CN115115626A - Machine part defect detection system and detection method - Google Patents
Machine part defect detection system and detection method Download PDFInfo
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Abstract
The invention belongs to the field of part detection, relates to a data analysis technology, and is used for solving the problem that the existing mechanical part defect detection system cannot carry out troubleshooting analysis on reasons causing abnormity when part machining is abnormal, in particular to a mechanical part defect detection system and a detection method, wherein the mechanical part defect detection system comprises a detection platform, and the detection platform is in communication connection with a surface detection module, an element detection module, a stretching detection module, an abnormity analysis module and a storage module; the surface detection module is used for detecting and analyzing the surface quality of the part through scratch data and stain data and sending an abnormal analysis signal to the detection platform when the surface quality does not meet the requirement; according to the invention, the abnormity analysis module can analyze the reason causing abnormity when the part detection result is abnormal, and the abnormal reason is connected with the processing equipment by the operation state of the part processing equipment, so that the abnormity processing efficiency is improved.
Description
Technical Field
The invention belongs to the field of part detection, relates to a data analysis technology, and particularly relates to a system and a method for detecting defects of mechanical parts.
Background
Mechanical parts, also called mechanical elements, are basic elements constituting a machine and are non-detachable single parts constituting the machine and the machine; the mechanical parts are a subject of researching and designing mechanical basic parts in various devices and are also a general term of parts and components.
The existing mechanical part defect detection system can only detect and analyze whether a part meets the processing requirement through various parameters of the part, but cannot check and analyze the reason causing the abnormality when the part is processed abnormally, so that the problem of low abnormality processing efficiency exists.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide a mechanical part defect detection system and a detection method, which are used for solving the problem that the conventional mechanical part defect detection system cannot check and analyze the reasons causing the abnormity when the part processing is abnormal;
the technical problems to be solved by the invention are as follows: provided is a mechanical part defect detection system capable of checking and analyzing reasons causing abnormity when part machining is abnormal.
The purpose of the invention can be realized by the following technical scheme:
a mechanical part defect detection system and a detection method comprise a detection platform, wherein the detection platform is in communication connection with a surface detection module, an element detection module, a stretching detection module, an anomaly analysis module and a storage module;
the surface detection module is used for detecting and analyzing the surface quality of the part through scratch data and stain data and sending an abnormal analysis signal to the detection platform when the surface quality does not meet the requirement;
the element detection module is used for detecting and analyzing the element content of the part and sending an abnormal analysis signal to the detection platform when the element content does not meet the requirement;
the stretching detection module is used for detecting and analyzing the stretching performance of the part and sending an abnormal analysis signal to the detection platform when the stretching performance does not meet the requirement;
the detection platform receives the anomaly analysis signal and then sends the anomaly analysis signal to the anomaly analysis module, and the anomaly analysis module receives the anomaly analysis signal and then monitors and analyzes the running state of the machining equipment of the mechanical part: acquiring temperature data WD, noise data ZS and vibration data ZD when machining equipment operates; obtaining an operation coefficient YX of the processing equipment by carrying out numerical calculation on the temperature data WD, the noise data ZS and the vibration data ZD, obtaining an operation threshold YXmax through a storage module, and comparing the operation coefficient YX of the processing equipment with the operation threshold YXmax:
if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the processing equipment meets the requirement, and sending a normal operation signal to the detection platform by the abnormality analysis module;
and if the operation coefficient YX is larger than or equal to the operation threshold YXmax, judging that the operation state of the processing equipment does not meet the requirement, and sending an operation abnormal signal to the detection platform by the abnormal analysis module.
As a preferred embodiment of the present invention, the specific process of the surface detection module for detecting and analyzing the surface quality of the part includes: acquiring the number of scratches on each surface of the part, and marking the sum of the number of scratches on each surface of the part as scratch data GS; acquiring the stain values of all surfaces of the part, and marking the average value of the stain values of all surfaces of the part as a stain expression value WB; by the formula
Calculating to obtain a surface coefficient BX of the part, wherein both alpha 1 and alpha 2 are proportional coefficients; acquiring a surface coefficient threshold value BMmax of the part through a storage module, and comparing the surface coefficient BM of the part with the surface coefficient threshold value BMmax: if BM<BMmax, then the surface quality of the part is judged to meet the processing requirement, and the surface detection module sends a surface qualified signal to the detection planeA stage; and if BM is larger than or equal to BMmax, judging that the surface quality of the part does not meet the machining requirement.
