

[0027] μ2 is the tooth profile deviation f fα The indicator weight values;

[0028] μ3 is the total deviation of the helix, f. β The indicator weight values.

[0029] Total profile deviation F α Within the calculated range, the distance between two designed tooth profile traces that encompass the actual tooth profile trace.

[0030] Tooth profile deviation f fα Within the calculated range, the distance between two curves that are exactly the same as the average tooth profile trace, encompassing the actual tooth profile trace.

[0031] Total deviation of the spiral F β Within the calculated range, the distance between two designed spiral traces that encompass the actual spiral trace.

[0032] Furthermore, the method for determining the weights in step (33) is as follows:

[0033] Let the factor set U = {u1, u2, u3, ..., u} n There are m experts, and each expert provides factor u. j The weight is (a 1j a 2j a 3j ,,..,a mj ) T Therefore, the weights given by all experts are represented by the following matrix:

[0034]

[0035] The weights are weighted averages, and the expression for the weights is:

[0036]

[0037] Furthermore, the construction process of the surface quality evaluation function R for hobbing gears is as follows:

[0038] By analyzing the total profile deviation F in the part sampling inspection report α Tooth profile shape deviation f fα and total deviation of the spiral f β The inspection indicators in the part inspection report and the time-domain evaluation index X of the hob spindle vibration signal are used to evaluate the part inspection indicators. arv (i) The function R is integrated into the surface quality evaluation function for hobbing gears, and its expression is:

[0039] R = ω1 * X arv (i)+ω2*Y t (i);

[0040] Where ω1+ω2=1;

[0041] ω1 is the weight value of the rectified average value;

[0042] ω2 is the weight value of the part sampling inspection report.

[0043] Furthermore, the rules for adjusting the weights in step (5) are as follows:

[0044] By machining the same batch of parts with the same cutting tool without changing the tool, the trend of surface finish changes during the machining process of the same batch of parts is measured. By adjusting the weights, the corresponding weights that can best distinguish the surface quality of gears machined at different wear stages of the cutting tool are found.

[0045] Furthermore, in step (6), the standard for evaluating the surface quality of the entire batch of parts using the gear hobbing surface quality evaluation function R is as follows: the value of the quality evaluation function R is divided into three intervals, namely [r1~r2), [r2~r3), and [r3~r4), where r1<r2<r4. 3＜ r4 corresponds to three levels of surface quality for parts: high precision, medium precision, and low precision.

[0046] The final constructed gear surface quality evaluation function R can effectively distinguish the surface quality of gears processed by different tools during the wear stage. Finally, the surface quality of the entire batch of parts is evaluated using the recorded hobbing gear surface quality evaluation function that integrates the hob spindle vibration signal and the part inspection report.

[0047] The present invention has the following beneficial effects:

[0048] (1) This invention evaluates the surface quality of gears by integrating multi-source information, deeply mines the data, and fully combines the advantages of each piece of information, thereby improving the accuracy of the evaluation.

[0049] (2) Based on the gear processing process, the present invention has a simple formula that can make a reasonable, accurate and timely evaluation of the surface quality of gears, improve the detection efficiency and has good application prospects.

[0050] (3) The present invention establishes a part surface quality evaluation function based on the vibration signal of the hob spindle and the part sampling inspection report, and integrates the vibration signal during the part processing and the inspection report index after the part processing to comprehensively evaluate the part processing surface quality, thereby reducing the possibility of subsequent part rework. Attached Figure Description

[0051] Figure 1 This is a flowchart of the evaluation method of the present invention.

[0052] Figure 2 This is a time-domain index diagram of the vibration signal of the hobbing spindle in this invention.

[0053] Figure 3 Y is the surface quality evaluation function for parts in this invention. t (i) is the construction flowchart.

[0054] Figure 4 The part inspection report shows the profile deviation F. α Schematic diagram.

[0055] Figure 5 The part inspection report shows the spiral deviation f. fα Schematic diagram.

[0056] Figure 6 This is a graph showing the surface quality evaluation function for hobbing gears. Detailed Implementation

[0057] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments are only specific illustrations of the invention and should not be regarded as limitations on the invention. The purpose of the embodiments is to enable those skilled in the art to better understand and reproduce the technical solution of the present invention. The scope of protection of the present invention should still be determined by the scope defined in the claims.

[0058] This embodiment provides a vibration signal acquisition system for the spindle of a CNC gear hobbing machine tool, enabling real-time acquisition of vibration signals and calculation of wear condition indicators. The system includes: a vibration acceleration sensor, acquisition software, and an acquisition card. The specific implementation process of this device is as follows:

[0059] The vibration acceleration sensor acquires the vibration signal in the z-direction of the hob spindle, transmits it to the acquisition software through the acquisition card, and captures and saves the vibration signal during the stable machining stage; the signal is subjected to feature calculation, and in this implementation scheme, the wear information in the vibration signal is extracted as a time-domain index.

