CN115519096A - Die casting deburring equipment state detection system and method based on big data - Google Patents
Die casting deburring equipment state detection system and method based on big data Download PDFInfo
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- CN115519096A CN115519096A CN202211137723.7A CN202211137723A CN115519096A CN 115519096 A CN115519096 A CN 115519096A CN 202211137723 A CN202211137723 A CN 202211137723A CN 115519096 A CN115519096 A CN 115519096A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D17/00—Pressure die casting or injection die casting, i.e. casting in which the metal is forced into a mould under high pressure
- B22D17/20—Accessories: Details
- B22D17/32—Controlling equipment
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D17/00—Pressure die casting or injection die casting, i.e. casting in which the metal is forced into a mould under high pressure
- B22D17/20—Accessories: Details
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D2/00—Arrangement of indicating or measuring devices, e.g. for temperature or viscosity of the fused mass
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D31/00—Cutting-off surplus material, e.g. gates; Cleaning and working on castings
- B22D31/002—Cleaning, working on castings
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/56—Investigating resistance to wear or abrasion
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/20—Metals
- G01N33/204—Structure thereof, e.g. crystal structure
- G01N33/2045—Defects
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- 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
Abstract
The invention discloses a die casting deburring equipment state detection system and method based on big data, wherein a using duration acquisition module takes a part, on deburring equipment, with burrs removed as a monitoring part, the side, on the monitoring part, with the burrs on a die casting to be deburred is taken as a monitoring side, when the deburring equipment finishes removing the burrs of one die casting, the using duration of the monitoring part is acquired, a duration comparison module compares the using duration of the monitoring part with a duration threshold, if the using duration of the monitoring part is less than or equal to the duration threshold, a prejudgment index acquisition and analysis module is used for acquiring the burr information of the die casting processed by the monitoring part, a prejudgment index of the monitoring part is calculated, the prejudgment index comparison module is used for judging whether to enable a wear detection control module to work or not according to the prejudgment index of the monitoring part, and if the using duration of the monitoring part is greater than the duration threshold, the wear detection control module is made to work.
Description
Technical Field
The invention relates to the technical field of die casting deburring equipment, in particular to a die casting deburring equipment state detection system and method based on big data.
Background
In the processing production process of die casting, because the problem of production technology, more or less can produce the burr on the die casting surface, the existence of burr can direct influence the assembly of the die casting of part, performance and working life, consequently after processing production die casting, need carry out burring work to the die casting. The deburring work is to smoothly process the outer surface of the die casting under the condition of ensuring the original state of the die casting to be unchanged, and remove burrs.
Along with the development of science and technology, the burr on the die casting is usually removed by deburring equipment at present, but the deburring equipment also can generate abrasion in the deburring process, if the abrasion of the deburring equipment is not timely maintained when being verified, the deburring efficiency of the die casting can be reduced, and the deburring effect is influenced.
Disclosure of Invention
The invention aims to provide a die casting deburring equipment state detection system and a die casting deburring equipment state detection method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the equipment state detection system comprises a use duration acquisition module, a duration comparison module, a prejudgment index acquisition module, a prejudgment index analysis module and a wear detection control module, wherein the use duration acquisition module takes a part on the deburring equipment for removing burrs as a monitoring part, the monitoring part takes the side for removing the burrs on a die casting to be deburred as a monitoring side, the use duration of the monitoring part is acquired after the deburring equipment removes the burrs of one die casting, the use duration comparison module compares the use duration of the monitoring part with a duration threshold, if the use duration of the monitoring part is less than or equal to the duration threshold, the prejudgment index acquisition and analysis module acquires the burr information of the die casting processed by the monitoring part and calculates the prejudgment index of the monitoring part, the prejudgment index analysis module judges whether the wear detection control module needs to work according to the prejudgment index of the monitoring part, if the use duration of the monitoring part is greater than the duration threshold, the wear detection control module works, and the wear detection control module is used for controlling and detecting the wear condition of the monitoring part on the deburring equipment.
Furthermore, the prejudgment index acquisition module comprises an influence burr acquisition module and a prejudgment index calculation module, the influence burr acquisition module sets burrs of the die casting processed by the monitoring part as influence burrs, the number of the influence burrs is k, and information of each influence burr is acquired, wherein the length of a certain influence burr in the direction of monitoring the edge of the die casting surface for removing burrs is the influence length of the certain influence burr, and the prejudgment index calculation module calculates the prejudgment index of the monitoring part
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i To monitor the length of time it takes to remove the ith influencing burr.
