CN114571287A - Machine tool workpiece online detection and analysis system based on big data - Google Patents
Machine tool workpiece online detection and analysis system based on big data Download PDFInfo
- Publication number
- CN114571287A CN114571287A CN202210238129.0A CN202210238129A CN114571287A CN 114571287 A CN114571287 A CN 114571287A CN 202210238129 A CN202210238129 A CN 202210238129A CN 114571287 A CN114571287 A CN 114571287A
- Authority
- CN
- China
- Prior art keywords
- detection
- workpiece
- square
- target
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/20—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring workpiece characteristics, e.g. contour, dimension, hardness
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q7/00—Arrangements for handling work specially combined with or arranged in, or specially adapted for use in connection with, machine tools, e.g. for conveying, loading, positioning, discharging, sorting
- B23Q7/12—Sorting arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses a machine tool workpiece online detection and analysis system based on big data, which comprises a workpiece detection and transmission mechanism setting module, a detection equipment setting module, a square workpiece machining quality online detection module, a workpiece machining quality database, a detection data analysis and judgment module and a square workpiece classification processing terminal, wherein each square workpiece in the same batch processed by a machine tool is sequentially placed on the workpiece detection and transmission mechanism by the workpiece detection and transmission mechanism and is transmitted to a detection area through an inlet transmission end for machining quality online detection, detection data corresponding to each machining quality index of each square workpiece are obtained, the detection data are intelligently analyzed, so that each square workpiece is automatically classified to a corresponding outlet transmission end according to an analysis result, the automatic integration of the machining quality online detection and classification of the square workpieces is realized, and the manual participation degree is effectively reduced, the detection efficiency and the classification accuracy are greatly improved.
Description
Technical Field
The invention belongs to the technical field of machine tool workpiece machining quality detection, and particularly relates to a machine tool workpiece online detection and analysis system based on big data.
Background
The workpiece refers to a processing object in a machining process, and is a square workpiece such as a rectangular workpiece and a square workpiece, and with the development of numerical control technology, the application of numerical control processing in the manufacturing industry is more and more extensive, and the numerical control machine tool becomes mainstream equipment in the current square workpiece processing and manufacturing industry. Although the machining process of the machine tool in China is rapidly developed, many factors still exist to influence the machining quality of the square workpiece, for example, various errors generated by a process system in the machining process of the machine tool can influence the machining precision of the square workpiece, and further influence the machining quality of the square workpiece, and in such a case, the machining quality detection of the square workpiece machined by the machine tool is required.
However, in the existing processing quality detection methods adopted for the processed square workpieces, corresponding detection devices are arranged according to processing quality indexes of the square workpieces, the processed square workpieces are manually and sequentially detected on the detection devices corresponding to the processing quality indexes to obtain detection results, the detection methods cannot realize direct automatic online processing quality detection after the square workpieces are processed due to the need of manual detection, so that the automatic detection level is low, the detection efficiency is low, on the other hand, the detection methods cannot automatically classify the square workpieces according to the detection results, the detection results are manually used for judging whether the square workpieces are qualified, and then the square workpieces are classified according to the judgment results, and the classification methods only depend on manual classification operation to easily cause classification confusion, thereby affecting the accuracy of the classification result.
Disclosure of Invention
In view of the above insufficiency, this application provides a lathe work piece on-line measuring analytic system based on big data to the square body work piece, through setting up work piece detection transport mechanism, will be through the batch square body work piece after machine tool machining accomplishes setting up work piece detection transport mechanism and carrying out the online automated inspection of processingquality to can carry out the automatic classification of batch square body work piece and deposit according to the testing result, realized the automatic integration operation of square body work piece processingquality on-line inspection and classification, effectively reduced artifical participation degree, improved detection efficiency and classification precision greatly.
The application is realized by adopting the following technical scheme: the machine tool workpiece online detection and analysis system based on the big data comprises a workpiece detection and transmission mechanism setting module used for setting a workpiece detection and transmission mechanism after the machine tool finishes processing, wherein the workpiece detection and transmission mechanism comprises an inlet transmission end, a transmission belt and a plurality of outlet transmission ends.
And the detection equipment setting module is connected with the workpiece detection and transmission mechanism setting module and used for defining a detection area on a transmission belt of the workpiece detection and transmission mechanism and setting detection equipment on the defined detection area.
And the square workpiece machining quality online detection module is connected with the detection equipment setting module and is used for sequentially placing each square workpiece in the same batch after the machining of the machine tool on the workpiece detection and transmission mechanism and transmitting the square workpiece to a detection area through the inlet transmission end to carry out machining quality online detection so as to obtain detection data of each square workpiece corresponding to each machining quality index.