[0060] like Figure 1 As shown, this invention provides a method for evaluating gear surface quality through multi-source information fusion, the method being:

[0061] S1, Conduct hobbing test to obtain vibration signal of hob spindle of hobbing machine;

[0062] S2, calculate the time-domain index of the hob spindle vibration signal to obtain the time-domain evaluation index X of the hob spindle vibration signal corresponding to its machining process. arv (i), the specific calculation process is as follows:

[0063] From the vibration signal of the hobbing spindle throughout the entire machining cycle, a 5-second segment of the steady-state phase is extracted, such as... Figure 2 As shown, the rectified average value in the time-domain index is selected as the time-domain evaluation index X of the hob spindle vibration signal. arv (i), the calculation process is as follows:

[0064]

[0065] Where A is the number of sampling points in the extracted hob spindle vibration signal sequence;

[0066] x k It is the amplitude of the kth sampling point in the sequence of the cutter spindle vibration signals;

[0067] X arv (i) is the i-th value in the time-domain index, i = 1, 2, 3, ...

[0068] S3. Sampling inspection is carried out on the batch of parts to obtain multiple inspection indicators, and a part surface quality evaluation function Y that integrates multiple inspection indicators is constructed. t (i), such as Figure 3 As shown, the construction process is as follows:

[0069] S31, Sampling inspection of parts on the processing production line to obtain multiple inspection indicators, including helix deviation F. αTooth profile deviation f fα ;

[0070] S32, Establish the part surface quality evaluation function Y based on the detection indicators. t (i), the expression is:

[0071] Y t (i)=μ1*F α +μ2*f fα +μ3*f β ;

[0072] Where μ1 + μ2 + μ3 = 1;

[0073] μ1 is the total profile deviation F α The indicator weight values;

[0074] μ2 is the tooth profile deviation f fα The indicator weight values;

[0075] μ3 is the total deviation of the helix, f. β The indicator weight values.

[0076] like Figure 4 As shown, the total deviation F of the tooth profile α Within the calculated range, the distance between two designed tooth profile traces that encompass the actual tooth profile trace.

[0077] like Figure 5 As shown, the tooth profile deviation f fα Within the calculated range, the distance between two curves that are exactly the same as the average tooth profile trace, encompassing the actual tooth profile trace.

[0078] Total deviation of the spiral F β Within the calculated range, the distance between two designed spiral traces that encompass the actual spiral trace.

[0079] S33, The weights of each parameter in the part surface quality evaluation function are determined using an expert evaluation statistical method. The weight determination method is as follows:

[0080] Let the factor set U = {u1, u2, u3, ..., u} n There are m experts, and each expert provides factor u. j The weight is (a 1j a 2j a 3j ,,..,a mj ) T Therefore, the weights given by all experts are represented by the following matrix:

[0081]

[0082] The weights are weighted averages, and the expression for the weights is:

[0083]

[0084] S4, further construct the time-domain evaluation index X of the fused vibration signal. arv (i) The surface quality evaluation function R for hobbing gears with multiple detection indicators is constructed as follows:

[0085] By analyzing the total profile deviation F in the part sampling inspection report α Tooth profile shape deviation f fα and total deviation of the spiral f β The inspection indicators in the part inspection report and the time-domain evaluation index X of the hob spindle vibration signal are used to evaluate the part inspection indicators. arv (i) The function R is integrated into the surface quality evaluation function for hobbing gears, and its expression is:

[0086] R = ω1 * X arv (i)+ω2*Y t (i);

[0087] Where ω1+ω2=1;

[0088] ω1 is the weight value of the rectified average value;

[0089] ω2 is the weight value of the part sampling inspection report.

[0090] S5, adjust the weight ratio of various indicators in the hobbing gear surface quality evaluation function R so that the final hobbing gear surface quality evaluation function R can better reflect the surface quality of the part.

[0091] The rules for adjusting the weights are:

[0092] By machining the same batch of parts with the same cutting tool without changing the tool, the trend of surface finish changes during the machining process of the same batch of parts is measured. By adjusting the weights, the corresponding weights that can best distinguish the surface quality of gears machined at different wear stages of the cutting tool are found.

[0093] S6. The surface quality of the entire batch of parts is evaluated using the surface quality evaluation function R for gear hobbing. The evaluation standard is to divide the value of the quality evaluation function R into three intervals: [r1~r2), [r2~r3), and [r3~r4), where r1 < r2 < r3 < r4, which correspond to three levels of surface quality of the parts: high precision, medium precision, and low precision.

