CN115519096B - 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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- 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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Abstract
The invention discloses a die casting deburring equipment state detection system and method based on big data, wherein a deburring part on a deburring equipment is used as a monitoring part by a using time length acquisition module, the edge of the monitoring part, from which the burrs on the die casting to be deburred are removed, is used as a monitoring edge, after the deburring equipment removes the burrs of one die casting, the using time length of the monitoring part is acquired, the using time length of the monitoring part is compared with a time length threshold value by a time length comparison module, if the using time length of the monitoring part is smaller than or equal to the time length threshold value, the burr information of the die casting processed by the monitoring part is acquired by a pre-judging index acquisition analysis module, the pre-judging index of the monitoring part is calculated, and the pre-judging index comparison module judges whether the wear detection control module works or not according to the pre-judging index of the monitoring part, and if the using time length of the monitoring part is longer than the time length threshold value, the wear detection control module works.
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 the die casting, burrs are more or less generated on the surface of the die casting due to the problem of the production process, and the assembly, the service performance and the service life of the die casting of the part are directly affected by the existence of the burrs, so that the die casting needs to be subjected to deburring after being processed and produced. The deburring work is to smoothly process the outer surface of the die casting and remove burrs under the condition of ensuring that the die casting is unchanged.
With the development of scientific technology, the deburring equipment is generally adopted at present to remove burrs on the die casting, but the deburring equipment also can produce wearing and tearing in the deburring process, if not in time maintain when the wearing and tearing comparison of deburring equipment is verified, can lead to the efficiency reduction to die casting burring, influences the effect of burring.
Disclosure of Invention
The invention aims to provide a die casting deburring equipment state detection system and method based on big data, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the equipment state detection system comprises a use time length acquisition module, a time length comparison module, a pre-judgment index acquisition module, a pre-judgment index analysis module and a wear detection control module, wherein the use time length acquisition module takes a part of the deburring equipment for removing burrs as a monitoring part, the side of the monitoring part for removing the burrs on the die casting to be deburred is the monitoring side, after the deburring equipment removes the burrs of one die casting, the use time length of the monitoring part is acquired, the time length comparison module compares the use time length of the monitoring part with a time length threshold, if the use time length of the monitoring part is smaller than or equal to the time length threshold, the pre-judgment index acquisition analysis module acquires burr information of the die casting processed by the monitoring part, the pre-judgment index of the monitoring part is calculated, the pre-judgment index analysis module judges whether the wear detection control module works according to the pre-judgment index of the monitoring part, and if the wear detection control module works, if the use time length of the monitoring part is longer than the time length threshold, the wear detection control module works, and the wear detection control module is used for controlling the wear condition of the monitoring part on the detection burr equipment.
Further, the pre-judging index obtaining module comprises an influencing burr obtaining module and a pre-judging index calculating module, wherein the influencing burr obtaining module is used for setting burrs of the die casting processed by the monitoring part as influencing burrsThe number of the influence burrs is k, information of each influence burr is obtained, wherein the length of a certain influence burr along the deburring direction of the surface of the die casting 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 i-th influencing burr, l i For the i-th influencing burr, L is the sum of the influencing lengths of all influencing burrs,/->T i To monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i The length of time it takes for the ith effect burr to be removed is monitored.
