Continuous detection method for large-scale high-precision long thread tooth profile

Abstract

The invention discloses a continuous detection method for a large-scale high-precision long thread tooth profile, and relates to the field of thread tooth profile detection, and the method specifically comprises the steps: S1, carrying out the modeling of a to-be-detected product; s2, obtaining a theoretical thread line of a to-be-detected product; s3, continuously obtaining a physical detection thread line and a machine vision thread line of the to-be-detected product; s4, taking the physical detection thread line and the machine vision thread line as a group of data to carry out primary iteration on the detection thread molded line, enabling the detection thread molded line to be closer to an actual thread molded line through an iteration mode, and improving the detection precision; s4, comparing the theoretical thread line with the detection thread molded line to obtain a detection result; according to the invention, detection of large-size and long threads is realized, an on-line detection function is also realized, errors caused by multiple times of clamping of a product in a product production and manufacturing process can be avoided, the production efficiency is improved, and the product precision is improved at the same time.

Description

Technical Field

[0001] The invention relates to the field of thread profile detection, and in particular to a continuous detection method for large-scale high-precision long thread profile. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Traditional thread measurement can be divided into two categories: single measurement and comprehensive measurement. The comprehensive detection method is based on the Taylor inclusion principle and uses a go and no-go gauge to perform functional inspection. The go gauge inspects the thread's screwability, and the no-go gauge inspects the manufacturing deviation of the thread's center diameter. The single detection method refers to the use of a measuring tool or a measuring tool to independently measure each parameter of the thread together. Commonly used single parameter detection methods include the needle method, the double ball method, and the imaging method.

[0004] However, there is no compensation in the single test results, which will contain significant system errors. The comprehensive test method usually results in different calibration plug gauges used for factory inspection and acceptance. Although both plug gauges are qualified, the test results are inconsistent. The controversial point is that the factory inspection and acceptance use different test methods. Although the thread comprehensive measuring machine can be used for testing, its detection size is limited and cannot meet the requirements of online measurement. Summary of the invention

[0005] The purpose of the present invention is to provide a large-scale high-precision continuous detection method for long thread tooth profile in response to the problems existing in the prior art, which combines machine vision, physical sampling, eigenvalue mask convolution sampling, associated position processing and other technologies to improve the detection accuracy, meet the detection needs, and thus solve the above-mentioned problems.

[0006] The technical solution of the present invention is as follows:

[0007] A continuous detection method for large-scale high-precision long thread tooth profile, comprising:

[0008] Step S1: Modeling the product to be inspected;

[0009] Step S2: Obtaining the theoretical thread line of the product to be inspected;

[0010] Step S3: continuously obtaining the physical detection thread line and the machine vision thread line of the product to be inspected;

[0011] Step S4: using the physical detection thread line and the machine vision thread line as a set of data to iterate the detection thread profile, so as to make the detection thread profile closer to the actual thread profile through iteration, thereby improving the detection accuracy;

[0012] Step S4: Compare the theoretical thread line with the detected thread profile line to obtain the detection result.

[0013] Furthermore, the step S1 comprises:

[0014] Modeling is done based on the parameters of the product to be inspected.

[0015] Furthermore, the step S2 comprises:

[0016] Step S21: Process the parameters of the product to be inspected to obtain theoretical thread parameters;

[0017] Step S22: forming a theoretical thread line based on the theoretical thread parameters.

[0018] Furthermore, the theoretical thread parameters include: nominal diameter, lead, lead accuracy, thread direction, thread profile and thread profile angle.

[0019] Furthermore, the step S22 includes:

[0020] Based on the theoretical thread parameters, the theoretical thread line is formed by drawing points.

[0021] Furthermore, the step S3 comprises:

[0022] Continuously obtain the physical detection thread line of the measurable part of the end, the continuous or intermittent machine vision thread line and the tool detection data.

[0023] Furthermore, the step S4 specifically includes the following steps:

[0024] Step S41: Obtaining a thread image of the product to be inspected;

[0025] Step S42: pre-processing the thread photo to remove interference items in the thread photo;

[0026] Step S43: extracting a single thread profile from the pre-processed thread photo; that is, the machine vision thread profile;

[0027] Step S44: importing path values, i.e., physically detecting the thread line;

[0028] Step S45: importing the inspection data of the tool;

[0029] Step S46: using the physical detection thread line, the machine vision thread line and the tool detection data as a set of data to iterate the detection thread profile line.