And the workpiece processing quality database is used for storing standard data of each processing quality index corresponding to the target batch of the square workpieces, wherein the target batch refers to a production batch corresponding to each square workpiece in the same batch after the machine tool finishes processing.
And the detection data analysis and judgment module is respectively connected with the square workpiece machining quality online detection module and the workpiece machining quality database and is used for comparing the detection data of each square workpiece corresponding to each machining quality index with the standard data of each machining quality index corresponding to the target batch of square workpieces stored in the workpiece machining quality database so as to judge whether the machining quality of each square workpiece is qualified.
And the square workpiece classification processing terminal is connected with the detection data analysis and judgment module and is used for identifying the outlet conveying end corresponding to each square workpiece based on the processing quality judgment result corresponding to each square workpiece, further conveying the outlet conveying end to the corresponding outlet conveying end for storage, and classifying the square workpieces stored in different outlet conveying ends by corresponding quality inspection personnel.
According to an optional implementation manner of the application, the inlet conveying end is used for placing the currently detected square workpiece, wherein the inlet transmission end is respectively provided with a pressure sensor and a marking device, the pressure sensor is used for sensing whether the square workpiece is placed at the inlet conveying end, and the marking device is used for labeling the square workpiece placed at the inlet conveying end.
According to an alternative embodiment of the present application, the plurality of outlet delivery ends includes a qualified workpiece delivery end, a single form factor off-grade delivery end, a single weight off-grade delivery end, a single surface defect delivery end, and a mixed off-grade delivery end.
According to an optional embodiment of the present application, the detection region is formed by a detection sub-region a, a detection sub-region B, a detection sub-region C, a detection sub-region D, a detection sub-region E, and a detection sub-region F at intervals side by side, each detection sub-region is provided with a detection device, and a pressure sensor is provided below each detection sub-region for sensing whether a square workpiece to be subjected to processing quality detection exists on each detection sub-region.
According to an optional embodiment of the present application, the detection apparatus includes a weight sensor, a profiler, and an intelligent camera, wherein the weight sensor is configured to detect a weight index of a square workpiece, the profiler is configured to detect an external dimension index of a target detection surface corresponding to the square workpiece, the intelligent camera is configured to detect a surface defect index of the target detection surface corresponding to the square workpiece, the square workpiece is composed of six surfaces, each detection sub-region corresponds to one detection surface of the square workpiece, at this time, a target detection surface corresponding to the detection sub-region a is designated as a detection surface, a target detection surface corresponding to the detection sub-region B is designated as a detection surface, a target detection area corresponding to the detection sub-region C is designated as a detection surface C, a target detection surface corresponding to the detection sub-region D is designated as a detection surface D, and a target detection surface corresponding to the detection sub-region E is designated as a detection surface E, and marking the target detection surface corresponding to the F detection subarea as an F detection surface, wherein the target detection surface corresponding to each detection subarea is preset.
According to an optional embodiment of the application, a detection gap exists between each two adjacent detection sub-regions, and a turning manipulator is arranged in the detection gap, and is used for turning the current detection square workpiece from a target detection surface corresponding to a previous detection sub-region to a target detection surface corresponding to a next detection sub-region in the adjacent detection sub-regions.
According to an alternative embodiment of the present application, the process quality indicators include a weight indicator, a physical dimension indicator, and a surface defect indicator.
According to an optional embodiment of the present application, the detection data of each processing quality index includes weight data corresponding to each detection sub-region, external dimension data of a target detection surface corresponding to each detection sub-region, and surface defect data of a target detection surface corresponding to each detection sub-region, where the external dimension data includes length and width, the surface defect data includes surface defect determination result data and surface defect specific data, where the surface defect determination result data includes presence of a surface defect and absence of a surface defect, and the surface defect specific data indicates a defect type corresponding to the presence of a surface defect, and the surface defect data detection step of the target detection surface corresponding to each detection sub-region includes: the first step is as follows: and carrying out image acquisition on the corresponding target detection surface on the currently detected square workpiece through the intelligent camera in the detection equipment corresponding to each detection subarea to obtain a surface image of each detection surface corresponding to the currently detected square workpiece.
The second step: and extracting standard surface images of the detection surfaces corresponding to the target batch of cube workpieces from the processing quality database.
The third step: comparing the surface image of each detection surface corresponding to the current detection square workpiece with the standard surface image of each detection surface corresponding to the target batch of square workpieces, if the surface image of a certain detection surface of the current detection square workpiece is inconsistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect, extracting an inconsistent area from the surface image of the detection surface, further determining the defect type corresponding to the inconsistent area as the specific surface defect data corresponding to the detection surface, and if the surface image of the certain detection surface of the current detection square workpiece is consistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect-free.
