CN113665079A - Plastic injection molding process control method and system - Google Patents
Plastic injection molding process control method and system Download PDFInfo
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- CN113665079A CN113665079A CN202110976691.9A CN202110976691A CN113665079A CN 113665079 A CN113665079 A CN 113665079A CN 202110976691 A CN202110976691 A CN 202110976691A CN 113665079 A CN113665079 A CN 113665079A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/77—Measuring, controlling or regulating of velocity or pressure of moulding material
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/78—Measuring, controlling or regulating of temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/80—Measuring, controlling or regulating of relative position of mould parts
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76003—Measured parameter
- B29C2945/76006—Pressure
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76003—Measured parameter
- B29C2945/7604—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76003—Measured parameter
- B29C2945/76056—Flow rate
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76003—Measured parameter
- B29C2945/76083—Position
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76003—Measured parameter
- B29C2945/7611—Velocity
- B29C2945/7612—Velocity rotational movement
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76929—Controlling method
- B29C2945/76939—Using stored or historical data sets
- B29C2945/76943—Using stored or historical data sets compare with thresholds
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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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a method and a system for controlling a plastic injection molding process, wherein the method comprises the following steps: collecting data of at least a machine platform, a mould and related auxiliary machines; analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves; respectively carrying out segmentation treatment on each standard process curve to obtain a segmentation result; respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result; and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product. Compared with the existing mode of manually determining the process parameters, the method can acquire and analyze the process parameters, can more effectively control the plastic injection molding process, further reduces the rejection rate of products and improves the stability of the product quality.
Description
Technical Field
The invention relates to the technical field of plastic injection molding, in particular to a method and a system for controlling a plastic injection molding process.
Background
At present, in the plastic injection molding industry, machine adjustment technicians are low in general cultural degree, process technologies are mostly derived from teachers and apprentices and experience accumulation, and parameter setting is performed by adopting a trial-and-error method during each starting production, so that the problems of high product rejection rate and unstable quality are easily caused.
Therefore, how to collect and analyze process parameters during plastic injection molding to control the plastic injection molding process in a more effective way, so as to reduce the rejection rate of products and improve the stability of product quality, which is a problem to be solved urgently.
Disclosure of Invention
In view of this, the invention provides a control method for a plastic injection molding process, which can collect and analyze process parameters and more effectively control the plastic injection molding process, compared with the existing mode of artificially determining the process parameters, thereby reducing the rejection rate of products and improving the stability of product quality.
The invention provides a control method of a plastic injection molding process, which is characterized by comprising the following steps:
collecting data of at least a machine platform, a mould and related auxiliary machines;
analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
respectively carrying out segmentation treatment on each standard process curve to obtain a segmentation result;
respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product.
Preferably, the method further comprises:
obtaining a feedback result of a judgment result of the quality personnel on the product quality;
and adjusting a threshold value for judging the quality of the product based on the judgment result of the quality of the product and the feedback result.
Preferably, the method further comprises:
clustering process curves corresponding to the defective products in the judgment result of the product quality to obtain the categories of the process curves corresponding to the defective products;
acquiring identification content for identifying the category of the process curve corresponding to the defective product by a process worker, wherein the identification content comprises: bad content and bad cause.
Preferably, the method further comprises:
judging the quality of a newly produced plastic injection molded product based on the adjusted threshold value for judging the quality of the product to obtain a judgment result of the quality of the newly produced product, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product;
and matching the process curve corresponding to the newly produced defective product with the category of the process curve corresponding to the defective product, and outputting the defective content and the defective reason of the newly produced defective product.
Preferably, the step of performing segmentation processing on each standard process curve respectively to obtain a segmentation result includes:
each standard process curve is divided into a filling stage, a feeding stage, a pressure maintaining stage and a cooling stage.
A plastic injection molding process control system comprising:
the data acquisition module is used for at least acquiring data of the machine platform, the mould and the related auxiliary machine;
the first analysis module is used for analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
the segmentation module is used for respectively carrying out segmentation processing on each standard process curve to obtain a segmentation result;
the second analysis module is used for respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
the first judging module is used for judging the quality of a plastic injection molding product based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judging result of the product quality, wherein the judging result of the product quality comprises a good product and a defective product.
