CN117382129A

CN117382129A - Injection molding machine data analysis system and electronic equipment

Info

Publication number: CN117382129A
Application number: CN202311379720.9A
Authority: CN
Inventors: 沈渐; 曾晰; 黄凯; 端木王赞; 郑国军
Original assignee: Wuxi Haitian Machinery Co ltd
Current assignee: Wuxi Haitian Machinery Co ltd
Priority date: 2023-10-23
Filing date: 2023-10-23
Publication date: 2024-01-12

Abstract

The invention provides an injection molding machine data analysis system and electronic equipment, comprising: the system comprises a data acquisition module, a data analysis module, a fault diagnosis and elimination module and a product analysis and reporting module; the data acquisition module is used for acquiring parameter data of the injection molding machine and the product based on the sensor and the camera; the data analysis module is used for outputting a prediction result of the parameter data based on the artificial intelligent model which is trained in advance; the fault diagnosis and elimination module is used for determining faults of the injection molding machine based on the prediction result; the product analysis and reporting module is used for determining defects of the product based on the prediction result. In the mode, the defects of complexity, inefficiency, difficulty in guaranteeing quality and the like existing in the injection molding production process of the traditional analysis method can be overcome, a more intelligent, efficient and reliable data analysis and optimization scheme is provided, and support is provided for improving competitiveness and sustainable development capability of enterprises.

Description

Injection molding machine data analysis system and electronic equipment

Technical Field

The invention relates to the technical field of injection molding production, in particular to an injection molding machine data analysis system and electronic equipment.

Background

At present, the analysis of products produced by an injection molding machine is mainly carried out by manually detecting and manually debugging parameters, and the research and analysis of the injection molding machine on the produced products are carried out through the experience of an analyst. However, the above-mentioned manner of manually performing the detection has problems of poor reliability, complexity, inefficiency, difficulty in guaranteeing quality, etc., thereby resulting in poor product quality and competitiveness.

Disclosure of Invention

Therefore, the invention aims to provide a data analysis system and electronic equipment of an injection molding machine, so as to improve the production efficiency and reliability, reduce the cost and improve the product quality and the competitiveness.

In a first aspect, an embodiment of the present invention provides an injection molding machine data analysis system, including: the system comprises a data acquisition module, a data analysis module, a fault diagnosis and elimination module and a product analysis and reporting module; the data acquisition module, the fault diagnosis and elimination module and the product analysis and reporting module are all connected with the data analysis module; the data acquisition module is used for acquiring parameter data of the injection molding machine and the product based on the sensor and the camera, and sending the parameter data to the data analysis module; the data analysis module is used for receiving the parameter data, outputting a predicted result of the parameter data based on the artificial intelligent model which is trained in advance, and sending the predicted result to the fault diagnosis and elimination module and the product analysis and reporting module; the prediction result comprises: injection molding machine parameter characteristics, injection molding machine operation characteristics and product quality characteristics, the injection molding machine parameter characteristics include: temperature, pressure and speed; the fault diagnosis and elimination module is used for determining faults of the injection molding machine based on the prediction result; the product analysis and reporting module is used for determining defects of the product based on the prediction result.

In an optional embodiment of the present application, the parameter data of the injection molding machine includes: injection molding process parameters, equipment operating signals, and environmental parameters; the parameter data of the product comprises: product defect information.

In an optional embodiment of the present application, the data analysis module is further configured to perform preprocessing on parameter data, where the preprocessing includes: data cleansing, outlier removal, missing value removal, and/or data normalization.

In an alternative embodiment of the present application, the artificial intelligence model includes: support vector machines, decision trees, random forests, or neural networks.

In an alternative embodiment of the present application, the artificial intelligence model is used for training based on a supervised learning mode, and the artificial intelligence model is optimized based on a cross-validation and regularization mode.

In an optional embodiment of the present application, the above-mentioned artificial intelligence model is further configured to determine a performance index of the artificial intelligence model based on the test data set, and adjust a parameter setting, a feature combination or a model structure of the artificial intelligence model based on the performance index; the performance indexes comprise: accuracy, recall, and/or precision.

In an optional embodiment of the present application, the fault diagnosis and removal module is further configured to locate a fault position, display a three-dimensional model of the injection molding machine on an operation interface, and mark the fault position in the three-dimensional model; the fault diagnosis and elimination module is also used for determining elimination suggestions of faults based on the knowledge base; the fault diagnosis and removal module is also used for recording and saving faults.

In an optional embodiment of the present application, the fault diagnosis and removal module is further configured to solve a fault; the fault diagnosis and elimination module is also used for adjusting elimination suggestions based on feedback of the user and updating the knowledge base.

