Intelligent detection method of electronic equipment shell production mold

Abstract

The invention relates to the technical field of intelligent detection, and particularly discloses an intelligent detection method for an electronic equipment shell production mold, which comprises the following steps: S1, acquiring historical use data of the mold, setting a standard input period according to the historical use data, and dividing the period into a safety stage and a loss stage; s2, if the current mold is in the safety stage, three-dimensional image data and sample image data of the finished shell are obtained in real time; performing grid division on the image data, and calculating a defect index; s3, when the mold enters a loss stage, continuing to obtain the flaw index in real time, and combining with the previously calculated environment flaw index to obtain a wear flaw index; a curve changing along with time can be generated through the abrasion defect indexes obtained through multiple times of measurement, and the curve is used for evaluating the abrasion condition of the mold; the die state is evaluated by monitoring data in real time, so that whether the die needs to be maintained or replaced is accurately judged, and the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to an intelligent detection method for an electronic equipment shell production mold. Background Art

[0002] Intelligent inspection of housing production molds is a comprehensive process involving multiple aspects to ensure the quality, precision and service life of the molds; including appearance inspection, size inspection, durability inspection and life inspection.

[0003] In the existing electronic equipment shell production technology, the quality inspection of the shell production mold usually relies on the defects of the finished product. However, in the actual production environment, there are many factors that affect the quality of the finished product. The quality of the finished product is jointly affected by many factors, including the quality of raw materials, production process parameters, operator skills, and equipment status. Therefore, when conducting quality inspections, it is not possible to arbitrarily assume that the quality of the finished product is caused by the quality of the mold based solely on the defects of the finished product, which will cause unnecessary waste of mold costs. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent detection method for electronic equipment housing production molds to solve the above technical problems.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An intelligent detection method for an electronic device housing production mold comprises the following steps:

[0007] Step S1: Acquire historical usage data of the mold, wherein the historical usage data is the input cycle of each mold used in the past, and the input cycle is the time period from when the mold is put into use to when it is replaced; set a standard input cycle according to the historical usage data, and divide the standard input cycle into a safety stage and a wear stage;

[0008] Step S2: Record the mold being used as the current mold; when the current mold is in the safe stage, obtain image data of the finished shell in real time, and obtain sample image data of the finished shell; the sample image data is image data of the sample of the finished shell, and the image data is a three-dimensional image of the finished shell on each surface;

[0009] The three-dimensional image is gridded to obtain a plurality of squares, and the three-dimensional image is placed in a three-dimensional coordinate system to obtain the coordinates of the center points of each square; the center points of the same column are numbered as i, i∈[1, 2, ..., n] in sequence and n is the total number of center points on the same column; the spacing between each adjacent center point on the same column is obtained |P i+1-P i |, where P i Represents the coordinates of the i-th center point;

[0010] According to the image data and the sample image data, a defect index is obtained:

[0011]

[0012] Where P i ′ represents the coordinates of the i-th center point on the same column in the sample image data, m is the total number of columns after the three-dimensional image grid is divided on one side, k∈[1,m], N is the total number of three-dimensional images in the image data, x∈[1,N]; according to all defect indices obtained in real time, the environmental defect index Di in the safe stage is obtained 环 ;

[0013] Step S3: When the current mold is in the wear stage, the defect index Di at this time is obtained in real time 损 , and according to the environmental defect index, the wear defect index Wi = Di is obtained 损 -Di 环 ; and according to the wear defect index obtained each time, generate a curve F(t) showing the wear defect index changing with time, where t is the timestamp corresponding to each time the wear defect index is obtained;

[0014] The average slope K of the curve F(t) is obtained in real time. If Wi>0 and K>0, the current mold is worn out at this time, and the replacement time of the current mold is predicted based on the curve F(t).

[0015] As a further solution of the present invention: the process of setting the standard input cycle includes:

[0016] Obtain the input cycle of each mold used in the past, and obtain the input cycle set Z = {C1, C2, ..., C num}, where C1 represents the input cycle of the first mold used in the past, and num is the total number of molds used in the past, and the standard input cycle is obtained. where g∈[1,num].