Preferably, the system further comprises:
the first acquisition module is used for acquiring a feedback result of a judgment result of the quality of the product by a quality worker;
and the adjusting module is used for adjusting the threshold value for judging the quality of the product based on the judgment result of the quality of the product and the feedback result.
Preferably, the system further comprises:
the clustering module is used for clustering the process curves corresponding to the defective products in the judgment result of the product quality to obtain the categories of the process curves corresponding to the defective products;
a second obtaining module, configured to obtain identification content for identifying, by a process worker, a category of a process curve corresponding to the defective product, where the identification content includes: bad content and bad cause.
Preferably, the system further comprises:
the second judgment module is used for judging the quality of the newly produced plastic injection molding product based on the adjusted threshold value for judging the quality of the product to obtain a judgment result of the quality of the newly produced product, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product;
and the matching module is used for matching the process curve corresponding to the newly produced defective product with the category of the process curve corresponding to the defective product and outputting the defective content and the defective reason of the newly produced defective product.
Preferably, the segmentation module is specifically configured to:
each standard process curve is divided into a filling stage, a feeding stage, a pressure maintaining stage and a cooling stage.
In summary, the present invention discloses a method for controlling a plastic injection molding process, wherein when the plastic injection molding process needs to be controlled, data of at least a machine, a mold and related auxiliary machines are collected, and then the data are analyzed by using a mahalanobis distance algorithm to generate a set of standard process curves, and each standard process curve is respectively processed in a segmented manner to obtain a segmented result; then based on the segmentation result, respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve; and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product. Compared with the existing mode of manually determining the process parameters, the method can acquire and analyze the process parameters, can more effectively control the plastic injection molding process, further reduces the rejection rate of products and improves the stability of the product quality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method of analyzing parameters of a plastic injection molding process according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of an example of data analysis using the Mahalanobis distance algorithm according to the present disclosure;
FIG. 3 is a schematic illustration of a process parameter fluctuation range disclosed herein;
FIG. 4 is a flowchart of a method of analyzing parameters of a plastic injection molding process according to embodiment 2 of the present invention;
FIG. 5 is a schematic structural diagram of an embodiment 1 of a system for analyzing parameters of a plastic injection molding process disclosed in the present invention;
fig. 6 is a schematic structural diagram of an embodiment 2 of a system for analyzing parameters of a plastic injection molding process disclosed in 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.
As shown in fig. 1, which is a flow chart of embodiment 1 of a method for controlling a plastic injection molding process disclosed in the present invention, the method may include the following steps:
s101, at least collecting data of a machine table, a mold and related auxiliary machines;
when the plastic injection molding process needs to be controlled more effectively, at least data of the machine, the mold and the related auxiliary machines are collected first, that is, the collected data includes, but is not limited to, data of the machine, the mold and the related auxiliary machines. For example, in data acquisition, the sensing data of the machine, the mold and the related auxiliary machines are acquired. Wherein, in order to make the control of the plastic injection molding process more effective, the data acquisition can be at least more than 50 times.
For example, pressure data of the machine can be acquired through a pressure sensor, speed data of the machine can be acquired through a speed sensor, and temperature data of the machine can be acquired through a first temperature sensor; the pressure data of the die is collected through the pressure sensor, the speed data of the die is collected through the speed sensor, and the temperature data of the die is collected through the temperature sensor.
S102, analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
after data of at least a machine, a mold and related auxiliary machines are collected, a mahalanobis distance algorithm is further adopted, variables are rotated according to principal components, the dimensions are mutually independent and then are normalized, the dimensions are distributed in the same way, and principal component analysis can know that the principal components are the directions of feature vectors, and the variance of each direction is a corresponding feature value, so that the principal components only need to be rotated according to the directions of the feature vectors, and then the feature values are scaled by times, for example, as shown in fig. 2, a point B in the figure is more likely to be a point in a sample, a point A is more likely to be an outlier, the inherent relation among the data is analyzed by the method, and a group of standard process curves are generated.
S103, performing segmentation treatment on each standard process curve respectively to obtain a segmentation result;
because the influence weight of each parameter on the product quality is different in different stages of plastic injection molding, after a standard process curve is generated, the standard process curve can be subjected to segmentation treatment to obtain a segmentation result.
Specifically, the standard process curve can be divided into a filling stage, a feeding stage, a pressure maintaining stage, and a cooling stage according to the plastic injection molding process.