Further, the prejudgment index analysis module comprises a prejudgment index comparison module, a region division module, an associated burr acquisition module, an attention index calculation module, a continuous index acquisition module, a proportion index calculation module and a proportion index comparison module, the prejudgment index comparison module compares a prejudgment index of a monitoring component with a prejudgment threshold, if the prejudgment index of the monitoring component is greater than or equal to the prejudgment threshold, the wear detection control module is made to work, if the prejudgment index of the monitoring component is smaller than the prejudgment threshold, the region division module averagely divides a monitoring edge into m sub-monitoring regions along a direction parallel to the deburring surface of the die casting, when the monitoring component removes the influence burrs on the die casting, if a certain influence burr is firstly contacted with a certain sub-monitoring region, the burr association acquisition module takes the certain influence burr as the associated burr of the sub-monitoring regions, the attention index calculation module respectively counts the total number of the associated burrs of each sub-monitoring region, if the total number of the associated burrs of a certain sub-monitoring region is greater than the number of the sub-monitoring region, the attention index of the sub-monitoring region is g/g, wherein the attention index of the monitoring region is the total number of g/g, and the attention index of the monitoring region is the attention index of the monitoring region; the continuity index obtaining module analyzes the position condition of each concerned area on the monitoring edge to obtain a continuity index y of the monitoring edge, the ratio index calculating module calculates the ratio index Q =0.5 x +0.5 y of the monitoring edge, the ratio index comparing module compares the ratio index of the monitoring edge with a ratio threshold, and if the ratio index of the monitoring edge is larger than or equal to the ratio threshold, the abrasion detection control module works.
Further, the consecutive index obtaining module includes a consecutive area processing module and a consecutive index calculating module, the consecutive area processing module takes each concerned area as an initial consecutive area, sequentially judges a relationship between the number of the sub-monitoring areas between the positions of two adjacent consecutive areas and the number of the intervals, if the number of the sub-monitoring areas between the positions of two adjacent consecutive areas is smaller than the number of the intervals, the two adjacent consecutive areas and the sub-monitoring areas between the positions of the two adjacent consecutive areas are combined into a consecutive area, and so on until the number of the sub-monitoring areas between the positions of all the adjacent consecutive areas is larger than the number of the intervals, the consecutive index calculating module sorts the number of the sub-monitoring areas included in each consecutive area in descending order, selects the first sorted number as a representative number d, and then the consecutive index y = d/m of the monitoring edge.
The die casting deburring equipment state detection method based on big data comprises the following steps:
a component for removing burrs on the deburring equipment is used as a monitoring component, the edge of the monitoring component for removing the burrs on the die casting to be deburred is used as a monitoring edge,
when the deburring device removes burrs of a die casting, the service life of the monitoring part is obtained,
if the service life of the monitoring part is less than or equal to the time threshold, acquiring burr information of the die casting processed by the monitoring part, calculating a prejudgment index of the monitoring part, judging whether to detect the wear condition of the monitoring part on the burr equipment or not according to the prejudgment index of the monitoring part,
detecting a wear condition of the monitoring component on the burred equipment if the length of time the monitoring component is in use is greater than the length of time threshold.
Further, the calculating the prediction index of the monitoring component includes:
setting burrs of a die casting processed by a monitoring part as influence burrs, and the number of the influence burrs as k, and acquiring information of each influence burr, wherein the length of a certain influence burr in the burr removing direction of the monitoring edge on the surface of the die casting is the influence length of the influence burr,
then the prejudgment index of the monitoring part
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i The length of time it takes for the edge to remove the ith impact spike is monitored.
Further, the determining whether to replace the monitoring component according to the predetermined index of the monitoring component includes:
if the prejudgment index of the monitoring part is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring part on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
the monitoring edge is divided into m sub-monitoring areas on average along the direction parallel to the deburring surface of the die casting,
when the monitoring part removes the influence burrs on the die casting, if a certain influence burr firstly contacts a certain sub-monitoring area, the certain influence burr is the related burr of the sub-monitoring area,
respectively counting the total number of the associated burrs of each sub-monitoring area, if the total number of the associated burrs of a certain sub-monitoring area is greater than a number threshold, the sub-monitoring area is an attention area, and the attention index x of a monitoring edge is = g/m, wherein g is the total number of the attention areas in the sub-monitoring areas;
analyzing the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge;
then the ratio index Q =0.5 x +0.5 y of the monitored side,
the duty index of the monitoring edge is compared to a duty threshold,
and if the ratio index of the monitoring edge is larger than or equal to the ratio threshold value, detecting the abrasion condition of the monitoring part on the burr equipment.