[0017] As a further solution of the present invention: the process of dividing the standard input cycle includes:

[0018] Obtain the minimum value in the investment cycle set Z, recorded as Min(Z); record the time when the mold is put into use as the use time T0, and record [T0, T0+Min(Z)-T′] as the safety stage, where T′ is the preset time period threshold, and 0<T′≤Min(Z) / 5; if the standard investment cycle C=[T0, T1], then the wear stage is [T0+Min(Z)-T′, ​​T1].

[0019] As a further solution of the present invention: the three-dimensional image is acquired based on laser scanning technology.

[0020] As a further solution of the present invention: the environmental defect index is an average value of all defect indices acquired in real time.

[0021] As a further solution of the present invention: the process of obtaining the average slope K of the curve F(t) includes:

[0022] M monitoring points are selected on the curve F(t), where M≥w, and w is the total number of defect indexes obtained during the wear stage; the slopes at the M monitoring points on the curve F(t) are obtained, and the average value of the M slopes is obtained, which is recorded as the average slope K of the curve F(t).

[0023] As a further solution of the present invention: if Wi≤0 or K≤0, then the current mold has not been worn out yet.

[0024] As a further solution of the present invention: the process of predicting the replacement time of the current mold according to the curve F(t) includes:

[0025] Set the defect index threshold DI, then get the wear defect index threshold WI = DI-Di 环 ; The timestamp corresponding to the current mold entering the wear and tear stage is recorded as t0, and the replacement time t′=t0+WI / K is obtained.

[0026] Beneficial effects of the present invention:

[0027] The present invention can more accurately identify the wear or damage that may occur in the mold during use by acquiring the three-dimensional image data of the finished shell in real time and comparing and analyzing it with the sample image data. This method can provide a detailed defect index, which is helpful to accurately evaluate the state of the mold. The use cycle of the mold is divided into a safe stage and a loss stage, and the mold state is monitored in real time during the loss stage. It can be repaired or replaced in time according to actual needs, thereby avoiding production quality problems caused by excessive wear of the mold and extending the service life of the mold. By monitoring the wear of the mold and generating a curve of the wear defect index changing over time, the enterprise can better predict the maintenance needs and replacement time of the mold, thereby reasonably arranging production plans and maintenance work, and reducing production interruptions caused by mold failure. According to the environmental defect index in the safe stage, the defect influence of the environment or external factors on the shell product is stripped out, and the misjudgment of the mold wear is reduced; by comparing the defect index obtained in real time with the environmental defect index, the defect influence of the environment or external factors on the shell product can be stripped out. The specific method is to subtract the environmental defect index from the defect index obtained in real time, and the result obtained is the mold defect wear defect index after stripping the influence of environmental factors. Based on the wear defect index obtained each time, a curve showing the change of the wear defect index over time is generated; this curve can intuitively show the changing trend of the wear condition of the mold during use, which helps companies to more accurately judge the maintenance needs and replacement time of the mold. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below in conjunction with the accompanying drawings.

[0029] Figure 1 The present invention is a schematic flow chart of an intelligent detection method for an electronic equipment housing production mold. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 As shown, the present invention is an intelligent detection method for an electronic device housing production mold, comprising the following steps:

[0032] Step S1: Acquire historical usage data of the mold, wherein the historical usage data is the input cycle of each mold used in the past, and the input cycle is the time period from when the mold is put into use to when it is replaced; set a standard input cycle according to the historical usage data, and divide the standard input cycle into a safety stage and a wear stage;

[0033] It is understandable that the historical usage data of each mold used in the past is collected, and the historical usage data includes the entire time period from the beginning of the use of the mold to the final replacement; by analyzing and sorting these historical data, the service life of different molds and various problems that may occur during use can be understood; in the safety stage, the use status of the mold is relatively stable, the degree of wear is small, and it can continue to be used normally; while in the wear stage, the degree of wear of the mold gradually increases, and various faults and problems may occur, requiring repair or replacement; by dividing the standard investment cycle into these two stages, the use of the mold can be better monitored, and potential problems can be discovered and handled in a timely manner;

