CN111105082A - Workpiece quality prediction model construction method and prediction method based on machine learning - Google Patents
Workpiece quality prediction model construction method and prediction method based on machine learning Download PDFInfo
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Abstract
The invention discloses a method for constructing a workpiece quality prediction model based on machine learning and a prediction method, belongs to the field of workpiece quality prediction, and aims to solve the technical problem of predicting the quality and the yield of workpieces with given parameters. The construction method comprises the following steps: collecting training data; constructing at least one polynomial characteristic, and selecting the polynomial characteristic with obvious distribution difference; and forming a training sample by the collected characteristics and the selected polynomial characteristics, and performing parameter optimization on the constructed prediction model to obtain the trained prediction model. The prediction method comprises the following steps: collecting test data; constructing at least one polynomial characteristic, and selecting the polynomial characteristic with obvious distribution difference; forming a test sample by the collected characteristics and the selected polynomial characteristics, and inputting the test sample into a trained prediction model; inputting the probability sample into an anomaly detection model to obtain a processed probability sample; and carrying out average calculation on the processed probability samples.
Description
Technical Field
The invention relates to the field of workpiece quality prediction, in particular to a workpiece quality prediction model construction method and a prediction method based on machine learning.
Background
In the actual production in the industrial manufacturing field, workpieces produced under the same set of process parameters can have various quality inspection results, so the quality inspection standard conformity rate is defined for each set of process parameters, namely the quality inspection results of the workpieces produced by the set of process parameters respectively accord with the ratios of four indexes of excellent, good, qualified and unqualified. Compared with the prediction of the quality inspection result of each workpiece, the prediction of the quality inspection standard conformity rate has more practical significance.
Workpiece production typically fixes certain parameters for machine fabrication. However, due to the influence of various environmental factors, material factors, machine conditions, etc., the quality of the workpiece with fixed production parameters is also determined.
Based on the above, how to predict the quality and yield of the workpiece with given parameters is a technical problem to be solved.
Disclosure of Invention
The technical task of the invention is to provide a workpiece quality prediction model construction method and a prediction method based on machine learning to solve the problems of quality prediction and yield prediction of workpieces with given parameters.
In a first aspect, the present invention provides a method for constructing a workpiece quality prediction model based on machine learning, comprising the following steps:
collecting the characteristics of the manufactured workpiece as training data, and preprocessing the training data;
constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
the method comprises the steps of forming a training sample by collected features and selected polynomial features, carrying out parameter optimization on a constructed prediction model by taking the training sample as input to obtain a trained prediction model, wherein the prediction model is a machine learning decision tree model.
Preferably, the training data is preprocessed, including:
carrying out standardization processing or normalization processing on the training data;
and/or, carrying out abnormal value processing on the training data;
and/or, carrying out vacancy value processing on the training data;
and/or performing data augmentation processing on the training data.
Preferably, the different tags include: excellent, good, qualified and unqualified.
Preferably, the machine learning decision tree model is a light tgbm model or a catbios model.
In a second aspect, the present invention provides a workpiece quality prediction method based on machine learning, including the following steps:
constructing a prediction model by the method for constructing the workpiece quality prediction model based on machine learning according to any one of the first aspect to obtain a trained prediction model;
collecting the characteristics of a workpiece to be tested as test data, and preprocessing the test data;
constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
forming a test sample by the collected characteristics and the selected polynomial characteristics, inputting the test sample into a trained prediction model, and obtaining the probability corresponding to each label;
forming a probability sample according to the probability corresponding to each label, inputting the probability sample into an anomaly detection model, and removing the anomaly probability in the probability sample to obtain a processed probability sample;
and carrying out average calculation on the processed probability samples, and taking the average as the yield prediction.
Preferably, the test data is preprocessed, including:
carrying out standardization processing or normalization processing on the test data;
and/or carrying out abnormal value processing on the test data;
and/or, carrying out vacancy value processing on the test data;
and/or performing data amplification processing on the test data.