As a preferred embodiment of the present invention, the process of acquiring the stain value of the surface of the part includes: the stain value acquisition process comprises the following steps: the method comprises the steps of shooting a picture of the surface of a part, amplifying the shot picture into a pixel grid picture, marking the pixel grid picture as an analysis picture, carrying out picture processing on the analysis picture to obtain a gray value of each pixel grid of the analysis picture, wherein the picture processing comprises picture enhancement and gray level transformation, obtaining a gray level threshold value through a storage module, subtracting the gray level threshold value from the gray level value of the pixel grid to obtain a gray level representation value of the pixel grid, establishing a rectangular coordinate system by using the gray level representation value of the pixel grid and the pixel grid number corresponding to the gray level representation value, taking an X axis as the gray level representation value of the pixel grid, taking a Y axis as the pixel grid number corresponding to the gray level representation value, drawing a curve on the rectangular coordinate system by using the gray level representation value of the pixel grid of the analysis picture, intercepting the curve on the right side of the Y axis and marking as the analysis curve, obtaining all inflection points of the analysis curve, and sequencing the inflection points from small to large by using horizontal coordinate values, marking the coordinates of the inflection points as (Xi, Yi), i =1, 2, …, n, establishing a set JH, JH = [ (Y1, Y2), (Y2, Y3), … …, (Yn-1, Yn) ] by using the longitudinal coordinate values of two adjacent inflection points, carrying out square error calculation on n-1 subsets in the set JH to obtain n-1 stain variances, and summing and averaging the n-1 stain variances to obtain the stain value of the surface of the part.
As a preferred embodiment of the present invention, the specific process of the element detection module for detecting and analyzing the element content of the part includes: element detection is carried out on the part, and the carbon element content, the sulfur element content and the silicon element content of the part are respectively marked as TH, LH and GH; obtaining an element content coefficient YH of the part by carrying out numerical calculation on TH, LH and GH; the element content threshold YHmin of the part is obtained through the storage module, the element content coefficient YH is compared with the element content threshold YHmin, and whether the element content of the part meets the requirement or not is judged according to the comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the element content coefficient YH with the element content threshold YHmin includes: if YH is less than or equal to YHmin, judging that the element content of the part does not meet the production requirement, and sending an abnormal analysis signal to a detection platform by an element detection module; if YH > YHmin, judging that the element content of the part meets the production requirement, and sending an element qualified signal to the detection platform by the element detection module.
As a preferred embodiment of the present invention, the specific process of detecting and analyzing the tensile property of the part by the tensile detection module includes: randomly extracting m parts from parts to be detected for tensile detection, and marking the randomly extracted m parts as detection parts; obtaining a tensile strength value, a yield strength value and an elongation of the detection part, and respectively marking the tensile strength value, the yield strength value and the elongation of the detection part as KL, QF and SC; by the formula
Obtaining the tensile coefficient LS of the detected part, wherein both c1 and c2 are proportionality coefficients; acquiring a stretching coefficient threshold LSmin through a storage module, and comparing a stretching coefficient LS of the detected part with the stretching coefficient threshold LSmin: if LS is less than or equal to LSmin, the tensile property of the part is judged not to meet the processing requirement, and a reworking signal is sent to the detection platform by the tensile detection module; if LS>LSmin, judging that the tensile property of the part meets the processing requirement; marking the number of the detection parts with tensile property meeting the processing requirement as u through a formula
And obtaining the qualification rate HG of the detected part, obtaining the qualification rate threshold HGmin of the detected part through the storage module, comparing the qualification rate HG of the detected part with the qualification rate threshold HGmin, and judging whether the tensile property of the detected part meets the requirement or not through the comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the yield HG with the yield threshold HGmin includes: if HG is less than HGmin, judging that the tensile property of the part does not meet the requirement; and if HG is larger than or equal to HGmin, judging that the tensile property of the part meets the requirement, and sending a tensile qualified signal to the detection platform by the tensile detection module.
