CN114701135A - Injection molding workpiece size prediction method based on virtual measurement model - Google Patents
Injection molding workpiece size prediction method based on virtual measurement model Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000001746 injection moulding Methods 0.000 title claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims abstract description 54
- 238000004519 manufacturing process Methods 0.000 claims abstract description 47
- 230000008569 process Effects 0.000 claims abstract description 22
- 238000004140 cleaning Methods 0.000 claims abstract description 8
- 230000002159 abnormal effect Effects 0.000 claims description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 14
- 238000002347 injection Methods 0.000 claims description 11
- 239000007924 injection Substances 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 238000010200 validation analysis Methods 0.000 claims description 5
- 238000002790 cross-validation Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000010801 machine learning Methods 0.000 abstract description 3
- 238000005070 sampling Methods 0.000 description 6
- 239000002699 waste material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000003292 glue Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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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
- 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
-
- 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
- B29C2945/76973—By counting
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Mechanical Engineering (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
The invention discloses an injection molding workpiece size prediction method based on a virtual measurement model, which comprises the following steps: s1: acquiring real-time monitoring data of a plurality of workpieces on a production line, and acquiring preset process parameter data and PLC monitoring data of the production line; s2: cleaning the real-time monitoring data, and acquiring a characteristic value of the real-time monitoring data of each workpiece; s3: and merging the process parameter data obtained in the step S1, the PCL monitoring data and the characteristic values obtained in the step S2 to obtain sample data, and constructing a virtual measurement model according to the sample data. The industrial big data technology is utilized to collect and analyze various parameters in the production process of the workpiece, the size of the workpiece is used as a label, the machine learning technology is utilized to construct a virtual measurement model of the workpiece, and then the size of the workpiece can be predicted in real time, so that the workpiece with an over-standard size can be found in time, the production quality monitoring mode of the workpiece can be optimized, and the production and operation cost of enterprises can be reduced.
Description
Technical Field
The invention relates to the technical field of injection molding production, in particular to an injection molding workpiece dimension prediction method based on a virtual measurement model.
Background
At present, an injection molding enterprise monitors the size of an injection molded workpiece in a mode of sampling and inspecting the produced workpiece and measuring a plurality of sizes of the workpiece by a measuring instrument. Due to the complex production environment, unstable factors in the injection molding processing process easily cause the phenomenon of the oversize of the workpiece, and economic loss is caused. After a certain amount of workpieces are produced, a certain amount of workpieces are extracted according to a pre-specified sampling standard, and then the sizes of the workpieces are measured by using a professional measuring instrument or measuring tool, so that the sampling result represents the size quality of the workpieces produced in a previous period of time. The workpiece size monitoring mode has the following defects:
1) the size quality effect of a large batch of workpieces is represented by a certain proportion of workpieces to be sampled and inspected, the reliability of the whole sampling and evaluation is unreasonable, and the batch of abnormal workpieces cannot be selected in time;
2) if the monitoring precision is improved, the sampling frequency needs to be increased, but the operation pressure is brought to a production unit;
3) because an injection molding system is complex, various factors can influence the size of a workpiece, and the batch is abnormal, the existing sampling inspection mode adopts a 'measurement strategy after processing', and after the oversize is found, a large number of oversize workpieces are produced, so that the waste of raw materials is caused.
Disclosure of Invention
The invention aims to provide an injection molding workpiece dimension prediction method based on a virtual measurement model, which utilizes an industrial big data technology to collect and analyze various parameters in the production process of a workpiece, takes the dimension of the workpiece as a label, and then utilizes a machine learning technology to construct a workpiece virtual measurement model, so that the dimension of the workpiece can be predicted in real time, the workpiece with an over-specification dimension can be found in time, the production quality monitoring mode of the workpiece can be optimized, and the production and operation cost of enterprises can be reduced.
In order to realize the purpose, the following technical scheme is adopted:
a method for predicting the size of an injection molding workpiece based on a virtual measurement model comprises the following steps:
s1: acquiring real-time monitoring data of a plurality of workpieces on a production line, and acquiring preset process parameter data and PLC monitoring data of the production line;
s2: cleaning the real-time monitoring data, and acquiring a characteristic value of the real-time monitoring data of each workpiece;
s3: merging the process parameter data obtained in the step S1, the PCL monitoring data and the characteristic value obtained in the step S2 to obtain sample data, and constructing a virtual measurement model according to the sample data;
s4: and collecting real-time monitoring data, process parameter data and PLC monitoring data of a new workpiece in the production process, extracting a characteristic value of the real-time monitoring data, merging the data, inputting the merged data into the virtual measurement model constructed in S3, and outputting the predicted production size of the workpiece.