According to an optional embodiment of the present application, the specific method for determining whether the machining quality of each square workpiece is qualified includes the following steps: and S1, extracting the weight data corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index, and carrying out average value calculation on the weight data of the same square workpiece in each detection subarea to obtain the weight average value corresponding to each square workpiece.
S2, extracting the standard range of the weight index corresponding to the target batch of square workpieces from the standard data of each processing quality index corresponding to the target batch of square workpieces in the workpiece processing quality database, comparing the weight average value corresponding to each square workpiece with the standard range of the weight index corresponding to the target batch of square workpieces, and if the weight average value corresponding to a square workpiece is not in the standard range of the weight index corresponding to the target batch of square workpieces, judging that the processing quality of the square workpiece is unqualified, and the processing quality unqualified category corresponding to the square workpiece is weight.
And S3, extracting the outline dimension data of the target detection surface corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index.
S4, extracting standard values of the contour dimension indexes of the target detection surfaces corresponding to the target batch of square workpieces from the standard data of the processing quality indexes corresponding to the target batch of square workpieces in the workpiece processing quality database, comparing the contour dimension data of the target detection surfaces corresponding to the detection subareas in the square workpieces with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, if the contour dimension data of the target detection surfaces corresponding to the detection subareas in the square workpieces is not consistent with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, judging that the processing quality of the square workpieces is unqualified, and the type of unqualified processing quality corresponding to the square workpieces is the contour dimension.
And S5, extracting the surface defect data of the target detection surface corresponding to each detection subarea from the detection data of each processing quality index corresponding to each square workpiece, and if the target detection surface corresponding to a certain detection subarea in a certain square workpiece has a surface defect, judging that the processing quality of the square workpiece is unqualified, wherein the processing quality unqualified type corresponding to the square workpiece is the surface defect.
S6, if the weight average value of a square workpiece is in the standard range of the weight index corresponding to the target batch of square workpieces, the external dimension data of the target detection surface corresponding to each detection subarea is consistent with the standard value of the external dimension index of the detection surface corresponding to the target batch of square workpieces, and the target detection surface corresponding to each detection subarea has no surface defect, the processing quality of the square workpiece is judged to be qualified.
According to an optional embodiment of the present application, the identifying the outlet transfer end corresponding to each square workpiece based on the processing quality determination result corresponding to each square workpiece specifically includes: (1) and if the processing quality corresponding to a certain square workpiece is qualified, the outlet conveying end corresponding to the square workpiece is a qualified workpiece conveying end.
(2) If the machining quality corresponding to a certain square workpiece is unqualified, counting the quantity of the unqualified machining quality categories corresponding to the square workpiece, if the square workpiece only has a single unqualified machining quality category, when the unqualified machining quality category is weight, the outlet conveying end corresponding to the square workpiece is a single unqualified weight conveying end, when the unqualified machining quality category is an external dimension, the outlet conveying end corresponding to the square workpiece is a single unqualified external dimension conveying end, and when the unqualified machining quality category is a surface defect, the outlet conveying end corresponding to the square workpiece is a single surface defect conveying end.
(3) And if the processing quality corresponding to a certain square workpiece is unqualified and the square workpiece has various types of unqualified processing quality, the outlet conveying end corresponding to the square workpiece is the mixed unqualified conveying end.
By combining all the technical schemes, the invention has the advantages and positive effects that: according to the invention, by arranging the workpiece detection and transmission mechanism, square workpieces in the same batch processed by the machine tool are sequentially placed on the workpiece detection and transmission mechanism and transmitted to the detection area through the inlet transmission end for online detection of the processing quality, detection data of each square workpiece corresponding to each processing quality index is obtained, and intelligent analysis is performed on the detection data, so that each square workpiece is automatically classified to the corresponding outlet transmission end according to an analysis result, automatic integration of online detection and classification of the processing quality of the square workpieces is realized, the manual participation degree is effectively reduced, the detection efficiency and classification accuracy are greatly improved, the cost of the machine tool workpiece in the aspect of labor detection is reduced, and the online detection level of the processing quality of the machine tool workpiece is also improved.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a schematic diagram of the system connection structure of the present invention.
Fig. 2 is a schematic structural diagram of a workpiece detecting and conveying mechanism according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Referring to fig. 1, an embodiment of the invention provides a machine tool workpiece online detection and analysis system based on big data, which comprises a workpiece detection and transmission mechanism setting module, a detection device setting module, a square workpiece machining quality online detection module, a workpiece machining quality database, a detection data analysis and judgment module and a square workpiece classification processing terminal.