Preferably, the different tags include: excellent, good, qualified and unqualified.
Preferably, the machine learning decision tree model is a light tgbm model or a catbios model.
Preferably, the abnormality detection model is an isolotionform model.
The invention discloses a method for constructing a workpiece quality prediction model based on machine learning and a prediction method thereof, which have the following advantages:
1. the decision tree model of machine learning is used as a prediction model, and strict requirements on data are not required, so that a complicated data preprocessing process is omitted;
2. and constructing polynomial characteristics based on the acquired characteristics, selecting the polynomial characteristics with obvious distribution according to the distribution of the polynomial characteristics among different labels, and taking the polynomial characteristics and the acquired characteristics as samples, so that the method has diversity.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block flow diagram of a method for constructing a workpiece quality prediction model based on machine learning according to embodiment 1;
fig. 2 is a flow chart of a workpiece quality prediction method based on machine learning according to embodiment 2.
Detailed Description
The present invention is further described in the following with reference to the drawings and the specific embodiments so that those skilled in the art can better understand the present invention and can implement the present invention, but the embodiments are not to be construed as limiting the present invention, and the embodiments and the technical features of the embodiments can be combined with each other without conflict.
The embodiment of the invention provides a workpiece quality prediction model construction method and a prediction method based on machine learning, which are used for solving the technical problems of quality prediction and yield prediction of workpieces with given parameters.
Example 1:
the invention discloses a workpiece quality prediction model construction method based on machine learning, which comprises the following steps:
s100, collecting the characteristics of the manufactured workpiece as training data, and preprocessing the training data;
s200, constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
s300, forming a training sample by the collected characteristics and the selected polynomial characteristics, and performing parameter optimization on the constructed prediction model by taking the training sample as input to obtain a trained prediction model, wherein the prediction model is a machine learning decision tree model.
In step S100, characteristics such as length and width of the manufactured workpiece corresponding to different manufacturing times are collected, the characteristics at different times form a matrix form, and each column in the matrix is a value corresponding to a certain characteristic at different times.
The characteristics are preprocessed as follows: carrying out standardization processing on the training data; processing abnormal values of the training data; and carrying out vacancy value processing on the training data and carrying out data augmentation processing on the training data.
In practical application, the pretreatment can be selectively carried out according to actual requirements.
Wherein, different labels of settlement include: excellent, good, qualified and unqualified.
The prediction model in this embodiment is the light tgbm model. In practical application, the prediction model is not limited to the light tgbm model, and a CATBOOST model can be used.
The method for constructing the workpiece quality prediction model based on machine learning can obtain a prediction model after training, and the prediction model after training predicts a test sample.
When prediction is carried out through the prediction model, the selection method of the test sample comprises the following steps: collecting the characteristics of a workpiece to be tested as test data, and preprocessing the test data; constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference; and forming a test sample by the collected characteristics and the selected polynomial characteristics.
Example 2:
the invention relates to a workpiece quality prediction method based on machine learning, which comprises the following steps:
s100, constructing a prediction model through the method for constructing the workpiece quality prediction model based on machine learning as disclosed in the embodiment 1 to obtain a prediction model after training;
s200, collecting the characteristics of the workpiece to be tested as test data, and preprocessing the test data;
s300, constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
s400, forming a test sample through the collected characteristics and the selected polynomial characteristics, inputting the test sample into a trained prediction model, and obtaining the probability corresponding to each label;
s500, forming a probability sample according to the probability corresponding to each label, inputting the probability sample into an anomaly detection model, and removing the anomaly probability in the probability sample to obtain a processed probability sample;
s600, calculating the average value of the processed probability samples, and taking the average value as the yield prediction.
In step S100, characteristics such as length and width of the workpiece to be measured corresponding to different manufacturing times are collected, the characteristics at different times form a matrix form, and each column in the matrix is a value corresponding to a certain characteristic at different times.
The characteristics are preprocessed as follows: carrying out standardized processing on the test data; processing abnormal values of the test data; and carrying out vacancy value processing on the test data and carrying out data amplification processing on the test data.