A method for detecting defects of mechanical parts comprises the following steps:
the method comprises the following steps: detecting and analyzing the surface quality of the part according to the scratch data and the stain data, acquiring the number of scratches on each surface of the part, and marking the sum of the number of scratches on each surface of the part as scratch data GS; acquiring the stain values of all surfaces of the part, marking the average value of the stain values of all the surfaces of the part as a stain expression value WB, performing numerical calculation on scratch data and the stain expression value to obtain a surface coefficient, and judging whether the surface quality of the part meets the requirement or not according to the numerical value of the surface coefficient;
step two: detecting and analyzing the element content of the part, acquiring the element content coefficient of the part, and judging whether the element content of the part meets the requirement or not through the element content coefficient;
step three: detecting and analyzing the tensile property of the part, acquiring the qualified rate of the detected part, and judging whether the tensile property of the part meets the requirement or not according to the numerical value of the qualified rate;
step four: the abnormity analysis module monitors and analyzes the operation state of the machining equipment of the mechanical part and obtains an operation coefficient, and whether the operation state of the machining equipment meets the requirement or not is judged according to the numerical value of the operation coefficient.
The invention has the following beneficial effects:
1. the abnormal reason can be analyzed through the abnormal analysis module when the part detection result is abnormal, the abnormal reason is connected with the processing equipment through the running state of the part processing equipment, the abnormal reason is locked as equipment failure when the processing equipment runs abnormally, and then the processing equipment is directly overhauled to eliminate the abnormality, so that the abnormality processing efficiency is improved;
2. the surface quality of the part can be monitored through scratch data and stain data through a surface detection module, the surface quality of the part is subjected to high-precision analysis by combining an image processing technology, and early warning and abnormity analysis are further performed when the surface of the part is abnormal;
3. the element content of the part can be analyzed through the element detection module through the element content of each item, the element content coefficient is obtained, the element content abundance degree of the part is monitored through the element content coefficient, and then early warning and abnormal analysis are timely carried out when the element content does not meet the requirements.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in figure 1, the mechanical part defect detection system comprises a detection platform, wherein the detection platform is in communication connection with a surface detection module, an element detection module, a stretching detection module, an anomaly analysis module and a storage module.
The surface detection module is used for detecting and analyzing the surface quality of the part through scratch data and stain data: acquiring the number of scratches of each surface of the part, and marking the sum of the number of scratches of each surface of the part as scratch data GS; acquiring stain values of all surfaces of the part, wherein the stain values are acquired by the process comprising the following steps: shooting the picture of the surface of the part, and obtaining the shot pictureAmplifying the picture into a pixel grid picture, marking the pixel grid picture as an analysis picture, carrying out picture processing on the analysis picture to obtain a gray value of each pixel grid of the analysis picture, wherein the picture processing comprises picture enhancement and gray level transformation, obtaining a gray threshold value through a storage module, subtracting the gray threshold value from the gray value of the pixel grid to obtain a gray level representation value of the pixel grid, establishing a rectangular coordinate system by using the gray level representation value of the pixel grid and the pixel grid number corresponding to the gray level representation value, taking an X axis as the gray level representation value of the pixel grid, taking a Y axis as the pixel grid number corresponding to the gray level representation value, drawing a curve on a rectangular coordinate system by using the gray level representation value of the pixel grid of the analysis picture, intercepting a curve on the right side of the Y axis and marking as the analysis curve, obtaining all inflection points of the analysis curve, sorting inflection points from small inflection points to large in terms of horizontal coordinate values, marking the coordinates of the inflection points as (Xi, Yi), i =1, 2, …, n, and sets JH, JH = [ (Y1, Y2), (Y2, Y3), … …, (Yn-1, Yn) by ordinate values of two adjacent inflection points]Carrying out square deviation calculation on n-1 subsets in the set JH to obtain n-1 stain variances, summing the n-1 stain variances and averaging to obtain stain values on the surfaces of the parts, and marking the average value of the stain values on all the surfaces of the parts as a stain expression value WB; by the formula
Calculating to obtain a surface coefficient BX of the part, wherein both alpha 1 and alpha 2 are proportional coefficients; acquiring a surface coefficient threshold value BMmax of the part through a storage module, and comparing the surface coefficient BM of the part with the surface coefficient threshold value BMmax: if BM<BMmax, then the surface quality of the part is judged to meet the machining requirement, and the surface detection module sends a surface qualified signal to the detection platform; if BM is larger than or equal to BMmax, judging that the surface quality of the part does not meet the processing requirement, and sending an abnormal analysis signal to a detection platform by a surface detection module; the surface quality of the part is monitored through scratch data and stain data, the surface quality of the part is analyzed with high precision by combining an image processing technology, and then early warning and abnormity analysis are carried out when the surface of the part is abnormal.