[0030] Furthermore, the step S42 includes:

[0031] The thread profile is sampled using a feature mask, and interference items in the thread photo are eliminated by sharpening, blurring, and grayscale processing.

[0032] Furthermore, the interference items include: noise points, non-thread profile lines and part bodies.

[0033] Furthermore, the detected thread profile is formed by splicing and stitching the extracted single thread profile using the path value and the detection data of the tool.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] A continuous detection method for large-scale high-precision long thread tooth profiles comprises: step S1: modeling a product to be inspected; step S2: obtaining theoretical thread lines of the product to be inspected; step S3: continuously obtaining physical detection thread lines and machine vision thread lines of the product to be inspected; step S4: taking the physical detection thread lines and the machine vision thread lines as a set of data to iterate the detection thread profile, so that the detection thread profile is closer to the actual thread profile by iteration, thereby improving the detection accuracy; step S4: comparing the theoretical thread lines with the detection thread profile to obtain the detection result; while solving the detection of large-size and long threads, the detection technology also has the function of online detection, which can avoid errors caused by multiple clamping of products in the product manufacturing process, improve the production efficiency, and improve the product accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The figure is a flow chart of a method for continuous detection of large-scale high-precision long thread tooth profile. DETAILED DESCRIPTION

[0037] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0038] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0039] Embodiment 1

[0040] See also Figure 1 , a continuous detection method for large-scale high-precision long thread tooth profile, specifically comprising the following steps:

[0041] Step S1: Modeling the product to be inspected;

[0042] Step S2: Obtaining the theoretical thread line of the product to be inspected;

[0043] Step S3: continuously obtaining the physical detection thread line and the machine vision thread line of the product to be inspected;

[0044] Step S4: using the physical detection thread line and the machine vision thread line as a set of data to iterate the detection thread profile, so as to make the detection thread profile closer to the actual thread profile through iteration, thereby improving the detection accuracy;

[0045] Step S4: Compare the theoretical thread line with the detected thread profile line to obtain the detection result.

[0046] In this embodiment, specifically, step S1 includes:

[0047] Modeling is done based on the parameters of the product to be inspected.

[0048] In this embodiment, specifically, step S2 includes:

[0049] Step S21: Process the parameters of the product to be inspected to obtain theoretical thread parameters;

[0050] Step S22: forming a theoretical thread line based on the theoretical thread parameters.

[0051] In this embodiment, specifically, the theoretical thread parameters include: nominal diameter, lead, lead accuracy, thread direction, thread profile and thread profile angle.

[0052] In this embodiment, specifically, step S22 includes:

[0053] Based on the theoretical thread parameters, the theoretical thread line is formed by drawing points.

[0054] In this embodiment, specifically, step S3 includes:

[0055] Real-time acquisition of physical inspection thread lines of the measurable part of the end, continuous or intermittent machine vision thread lines, and tool inspection data.

[0056] In this embodiment, specifically, step S4 includes the following steps:

[0057] Step S41: Obtaining a thread image of the product to be inspected;

[0058] Step S42: pre-processing the thread photo to remove interference items in the thread photo;

[0059] Step S43: extracting a single thread profile from the pre-processed thread photo; that is, the machine vision thread profile;

[0060] Step S44: importing path values, i.e., physically detecting the thread line;

[0061] Step S45: importing the inspection data of the tool;

[0062] Step S46: using the physical detection thread line, the machine vision thread line and the tool detection data as a set of data to iterate the detection thread profile line.

[0063] In this embodiment, specifically, step S42 includes:

[0064] The thread profile is sampled using a feature mask, and interference items in the thread photo are eliminated by sharpening, blurring, and grayscale processing.

[0065] In this embodiment, specifically, the interference items include: noise points, non-thread profile lines and the part body.

[0066] In this embodiment, specifically, the detected thread profile is formed by splicing and stitching the extracted single thread profile using the path value and the detection data of the tool.