In this embodiment, the prediction model is a light tgbm model, and the abnormality detection model is an isolatonforret model. In practical application, the prediction model is not limited to the light tgbm model, and a CATBOOST model can be used.
The workpiece quality prediction method based on machine learning can predict the yield of workpieces.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. The method for constructing the workpiece quality prediction model based on machine learning is characterized by comprising the following steps of:
collecting the characteristics of the manufactured workpiece as training data, and preprocessing the training data;
constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
the method comprises the steps of forming a training sample by collected features and selected polynomial features, carrying out parameter optimization on a constructed prediction model by taking the training sample as input to obtain a trained prediction model, wherein the prediction model is a machine learning decision tree model.
2. The method of claim 1 in which the training data is preprocessed by a method comprising:
carrying out standardization processing or normalization processing on the training data;
and/or, carrying out abnormal value processing on the training data;
and/or, carrying out vacancy value processing on the training data;
and/or performing data augmentation processing on the training data.
3. The machine-learning based workpiece quality prediction model construction method of claim 1, wherein the different labels comprise: excellent, good, qualified and unqualified.
4. The method of claim 1, wherein the machine learning decision tree model is a light tbm model or a catshop model.
5. The workpiece quality prediction method based on machine learning is characterized by comprising the following steps:
constructing a prediction model by the method for constructing the workpiece quality prediction model based on machine learning according to any one of claims 1 to 4 to obtain a trained prediction model;
collecting the characteristics of a workpiece to be tested as test data, and preprocessing the test data;
constructing at least one polynomial characteristic based on the collected characteristics, calculating the distribution of the polynomial characteristics among different labels for each polynomial characteristic, and selecting the polynomial characteristics with obvious distribution difference;
forming a test sample by the collected characteristics and the selected polynomial characteristics, inputting the test sample into a trained prediction model, and obtaining the probability corresponding to each label;
forming a probability sample according to the probability corresponding to each label, inputting the probability sample into an anomaly detection model, and removing the anomaly probability in the probability sample to obtain a processed probability sample;
and carrying out average calculation on the processed probability samples, and taking the average as the yield prediction.
6. The machine-learning based workpiece quality prediction method of claim 5, wherein preprocessing the test data comprises:
carrying out standardization processing or normalization processing on the test data;
and/or carrying out abnormal value processing on the test data;
and/or, carrying out vacancy value processing on the test data;
and/or performing data amplification processing on the test data.
7. The machine-learning based workpiece quality prediction method of claim 5 or 6, characterized in that the different labels comprise: excellent, good, qualified and unqualified.
8. The machine learning based work piece quality prediction method of claim 5 or 6, characterized in that the machine learning decision tree model is a LIGHT TGBM model or a CATBOOST model.
9. The machine-learning based workpiece quality prediction method of claim 5 or 6, wherein the anomaly detection model is an isolotionform model.
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| CN112884081A (en) * | 2021-03-31 | 2021-06-01 | 福州大学 | Multi-variety small-batch-production workpiece quality online intelligent prediction method |
| CN113421264A (en) * | 2021-08-24 | 2021-09-21 | 深圳市信润富联数字科技有限公司 | Wheel hub quality detection method, device, medium, and computer program product |
| CN113537285A (en) * | 2021-06-08 | 2021-10-22 | 内蒙古卫数数据科技有限公司 | Novel clinical mismatching sample identification method based on machine learning technology by utilizing patient historical comparison data |
| CN114701135A (en) * | 2022-05-20 | 2022-07-05 | 深圳市信润富联数字科技有限公司 | Injection molding workpiece size prediction method based on virtual measurement model |
| CN115482227A (en) * | 2022-09-26 | 2022-12-16 | 中机生产力促进中心有限公司 | Machine vision self-adaptive imaging environment adjusting method |
| CN117112857A (en) * | 2023-10-23 | 2023-11-24 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | Machining path recommending method suitable for industrial intelligent manufacturing |
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