The element detection module is used for detecting and analyzing the element content of the partThe specific detection and analysis process comprises the following steps: element detection is carried out on the part, and the carbon element content, the sulfur element content and the silicon element content of the part are respectively marked as TH, LH and GH; by the formula
Obtaining the element content coefficient YH of the part, wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 1>β2>β3>0; acquiring an element content threshold YHmin of the part through a storage module, and comparing the element content coefficient YH with the element content threshold YHmin: if YH is less than or equal to YHmin, judging that the element content of the part does not meet the production requirement, and sending an abnormal analysis signal to a detection platform by an element detection module; if YH>YHmin, judging that the element content of the part meets the production requirement, and sending an element qualified signal to a detection platform by an element detection module; carry out element content analysis and obtain the element content coefficient to the part through each item element content, monitor the element content abundance degree of part through the element content coefficient, and then in time carry out early warning and abnormal analysis when the unsatisfied requirement of element content.
The tensile detection module is used for detecting and analyzing the tensile property of the part, and the specific detection process comprises the following steps: randomly extracting m parts from parts to be detected for tensile detection, and marking the randomly extracted m parts as detection parts; obtaining a tensile strength value, a yield strength value and an elongation of the detection part, and respectively marking the tensile strength value, the yield strength value and the elongation of the detection part as KL, QF and SC; by the formula
Obtaining the tensile coefficient LS of the detected part, wherein both c1 and c2 are proportionality coefficients; acquiring a stretching coefficient threshold LSmin through a storage module, and comparing a stretching coefficient LS of the detected part with the stretching coefficient threshold LSmin: if LS is less than or equal to LSmin, the tensile property of the part is judged not to meet the processing requirement, and a reworking signal is sent to the detection platform by the tensile detection module; if LS>LSmin, judging that the tensile property of the part meets the processing requirement; detection part with tensile property meeting processing requirementIs marked as u by the formula
Obtaining the qualification rate HG of the detected part, obtaining the qualification rate threshold HGmin of the detected part through the storage module, and comparing the qualification rate HG of the detected part with the qualification rate threshold HGmin: if HG<HGmin, judging that the tensile property of the part does not reach the standard, and sending an abnormal analysis signal to the detection platform by the tensile detection module; and if HG is larger than or equal to HGmin, judging that the tensile property of the part reaches the standard, and sending a tensile qualified signal to the detection platform by the tensile detection module.