Further, the analyzing the position condition of each region of interest on the monitoring edge includes:
taking each concerned area as an initial continuous area, sequentially judging the relationship between the number of the sub-monitoring areas between the positions of two adjacent continuous areas and the number of intervals, if the number of the sub-monitoring areas between the positions of two adjacent continuous areas is less than the number of intervals, combining the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into a continuous area, and repeating the steps until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is more than the number of intervals,
and sequencing the number of the sub-monitoring areas contained in each continuous area from large to small, and selecting the first sequenced number as a representative number d, so that the continuous index y = d/m of the monitoring edge.
Further, the comparing the duty ratio index of the monitoring edge with the duty ratio threshold further includes:
and if the ratio index of the monitoring edge is smaller than the ratio threshold value, deburring by the deburring equipment for deburring the next die casting.
Compared with the prior art, the invention has the following beneficial effects: the method and the device can judge whether to detect the abrasion condition of the deburring device or not by analyzing the service time of the deburring component in the deburring device and the abrasion condition of the deburring component by the burr, so that the deburring device can find out the abrasion condition in time when the deburring component is seriously abraded, and the deburring precision and efficiency of the deburring device are ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic block diagram of a die casting deburring apparatus condition detection system of 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, the present invention provides the following technical solutions: the equipment state detection system comprises a use duration acquisition module, a duration comparison module, a prejudgment index acquisition module, a prejudgment index analysis module and a wear detection control module, wherein the use duration acquisition module takes a part on the deburring equipment for removing burrs as a monitoring part, the edge on the monitoring part for removing the burrs on the die casting to be deburred is a monitoring edge, the use duration of the monitoring part is acquired after the deburring equipment removes the burrs of one die casting, the use duration comparison module compares the use duration of the monitoring part with a duration threshold, if the use duration of the monitoring part is less than or equal to the duration threshold, the prejudgment index acquisition and analysis module acquires the burr information of the die casting processed by the monitoring part and calculates the prejudgment index of the monitoring part, the prejudgment index analysis module judges whether the wear detection control module needs to work according to the prejudgment index of the monitoring part, if the use duration of the monitoring part is greater than the duration threshold, the wear detection control module works, and the wear detection control module is used for controlling and detecting the wear condition of the monitoring part on the burrs.
The prejudgment index obtaining module comprises an influence burr obtaining module and a prejudgment index calculating module, the influence burr obtaining module is used for setting burrs of die castings processed by monitoring parts as influence burrs, the number of the influence burrs is k, and information of all the influence burrs is obtained, wherein the length of a certain influence burr in the direction of removing burrs on the surfaces of the die castings at the monitoring edges is the influence length of the certain influence burr, and the prejudgment index calculating module is used for calculating the prejudgment index of the monitoring parts
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all the influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i To monitor the length of time it takes for the edge to remove the ith influencing spur.
The device comprises a pre-judgment index analysis module, a pre-judgment index comparison module, a region division module, an associated burr acquisition module, an attention index calculation module, a continuous index acquisition module, a proportion index calculation module and a proportion index comparison module, wherein the pre-judgment index comparison module compares a pre-judgment index of a monitoring component with a pre-judgment threshold value, if the pre-judgment index of the monitoring component is larger than or equal to the pre-judgment threshold value, a wear detection control module is enabled to work, if the pre-judgment index of the monitoring component is smaller than the pre-judgment threshold value, a monitoring edge is averagely divided into m sub-monitoring regions along the direction parallel to the deburring surface of a die casting, when the monitoring component removes the influence burrs on the die casting, if a certain influence burr is firstly contacted with a certain sub-monitoring region, the associated burr acquisition module takes the influence burr as the associated burr of the sub-monitoring region, the attention index calculation module respectively counts the total number of the associated burrs of each sub-monitoring region, if the total number of the associated burrs of a certain sub-monitoring region is larger than the threshold number of the associated burrs, the sub-monitoring region is the attention index comparison region, x = g/m, wherein the attention index of the monitoring region is the total number of the attention region; the continuity index obtaining module analyzes the position condition of each concerned area on the monitoring edge to obtain a continuity index y of the monitoring edge, the ratio index calculating module calculates the ratio index Q =0.5 x +0.5 y of the monitoring edge, the ratio index comparing module compares the ratio index of the monitoring edge with a ratio threshold, and if the ratio index of the monitoring edge is larger than or equal to the ratio threshold, the abrasion detection control module works.