Further, the step of collecting real-time monitoring data of a plurality of workpieces on the production line in S1 specifically includes the following steps:
s11: the following high-frequency sensors were previously mounted on the mold: an in-mold pressure sensor, an in-mold temperature sensor, a mold temperature machine water flow meter, an actual screw sensor, a molten liquid injection pressure sensor and a mold core temperature sensor;
s12: the following high-frequency sensors are mounted on a mold temperature controller in advance: a cold water temperature sensor, a hot water temperature sensor and a backwater temperature sensor;
s13: the real-time monitoring data of a plurality of workpieces are acquired through a plurality of sensors arranged on a die and a die temperature controller.
Further, the process parameter data in S1 includes clamping pressure, clamping speed, holding pressure speed, holding pressure time, and cooling time.
Further, the PLC monitoring data in S1 includes mold clamping pressure, injection pressure, mold opening time, mold clamping time, pressure maintaining time, and hot runner temperature.
Further, the S2 specifically includes the following steps:
s21: based on a dynamic time warping algorithm, carrying out time alignment on data;
s22: acquiring abnormal values in data and eliminating the abnormal values;
s23: based on a high-pass filtering method, cleaning and denoising the data;
s24: and constructing envelope characteristics for the data after noise reduction based on an envelope construction method, and segmenting the data according to the workpiece processing stages to extract corresponding characteristic values in each processing stage.
Further, the S22 specifically includes the following steps:
s221: capturing abnormal values in the data based on a boxplot segmentation method;
s222: the abnormal value captured in S221 is eliminated by the moving average method.
Further, the S3 specifically includes the following steps:
s31: merging the process parameter data, the PCL monitoring data and the characteristic value according to the production number of each workpiece to obtain sample data;
s32: based on a cross validation method, randomly dividing sample data into a training set, a test set and a validation set according to the proportion of 5:1: 1;
s33: and constructing a virtual measurement model by adopting a polynomial regression model based on the training set, the test set and the verification set, and optimizing the model based on the MAE function.
By adopting the scheme, the invention has the beneficial effects that:
the method utilizes an industrial big data technology to collect and analyze various parameters in the production process of the workpiece, uses the size of the workpiece as a label, and then utilizes a machine learning technology to construct a virtual measurement model of the workpiece, so that the size of the workpiece can be predicted in real time, the workpiece with an over-specification size can be found in time, the production quality monitoring mode of the workpiece can be optimized, and the production and operation cost of enterprises can be reduced.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
Referring to fig. 1, the present invention provides a method for predicting a dimension of an injection molding workpiece based on a virtual metrology model, comprising the following steps:
s1: acquiring real-time monitoring data of a plurality of workpieces on a production line, and acquiring preset process parameter data and PLC monitoring data of the production line;
s2: cleaning the real-time monitoring data, and acquiring a characteristic value of the real-time monitoring data of each workpiece;
s3: merging the process parameter data obtained in the step S1, the PCL monitoring data and the characteristic value obtained in the step S2 to obtain sample data, and constructing a virtual measurement model according to the sample data;
s4: and collecting real-time monitoring data, process parameter data and PLC monitoring data of a new workpiece in the production process, extracting a characteristic value of the real-time monitoring data, merging the data, inputting the merged data into the virtual measurement model constructed in S3, and outputting the predicted production size of the workpiece.
Wherein, the step of collecting real-time monitoring data of a plurality of workpieces on the production line in the step S1 specifically includes the following steps:
s11: the following high-frequency sensors were previously mounted on the mold: an in-mold pressure sensor, an in-mold temperature sensor, a mold temperature machine water flow meter, an actual screw sensor, a molten liquid injection pressure sensor and a mold core temperature sensor;
s12: the following high-frequency sensors are mounted on a mold temperature controller in advance: a cold water temperature sensor, a hot water temperature sensor and a return water temperature sensor;
s13: the real-time monitoring data of a plurality of workpieces are acquired through a plurality of sensors arranged on a die and a die temperature controller.
The process parameter data in the S1 comprises die clamping pressure, die clamping speed, pressure maintaining pressure, pressure maintaining speed, pressure maintaining time and cooling time; and the PLC monitoring data in the S1 comprise mold locking pressure, injection pressure, mold opening time, mold closing time, pressure maintaining time and hot runner temperature.