The detection platform sends the abnormal analysis signal to the abnormal analysis module after receiving the abnormal analysis signal, and the abnormal analysis module monitors and analyzes the running state of the machining equipment of the mechanical part after receiving the abnormal analysis signal: acquiring temperature data WD, noise data ZS and vibration data ZD of the processing equipment during operation, wherein the temperature data is an average value of temperature values of all surfaces of the processing equipment, the noise data is a decibel value of noise generated during the operation of the processing equipment, and the vibration data is a frequency value of vibration generated during the operation of the processing equipment; by the formula
Obtaining the operation coefficient of the processing equipment, wherein gamma 1, gamma 2 and gamma 3 are proportionality coefficients, and gamma 1 is more than gamma 2 and more than gamma 3 is more than 1; acquiring an operation threshold YXmax through a storage module, and comparing the operation coefficient YX of the processing equipment with the operation threshold YXmax: if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the processing equipment meets the requirement, and sending a normal operation signal to the detection platform by the abnormity analysis module; if the operation coefficient YX is larger than or equal to the operation threshold YXmax, judging that the operation state of the processing equipment does not meet the requirement, and sending an operation abnormal signal to the detection platform by the abnormal analysis module; analyzing the reasons causing the abnormity when the part detection result is abnormal, establishing connection between the abnormal reasons and the processing equipment through the running state of the part processing equipment, locking the abnormal reasons into equipment faults when the processing equipment runs abnormally, and then directly overhauling the processing equipmentAnd (4) eliminating the exception and improving exception handling efficiency.
Example two
As shown in fig. 2, a method for detecting defects of a mechanical part includes the following steps:
the method comprises the following steps: detecting and analyzing the surface quality of the part according to the scratch data and the stain data, acquiring the number of scratches on each surface of the part, and marking the sum of the number of scratches on each surface of the part as scratch data GS; acquiring the stain values of all surfaces of the part, marking the average value of the stain values of all the surfaces of the part as a stain expression value WB, performing numerical calculation on scratch data and the stain expression value to obtain a surface coefficient, and judging whether the surface quality of the part meets the requirement or not according to the numerical value of the surface coefficient;
step two: detecting and analyzing the element content of the part, acquiring the element content coefficient of the part, and judging whether the element content of the part meets the requirement or not through the element content coefficient;
step three: detecting and analyzing the tensile property of the part, acquiring the qualification rate of the detected part, and judging whether the tensile property of the part meets the requirement or not according to the numerical value of the qualification rate;
step four: the abnormity analysis module monitors and analyzes the operation state of the machining equipment of the mechanical part and obtains an operation coefficient, and whether the operation state of the machining equipment meets the requirement or not is judged according to the numerical value of the operation coefficient.
A machine part defect detection system and a detection method are disclosed, wherein when the system works, the surface quality of a part is detected and analyzed through scratch data and stain data, a surface coefficient is obtained through numerical calculation of the scratch data and a stain expression value, and whether the surface quality of the part meets requirements or not is judged through the numerical value of the surface coefficient; detecting and analyzing the element content of the part, acquiring the element content coefficient of the part, and judging whether the element content of the part meets the requirement or not through the element content coefficient; detecting and analyzing the tensile property of the part, acquiring the qualification rate of the detected part, and judging whether the tensile property of the part meets the requirement or not according to the numerical value of the qualification rate; the abnormity analysis module monitors and analyzes the operation state of the machining equipment of the mechanical part and obtains an operation coefficient, and whether the operation state of the machining equipment meets the requirement or not is judged according to the numerical value of the operation coefficient.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula (II)
(ii) a Collecting multiple groups of sample data and setting corresponding operation coefficients for each group of sample data by a person skilled in the art; substituting the set operation coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of gamma 1, gamma 2 and gamma 3 which are respectively 3.68, 2.79 and 2.15;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding operation coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the operation coefficient is proportional to the value of the temperature data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A mechanical part defect detection system and a detection method comprise a detection platform, and are characterized in that the detection platform is in communication connection with a surface detection module, an element detection module, a stretching detection module, an anomaly analysis module and a storage module;
the surface detection module is used for detecting and analyzing the surface quality of the part through scratch data and stain data and sending an abnormal analysis signal to the detection platform when the surface quality does not meet the requirement;
the element detection module is used for detecting and analyzing the element content of the part and sending an abnormal analysis signal to the detection platform when the element content does not meet the requirement;
the stretching detection module is used for detecting and analyzing the stretching performance of the part and sending an abnormal analysis signal to the detection platform when the stretching performance does not meet the requirement;
the detection platform receives the anomaly analysis signal and then sends the anomaly analysis signal to the anomaly analysis module, and the anomaly analysis module receives the anomaly analysis signal and then monitors and analyzes the running state of the machining equipment of the mechanical part: acquiring temperature data WD, noise data ZS and vibration data ZD when the processing equipment operates; obtaining an operation coefficient YX of the processing equipment by carrying out numerical calculation on the temperature data WD, the noise data ZS and the vibration data ZD, obtaining an operation threshold YXmax through a storage module, and comparing the operation coefficient YX of the processing equipment with the operation threshold YXmax:
if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the processing equipment meets the requirement, and sending a normal operation signal to the detection platform by the abnormality analysis module;
and if the operation coefficient YX is larger than or equal to the operation threshold YXmax, judging that the operation state of the processing equipment does not meet the requirement, and sending an operation abnormal signal to the detection platform by the abnormal analysis module.