[0034] As a preferred embodiment of the present invention, the process of setting the standard input cycle includes:

[0035] Obtain the input cycle of each mold used in the past, and obtain the input cycle set Z = {C1, C2, ..., C num}, where C1 represents the input cycle of the first mold used in the past, and num is the total number of molds used in the past, and the standard input cycle is obtained. Where g∈[1,num];

[0036] The process of dividing the standard investment cycle includes:

[0037] Obtain the minimum value in the investment cycle set Z, recorded as Min(Z); record the time when the mold is put into use as the use time T0, and record [T0, T0+Min(Z)-T′] as the safety stage, where T′ is the preset time period threshold, and 0<T′≤Min(Z) / 5; if the standard investment cycle C=[T0, T1], then the wear stage is [T0+Min(Z)-T′, ​​T1];

[0038] It is understandable that by calculating the average value of historical investment cycles, a relatively accurate standard investment cycle can be obtained, and this standard value can be used as the basis for predicting the service life of the mold; the minimum value Min(Z) represents the shortest service life of all molds used in the past, and can be used as an important reference for judging whether the mold has entered the wear and tear stage; in the safety stage, the mold is in a relatively stable use state and the degree of wear is small; in the wear and tear stage, the degree of wear of the mold gradually increases, and various faults and problems may occur, requiring repair or replacement;

[0039] Step S2: Record the mold being used as the current mold; when the current mold is in the safe stage, obtain image data of the finished shell in real time, and obtain sample image data of the finished shell; the sample image data is image data of the sample of the finished shell, and the image data is a three-dimensional image of the finished shell on each surface;

[0040] The three-dimensional image is gridded to obtain a plurality of squares, and the three-dimensional image is placed in a three-dimensional coordinate system to obtain the coordinates of the center points of each square; the center points of the same column are numbered as i, i∈[1, 2, ..., n] in sequence and n is the total number of center points on the same column; the spacing between each adjacent center point on the same column is obtained |P i+1 -P i |, where P i Represents the coordinates of the i-th center point;

[0041] According to the image data and the sample image data, a defect index is obtained:

[0042]

[0043] Where P i ′ represents the coordinates of the i-th center point on the same column in the sample image data, m is the total number of columns after the three-dimensional image grid is divided on one side, k∈[1,m], N is the total number of three-dimensional images in the image data, x∈[1,N]; according to all defect indices obtained in real time, the environmental defect index Di in the safe stage is obtained 环 ;

[0044] It is understandable that when the mold being used is in a safe stage, image data of the finished shell is acquired in real time; the image data is a three-dimensional image of the finished shell in all aspects, which can fully reflect the surface condition of the finished shell; the sample image data represents the surface condition of the finished shell in an ideal state;

[0045] It is worth noting that the three-dimensional image acquired in real time is gridded and divided into several small squares, so that the image difference of each small area can be more conveniently compared; if there are potholes or other defects on the surface of the finished shell, the area where the potholes or other defects are located is recorded as a defective area, and the distance between adjacent center points at the corresponding position of the defective area on the image data will be larger than that of the sample image data; therefore, the larger the defect index is, the larger the defect of the finished shell on the surface is compared with the sample finished shell;

[0046] As a preferred embodiment of the present invention, the three-dimensional image is obtained based on laser scanning technology;

[0047] It should be noted that the three-dimensional images are obtained based on laser scanning technology, which uses laser beams to scan the surface of an object with high precision to capture the detailed geometry and surface features of the object and provide comprehensive three-dimensional data;