The S2 specifically includes the following steps:
s21: based on a dynamic time warping algorithm, carrying out time alignment on data;
s22: acquiring abnormal values in data and eliminating the abnormal values;
s23: based on a high-pass filtering method, cleaning and denoising the data;
s24: and constructing envelope characteristics for the data after noise reduction based on an envelope construction method, and segmenting the data according to the workpiece processing stages to extract corresponding characteristic values in each processing stage.
The S22 specifically includes the following steps:
s221: capturing abnormal values in the data based on a boxplot segmentation method;
s222: the abnormal value captured in S221 is eliminated by the moving average method.
The S3 specifically includes the following steps:
s31: merging the process parameter data, the PCL monitoring data and the characteristic value according to the production number of each workpiece to obtain sample data;
s32: based on a cross validation method, randomly dividing sample data into a training set, a test set and a validation set according to the proportion of 5:1: 1;
s33: and constructing a virtual measurement model by adopting a polynomial regression model based on the training set, the test set and the verification set, and optimizing the model based on the MAE function.
The working principle of the invention is as follows:
with reference to fig. 1, in this embodiment, the method may be applied to the field of injection molding workpiece production, and may predict the production size of the produced injection molding workpiece in real time, and when the size does not reach the preset qualified size threshold range, may issue a warning to the outside, so that the worker may adjust and check the production line, avoid producing a large amount of unqualified products, and improve the working efficiency, specifically:
firstly, a virtual measurement model needs to be constructed, and when the virtual measurement model is constructed, a large amount of workpiece production data needs to be acquired to acquire a sample; the existing workpiece injection molding system generally comprises an injection molding machine and a mold temperature controller, so that the following sensors are required to be installed on a mold in advance: an in-mold pressure sensor, an in-mold temperature sensor, a mold temperature machine water flow meter, an actual screw sensor, a molten liquid injection pressure sensor and a mold core temperature sensor; the following high-frequency sensors are installed on the mold temperature controller: a cold water temperature sensor, a hot water temperature sensor and a return water temperature sensor; then, the sensors mounted above are used for acquiring real-time monitoring data (including pressure, temperature, three main sizes of the workpiece and the like in the production process of the workpiece, which can be understood as length, width, height and the like) of a plurality of workpieces produced within a certain time period, and storing the data according to the production number of each workpiece, wherein the time length of single injection molding is generally 37.6s, in the embodiment, when the sensors on the mold acquire data, the first 4s of the sensors acquire data at a frequency of 20/s, and the second 4s to 31s of the sensors acquire data at a frequency of 50/s; and in this embodiment, the number of workpieces collected is 24242456.
Subsequently, the process parameter data and PLC monitoring data set by the production staff are acquired from the machine PLC, and are recorded according to the ID of each workpiece, in this embodiment, the process parameter data to be acquired include: the mold closing pressure, the mold closing speed, the mold closing end point, the pressure maintaining pressure, the pressure maintaining speed, the pressure maintaining time and the cooling time, and the PLC monitoring data required to be obtained are as follows: mold locking pressure, injection pressure, mold opening time, mold closing time, pressure maintaining time and hot runner temperature; after data acquisition is finished, in order to avoid influencing the accuracy of acquired real-time monitoring data due to reasons such as sensor abnormity, the acquired real-time monitoring data needs to be cleaned, the data is firstly subjected to time alignment through a dynamic time warping algorithm (DWT) so as to solve the problem of non-uniform time of sensor data acquisition starting, then, abnormal values in the data are grabbed based on a box line graph segmentation method, the abnormal values are removed by using a sliding average method, and finally, the data are cleaned and subjected to noise reduction through a high-pass filtering method.
After the data are cleaned, constructing envelope characteristics for the data subjected to noise reduction based on an envelope construction method, and segmenting the data according to the workpiece processing stages to extract corresponding characteristic values in each processing stage; in this embodiment, a single workpiece comprises several stages of processing:
1. closing the mold, and corresponding time: 0-3 s;
2. injection, corresponding time: 4-6 s;
3. pressure maintaining, corresponding time: 6-20 s;
4. and (3) melting glue stage, corresponding time: 20-25 s;
5. back loosening, corresponding time: 25-26 s;
6. cooling, corresponding time: 26-31 s;
7. opening the mold, and corresponding time: 32-34 s;
in this embodiment, the average characteristics (e.g., mold temperature, average value of pressure), maximum and minimum values (e.g., maximum and minimum values of mold temperature, pressure, etc.), effective value, square root amplitude, variance, peak-to-peak value, skewness index, kurtosis index, peak index, waveform index, pulse index, margin index, variation coefficient, etc. of the data are extracted during the injection, holding pressure, and cooling stages.