2. The system and the method for detecting the defects of the mechanical parts according to claim 1, wherein the specific process of the surface detection module for detecting and analyzing the surface quality of the parts comprises the following steps: acquiring the number of scratches of each surface of the part, and marking the sum of the number of scratches of each surface of the part as scratch data GS; acquiring the stain values of all surfaces of the part, and marking the average value of the stain values of all surfaces of the part as a stain expression value WB; by the formula
Calculating to obtain a surface coefficient BX of the part, wherein both alpha 1 and alpha 2 are proportional coefficients; acquiring a surface coefficient threshold value BMmax of the part through a storage module, and comparing the surface coefficient BM of the part with the surface coefficient threshold value BMmax: if BM<BMmax, then the surface quality of the part is judged to meet the machining requirement, and the surface detection module sends a surface qualified signal to the detection platform; and if BM is larger than or equal to BMmax, judging that the surface quality of the part does not meet the machining requirement.
3. The system and the method for detecting the defects of the mechanical part according to claim 2, wherein the process of acquiring the stain value of the surface of the part comprises the following steps: the stain value acquisition process comprises the following steps: the method comprises the steps of shooting a picture of the surface of a part, amplifying the shot picture into a pixel grid picture, marking the pixel grid picture as an analysis picture, carrying out picture processing on the analysis picture to obtain a gray value of each pixel grid of the analysis picture, wherein the picture processing comprises picture enhancement and gray level transformation, obtaining a gray level threshold value through a storage module, subtracting the gray level threshold value from the gray level value of the pixel grid to obtain a gray level representation value of the pixel grid, establishing a rectangular coordinate system by using the gray level representation value of the pixel grid and the pixel grid number corresponding to the gray level representation value, taking an X axis as the gray level representation value of the pixel grid, taking a Y axis as the pixel grid number corresponding to the gray level representation value, drawing a curve on the rectangular coordinate system by using the gray level representation value of the pixel grid of the analysis picture, intercepting the curve on the right side of the Y axis and marking as the analysis curve, obtaining all inflection points of the analysis curve, and sequencing the inflection points from small to large by using horizontal coordinate values, marking the coordinates of the inflection points as (Xi, Yi), i =1, 2, …, n, establishing a set JH, JH = [ (Y1, Y2), (Y2, Y3), … …, (Yn-1, Yn) ] by using the longitudinal coordinate values of two adjacent inflection points, carrying out square error calculation on n-1 subsets in the set JH to obtain n-1 stain variances, and summing and averaging the n-1 stain variances to obtain the stain value of the surface of the part.
4. The system and the method for detecting the defects of the mechanical parts according to claim 1, wherein the specific process of detecting and analyzing the element content of the parts by the element detection module comprises the following steps: element detection is carried out on the part, and the carbon element content, the sulfur element content and the silicon element content of the part are respectively marked as TH, LH and GH; obtaining an element content coefficient YH of the part by carrying out numerical calculation on TH, LH and GH; the element content threshold YHmin of the part is obtained through the storage module, the element content coefficient YH is compared with the element content threshold YHmin, and whether the element content of the part meets the requirement or not is judged according to the comparison result.
5. The system and method for detecting defects on a mechanical part according to claim 3, wherein the comparing of the element content coefficient YH with the element content threshold YHmin comprises: if YH is less than or equal to YHmin, judging that the element content of the part does not meet the production requirement, and sending an abnormal analysis signal to a detection platform by an element detection module; if YH > YHmin, judging that the element content of the part meets the production requirement, and sending an element qualified signal to the detection platform by the element detection module.