[0048] Step S3: When the current mold is in the wear stage, the defect index Di at this time is obtained in real time 损 , and according to the environmental defect index, the wear defect index Wi = Di is obtained 损 -Di 环 ; and according to the wear defect index obtained each time, generate a curve F(t) showing the wear defect index changing with time, where t is the timestamp corresponding to each time the wear defect index is obtained;

[0049] The average slope K of the curve F(t) is obtained in real time. If Wi>0 and K>0, the current mold is worn out at this time, and the replacement time of the current mold is predicted according to the curve F(t);

[0050] It can be understood that the environmental defect index is a reference defect index determined in the safety stage; the calculated wear defect index represents the increase in the current wear degree of the mold relative to the safety stage; according to each wear defect index obtained, a curve F(t) of the wear defect index changing with time is generated, and the curve can intuitively show the cumulative effect of mold wear over time, and help analyze the wear trend of the mold;

[0051] It should be noted that the average slope reflects the overall rate of change of the defect index over time; if Wi>0 and K>0, it is determined that the current mold is worn. At this time, the increase in the defect index and the upward trend of the curve together indicate the decline in mold performance and the increase in wear, which means that the current mold is experiencing wear and the wear rate is increasing; this is a clear signal that it is necessary to pay close attention to the status of the mold and consider maintenance or replacement;

[0052] As a preferred embodiment of the present invention, the process of obtaining the average slope K of the curve F(t) includes:

[0053] Select M monitoring points on the curve F(t), where M≥w, and w is the total number of defect indexes obtained in the wear stage; obtain the slopes at the M monitoring points on the curve F(t), and obtain the average value of the M slopes, which is recorded as the average slope K of the curve F(t);

[0054] It can be understood that M monitoring points are selected on the curve F(t); M is a value greater than or equal to w, where w represents the total number of defect indexes obtained in the wear stage; selecting enough monitoring points can ensure that the calculated average slope is more accurate and reliable; for the M monitoring points on the curve F(t), the slope of each point is calculated respectively; the slope is a measure of the degree of inclination of the tangent of the curve at a certain point, which can be solved by mathematical methods (such as differentiation); the slope values ​​of all monitoring points are summed up and then divided by the total number of monitoring points M, and the result is the average slope K of the curve F(t), which reflects the overall change trend of the curve in the entire wear stage; if the K value is positive, it means that the curve is on an upward trend, that is, the wear defect index of the mold increases over time, indicating that the mold is gradually wearing out;

[0055] As a preferred embodiment of the present invention, if Wi≤0 or K≤0, then the current mold has not been worn out;

[0056] It can be understood that when the calculated wear defect index Wi≤0, it means that the defect index of the current mold has not increased compared with the defect index of the baseline environment, indicating that the performance of the mold remains stable or has improved, and no obvious wear has occurred; the average slope K of the curve F(t) reflects the change trend of the wear defect index over time; if K≤0, it means that the curve F(t) presents a non-increasing trend, that is, the wear condition of the mold has not deteriorated, which also indicates that the current mold has not yet experienced wear that requires attention;

[0057] As a preferred embodiment of the present invention, the process of predicting the replacement time of the current mold according to the curve F(t) includes:

[0058] Set the defect index threshold DI, then get the wear defect index threshold WI = DI-Di 环 ; The timestamp corresponding to the current mold entering the wear stage is recorded as t0, and the replacement time t′=t0+WI / K is obtained;

[0059] It is understandable that a defect index threshold DI needs to be set. This threshold is a predefined value used to determine whether the mold has reached the level of needing to be replaced. The setting of DI is based on the performance requirements of the mold, production quality standards, and historical data analysis; the wear defect index threshold represents the degree of wear of the mold from the safe stage to the critical state of needing to be replaced;

[0060] It is worth noting that WI / K is the predicted time increment required to reach the threshold based on the current wear rate.