Further, the pre-judging index analysis module comprises a pre-judging index comparison module, a region dividing module, an associated burr obtaining module, a concerned index calculation module, a continuous index obtaining module, a duty ratio index calculation module and a duty ratio index comparison module, wherein the pre-judging index comparison module compares the pre-judging index of the monitoring component with a pre-judging threshold value, if the pre-judging index of the monitoring component is greater than or equal to the pre-judging threshold value, the abrasion detection control module works, if the pre-judging index of the monitoring component is smaller than the pre-judging threshold value, the region dividing module averagely divides the monitoring edge into m sub-monitoring regions along the 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 burrs firstly contact a certain sub-monitoring region, the associated burr obtaining module takes the influence burrs as the associated burrs of the sub-monitoring region, the concerned 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 greater than the number threshold value, the concerned index of the monitoring region is the concerned index of the monitoring edge, x=g/g/m, and the concerned index of the concerned edge is the concerned region; the continuous index acquisition module analyzes the position condition of each concerned region on the monitoring edge to obtain a continuous index y of the monitoring edge, the duty ratio index calculation module calculates the duty ratio index Q=0.5 x+0.5 x y of the monitoring edge, the duty ratio index comparison module compares the duty ratio index of the monitoring edge with a duty ratio threshold value, and if the duty ratio index of the monitoring edge is greater than or equal to the duty ratio threshold value, the abrasion detection control module works.
Further, the continuous index obtaining module includes a continuous area processing module and a continuous index calculating module, the continuous area processing module uses each concerned area as an initial continuous area, sequentially judges the relation between the number of the sub-monitoring areas between the positions of the two adjacent continuous areas and the interval number, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is smaller than the interval number, the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas are combined into one continuous area, and so on until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is greater than the interval number, the continuous index calculating module sorts the number of the sub-monitoring areas included in each continuous area in the order from large to small, selects the first sorted number as the 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:
the deburring component on the deburring device 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 one die casting, the service time of the monitoring part is obtained,
if the using time of the monitoring component is less than or equal to the time threshold value, acquiring the burr information of the die casting processed by the monitoring component, calculating the pre-judging index of the monitoring component, judging whether the abrasion condition of the monitoring component on the burr equipment is to be detected or not according to the pre-judging index of the monitoring component,
if the usage time of the monitoring component is greater than the time threshold, detecting the wear condition of the monitoring component on the burr equipment.
Further, the calculating the predictive index of the monitoring component includes:
the burrs of the die casting processed by the monitoring part are used as influencing burrs, the number of the influencing burrs is k, the information of each influencing burr is obtained, wherein the length of a certain influencing burr along the deburring direction of the surface of the die casting is the influencing length of the influencing burr,
then the predictive index of the monitoring component
Where i is the i-th influencing burr, l i For the i-th influence length of the influence burr, L is the sum of the influence lengths of all influence burrs,T i to monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i The length of time it takes for the ith effect burr to be removed by the edge is monitored.
Further, the determining whether to replace the monitoring component according to the pre-determination index of the monitoring component includes:
if the prejudgment index of the monitoring component is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring component on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
dividing the monitoring edge into m sub-monitoring areas on average along the direction parallel to the deburring surface of the die casting,
when the monitoring component removes the influencing burr on the die casting, if a certain influencing burr first contacts a certain sub-monitoring area, the influencing 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 larger than a number threshold, then the sub-monitoring area is a concerned area, and then the concerned index x=g/m of the monitoring edge, wherein g is the total number of the concerned areas in the sub-monitoring area;
analyzing the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge;
the duty cycle index q=0.5 x+0.5 x y of the monitored edge,
the duty cycle index of the monitoring edge is compared to a duty cycle threshold,
and if the duty ratio index of the monitoring edge is larger than or equal to the duty 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:
sequentially judging the relation between the number of the sub-monitoring areas and the number of intervals by taking each concerned area as an initial continuous area, merging the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into one continuous area 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, and so on 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,
and sequencing the number of the sub-monitoring areas contained in each continuous area according to the sequence from big to small, and selecting the first sequencing number as the representative number d, so that the continuous index y=d/m of the monitoring edge.