S104, respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
after the standard process curve is processed in a segmented mode, each parameter point on each segment of the curve is further analyzed, and the fluctuation range of each parameter point is obtained. For example, as shown in fig. 3, the solid line in the figure is a standard process curve, and the upper and lower dashed lines are the allowable fluctuation ranges of each parameter point, so it can be seen that the allowable fluctuation ranges of each parameter point are different.
Wherein the parameter points include, but are not limited to: the mold cooling medium flow rate and the inlet and outlet temperature, the material drying temperature, the temperature of each section of the charging barrel, the ambient temperature and the like.
And S105, judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises good products and defective products.
After the fluctuation range of each parameter point on each section of curve of each standard process curve is analyzed, in the plastic injection molding process, the quality of the plastic injection molded product is further judged according to the fluctuation range of each parameter point on each section of curve of each standard process curve, and the judgment result of the quality of the plastic injection molded product is obtained. For example, the plastic injection molded product is determined to be good or defective according to the fluctuation range of each parameter point on each section of each standard process curve.
In summary, in the above embodiments, when the plastic injection molding process needs to be controlled, at least data of the machine, the mold and the related auxiliary machines are collected, and then the data are analyzed by using the mahalanobis distance algorithm to generate a set of standard process curves, and each standard process curve is subjected to segmentation processing to obtain a segmentation result; then based on the segmentation result, respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve; and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product. Compared with the existing mode of manually determining the process parameters, the method can acquire and analyze the process parameters, can more effectively control the plastic injection molding process, further reduces the rejection rate of products and improves the stability of product quality.
As shown in fig. 4, which is a flowchart of embodiment 2 of a method for controlling a plastic injection molding process disclosed in the present invention, the method may include the following steps:
s401, at least collecting data of a machine table, a mold and related auxiliary machines;
when the plastic injection molding process needs to be controlled more effectively, at least data of the machine, the mold and the related auxiliary machines are collected first, that is, the collected data includes, but is not limited to, data of the machine, the mold and the related auxiliary machines. For example, in data acquisition, the sensing data of the machine, the mold and the related auxiliary machines are acquired. Wherein, in order to make the control of the plastic injection molding process more effective, the data acquisition can be at least more than 50 times.
For example, pressure data of the machine can be acquired through a pressure sensor, speed data of the machine can be acquired through a speed sensor, and temperature data of the machine can be acquired through a first temperature sensor; the pressure data of the die is collected through the pressure sensor, the speed data of the die is collected through the speed sensor, and the temperature data of the die is collected through the temperature sensor.
S402, analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
after data of at least a machine, a mold and related auxiliary machines are collected, a mahalanobis distance algorithm is further adopted, variables are rotated according to principal components, the dimensions are mutually independent and then are normalized, the dimensions are distributed in the same way, and principal component analysis can know that the principal components are the directions of feature vectors, and the variance of each direction is a corresponding feature value, so that the principal components only need to be rotated according to the directions of the feature vectors, and then the feature values are scaled by times, for example, as shown in fig. 2, a point B in the figure is more likely to be a point in a sample, a point A is more likely to be an outlier, the inherent relation among the data is analyzed by the method, and a group of standard process curves are generated.
S403, performing segmentation treatment on each standard process curve respectively to obtain a segmentation result;
because the influence weight of each parameter on the product quality is different in different stages of plastic injection molding, after a standard process curve is generated, the standard process curve can be subjected to segmentation treatment to obtain a segmentation result.
Specifically, the standard process curve can be divided into a filling stage, a feeding stage, a pressure maintaining stage, and a cooling stage according to the plastic injection molding process.
S404, respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
after the standard process curve is processed in a segmented mode, each parameter point on each segment of the curve is further analyzed, and the fluctuation range of each parameter point is obtained. For example, as shown in fig. 3, the solid line in the figure is a standard process curve, and the upper and lower dashed lines are the allowable fluctuation ranges of each parameter point, so it can be seen that the allowable fluctuation ranges of each parameter point are different.
Wherein the parameter points include, but are not limited to: the mold cooling medium flow rate and the inlet and outlet temperature, the material drying temperature, the temperature of each section of the charging barrel, the ambient temperature and the like.