A workpiece detection and transmission mechanism setting module, configured to set a workpiece detection and transmission mechanism after machining of a machine tool is completed, referring to fig. 2, where the workpiece detection and transmission mechanism includes an inlet transmission end, a transmission belt, and a plurality of outlet transmission ends, where the inlet transmission end is configured to place a currently detected square workpiece, the inlet transmission end is respectively provided with a pressure sensor and a marking device, the pressure sensor is configured to sense whether the inlet transmission end places the square workpiece, the marking device is configured to label-mark the square workpiece placed at the inlet transmission end, and sequentially obtain label marks corresponding to the square workpieces, where the mentioned label device may be an electronic label device, where the transmission belt is configured to detect the currently detected square workpiece and transmit the currently detected square workpiece to the corresponding outlet transmission end according to a subsequently identified outlet transmission end corresponding to the currently detected square workpiece, wherein the plurality of outlet transport ends include a qualified workpiece transport end, a single form factor reject transport end, a single weight reject transport end, a single surface defect transport end, and a mixed reject transport end.
The detection device setting module is connected with the workpiece detection and transmission mechanism setting module and used for defining a detection area on a transmission belt of the workpiece detection and transmission mechanism, wherein the detection area is formed by a detection subarea A, a detection subarea B, a detection subarea C, a detection subarea D, a detection subarea E and a detection subarea F which are arranged side by side at intervals, and each detection subarea is respectively provided with a detection device which comprises a weight sensor, a profiler and an intelligent camera, wherein the weight sensor is used for detecting the weight index of the square workpiece, the profiler is used for detecting the outline dimension index of the square workpiece corresponding to a target detection surface, the intelligent camera is used for detecting the surface defect index of the square workpiece corresponding to the target detection surface, and a pressure sensor is respectively arranged below each detection subarea and used for sensing whether the square workpiece to be processed and detected exists on each detection subarea or not, in the above, since the square workpiece is composed of six surfaces, each detection sub-region corresponds to one detection surface of the square workpiece, at this time, the target detection surface corresponding to the detection sub-region a is denoted as a detection surface, the target detection surface corresponding to the detection sub-region B is denoted as B detection surface, and the target detection area corresponding to the detection sub-region C is denoted as C detection surface, the target detection surface corresponding to the detection sub-region D is denoted as D detection surface, the target detection surface corresponding to the detection sub-region E is denoted as E detection surface, the target detection surface corresponding to the detection sub-region F is denoted as F detection surface, and the target detection surface corresponding to each detection sub-region is preset.
And the turning manipulator is arranged on the detection gap between every two adjacent detection subareas and used for turning the current detection square workpiece from the target detection surface corresponding to the previous detection subarea to the target detection surface corresponding to the next detection subarea.
It should be noted that the specific detection principle corresponding to the workpiece detecting and conveying mechanism is to place the currently detected square workpiece at the inlet conveying end of the workpiece detecting and conveying mechanism, when the pressure sensor on the inlet conveying end senses the placing pressure of the currently detected square workpiece, start the marking device to mark the currently detected square workpiece with a label to obtain a label number corresponding to the currently detected square workpiece, then convey the currently detected square workpiece to the detection area on the conveying belt, when the pressure sensor below the detection sub-area A senses the placing of the currently detected square workpiece, at this time, control the conveying belt to suspend conveying, perform weight index detection on the currently detected square workpiece by the detection device on the detection sub-area A, perform outline dimension index detection and surface defect index detection on the detection surface A, and control the conveying belt to continue conveying after the detection is finished, and then the currently detected square workpiece is turned over from the A detection surface to the B detection surface by the turning manipulator in the detection gap between the A detection subregion and the B detection subregion and then is conveyed to the B detection subregion, when a pressure sensor below the B detection subregion senses that the currently detected square workpiece is placed, the conveying belt is controlled to pause conveying, the detection equipment on the B detection subregion detects each processing quality index of the currently detected square workpiece, the detection is continued according to the above mode until the F detection subregion detects each processing quality index of the currently detected square workpiece.
And after the detection is finished, placing the next square workpiece at the inlet conveying end according to the detection mode to carry out processing quality detection until the processing quality detection of the last square workpiece is finished.
The square workpiece machining quality online detection module is connected with the detection equipment setting module and is used for sequentially placing each square workpiece in the same batch after the machining of the machine tool on the workpiece detection and transmission mechanism and transmitting the square workpiece to the detection area through the inlet transmission end for machining quality online detection to obtain detection data of each machining quality index corresponding to each square workpiece, wherein each machining quality index comprises a weight index, an outline dimension index and a surface defect index, the detection data of each machining quality index comprises weight data corresponding to each detection subarea, outline dimension data of a target detection surface corresponding to each detection subarea and surface defect data of a target detection surface corresponding to each detection subarea, the outline dimension data comprises length and width, the surface defect data comprises surface defect judgment result data and surface defect specific data, and the surface defect judgment result data comprises surface defects and no surface defects, the surface defect specific data represents a corresponding defect type under the condition that the surface defect exists, and the surface defect data detection steps of each detection subarea corresponding to the target detection surface are as follows: the first step is as follows: and carrying out image acquisition on the corresponding target detection surface on the currently detected square workpiece through the intelligent camera in the detection equipment corresponding to each detection subarea to obtain a surface image of each detection surface corresponding to the currently detected square workpiece.