Further, comparing the duty cycle index of the monitoring edge with the duty cycle threshold value further comprises:
and if the duty ratio index of the monitoring edge is smaller than the duty ratio threshold value, deburring equipment deburrs the next die casting.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, through analyzing the use time of the deburring part in the deburring device and the abrasion condition of the deburring part by the deburring, whether the abrasion condition of the deburring device is detected is judged, so that the deburring device can timely find out when the abrasion of the deburring part is serious, and the deburring precision and efficiency of the deburring device are ensured.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic block diagram of a state detection system of a die casting deburring apparatus of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the equipment state detection system comprises a use time length acquisition module, a time length comparison module, a pre-judgment index acquisition module, a pre-judgment index analysis module and a wear detection control module, wherein the use time length acquisition module takes a part of the deburring equipment for removing burrs as a monitoring part, the side of the monitoring part for removing the burrs on the die casting to be deburred is the monitoring side, after the deburring equipment removes the burrs of one die casting, the use time length of the monitoring part is acquired, the time length comparison module compares the use time length of the monitoring part with a time length threshold, if the use time length of the monitoring part is smaller than or equal to the time length threshold, the pre-judgment index acquisition analysis module acquires burr information of the die casting processed by the monitoring part, the pre-judgment index of the monitoring part is calculated, the pre-judgment index analysis module judges whether the wear detection control module works according to the pre-judgment index of the monitoring part, and if the wear detection control module works, if the use time length of the monitoring part is longer than the time length threshold, the wear detection control module works, and the wear detection control module is used for controlling the wear condition of the monitoring part on the detection burr equipment.
The pre-judging index obtaining module comprises an influencing burr obtaining module and a pre-judging index calculating module, wherein the influencing burr obtaining module is used for setting burrs of the die casting processed by the monitoring component as influencing burrs, the number of the influencing burrs is k, information of each influencing burr is obtained, the length of a certain influencing burr in the direction of removing the burrs on the surface of the die casting along the monitoring edge is the influence length of the influencing burrs, and the pre-judging index calculating module is used for calculating the pre-judging index of the monitoring componentWhere i is the i-th influencing burr, l i For the i-th influencing burr, L is the sum of the influencing lengths of all influencing burrs,/-> T i To monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i The length of time it takes for the ith effect burr to be removed is monitored.
The pre-judging index analysis module comprises a pre-judging index comparison module, a region dividing module, an associated burr obtaining module, a concerned index calculating module, a continuous index obtaining module, a duty ratio index calculating module and a duty ratio index comparing module, wherein the pre-judging index comparing module compares the pre-judging index of the monitoring component with a pre-judging threshold value, if the pre-judging index of the monitoring component is larger than or equal to the pre-judging threshold value, the abrasion detection control module works, if the pre-judging index of the monitoring component is smaller than the pre-judging threshold value, the region dividing module averagely divides the monitoring edge into m sub-monitoring regions along the 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 associated burr obtaining module takes the influence burrs as the associated burrs of the sub-monitoring region, the concerned index calculating 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 larger than the number threshold value, the sub-monitoring region is the concerned index x=g/m, and the concerned index of the edge is the monitored region, and the concerned index g/m is the monitored region; the continuous index acquisition module analyzes the position condition of each concerned region on the monitoring edge to obtain a continuous index y of the monitoring edge, the duty ratio index calculation module calculates the duty ratio index Q=0.5 x+0.5 x y of the monitoring edge, the duty ratio index comparison module compares the duty ratio index of the monitoring edge with a duty ratio threshold value, and if the duty ratio index of the monitoring edge is greater than or equal to the duty ratio threshold value, the abrasion detection control module works.