S405, judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises good products and defective products;
after the fluctuation range of each parameter point on each section of curve of each standard process curve is analyzed, in the plastic injection molding process, the quality of the plastic injection molded product is further judged according to the fluctuation range of each parameter point on each section of curve of each standard process curve, and the judgment result of the quality of the plastic injection molded product is obtained. For example, the plastic injection molded product is determined to be good or defective according to the fluctuation range of each parameter point on each section of each standard process curve.
S406, obtaining a feedback result of the judgment result of the quality personnel on the product quality;
after the result of judging the quality of the product formed by injection molding of the plastic according to the fluctuation range of each parameter point on each section of curve of each standard process curve is obtained, the judgment result of the quality of the product can be further positively or negatively fed back by quality personnel, and the feedback result of the judgment result of the quality personnel on the quality of the product can be obtained.
S407, adjusting a threshold value for judging the quality of the product based on the judgment result and the feedback result of the quality of the product;
after the feedback result of the quality personnel on the judgment result of the product quality is obtained, the threshold value for judging the quality of the product can be further adjusted according to the judgment result of the product quality and the feedback result, so that the accuracy of judging the product quality can be improved through the adjusted threshold value.
For example, the standard deviation of 1000 positive samples can be calculated according to a normal distribution variance calculation formula, then the median offset position of the normal distribution of the test sample is calculated, if the program initially sets that the threshold is within 5 standard deviations, the test sample is within 4.5 standard deviations, and the quality worker judges that the product is not qualified, and after the program is fed back, the program automatically adjusts the threshold to 5- (5-4.5) × 1.2 to 4.4 standard deviations, and the quality worker judges that the product in 5.5 standard deviations is qualified, and the program automatically adjusts the threshold to 5+ (5.5-5) × 0.8 to 5.4 standard deviations, so that the program judgment result meets the product quality requirement. It should be noted that the above method is only one of many means for adjusting the threshold, and the method for adjusting the threshold includes, but is not limited to, this.
S408, clustering process curves corresponding to the defective products in the judgment result of the product quality to obtain the categories of the process curves corresponding to the defective products;
after the result of judging the quality of the product formed by plastic injection molding according to the fluctuation range of each parameter point on each section of curve of each standard process curve is obtained, the process curves corresponding to the defective products can be further clustered to obtain the categories of the process curves corresponding to the defective products.
S409, obtaining identification content for identifying the category of the process curve corresponding to the defective product by the process personnel, wherein the identification content comprises: bad content and bad cause;
after the category of the process curve corresponding to the defective product is obtained, the category of the process curve corresponding to the defective product can be further identified through a process worker, and the defective content and the defective reason can be identified.
S410, judging the quality of the newly produced plastic injection molding product based on the adjusted threshold value for judging the quality of the product to obtain a judgment result of the quality of the newly produced product, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product;
after the adjusted threshold value for judging the quality of the product is obtained, the quality of the new product can be judged according to the adjusted threshold value for judging the quality of the product when the new product formed by plastic injection molding is produced, and a judgment result of the quality of the newly produced product is obtained, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product.
S411, matching the process curve corresponding to the defective product newly produced with the type of the process curve corresponding to the defective product, and outputting the defective content and the defective reason of the defective product newly produced.
And in the judging result of the quality of the newly produced product, further matching the process curve corresponding to the newly produced defective product with the type of the process curve corresponding to the defective product, and outputting the defective content and the defective reason of the newly produced defective product according to the matching result. Therefore, in the plastic injection molding process, process personnel can visually obtain the defective content and the defective reason of the defective products.
As shown in fig. 5, which is a schematic structural diagram of an embodiment 1 of a control system for a plastic injection molding process disclosed in the present invention, the system may include:
a data acquisition module 501, configured to at least acquire data of a machine, a mold, and a related auxiliary machine;
when the plastic injection molding process needs to be controlled more effectively, at least data of the machine, the mold and the related auxiliary machines are collected first, that is, the collected data includes, but is not limited to, data of the machine, the mold and the related auxiliary machines. For example, in data acquisition, the sensing data of the machine, the mold and the related auxiliary machines are acquired. Wherein, in order to make the control of the plastic injection molding process more effective, the data acquisition can be at least more than 50 times.
For example, pressure data of the machine can be acquired through a pressure sensor, speed data of the machine can be acquired through a speed sensor, and temperature data of the machine can be acquired through a first temperature sensor; the pressure data of the die is collected through the pressure sensor, the speed data of the die is collected through the speed sensor, and the temperature data of the die is collected through the temperature sensor.