The second step is that: and extracting standard surface images of the detection surfaces corresponding to the target batch of cube workpieces from the processing quality database.
The third step: comparing the surface image of each detection surface corresponding to the current detection square workpiece with the standard surface image of each detection surface corresponding to the target batch of square workpieces, if the surface image of a certain detection surface of the current detection square workpiece is inconsistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect, extracting an inconsistent area from the surface image of the detection surface, further determining the defect type corresponding to the inconsistent area as the specific surface defect data corresponding to the detection surface, and if the surface image of the certain detection surface of the current detection square workpiece is consistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect-free.
Specifically, the types of defects mentioned above include color inconsistency, surface scratches, surface smudges, surface blisters, surface depressions, and the like.
And the workpiece processing quality database is used for storing standard data of each processing quality index corresponding to the target batch of the square workpieces, wherein the target batch refers to a production batch corresponding to each square workpiece in the same batch after the machine tool finishes processing.
After detection data of each square workpiece corresponding to each processing quality index are obtained through detection, the detection data correspond to label numbers corresponding to each square workpiece and are uploaded to a workpiece detection data cloud storage space, and a detection data basis is provided for follow-up remediation treatment of unqualified square workpieces.
The detection data analysis and judgment module is respectively connected with the cube workpiece machining quality online detection module and the workpiece machining quality database and is used for comparing the detection data of each cube workpiece corresponding to each machining quality index with the standard data of each machining quality index corresponding to a target batch of cube workpieces stored in the workpiece machining quality database so as to judge whether the machining quality of each cube workpiece is qualified or not, and the specific judgment steps comprise: and S1, extracting the weight data corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index, and carrying out average value calculation on the weight data of the same square workpiece in each detection subarea to obtain the weight average value corresponding to each square workpiece, wherein the calculation of the weight average value can improve the accuracy of the detection result and avoid the detection error phenomenon caused by single detection.
S2, extracting the standard range of the weight index corresponding to the target batch of square workpieces from the standard data of each processing quality index corresponding to the target batch of square workpieces in the workpiece processing quality database, comparing the weight average value corresponding to each square workpiece with the standard range of the weight index corresponding to the target batch of square workpieces, and if the weight average value corresponding to a square workpiece is not in the standard range of the weight index corresponding to the target batch of square workpieces, judging that the processing quality of the square workpiece is unqualified, and the processing quality unqualified category corresponding to the square workpiece is weight.
And S3, extracting the outline dimension data of the target detection surface corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index.
S4, extracting standard values of the contour dimension indexes of the target detection surfaces corresponding to the target batch of square workpieces from the standard data of the processing quality indexes corresponding to the target batch of square workpieces in the workpiece processing quality database, comparing the contour dimension data of the target detection surfaces corresponding to the detection subareas in the square workpieces with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, if the contour dimension data of the target detection surfaces corresponding to the detection subareas in the square workpieces is not consistent with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, judging that the processing quality of the square workpieces is unqualified, and the type of unqualified processing quality corresponding to the square workpieces is the contour dimension.
And S5, extracting the surface defect data of the target detection surface corresponding to each detection subarea from the detection data of each processing quality index corresponding to each square workpiece, and if the target detection surface corresponding to a certain detection subarea in a certain square workpiece has a surface defect, judging that the processing quality of the square workpiece is unqualified, wherein the processing quality unqualified type corresponding to the square workpiece is the surface defect.
S6, if the weight average value of a square workpiece is in the standard range of the weight index corresponding to the target batch of square workpieces, the external dimension data of the target detection surface corresponding to each detection subarea is consistent with the standard value of the external dimension index of the detection surface corresponding to the target batch of square workpieces, and the target detection surface corresponding to each detection subarea has no surface defect, the processing quality of the square workpiece is judged to be qualified.
The square workpiece classification processing terminal is connected with the detection data analysis and judgment module, and is used for identifying the outlet conveying end corresponding to each square workpiece based on the processing quality judgment result corresponding to each square workpiece, and the method specifically comprises the following steps: (1) and if the processing quality corresponding to a certain square workpiece is qualified, the outlet conveying end corresponding to the square workpiece is a qualified workpiece conveying end.