The continuous index acquisition module comprises a continuous area processing module and a continuous index calculation module, wherein the continuous area processing module takes each concerned area as an initial continuous area, sequentially judges the relation between the number of sub-monitoring areas between the positions of two adjacent continuous areas and the interval number, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is smaller than the interval number, the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas are combined into one continuous area, and so on until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is larger than the interval number, the continuous index calculation module sorts the number of the sub-monitoring areas contained in each continuous area in the order from large to small, selects the first number of the sorted sub-monitoring areas as the 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:
the deburring device comprises a deburring device, a deburring tool and a deburring tool, wherein the deburring device is used for deburring a die casting to be deburred on the deburring device;
when the deburring device removes burrs of one die casting, the service time of the monitoring part is obtained,
detecting the wear condition of the monitoring component on the burr equipment if the use time of the monitoring component is greater than a time threshold;
if the using time of the monitoring component is less than or equal to the time threshold value, acquiring the burr information of the die casting processed by the monitoring component, calculating the predictive index of the monitoring component,
the calculating the predictive index of the monitoring component includes:
the method comprises the steps that burrs of a die casting processed by a monitoring part are used as influence burrs, the number of the influence burrs is k, information of each influence burr is obtained, wherein the length of a certain influence burr along the direction of removing the burrs on the surface of the die casting is the influence length of the influence burr, for example, when the part for removing the burrs is a cutter, the length of the cutter for removing the certain burrs on the die casting is the influence length of the burr when the certain burrs on the die casting are removed;
then the predictive index of the monitoring component
Where i is the i-th influencing burr, l i For the i-th influence length of the influence burr, L is the sum of the influence lengths of all influence burrs,T i to monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i Monitoring the length of time spent by the ith affected burr removed by the edge; when i=k, h can be set to correspond to all of the influence burrs previously describedOr, a specified value may be directly set; the application considers that the monitoring component can generate heat when removing burrs, if the monitoring component is in a state of long heating time, the monitoring component is more easy to wear in the deburring process, thus, when +.>The larger the size, the more likely the monitoring part will wear, and at the same time, will +>As->The weight of the monitoring component is more reasonable, so that the influence on whether the monitoring component is easy to wear or not is more reasonable when each burr is removed, and the accuracy of the subsequent judgment on whether the monitoring component is to trample the wear detection is improved;
judging whether to detect the abrasion condition of the monitoring component on the burr equipment according to the pre-judging index of the monitoring component, comprising:
if the prejudgment index of the monitoring component is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring component on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
dividing the monitoring edge 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 influencing burrs on the die casting, if a certain influencing burr firstly contacts a certain sub-monitoring area, the influencing burr is the relevant burr of the sub-monitoring area, the pressure born by the opposite part of the sub-monitoring area is larger, and if the sub-monitoring area is subjected to larger pressure, the sub-monitoring area is easy to wear 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, then the sub-monitoring area is a concerned area, and then the concerned index x=g/m of the monitoring edge, wherein g is the total number of the concerned areas in the sub-monitoring area;
analyzing the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge, wherein the continuous index y comprises the following steps:
the method comprises the steps of taking each concerned area as an initial continuous area, sequentially judging the relation between the number of sub-monitoring areas between the positions of two adjacent continuous areas and the number of intervals, merging the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into one continuous area 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, and so on 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, wherein if the number of the intervals between the two continuous areas is relatively independent, the two continuous areas are not easily influenced by the force of the opposite area, and if the number of the intervals between the two continuous areas is relatively less, the two continuous areas are relatively related, are easily influenced by the force of the opposite area, and the abrasion of the two continuous areas is likely to spread, so that the abrasion area is increased;
sequencing the number of the sub-monitoring areas contained in each continuous area according to the sequence from big to small, and selecting the first sequencing number as the representative number d, wherein the continuous index y=d/m of the monitoring edge;
when the monitoring edge is a knife edge, the knife edge is divided into a plurality of sub-monitoring areas, the attention index of the monitoring edge is used for judging how much more places the sub-monitoring areas on the knife edge are likely to be worn, and the continuous index of the monitoring edge is used for judging how much more areas on the knife edge are likely to be worn; when the duty ratio index is larger, the possible abrasion places on the knife edge are more, the abrasion area on the knife edge is larger, the abrasion on the knife edge can be serious, the subsequent burr removal can be influenced, and therefore the monitoring edge is checked;