A first analysis module 502, configured to analyze the data by using a mahalanobis distance algorithm to generate a set of standard process curves;
after data of at least a machine, a mold and related auxiliary machines are collected, a mahalanobis distance algorithm is further adopted, variables are rotated according to principal components, the dimensions are mutually independent and then are normalized, the dimensions are distributed in the same way, and principal component analysis can know that the principal components are the directions of feature vectors, and the variance of each direction is a corresponding feature value, so that the principal components only need to be rotated according to the directions of the feature vectors, and then the feature values are scaled by times, for example, as shown in fig. 2, a point B in the figure is more likely to be a point in a sample, a point A is more likely to be an outlier, the inherent relation among the data is analyzed by the method, and a group of standard process curves are generated.
A segmentation module 503, configured to perform segmentation processing on each standard process curve respectively to obtain a segmentation result;
because the influence weight of each parameter on the product quality is different in different stages of plastic injection molding, after a standard process curve is generated, the standard process curve can be subjected to segmentation treatment to obtain a segmentation result.
Specifically, the standard process curve can be divided into a filling stage, a feeding stage, a pressure maintaining stage, and a cooling stage according to the plastic injection molding process.
The second analysis module 504 is configured to analyze a fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
after the standard process curve is processed in a segmented mode, each parameter point on each segment of the curve is further analyzed, and the fluctuation range of each parameter point is obtained. For example, as shown in fig. 3, the solid line in the figure is a standard process curve, and the upper and lower dashed lines are the allowable fluctuation ranges of each parameter point, so it can be seen that the allowable fluctuation ranges of each parameter point are different.
Wherein the parameter points include, but are not limited to: the mold cooling medium flow rate and the inlet and outlet temperature, the material drying temperature, the temperature of each section of the charging barrel, the ambient temperature and the like.
The first judging module 505 is configured to judge the quality of a plastic injection molded product based on the fluctuation range of each parameter point on each section of curve of each standard process curve, and obtain a judgment result of the product quality, where the judgment result of the product quality includes a good product and a defective product.
After the fluctuation range of each parameter point on each section of curve of each standard process curve is analyzed, in the plastic injection molding process, the quality of the plastic injection molded product is further judged according to the fluctuation range of each parameter point on each section of curve of each standard process curve, and the judgment result of the quality of the plastic injection molded product is obtained. For example, the plastic injection molded product is determined to be good or defective according to the fluctuation range of each parameter point on each section of each standard process curve.
In summary, in the above embodiments, when the plastic injection molding process needs to be controlled, at least data of the machine, the mold and the related auxiliary machines are collected, and then the data are analyzed by using the mahalanobis distance algorithm to generate a set of standard process curves, and each standard process curve is subjected to segmentation processing to obtain a segmentation result; then based on the segmentation result, respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve; and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product. Compared with the existing mode of manually determining the process parameters, the method can acquire and analyze the process parameters, can more effectively control the plastic injection molding process, further reduces the rejection rate of products and improves the stability of product quality.
As shown in fig. 6, which is a schematic structural diagram of an embodiment 2 of a control system of a plastic injection molding process disclosed in the present invention, the system may include:
the data acquisition module 601 is used for at least acquiring data of the machine, the die and the related auxiliary machine;
when the plastic injection molding process needs to be controlled more effectively, at least data of the machine, the mold and the related auxiliary machines are collected first, that is, the collected data includes, but is not limited to, data of the machine, the mold and the related auxiliary machines. For example, in data acquisition, the sensing data of the machine, the mold and the related auxiliary machines are acquired. Wherein, in order to make the control of the plastic injection molding process more effective, the data acquisition can be at least more than 50 times.
For example, pressure data of the machine can be acquired through a pressure sensor, speed data of the machine can be acquired through a speed sensor, and temperature data of the machine can be acquired through a first temperature sensor; the pressure data of the die is collected through the pressure sensor, the speed data of the die is collected through the speed sensor, and the temperature data of the die is collected through the temperature sensor.