(2) If the machining quality corresponding to a certain square workpiece is unqualified, counting the quantity of the unqualified machining quality categories corresponding to the square workpiece, if the square workpiece only has a single unqualified machining quality category, when the unqualified machining quality category is weight, the outlet conveying end corresponding to the square workpiece is a single unqualified weight conveying end, when the unqualified machining quality category is an external dimension, the outlet conveying end corresponding to the square workpiece is a single unqualified external dimension conveying end, and when the unqualified machining quality category is a surface defect, the outlet conveying end corresponding to the square workpiece is a single surface defect conveying end.
(3) And if the processing quality corresponding to a certain square workpiece is unqualified and the square workpiece has various types of unqualified processing quality, the outlet conveying end corresponding to the square workpiece is the mixed unqualified conveying end.
After the outlet conveying ends corresponding to the square workpieces are identified, the square workpiece classification processing terminal conveys the square workpieces to the corresponding outlet conveying ends for storage, and corresponding quality inspection personnel classify the square workpieces stored at different outlet conveying ends.
According to the embodiment of the invention, the workpiece detection and transmission mechanism is arranged, square workpieces in the same batch processed by a machine tool are sequentially placed on the workpiece detection and transmission mechanism and are transmitted to the detection area through the inlet transmission end to carry out online detection on the processing quality, detection data of each square workpiece corresponding to each processing quality index are obtained, and intelligent analysis is carried out on the detection data, so that each square workpiece is automatically classified to the corresponding outlet transmission end according to an analysis result, the online detection and classification of the processing quality of the square workpieces are automatically integrated, the manual participation degree is effectively reduced, the detection efficiency and classification accuracy are greatly improved, the cost of the machine tool workpiece detection in the manual aspect is reduced, and the online detection level of the processing quality of the machine tool workpiece is also improved.
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.
Claims (10)
1. An online detection and analysis system for machine tool workpieces based on big data is characterized by comprising:
the workpiece detection and transmission mechanism setting module is used for setting the workpiece detection and transmission mechanism after the machining of the machine tool is finished, wherein the workpiece detection and transmission mechanism comprises an inlet transmission end, a transmission belt and a plurality of outlet transmission ends;
the detection equipment setting module is connected with the workpiece detection and transmission mechanism setting module and used for defining a detection area on a transmission belt of the workpiece detection and transmission mechanism and setting detection equipment on the defined detection area;
the square workpiece machining quality online detection module is connected with the detection equipment setting module and used for sequentially placing each square workpiece in the same batch after machining of the machine tool on the workpiece detection and transmission mechanism and transmitting the square workpiece to a detection area through the inlet transmission end to perform machining quality online detection so as to obtain detection data of each square workpiece corresponding to each machining quality index;
the workpiece processing quality database is used for storing standard data of each processing quality index corresponding to a target batch of square workpieces, wherein the target batch refers to a production batch corresponding to each square workpiece in the same batch after the machine tool finishes processing;
the detection data analysis and judgment module is respectively connected with the square workpiece machining quality online detection module and the workpiece machining quality database and is used for comparing the detection data of each square workpiece corresponding to each machining quality index with the standard data of each machining quality index corresponding to a target batch of square workpieces stored in the workpiece machining quality database so as to judge whether the machining quality of each square workpiece is qualified or not;
and the square workpiece classification processing terminal is connected with the detection data analysis and judgment module and is used for identifying the outlet conveying end corresponding to each square workpiece based on the processing quality judgment result corresponding to each square workpiece, further conveying the outlet conveying end to the corresponding outlet conveying end for storage, and classifying the square workpieces stored in different outlet conveying ends by corresponding quality inspection personnel.
2. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the entrance conveying end is used for placing the square workpiece to be detected currently, a pressure sensor and marking equipment are respectively arranged on the entrance transmission end, the pressure sensor is used for sensing whether the square workpiece is placed on the entrance conveying end, and the marking equipment is used for labeling the square workpiece placed on the entrance conveying end.
3. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the plurality of outlet delivery ends include a qualified workpiece delivery end, a single form factor non-conforming delivery end, a single weight non-conforming delivery end, a single surface defect delivery end, and a mixed non-conforming delivery end.
4. The big data based on-line detection and analysis system for the machine tool workpiece is characterized in that: the detection area is composed of a detection subarea A, a detection subarea B, a detection subarea C, a detection subarea D, a detection subarea E and a detection subarea F at intervals side by side, detection equipment is arranged on each detection subarea, and a pressure sensor is arranged below each detection subarea and used for sensing whether a square workpiece to be subjected to machining quality detection exists on each detection subarea.