[0067] It should be noted that the detection accuracy can reach 0.0005mm by this method. In theory, the detection accuracy can be further improved by increasing the CCD size, lens magnification, and sampling quantity.

[0068] It should be noted that a corresponding detection device can also be provided, and the detection device includes: a high-precision detection table, a servo motor, a track, and an adjustable supporting device; the track and the adjustable supporting device are both arranged on the high-precision detection table, and are used to adjust the supporting device to ensure that the product is in a natural horizontal state; the servo motor is arranged on the high-precision detection table, and is used to connect with the product to be inspected so that the product to be inspected can rotate; the high-precision detection table is provided with a detection probe; the high-precision detection table is also provided with a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of the above-mentioned method for continuous detection of large-scale high-precision long thread tooth profile are implemented; preferably, the computer program can be executed on a terminal device, such as a personal computer.

[0069] When in use, adjust the supporting equipment to ensure that the product to be inspected is in a naturally horizontal state, set the detection probe to an appropriate position, and enter the parameters of the product to be inspected into the detection program for automatic detection. The full stroke of the thread can be detected, and the thread phase can also be detected.

[0070] In this embodiment, it should also be noted that, based on the above-mentioned detection method, those skilled in the art can prepare the above-mentioned detection device without any creative work, so the specific structure and design of the detection device will not be described in detail here.

[0071] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

[0072] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A continuous detection method for large-scale high-precision long thread profiles, It is characterized in that include: Step S1: Modeling the product to be inspected; Step S2: Obtaining the theoretical thread line of the product to be inspected; Step S3: continuously obtaining the physical detection thread line and the machine vision thread line of the product to be inspected; Step S4: using the physical detection thread line and the machine vision thread line as a set of data to iterate the detection thread profile, so as to make the detection thread profile closer to the actual thread profile through iteration, thereby improving the detection accuracy; Step S4: Compare the theoretical thread line with the detected thread profile line to obtain the detection result.

2. A continuous detection method for large-scale high-precision long thread profile according to claim 1, It is characterized in that The step S1 comprises: Modeling is done based on the parameters of the product to be inspected.

3. A continuous detection method for large-scale high-precision long thread profile according to claim 1, It is characterized in that The step S2 comprises: Step S21: Process the parameters of the product to be inspected to obtain theoretical thread parameters; Step S22: forming a theoretical thread line based on the theoretical thread parameters.

4. A continuous detection method for large-scale high-precision long thread profile according to claim 3, It is characterized in that The theoretical thread parameters include: nominal diameter, lead, lead accuracy, thread direction, thread profile and thread profile angle.

5. A continuous detection method for large-scale high-precision long thread profile according to claim 3, It is characterized in that The step S22 comprises: Based on the theoretical thread parameters, the theoretical thread line is formed by drawing points.

6. A continuous detection method for large-scale high-precision long thread profile according to claim 1, It is characterized in that The step S3 comprises: Continuously obtain the physical detection thread line of the measurable part of the end, the continuous or intermittent machine vision thread line and the tool detection data.

7. A continuous detection method for large-scale high-precision long thread profile according to claim 6, It is characterized in that The step S4 specifically includes the following steps: Step S41: Obtaining a thread image of the product to be inspected; Step S42: pre-processing the thread photo to remove interference items in the thread photo; Step S43: extracting a single thread profile from the pre-processed thread photo; that is, the machine vision thread profile; Step S44: importing path values, i.e., physically detecting the thread line; Step S45: importing the inspection data of the tool; Step S46: using the physical detection thread line, the machine vision thread line and the tool detection data as a set of data to iterate the detection thread profile line.

8. A continuous detection method for large-scale high-precision long thread profile according to claim 7, It is characterized in that The step S42 comprises: The thread profile is sampled using a feature mask, and interference items in the thread photo are eliminated by sharpening, blurring, and grayscale processing.

9. A continuous detection method for large-scale high-precision long thread profile according to claim 8, It is characterized in that The interference items include: noise points, non-thread profile lines and part bodies.

10. A continuous detection method for large-scale high-precision long thread profile according to claim 9, It is characterized in that The detected thread profile is formed by splicing and stitching the extracted single thread profile using the path value and the detection data of the tool.

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