The continuous index obtaining module comprises a continuous area processing module and a continuous index calculating module, wherein the continuous area processing module takes each concerned area as an initial continuous area, sequentially judges the relationship between the number of the sub-monitoring areas between the positions of two adjacent continuous areas and the number of the intervals, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is less than the number of the intervals, the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas are combined into a continuous area, and the like is performed in sequence until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is greater than the number of the intervals, the continuous index calculating module sorts the number of the sub-monitoring areas contained in each continuous area from large to small, selects the first sorted number as a representative number d, and then the continuous index y = d/m of the monitoring edge.
The die casting deburring equipment state detection method based on big data comprises the following steps:
setting a deburring component on the deburring equipment as a monitoring component, and setting the edge of the monitoring component, which is used for removing burrs on a die casting to be deburred, as a monitoring edge, for example, when the deburring component is a cutter, the monitoring edge is a cutter edge on the cutter for removing burrs;
when the deburring device removes burrs of a die casting, the service life of the monitoring part is obtained,
detecting the abrasion condition of the monitoring part on the burr equipment if the service time of the monitoring part is longer than a time threshold;
if the service life of the monitoring part is less than or equal to the time threshold, acquiring burr information of the die casting processed by the monitoring part, calculating a prejudgment index of the monitoring part,
the calculating the prejudgment index of the monitoring component comprises the following steps:
setting burrs of a die casting processed by a monitoring part as influence burrs, and the number of the influence burrs as k, and acquiring information of each influence burr, wherein the length of a certain influence burr in the direction of removing the burrs along the surface of the die casting along the monitoring edge is the influence length of the certain influence burr, for example, when the deburring part is a cutter and the certain burr on the die casting is removed, the length of the cutter for removing the burr on the die casting is the influence length of the burr;
then the predictive index of the monitoring component
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all the influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i Monitoring the time length spent on removing the ith influence burr; when i = k, h may be set as an average value of h corresponding to all the previous influence spikes, or a specified value may be directly set; the present application considers that heat is generated when the monitoring part removes the burr, and if the state that the monitoring part is in the heating time is long, the monitoring part is more likely to cause abrasion in the process of removing the burr, and therefore, when the monitoring part is removed from the burr
The larger the size, the more easily the monitoring member will wear and at the same time, will
As
The weight of the monitoring part is more reasonable in influence on whether the monitoring part is easy to wear or not when each burr is removed, and the accuracy of judging whether the monitoring part is to wear or not to detect in the follow-up process is improved;
judging whether to detect the wear condition of the monitoring part on the burr equipment according to the prejudgment index of the monitoring part, comprising the following steps:
if the prejudgment index of the monitoring part is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring part on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
the monitoring edge is averagely divided into m sub-monitoring areas along the direction parallel to the deburring surface of the die casting,
when the influence burrs on the die casting are removed by the monitoring part, if a certain influence burr firstly contacts a certain sub-monitoring area, the influence burr is a related burr of the sub-monitoring area, the pressure applied to the corresponding part of the sub-monitoring area is higher, and if the sub-monitoring area is always applied with higher pressure, the sub-monitoring area is easy to wear more seriously;
respectively counting the total number of the associated burrs of each sub-monitoring area, if the total number of the associated burrs of a certain sub-monitoring area is larger than a number threshold, the sub-monitoring area is an attention area, and the attention index x of a monitoring edge is = g/m, wherein g is the total number of the attention areas in the sub-monitoring areas;
analyzing the position condition of each concerned area on the monitoring edge to obtain the continuous index y of the monitoring edge, which comprises the following steps:
taking each concerned area as an initial continuous area, sequentially judging the relationship between the number of the sub-monitoring areas between the positions of two adjacent continuous areas and the number of intervals, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is smaller than the number of the intervals, combining the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into a continuous area, and repeating the steps until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is larger than the number of the intervals, if the number of the intervals between the two continuous areas is more, the two continuous areas are relatively independent and are not easily influenced by the force of the other area, if the number of the intervals between the two continuous areas is less, the two continuous areas are relatively associated and are easily influenced by the force of the other area, and the abrasion of the two continuous areas can possibly spread to cause the increase of the abrasion area;