5. The big data based on-line detection and analysis system for the machine tool workpiece is characterized in that: the detection equipment comprises a weight sensor, a contourgraph and an intelligent camera, wherein the weight sensor is used for detecting the weight index of a square workpiece, the contourgraph is used for detecting the outline dimension index of a target detection surface corresponding to the square workpiece, the intelligent camera is used for detecting the surface defect index of the target detection surface corresponding to the square workpiece, the square workpiece is composed of six surfaces, each detection subarea is respectively corresponding to one detection surface of the square workpiece, at the moment, the target detection surface corresponding to the detection subarea A is marked as the detection surface A, the target detection surface corresponding to the detection subarea B is marked as the detection surface B, the target detection area corresponding to the detection subarea C is marked as the detection surface C, the target detection surface corresponding to the detection subarea D is marked as the detection surface D, the target detection surface corresponding to the detection subarea E is marked as the detection surface E, and the target detection surface corresponding to the detection subarea F is marked as the detection surface F, and the target detection surface corresponding to each detection subarea is preset.
6. The big data based on-line detection and analysis system for the machine tool workpiece is characterized in that: and a detection gap exists between every two adjacent detection subareas, and a turning manipulator is arranged in the detection gap and used for turning the current detection square workpiece from a target detection surface corresponding to a previous detection subarea to a target detection surface corresponding to a next detection subarea in the adjacent detection subareas.
7. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the machining quality indexes comprise a weight index, an outline dimension index and a surface defect index.
8. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the detection data of each processing quality index comprises weight data corresponding to each detection subarea, overall dimension data of a target detection surface corresponding to each detection subarea and surface defect data of the target detection surface corresponding to each detection subarea, wherein the overall dimension data comprises length and width, the surface defect data comprises surface defect judgment result data and surface defect specific data, the surface defect judgment result data comprises surface defects and non-surface defects, and the surface defect specific data represents corresponding defect types under the condition that the surface defects exist, wherein the surface defect data detection steps of the target detection surfaces corresponding to each detection subarea are as follows:
the first step is as follows: acquiring images of a corresponding target detection surface of a currently detected square workpiece through an intelligent camera in detection equipment corresponding to each detection subarea to obtain surface images of each detection surface corresponding to the currently detected square workpiece;
the second step is that: extracting standard surface images of the detection surfaces corresponding to the target batch of cube workpieces from a processing quality database;
the third step: comparing the surface image of each detection surface corresponding to the current detection square workpiece with the standard surface image of each detection surface corresponding to the target batch of square workpieces, if the surface image of a certain detection surface of the current detection square workpiece is inconsistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect, extracting an inconsistent area from the surface image of the detection surface, further determining the defect type corresponding to the inconsistent area as the specific surface defect data corresponding to the detection surface, and if the surface image of the certain detection surface of the current detection square workpiece is consistent with the standard surface image of the corresponding detection surface of the target batch of workpieces, indicating that the surface defect judgment result data corresponding to the detection surface is surface defect-free.
9. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the specific judgment method for judging whether the machining quality of each square workpiece is qualified or not comprises the following steps:
s1, extracting the weight data corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index, and carrying out average value calculation on the weight data of the same square workpiece in each detection subarea to obtain the weight average value corresponding to each square workpiece;
s2, extracting the standard range of the weight index corresponding to the target batch of square workpieces from the standard data of each processing quality index corresponding to the target batch of square workpieces in the workpiece processing quality database, further comparing the weight average value corresponding to each square workpiece with the standard range of the weight index corresponding to the target batch of square workpieces, if the weight average value corresponding to a certain square workpiece is not in the standard range of the weight index corresponding to the target batch of square workpieces, judging that the processing quality of the square workpiece is unqualified, and the processing quality unqualified category corresponding to the square workpiece is weight;
s3, extracting the outline dimension data of the target detection surface corresponding to each detection subarea from the detection data of each square workpiece corresponding to each processing quality index;
s4, extracting standard values of the contour dimension indexes of the target detection surfaces corresponding to the target batch of square workpieces from the standard data of the target batch of square workpieces corresponding to the processing quality indexes in the workpiece processing quality database, comparing the contour dimension data of the target detection surfaces corresponding to the detection subareas in the square workpieces with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, if the contour dimension data of the target detection surfaces corresponding to a detection subarea in a square workpiece is not consistent with the standard values of the contour dimension indexes of the detection surfaces corresponding to the target batch of square workpieces, judging that the processing quality of the square workpieces is unqualified, and the unqualified category of the processing quality corresponding to the square workpieces is contour dimension;
s5, extracting the surface defect data of the target detection surface corresponding to each detection subarea from the detection data of each processing quality index corresponding to each square workpiece, if the target detection surface corresponding to a detection subarea in a square workpiece has a surface defect, judging that the processing quality of the square workpiece is unqualified, and the processing quality unqualified category corresponding to the square workpiece is the surface defect;
s6, if the weight average value of a square workpiece is in the standard range of the weight index corresponding to the target batch of square workpieces, the external dimension data of the target detection surface corresponding to each detection subarea is consistent with the standard value of the external dimension index of the detection surface corresponding to the target batch of square workpieces, and the target detection surface corresponding to each detection subarea has no surface defect, the processing quality of the square workpiece is judged to be qualified.