the duty cycle index of the monitoring edge is compared to a duty cycle threshold,
if the duty ratio index of the monitoring edge is greater than or equal to the duty ratio threshold, detecting the wear condition of the monitoring component on the burr equipment,
and if the duty ratio index of the monitoring edge is smaller than the duty ratio threshold value, deburring equipment deburrs the next die casting.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. 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 die casting deburring equipment state detection system based on big data is characterized by comprising a using time length acquisition module, a time length comparison module, a pre-judging index acquisition module, a pre-judging index analysis module and a wear detection control module, wherein the using time length acquisition module takes a part of deburring equipment for deburring as a monitoring part, the side of the monitoring part for deburring on the die casting to be deburred is the monitoring side, after the deburring equipment removes the burr of one die casting, the using time length of the monitoring part is acquired, the time length comparison module compares the using time length of the monitoring part with a time length threshold, if the using time length of the monitoring part is smaller than or equal to the time length threshold, the pre-judging index acquisition module acquires burr information of the die casting processed by the monitoring part, the pre-judging index of the monitoring part is calculated, the pre-judging index analysis module judges whether the wear detection control module works according to the pre-judging index of the monitoring part, and if the wear detection control module works when the monitoring part is longer than the time length threshold, the wear detection control module works and is used for controlling the wear condition of the monitoring part on the detection equipment.
2. The big data based die casting deburring equipment status detection system as set forth in claim 1, wherein: the pre-judging index obtaining module comprises an influencing burr obtaining module and a pre-judging index calculating module, wherein the influencing burr obtaining module is used for setting burrs of the die casting processed by the monitoring component as influencing burrs, the number of the influencing burrs is k, information of each influencing burr is obtained, the length of a certain influencing burr in the direction of removing the burrs on the surface of the die casting along the monitoring edge is the influence length of the influencing burrs, and the pre-judging index calculating module is used for calculating the pre-judging index of the monitoring component Where i is the i-th influencing burr, l i For the i-th influencing burr, L is the sum of the influencing lengths of all influencing burrs,/->T i To monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i The length of time it takes for the ith effect burr to be removed is monitored.
3. The big data based die casting deburring equipment status detection system as claimed in claim 2, wherein: the pre-judging index analysis module comprises a pre-judging index comparison module, a region dividing module, an associated burr obtaining module, a concerned index calculating module, a continuous index obtaining module, a duty ratio index calculating module and a duty ratio index comparing module, wherein the pre-judging index comparing module compares the pre-judging index of the monitoring component with a pre-judging threshold value, if the pre-judging index of the monitoring component is larger than or equal to the pre-judging threshold value, the abrasion detection control module works, if the pre-judging index of the monitoring component is smaller than the pre-judging threshold value, the region dividing module averagely divides the monitoring edge into m sub-monitoring regions along the 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 associated burr obtaining module takes the influence burrs as the associated burrs of the sub-monitoring region, the concerned index calculating 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 larger than the number threshold value, the sub-monitoring region is the concerned index x=g/m, and the concerned index of the edge is the monitored region, and the concerned index g/m is the monitored region; the continuous index acquisition module analyzes the position condition of each concerned region on the monitoring edge to obtain a continuous index y of the monitoring edge, the duty ratio index calculation module calculates the duty ratio index Q=0.5 x+0.5 x y of the monitoring edge, the duty ratio index comparison module compares the duty ratio index of the monitoring edge with a duty ratio threshold value, and if the duty ratio index of the monitoring edge is greater than or equal to the duty ratio threshold value, the abrasion detection control module works.
4. The big data based die casting deburring equipment status detection system as claimed in claim 3, wherein: the continuous index acquisition module comprises a continuous area processing module and a continuous index calculation module, wherein the continuous area processing module takes each concerned area as an initial continuous area, sequentially judges the relation between the number of sub-monitoring areas between the positions of two adjacent continuous areas and the interval number, if the number of the sub-monitoring areas between the positions of the two adjacent continuous areas is smaller than the interval number, the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas are combined into one continuous area, and so on until the number of the sub-monitoring areas between the positions of all the adjacent continuous areas is larger than the interval number, the continuous index calculation module sorts the number of the sub-monitoring areas contained in each continuous area in the order from large to small, selects the first number of the sorted sub-monitoring areas as the representative number d, and then the continuous index y=d/m of the monitoring edge.