[0094] like Figure 6As shown, based on experience and the overall trend of the curve, the R value is divided into stages, and the value of the quality assessment function R is divided into three intervals:

[0095] [0~0.2), the R value of parts number 1-4 in the range is relatively stable, corresponding to a high precision level;

[0096] [0.2~0.7), the R value of part number 5-11 in the range first rises and then tends to ease, corresponding to medium precision quality;

[0097] [0.7~1.4), the R value of part numbers 12-17 in the range increases again, corresponding to a low precision quality level.

[0098] As can be seen from the figure, point 1 is in the range of [0.2~0.7), and the surface quality of the part corresponds to medium precision quality; point 2 is in the range of [0.7~1.4), and the surface quality of the part is low precision quality level.

[0099] The final constructed gear surface quality evaluation function R can effectively distinguish the surface quality of gears processed by different tools during the wear stage. Finally, the surface quality of the entire batch of parts is evaluated using the recorded hobbing gear surface quality evaluation function that integrates the hob spindle vibration signal and the part inspection report.

[0100] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

Claims

1. A method for evaluating gear surface quality based on multi-source information fusion, characterized in that, The method is as follows: (1) Conduct hobbing test to obtain vibration signal of hobbing machine spindle; (2) Calculate the time-domain index of the vibration signal of the hob spindle to obtain the time-domain evaluation index of the hob spindle vibration signal corresponding to its machining process. , To extract the rectified average value of the same phase in the steady phase of the hob spindle vibration signal during the entire machining cycle; (3) Construct a part surface quality evaluation function that integrates multiple detection indicators. Specifically, it includes: (31) Sample and inspect parts on the processing production line to obtain multiple inspection indicators, including total tooth profile deviation. , Tooth profile shape deviation and total deviation of the spiral ; (32) Establish a surface quality evaluation function for parts based on the detection indicators. : (33) The weights of each parameter in the surface quality evaluation function of the parts are determined by the expert evaluation statistical method; (4) Further construct time-domain evaluation index for fused vibration signals And a part surface quality evaluation function with multiple detection indicators R is the function for evaluating the surface quality of hobbing gears. (5) Adjust the weight ratio of various indicators in the surface quality evaluation function R for hobbing gears; (6) Use the surface quality evaluation function R of gear hobbing to evaluate the surface quality of the entire batch of parts.

2. The gear surface quality evaluation method based on multi-source information fusion according to claim 1, characterized in that, The specific calculation process in step (2) is as follows: From the hob spindle vibration signal throughout the entire machining cycle, the same stable phase is extracted, and the rectified average value in the time-domain index is selected as the time-domain evaluation index of the hob spindle vibration signal. The calculation process is as follows: ； Where A is the number of sampling points in the extracted hob spindle vibration signal sequence; It is the amplitude of the kth sampling point in the sequence of vibration signals of the cutter spindle.

3. The gear surface quality evaluation method based on multi-source information fusion according to claim 1, characterized in that, The surface quality evaluation function of the part The expression is: ； in, ; Total deviation of tooth profile The indicator weight values; It is a deviation in tooth profile. The indicator weight values; It is the total deviation of the spiral. The indicator weight values.

4. The gear surface quality evaluation method based on multi-source information fusion according to claim 1, characterized in that, The method for determining the weights in step (33) is as follows: Let the factor set There are m experts, and each expert provides the following factors. The weight is Therefore, the weights given by all experts are represented by the following matrix: ; The weights are weighted averages, and the expression for the weights is: ， j=1,2,3,...,n 。 5. The gear surface quality evaluation method based on multi-source information fusion according to claim 3, characterized in that, The process for constructing the surface quality evaluation function R for hobbing gears is as follows: By analyzing the total profile deviation in the part sampling inspection report , Tooth profile shape deviation and total deviation of the spiral The inspection indicators in the parts inspection report and the time-domain evaluation indicators of the hob spindle vibration signal are used to evaluate the parts. The function R, which is integrated into the surface quality evaluation function for hobbing gears, is expressed as follows: ； in, ; It is the weight value of the rectified average value; It is the weight value of the parts sampling inspection report.

6. The gear surface quality evaluation method based on multi-source information fusion according to claim 1, characterized in that, The rules for adjusting the weights in step (5) are as follows: By machining the same batch of parts with the same cutting tool without changing the tool, the trend of surface finish changes during the machining process of this batch of parts is measured. By adjusting the weights, the weights that can best distinguish the surface quality of gears machined at different wear stages of the cutting tool are found.

7. The gear surface quality evaluation method based on multi-source information fusion according to claim 1, characterized in that, The standard for evaluating the surface quality of the whole batch of parts by using the gear surface quality evaluation function R in step (6) is that the value of the quality evaluation function R is divided into three intervals, respectively [r1~r2), [r2~r3), [r3~r4), wherein r 1< r 2< r 3< r4, respectively corresponding to three levels of part surface quality, respectively high precision level, medium precision level and low precision level.

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