6. The system and the method for detecting the defects of the mechanical parts according to claim 1, wherein the specific process of detecting and analyzing the tensile property of the parts by the tensile detection module comprises the following steps: randomly extracting m parts from the parts to be detected for tensile detection, and marking the randomly extracted m parts as detection parts; obtaining a tensile strength value, a yield strength value and an elongation of the detection part, and respectively marking the tensile strength value, the yield strength value and the elongation of the detection part as KL, QF and SC; by the formula
Obtaining the tensile coefficient LS of the detected part, wherein both c1 and c2 are proportionality coefficients; acquiring a stretching coefficient threshold LSmin through a storage module, and comparing a stretching coefficient LS of the detected part with the stretching coefficient threshold LSmin: if LS is less than or equal to LSmin, the tensile property of the part is judged not to meet the processing requirement, and a reworking signal is sent to the detection platform by the tensile detection module; if LS>LSmin, judging that the tensile property of the part meets the processing requirement; marking the number of the detection parts with tensile property meeting the processing requirement as u through a formula
And obtaining the qualification rate HG of the detected part, obtaining the qualification rate threshold HGmin of the detected part through the storage module, comparing the qualification rate HG of the detected part with the qualification rate threshold HGmin, and judging whether the tensile property of the detected part meets the requirement or not through the comparison result.
7. The system and the method for detecting the defects of the mechanical parts according to claim 6, wherein the specific process of comparing the yield HG with the yield threshold HGmin comprises the following steps: if HG is less than HGmin, judging that the tensile property of the part does not meet the requirement; and if HG is larger than or equal to HGmin, judging that the tensile property of the part meets the requirement, and sending a tensile qualified signal to the detection platform by the tensile detection module.
8. A method for detecting defects of mechanical parts is characterized by comprising the following steps:
the method comprises the following steps: detecting and analyzing the surface quality of the part according to the scratch data and the stain data, acquiring the number of scratches on each surface of the part, and marking the sum of the number of scratches on each surface of the part as scratch data GS; acquiring the stain values of all surfaces of the part, marking the average value of the stain values of all the surfaces of the part as a stain expression value WB, performing numerical calculation on scratch data and the stain expression value to obtain a surface coefficient, and judging whether the surface quality of the part meets the requirement or not according to the numerical value of the surface coefficient;
step two: detecting and analyzing the element content of the part, acquiring the element content coefficient of the part, and judging whether the element content of the part meets the requirement or not through the element content coefficient;
step three: detecting and analyzing the tensile property of the part, acquiring the qualification rate of the detected part, and judging whether the tensile property of the part meets the requirement or not according to the numerical value of the qualification rate;
step four: and the abnormity analysis module monitors and analyzes the operation state of the machining equipment of the mechanical part to obtain an operation coefficient, and judges whether the operation state of the machining equipment meets the requirement or not according to the numerical value of the operation coefficient.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101600295B1 (en) * | 2015-01-06 | 2016-03-21 | 한국인터넷진흥원 | System for detecting abnomal behaviors using personalized the whole access period use behavior pattern analsis |
| CN113052830A (en) * | 2021-04-07 | 2021-06-29 | 深圳市磐锋精密技术有限公司 | Product performance detection system based on big data |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101600295B1 (en) * | 2015-01-06 | 2016-03-21 | 한국인터넷진흥원 | System for detecting abnomal behaviors using personalized the whole access period use behavior pattern analsis |
| CN113052830A (en) * | 2021-04-07 | 2021-06-29 | 深圳市磐锋精密技术有限公司 | Product performance detection system based on big data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117373635A (en) * | 2023-08-01 | 2024-01-09 | 上海昆亚医疗器械股份有限公司 | Intelligent management method and system for medical equipment |
| CN117373635B (en) * | 2023-08-01 | 2024-04-09 | 上海昆亚医疗器械股份有限公司 | Intelligent management method and system for medical equipment |
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