5. The die casting deburring equipment state detection method based on big data is characterized by comprising the following steps of: the equipment state detection method comprises the following steps:
the deburring component on the deburring device 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 one die casting, the service time of the monitoring part is obtained,
if the using time of the monitoring component is less than or equal to the time threshold value, acquiring the burr information of the die casting processed by the monitoring component, calculating the pre-judging index of the monitoring component, judging whether the abrasion condition of the monitoring component on the burr equipment is to be detected or not according to the pre-judging index of the monitoring component,
if the usage time of the monitoring component is greater than the time threshold, detecting the wear condition of the monitoring component on the burr equipment.
6. The big data based die casting deburring equipment state detection method as claimed in claim 5, wherein: the calculating the predictive index of the monitoring component includes:
the burrs of the die casting processed by the monitoring part are used as influencing burrs, the number of the influencing burrs is k, the information of each influencing burr is obtained, wherein the length of a certain influencing burr along the deburring direction of the surface of the die casting is the influencing length of the influencing burr,
then the predictive index of the monitoring component
Where i is the i-th influencing burr, l i For the i-th influence length of the influence burr, L is the sum of the influence lengths of all influence burrs,T i to monitor the interval duration between the time node when the edge starts to remove the ith influencing burr and the time before the monitoring edge starts to remove the (i+1) th burr, h i The length of time it takes for the ith effect burr to be removed by the edge is monitored.
7. The big data based die casting deburring equipment state detection method as claimed in claim 6, wherein: the judging whether to replace the monitoring component according to the pre-judging index of the monitoring component comprises the following steps:
if the prejudgment index of the monitoring component is larger than or equal to the prejudgment threshold value, detecting the abrasion condition of the monitoring component on the burr equipment;
if the predictive index of the monitoring component is less than the predictive threshold,
dividing the monitoring edge into m sub-monitoring areas on average along the direction parallel to the deburring surface of the die casting,
when the monitoring component removes the influencing burr on the die casting, if a certain influencing burr first contacts a certain sub-monitoring area, the influencing 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 larger than a number threshold, then the sub-monitoring area is a concerned area, and then the concerned index x=g/m of the monitoring edge, wherein g is the total number of the concerned areas in the sub-monitoring area;
analyzing the position condition of each concerned area on the monitoring edge to obtain a continuous index y of the monitoring edge;
the duty cycle index q=0.5 x+0.5 x y of the monitored edge,
the duty cycle index of the monitoring edge is compared to a duty cycle threshold,
and if the duty ratio index of the monitoring edge is larger than or equal to the duty ratio threshold value, detecting the abrasion condition of the monitoring part on the burr equipment.
8. The big data based die casting deburring equipment state detection method as claimed in claim 7, wherein: the analyzing the position condition of each region of interest on the monitoring edge comprises the following steps:
sequentially judging the relation between the number of the sub-monitoring areas and the number of intervals by taking each concerned area as an initial continuous area, merging the two adjacent continuous areas and the sub-monitoring areas between the positions of the two adjacent continuous areas into one continuous area 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, and so on 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,
and sequencing the number of the sub-monitoring areas contained in each continuous area according to the sequence from big to small, and selecting the first sequencing number as the representative number d, so that the continuous index y=d/m of the monitoring edge.
9. The big data based die casting deburring equipment state detection method as claimed in claim 7, wherein: the comparing the duty cycle index of the monitored edge to the duty cycle threshold further comprises:
and if the duty ratio index of the monitoring edge is smaller than the duty ratio threshold value, deburring equipment deburrs the next die casting.
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