[0061] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent detection method for electronic equipment housing production mold, characterized in that: The following steps are involved: Step S1: Acquire historical usage data of the mold, wherein the historical usage data is the input cycle of each mold used in the past, and the input cycle is the time period from when the mold is put into use to when it is replaced; set a standard input cycle according to the historical usage data, and divide the standard input cycle into a safety stage and a wear stage; Step S2: Record the mold being used as the current mold; when the current mold is in the safe stage, obtain image data of the finished shell in real time, and obtain sample image data of the finished shell; the sample image data is image data of the sample of the finished shell, and the image data is a three-dimensional image of the finished shell on each surface; The three-dimensional image is gridded to obtain a plurality of squares, and the three-dimensional image is placed in a three-dimensional coordinate system to obtain the coordinates of the center points of each square; the center points of the same column are numbered as i, i∈[1, 2, ..., n] in sequence and n is the total number of center points on the same column; the spacing between each adjacent center point on the same column is obtained |P i+1 -P i |, where P i Represents the coordinates of the i-th center point; According to the image data and the sample image data, a defect index is obtained: Where P i ′ represents the coordinates of the i-th center point on the same column in the sample image data, m is the total number of columns after the three-dimensional image grid is divided on one side, k∈[1,m], N is the total number of three-dimensional images in the image data, x∈[1,N]; according to all defect indices obtained in real time, the environmental defect index Di in the safe stage is obtained 环 ; Step S3: When the current mold is in the wear stage, the defect index Di at this time is obtained in real time 损 , and according to the environmental defect index, the wear defect index Wi = Di is obtained 损 -Di 环 ; and according to the wear defect index obtained each time, generate a curve F(t) showing the wear defect index changing with time, where t is the timestamp corresponding to each time the wear defect index is obtained; The average slope K of the curve F(t) is obtained in real time. If Wi>0 and K>0, the current mold is worn out at this time, and the replacement time of the current mold is predicted based on the curve F(t).

2. The intelligent detection method for electronic equipment housing production mold according to claim 1 is characterized in that: In step S1, the process of setting the standard input cycle includes: Obtain the input cycle of each mold used in the past, and obtain the input cycle set Z = {C1, C2, ..., C num }, where C1 represents the input cycle of the first mold used in the past, and num is the total number of molds used in the past, and the standard input cycle is obtained where g∈[1,num].

3. The intelligent detection method for electronic equipment housing production mold according to claim 2 is characterized in that: In step S1, the process of dividing the standard input cycle includes: Obtain the minimum value in the investment cycle set Z, recorded as Min(Z); record the time when the mold is put into use as the use time T0, and record [T0, T0+Min(Z)-T′] as the safety stage, where T′ is the preset time period threshold, and 0<T′≤Min(Z) / 5; if the standard investment cycle C=[T0, T1], then the wear stage is [T0+Min(Z)-T′, ​​T1].

4. The intelligent detection method for electronic equipment housing production mold according to claim 1 is characterized in that: In step S2, the three-dimensional image is acquired based on laser scanning technology.

5. The intelligent detection method for electronic equipment housing production mold according to claim 1 is characterized in that: In step S2, the environmental defect index is an average value of all defect indices acquired in real time.

6. The intelligent detection method for electronic equipment housing production mold according to claim 1, characterized in that: In step S3, the process of obtaining the average slope K of the curve F(t) includes: M monitoring points are selected on the curve F(t), where M≥w, and w is the total number of defect indexes obtained during the wear stage; the slopes at the M monitoring points on the curve F(t) are obtained, and the average value of the M slopes is obtained, which is recorded as the average slope K of the curve F(t).

7. The intelligent detection method for electronic equipment housing production mold according to claim 1 is characterized in that: In step S3, if Wi≤0 or K≤0, the current mold has not been worn out yet.

8. The intelligent detection method for electronic equipment housing production mold according to claim 1 is characterized in that: In step S3, the process of predicting the replacement time of the current mold according to the curve F(t) includes: Set the defect index threshold DI, then get the wear defect index threshold WI = DI-Di 环 ; The timestamp corresponding to the current mold entering the wear and tear stage is recorded as t0, and the replacement time t′=t0+WI / K is obtained.

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