A first analysis module 602, configured to analyze the data by using a mahalanobis distance algorithm to generate a set of standard process curves;
after data of at least a machine, a mold and related auxiliary machines are collected, a mahalanobis distance algorithm is further adopted, variables are rotated according to principal components, the dimensions are mutually independent and then are normalized, the dimensions are distributed in the same way, and principal component analysis can know that the principal components are the directions of feature vectors, and the variance of each direction is a corresponding feature value, so that the principal components only need to be rotated according to the directions of the feature vectors, and then the feature values are scaled by times, for example, as shown in fig. 2, a point B in the figure is more likely to be a point in a sample, a point A is more likely to be an outlier, the inherent relation among the data is analyzed by the method, and a group of standard process curves are generated.
A segmentation module 603, configured to perform segmentation processing on each standard process curve respectively to obtain a segmentation result;
because the influence weight of each parameter on the product quality is different in different stages of plastic injection molding, after a standard process curve is generated, the standard process curve can be subjected to segmentation treatment to obtain a segmentation result.
Specifically, the standard process curve can be divided into a filling stage, a feeding stage, a pressure maintaining stage, and a cooling stage according to the plastic injection molding process.
The second analysis module 604 is configured to analyze a fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
after the standard process curve is processed in a segmented mode, each parameter point on each segment of the curve is further analyzed, and the fluctuation range of each parameter point is obtained. For example, as shown in fig. 3, the solid line in the figure is a standard process curve, and the upper and lower dashed lines are the allowable fluctuation ranges of each parameter point, so it can be seen that the allowable fluctuation ranges of each parameter point are different.
Wherein the parameter points include, but are not limited to: the mold cooling medium flow rate and the inlet and outlet temperature, the material drying temperature, the temperature of each section of the charging barrel, the ambient temperature and the like.
The first judging module 605 is configured to judge the quality of a product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, where the judgment result of the product quality includes a good product and a defective product;
after the fluctuation range of each parameter point on each section of curve of each standard process curve is analyzed, in the plastic injection molding process, the quality of the plastic injection molded product is further judged according to the fluctuation range of each parameter point on each section of curve of each standard process curve, and the judgment result of the quality of the plastic injection molded product is obtained. For example, the plastic injection molded product is determined to be good or defective according to the fluctuation range of each parameter point on each section of each standard process curve.
A first obtaining module 606, configured to obtain a feedback result of a result of the quality determination performed by the quality staff;
after the result of judging the quality of the product formed by injection molding of the plastic according to the fluctuation range of each parameter point on each section of curve of each standard process curve is obtained, the judgment result of the quality of the product can be further positively or negatively fed back by quality personnel, and the feedback result of the judgment result of the quality personnel on the quality of the product can be obtained.
An adjusting module 607 for adjusting the threshold for determining the quality of the product based on the determination result of the quality of the product and the feedback result;
after the feedback result of the quality personnel on the judgment result of the product quality is obtained, the threshold value for judging the quality of the product can be further adjusted according to the judgment result of the product quality and the feedback result, so that the accuracy of judging the product quality can be improved through the adjusted threshold value.
For example, the standard deviation of 1000 positive samples can be calculated according to a normal distribution variance calculation formula, then the median offset position of the normal distribution of the test sample is calculated, if the program initially sets that the threshold is within 5 standard deviations, the test sample is within 4.5 standard deviations, and the quality worker judges that the product is not qualified, and after the program is fed back, the program automatically adjusts the threshold to 5- (5-4.5) × 1.2 to 4.4 standard deviations, and the quality worker judges that the product in 5.5 standard deviations is qualified, and the program automatically adjusts the threshold to 5+ (5.5-5) × 0.8 to 5.4 standard deviations, so that the program judgment result meets the product quality requirement. It should be noted that the above method is only one of many means for adjusting the threshold, and the method for adjusting the threshold includes, but is not limited to, this.
The clustering module 608 is configured to cluster the process curves corresponding to the defective products in the determination result of the product quality to obtain categories of the process curves corresponding to the defective products;
after the result of judging the quality of the product formed by plastic injection molding according to the fluctuation range of each parameter point on each section of curve of each standard process curve is obtained, the process curves corresponding to the defective products can be further clustered to obtain the categories of the process curves corresponding to the defective products.
A second obtaining module 609, configured to obtain identification content for identifying the category of the process curve corresponding to the defective product by the process worker, where the identification content includes: bad content and bad cause;
after the category of the process curve corresponding to the defective product is obtained, the category of the process curve corresponding to the defective product can be further identified through a process worker, and the defective content and the defective reason can be identified.