sorting the number of the sub-monitoring areas contained in each continuous area from large to small, selecting the first sorted number as a representative number d, and then monitoring the continuous index y of the edge = d/m;
then, the proportion index Q =0.5 x +0.5 y of the monitoring edge, when the monitoring edge is a knife edge, the knife edge is averagely divided into a plurality of sub-monitoring regions, the attention index of the monitoring edge is used for judging that the parts of the sub-monitoring regions on the knife edge, which are possibly seriously worn, are more and less, and the continuity index of the monitoring edge is used for judging that the regions on the knife edge, which are possibly worn, are large and not large; when the ratio index is larger, it is indicated that there are more places on the knife edge which may be worn, a certain worn area is larger, the knife edge may be worn seriously, and the subsequent burr removal may be affected, so that the monitoring edge needs to be inspected;
the duty ratio index of the monitored edge is compared to a duty ratio threshold,
if the ratio index of the monitoring edge is larger than or equal to the ratio threshold value, detecting the abrasion condition of the monitoring part on the burr equipment,
and if the ratio index of the monitoring edge is smaller than the ratio threshold value, deburring by the deburring equipment for deburring the next die casting.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The equipment state detection system is characterized by comprising a use duration acquisition module, a duration comparison module, a prejudgment index acquisition module, a prejudgment index analysis module and a wear detection control module, wherein the use duration acquisition module is used for taking a part, on the deburring equipment, for removing burrs as a monitoring part, the side, on the monitoring part, for removing the burrs on a die casting to be deburred is taken as a monitoring side, after the deburring equipment removes the burrs of one die casting, the use duration of the monitoring part is acquired, the duration comparison module is used for comparing the use duration of the monitoring part with a duration threshold, if the use duration of the monitoring part is less than or equal to the duration threshold, the prejudgment index acquisition and analysis module is used for acquiring the burr information of the die casting processed by the monitoring part and calculating the prejudgment index of the monitoring part, the prejudgment index analysis module is used for judging whether the wear detection control module needs to work according to the prejudgment index of the monitoring part, and if the use duration of the monitoring part is greater than the duration threshold, the wear detection control module is used for controlling the wear detection of the monitoring part on the wear detection equipment.
2. The big-data-based die casting deburring equipment state detection system of claim 1, wherein: the prejudgment index acquisition module comprises an influence burr acquisition module and a prejudgment index calculation module, the influence burr acquisition module is used for setting burrs of die castings processed by monitoring parts as influence burrs, the number of the influence burrs is k, and information of each influence burr is acquired, wherein burr removal burr on the surfaces of the die castings is carried out on a certain influence burr at the monitoring edgeThe length in the direction is the influence length of the influence burr, and the pre-judgment index calculation module calculates the pre-judgment index of the monitoring part
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i To monitor the length of time it takes for the edge to remove the ith influencing spur.
3. The die casting deburring equipment state detecting system based on big data of claim 2, characterized in that: the pre-judgment index analysis module comprises a pre-judgment index comparison module, a region division module, an associated burr acquisition module, an attention index calculation module, a continuous index acquisition module, a proportion index calculation module and a proportion index comparison module, wherein the pre-judgment index comparison module compares a pre-judgment index of a monitoring part with a pre-judgment threshold, if the pre-judgment index of the monitoring part is more than or equal to the pre-judgment threshold, a wear detection control module is enabled to work, if the pre-judgment index of the monitoring part is less than the pre-judgment threshold, the region division module averagely divides a monitoring edge into m sub-monitoring regions along the direction parallel to the deburring surface of the die casting, when the monitoring part removes the influence burrs on the die casting, if a certain influence burr is firstly contacted with a certain sub-monitoring region, the associated burr acquisition module takes the certain influence burr as the associated burr of the sub-monitoring regions, the attention index calculation module respectively counts the total number of the associated burrs of each sub-monitoring region, if the total number of the associated burrs of the certain sub-monitoring region is more than the number threshold, the sub-monitoring region is the attention index of the attention region, and the monitoring edge x/m, wherein the attention index g is the total number of the monitoring region; the continuous index obtaining module analyzes the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge, the ratio index calculating module calculates the ratio index Q =0.5 x +0.5 y of the monitoring edge, the ratio index comparing module compares the ratio index of the monitoring edge with a ratio threshold, and if the ratio index of the monitoring edge is larger than or equal to the ratio threshold, the abrasion detection control module works.