10. The big data based on-line detection and analysis system for the machine tool workpiece as claimed in claim 1, wherein: the identifying of the outlet delivery end corresponding to each square workpiece based on the machining quality judgment result corresponding to each square workpiece specifically includes:
(1) if the processing quality corresponding to a certain square workpiece is qualified, the outlet conveying end corresponding to the square workpiece is a qualified workpiece conveying end;
(2) if the machining quality corresponding to a certain square workpiece is unqualified, counting the quantity of unqualified machining quality categories corresponding to the square workpiece, if the square workpiece only has a single unqualified machining quality category, when the unqualified machining quality category is weight, the outlet conveying end corresponding to the square workpiece is a single unqualified weight conveying end, when the unqualified machining quality category is an external dimension, the outlet conveying end corresponding to the square workpiece is a single unqualified external dimension conveying end, and when the unqualified machining quality category is a surface defect, the outlet conveying end corresponding to the square workpiece is a single surface defect conveying end;
(3) and if the processing quality corresponding to a certain square workpiece is unqualified and the square workpiece has various types of unqualified processing quality, the outlet conveying end corresponding to the square workpiece is the mixed unqualified conveying end.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210238129.0A CN114571287A (en) | 2022-03-11 | 2022-03-11 | Machine tool workpiece online detection and analysis system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210238129.0A CN114571287A (en) | 2022-03-11 | 2022-03-11 | Machine tool workpiece online detection and analysis system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114571287A true CN114571287A (en) | 2022-06-03 |
Family
ID=81780669
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210238129.0A Withdrawn CN114571287A (en) | 2022-03-11 | 2022-03-11 | Machine tool workpiece online detection and analysis system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114571287A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118009889A (en) * | 2024-04-09 | 2024-05-10 | 常州铭赛机器人科技股份有限公司 | Method for measuring position of workpiece dispensing slot |
-
2022
- 2022-03-11 CN CN202210238129.0A patent/CN114571287A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118009889A (en) * | 2024-04-09 | 2024-05-10 | 常州铭赛机器人科技股份有限公司 | Method for measuring position of workpiece dispensing slot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103808734B (en) | Auto-parts fully-automated synthesis equipment | |
| CN212041552U (en) | Automatic detection device for surface defects and thickness of gasket | |
| CN102095733A (en) | Machine vision-based intelligent detection method for surface defect of bottle cap | |
| CN112150439B (en) | Automatic sorting equipment and sorting method for injection molding parts | |
| CN105784719A (en) | Machine vision inspection terminal capable of sorting goods | |
| CN110517233A (en) | A kind of defect classification learning system and its classification method based on artificial intelligence | |
| CN114571287A (en) | Machine tool workpiece online detection and analysis system based on big data | |
| CN113867246A (en) | Processing line mistake proofing system, processing line mistake proofing method, storage medium, electronic device and production line | |
| CN111711765A (en) | Part production process inspection system | |
| CN110108713A (en) | A kind of Superficial Foreign Body defect fast filtering method and system | |
| CN107478529A (en) | One kind automation aluminium section bar knockout detection means | |
| CN112881427B (en) | Electronic component defect detection device and method based on visible light and infrared thermal imaging | |
| Jiang et al. | Machine vision based inspection of oil seals | |
| CN112585551A (en) | Server and control method thereof | |
| CN109454010B (en) | Lens detection method | |
| CN113305017B (en) | Comprehensive intelligent detection and sorting method for full-automatic valve element | |
| CN206230527U (en) | A kind of vision guide robot automatic thread detecting system | |
| KR101011437B1 (en) | Accuracy screw's automatic inspection and sorting system using vision | |
| KR102049563B1 (en) | PCB overlap detection system and method in PCB manufacturing process | |
| KR102020873B1 (en) | Ogive's automatic measurement and inspection system | |
| KR101434270B1 (en) | An auto testing device for power window motor shaft | |
| CN205580384U (en) | Metal sheet surface detection device | |
| CN217058685U (en) | System for be used for camshaft axiality to detect | |
| CN111539933B (en) | Direct-insert element detection method and system | |
| CN215239704U (en) | External measurement and control device for improving precision of machining part of numerically controlled lathe |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220603 |
|
| WW01 | Invention patent application withdrawn after publication |