A second judging module 610, configured to judge the quality of a newly produced plastic injection molded product based on the adjusted threshold for judging the quality of the product, to obtain a judgment result of the quality of the newly produced product, where the judgment result of the quality of the newly produced product includes a good product and a defective product;
after the adjusted threshold value for judging the quality of the product is obtained, the quality of the new product can be judged according to the adjusted threshold value for judging the quality of the product when the new product formed by plastic injection molding is produced, and a judgment result of the quality of the newly produced product is obtained, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product.
The matching module 611 is configured to match the process curve corresponding to the newly produced defective product with the category of the process curve corresponding to the defective product, and output the defective content and the defective reason of the newly produced defective product.
And in the judging result of the quality of the newly produced product, further matching the process curve corresponding to the newly produced defective product with the type of the process curve corresponding to the defective product, and outputting the defective content and the defective reason of the newly produced defective product according to the matching result. Therefore, in the plastic injection molding process, process personnel can visually obtain the defective content and the defective reason of the defective products.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A control method for a plastic injection molding process is characterized by comprising the following steps:
collecting data of at least a machine platform, a mould and related auxiliary machines;
analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
respectively carrying out segmentation treatment on each standard process curve to obtain a segmentation result;
respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
and judging the quality of the product formed by plastic injection molding based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judgment result of the product quality, wherein the judgment result of the product quality comprises a good product and a defective product.
2. The method of claim 1, further comprising:
obtaining a feedback result of a judgment result of the quality personnel on the product quality;
and adjusting a threshold value for judging the quality of the product based on the judgment result of the quality of the product and the feedback result.
3. The method of claim 2, further comprising:
clustering process curves corresponding to the defective products in the judgment result of the product quality to obtain the categories of the process curves corresponding to the defective products;
acquiring identification content for identifying the category of the process curve corresponding to the defective product by a process worker, wherein the identification content comprises: bad content and bad cause.
4. The method of claim 3, further comprising:
judging the quality of a newly produced plastic injection molded product based on the adjusted threshold value for judging the quality of the product to obtain a judgment result of the quality of the newly produced product, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product;
and matching the process curve corresponding to the newly produced defective product with the category of the process curve corresponding to the defective product, and outputting the defective content and the defective reason of the newly produced defective product.
5. The method of claim 1, wherein the step of separately segmenting each standard process curve to obtain segmented results comprises:
each standard process curve is divided into a filling stage, a feeding stage, a pressure maintaining stage and a cooling stage.
6. A plastic injection molding process control system, comprising:
the data acquisition module is used for at least acquiring data of the machine platform, the mould and the related auxiliary machine;
the first analysis module is used for analyzing the data by adopting a Mahalanobis distance algorithm to generate a group of standard process curves;
the segmentation module is used for respectively carrying out segmentation processing on each standard process curve to obtain a segmentation result;
the second analysis module is used for respectively analyzing the fluctuation range of each parameter point on each section of curve of each standard process curve based on the segmentation result;
the first judging module is used for judging the quality of a plastic injection molding product based on the fluctuation range of each parameter point on each section of curve of each standard process curve to obtain a judging result of the product quality, wherein the judging result of the product quality comprises a good product and a defective product.
7. The system of claim 6, further comprising:
the first acquisition module is used for acquiring a feedback result of a judgment result of the quality of the product by a quality worker;
and the adjusting module is used for adjusting the threshold value for judging the quality of the product based on the judgment result of the quality of the product and the feedback result.
8. The system of claim 7, further comprising:
the clustering module is used for clustering the process curves corresponding to the defective products in the judgment result of the product quality to obtain the categories of the process curves corresponding to the defective products;
a second obtaining module, configured to obtain identification content for identifying, by a process worker, a category of a process curve corresponding to the defective product, where the identification content includes: bad content and bad cause.
9. The system of claim 8, further comprising:
the second judgment module is used for judging the quality of the newly produced plastic injection molding product based on the adjusted threshold value for judging the quality of the product to obtain a judgment result of the quality of the newly produced product, wherein the judgment result of the quality of the newly produced product comprises a good product and a defective product;
and the matching module is used for matching the process curve corresponding to the newly produced defective product with the category of the process curve corresponding to the defective product and outputting the defective content and the defective reason of the newly produced defective product.
10. The system of claim 6, wherein the segmentation module is specifically configured to:
each standard process curve is divided into a filling stage, a feeding stage, a pressure maintaining stage and a cooling stage.
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