4. The big data based die casting deburring equipment state detecting system of claim 3, characterized in that: the continuous index obtaining module comprises a continuous area processing module and a continuous index calculating module, wherein the continuous area processing module takes each concerned area as an initial continuous area, sequentially judges the relationship between the number of the sub-monitoring areas between the positions of two adjacent continuous areas and the number of intervals, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is smaller than the number of the intervals, the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas are combined into a continuous area, and the like in sequence until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is larger than the number of the intervals, the continuous index calculating module sorts the number of the sub-monitoring areas contained in each continuous area in descending order, selects the first sorted number as a representative number d, and then the continuous index y = d/m of the monitoring edge.
5. A die casting deburring equipment state detection method based on big data is characterized in that: the equipment state detection method comprises the following steps:
setting a component for removing burrs on the deburring equipment as a monitoring component, setting the edge of the monitoring component for removing the burrs on the die casting to be deburred as a monitoring edge,
when the deburring device removes burrs of a die casting, the service life of the monitoring part is obtained,
if the service life of the monitoring component is less than or equal to the time threshold, acquiring burr information of the die casting processed by the monitoring component, calculating a pre-judging index of the monitoring component, judging whether to detect the wear condition of the monitoring component on the burr equipment according to the pre-judging index of the monitoring component,
and if the service time of the monitoring part is longer than the time threshold, detecting the wear condition of the monitoring part on the burr equipment.
6. The die casting deburring device state detection method based on big data as claimed in claim 5, characterized in that: the calculating of the anticipation index of the monitoring part includes:
setting burrs of the die casting processed by the monitoring part as influence burrs, and the number of the influence burrs as k, acquiring information of each influence burr, wherein the length of a certain influence burr in the direction of removing the burrs from the surface of the die casting at the monitoring edge is the influence length of the influence burr,
then the predictive index of the monitoring component
Where i is the ith influencing spur, l i Is the influence length of the ith influencing spur, L is the sum of the influence lengths of all influencing spurs,
T i the interval duration h from the time node when the monitoring edge starts to remove the ith influence burr to the time point before the monitoring edge starts to remove the (i + 1) th burr i The length of time it takes for the edge to remove the ith influencing spur is monitored.
7. The die casting deburring equipment state detection method based on big data as claimed in claim 6, characterized in that: the judging whether to replace the monitoring part according to the pre-judging index of the monitoring part comprises the following steps:
if the prejudgment index of the monitoring part is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring part on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
the monitoring edge is averagely divided into m sub-monitoring areas along the direction parallel to the deburring surface of the die casting,
when the monitoring part removes the influence burrs on the die casting, if a certain influence burr firstly contacts a certain sub-monitoring area, the influence burr is the associated burr of the sub-monitoring area,
respectively counting the total number of the associated burrs of each sub-monitoring area, if the total number of the associated burrs of a certain sub-monitoring area is greater than a number threshold, the sub-monitoring area is an attention area, and the attention index x of a monitoring edge is = g/m, wherein g is the total number of the attention areas in the sub-monitoring areas;
analyzing the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge;
then the ratio index of the monitoring edge Q =0.5 x +0.5 y,
the duty ratio index of the monitored edge is compared to a duty ratio threshold,
and if the ratio index of the monitoring edge is larger than or equal to the ratio threshold value, detecting the abrasion condition of the monitoring part on the burr equipment.
8. The die casting deburring device state detection method based on big data as claimed in claim 7, characterized in that: the analyzing the position condition of each attention area on the monitoring edge comprises:
taking each concerned area as an initial continuous area, sequentially judging the relationship between the number of the sub-monitoring areas between the positions of two adjacent continuous areas and the number of intervals, if the number of the sub-monitoring areas between the positions of two adjacent continuous areas is less than the number of the intervals, combining the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into a continuous area, and repeating the steps until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is more than the number of the intervals,
and sequencing the number of the sub-monitoring areas contained in each continuous area from large to small, and selecting the first sequenced number as a representative number d, so that the continuous index y = d/m of the monitoring edge.
9. The die casting deburring equipment state detection method based on big data as claimed in claim 7, characterized in that: the comparing the duty ratio index of the monitoring edge with the duty ratio threshold further comprises:
and if the ratio index of the monitoring edge is smaller than the ratio threshold value, deburring by the deburring equipment for deburring the next die casting.
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