Then, according to the production number of the workpiece, merging (merging and summarizing) the process parameter data, the PCL monitoring data and the acquired characteristic value to obtain sample data; based on a cross-validation method, randomly dividing the combined data into a training set, a testing set and a validation set according to the proportion of 5:1:1, wherein the training set is used for constructing a virtual measurement model, the testing set is used for testing the virtual measurement model, parameters of the virtual measurement model are adjusted according to a testing result, and the validation set is used for testing the generalization capability of the virtual measurement model; then, taking the data characteristics of the training set samples as the training set sample data, taking the variable corresponding to each sample as a sample label, constructing a virtual measurement model by adopting a polynomial regression model, and optimizing the model based on an MAE function so that the constructed virtual measurement model is closer to the actual workpiece production condition, wherein the construction of the virtual measurement model is finished, and the model specifically comprises the following execution steps:
1) data cleaning;
2) extracting characteristics;
3) merging data;
4) inputting the merged data to produce a predicted production size of the workpiece;
when the method is specifically applied, real-time monitoring data, process parameter data and PLC monitoring data of a new workpiece are collected in the production process of the new workpiece, the data are transmitted to the model, the steps (1) to (4) are automatically executed, the predicted production size of the workpiece can be produced, a size qualified threshold value can be preset according to the size requirement of the injection molding workpiece for automatic comparison and judgment, if the predicted size data of the workpiece exceeds the size qualified threshold value, the production size of the workpiece is indicated to be abnormal, and warning information is sent to the outside, so that production personnel can adjust the size in time, resource waste is avoided, and production cost is saved.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An injection molding workpiece dimension prediction method based on a virtual measurement model is characterized by comprising the following steps:
s1: acquiring real-time monitoring data of a plurality of workpieces on a production line, and acquiring preset process parameter data and PLC monitoring data of the production line;
s2: cleaning the real-time monitoring data, and acquiring a characteristic value of the real-time monitoring data of each workpiece;
s3: merging the process parameter data obtained in the step S1, the PCL monitoring data and the characteristic value obtained in the step S2 to obtain sample data, and constructing a virtual measurement model according to the sample data;
s4: and collecting real-time monitoring data, process parameter data and PLC monitoring data of a new workpiece in the production process, extracting a characteristic value of the real-time monitoring data, merging the data, inputting the merged data into the virtual measurement model constructed in S3, and outputting the predicted production size of the workpiece.
2. The method of claim 1, wherein the step of collecting real-time monitoring data of a plurality of workpieces in the production line in step S1 comprises the steps of:
s11: the following high-frequency sensors were previously mounted on the mold: an in-mold pressure sensor, an in-mold temperature sensor, a mold temperature machine water flow meter, an actual screw sensor, a molten liquid injection pressure sensor and a mold core temperature sensor;
s12: the following high-frequency sensors are mounted on a mold temperature controller in advance: a cold water temperature sensor, a hot water temperature sensor and a backwater temperature sensor;
s13: the real-time monitoring data of a plurality of workpieces are acquired through a plurality of sensors arranged on a die and a die temperature controller.
3. The method of claim 1, wherein the process parameter data in S1 includes clamping pressure, clamping speed, holding pressure speed, holding pressure time, and cooling time.
4. The virtual metrology model-based injection molding workpiece dimension prediction method of claim 1, wherein the PLC monitoring data in S1 comprises mold clamping pressure, injection pressure, mold opening time, mold closing time, dwell time, hot runner temperature.
5. The method of claim 1, wherein the step S2 comprises the steps of:
s21: based on a dynamic time warping algorithm, carrying out time alignment on data;
s22: acquiring abnormal values in data and eliminating the abnormal values;
s23: based on a high-pass filtering method, cleaning and denoising the data;
s24: and constructing envelope characteristics for the data after noise reduction based on an envelope construction method, and segmenting the data according to the workpiece processing stages to extract corresponding characteristic values in each processing stage.
6. The method of claim 5, wherein the step S22 comprises the steps of:
s221: capturing abnormal values in the data based on a boxplot segmentation method;
s222: the abnormal value captured in S221 is eliminated by the moving average method.
7. The method of claim 1, wherein the step S3 comprises the steps of:
s31: merging the process parameter data, the PCL monitoring data and the characteristic value according to the production number of each workpiece to obtain sample data;
s32: based on a cross validation method, randomly dividing sample data into a training set, a test set and a validation set according to the proportion of 5:1: 1;
s33: and constructing a virtual measurement model by adopting a polynomial regression model based on the training set, the test set and the verification set, and optimizing the model based on the MAE function.
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