In an optional embodiment of the present application, the product analysis and reporting module is further configured to obtain defects of a plurality of products, and determine statistical data of the plurality of products based on the defects of the plurality of products; the statistical data comprise defective rate and quality trend; the product analysis and reporting module is also used for generating a product analysis report based on the statistical data; the product analysis and reporting module is also used for visually displaying the product analysis report.

In a second aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes the injection molding machine data analysis system.

The embodiment of the invention has the following beneficial effects:

the embodiment of the invention provides an injection molding machine data analysis system and electronic equipment, which can automatically collect parameter data of an injection molding machine and a product, analyze a prediction result of the parameter data, and determine faults of the injection molding machine and defects of the product based on the prediction result. The method can make up for the defects of complexity, inefficiency, difficulty in guaranteeing quality and the like in the injection molding production process of the traditional analysis method, provides a more intelligent, efficient and reliable data analysis and optimization scheme, and provides support for improving competitiveness and sustainable development capability of enterprises.

Additional features and advantages of the disclosure will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the techniques of the disclosure.

The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a data analysis system of an injection molding machine according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of another system for analyzing data of an injection molding machine according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

With the continuous development of technology, the importance of AI (Artificial Intelligenc, artificial intelligence) technology in the field of injection molding plastic production is increasing. Injection molding involves complex physical distribution, temperature, pressure, materials and other parameters, and is difficult to understand the rules and optimization space through traditional manual analysis. While AI technology can process large amounts of data and utilize machine learning and deep learning algorithms to help solve these difficult-to-analyze problems.

Based on the above, the embodiment of the invention provides an injection molding machine data analysis system and electronic equipment, and in particular provides an injection molding machine data analysis system based on an AI technology, wherein the AI technology can help to optimize parameters, predict faults, control quality and provide data-driven decision support in injection molding plastic production, so that the production efficiency is improved, the cost is reduced, and the product quality and competitiveness are improved.

For the convenience of understanding the present embodiment, a data analysis system of an injection molding machine disclosed in the embodiment of the present invention will be described in detail.

Embodiment one:

the embodiment of the invention provides an injection molding machine data analysis system, referring to a structural schematic diagram of the injection molding machine data analysis system shown in fig. 1, the injection molding machine data analysis system comprises: the system comprises a data acquisition module, a data analysis module, a fault diagnosis and elimination module and a product analysis and reporting module; the data acquisition module, the fault diagnosis and elimination module and the product analysis and reporting module are all connected with the data analysis module;

the data acquisition module is used for acquiring parameter data of the injection molding machine and the product based on the sensor and the camera, and sending the parameter data to the data analysis module; the data analysis module is used for receiving the parameter data, outputting a predicted result of the parameter data based on the artificial intelligent model which is trained in advance, and sending the predicted result to the fault diagnosis and elimination module and the product analysis and reporting module; the prediction result comprises: injection molding machine parameter characteristics, injection molding machine operation characteristics and product quality characteristics, the injection molding machine parameter characteristics include: temperature, pressure and speed; the fault diagnosis and elimination module is used for determining faults of the injection molding machine based on the prediction result; the product analysis and reporting module is used for determining defects of the product based on the prediction result.

The data acquisition module in the embodiment can acquire key parameter data of the injection molding machine and the product in real time through a data acquisition device (comprising various sensors, cameras and other devices).

The data analysis module in this embodiment may receive the parameter data, and output a prediction result of the parameter data based on the artificial intelligence model that is trained in advance. The artificial intelligent model can train a prediction model of the injection molding machine data by using a large amount of historical data and a machine learning algorithm so as to improve the accurate prediction capability of the operation state and faults of the injection molding machine. The artificial intelligent model can output the parameter characteristics of the injection molding machine, the operation characteristics of the injection molding machine, the quality characteristics of products and the like as prediction results.

The fault diagnosis and elimination module in the embodiment can determine faults of the injection molding machine based on the prediction result, and automatically detect, diagnose and eliminate possible faults and problems by analyzing the operation data and the product quality data of the injection molding machine.

The product analysis and reporting module in the embodiment can determine defects of the product based on the prediction result, help a user to comprehensively analyze the quality of the injection molding product, and generate a detailed product analysis report.

The embodiment of the invention provides a data analysis system of an injection molding machine, which can automatically collect parameter data of the injection molding machine and products, analyze prediction results of the parameter data and determine faults of the injection molding machine and defects of the products based on the prediction results. The method can make up for the defects of complexity, inefficiency, difficulty in guaranteeing quality and the like in the injection molding production process of the traditional analysis method, provides a more intelligent, efficient and reliable data analysis and optimization scheme, and provides support for improving competitiveness and sustainable development capability of enterprises.

Embodiment two:

the embodiment provides another injection molding machine data analysis system, which is implemented on the basis of the above embodiment, referring to a schematic structural diagram of another injection molding machine data analysis system shown in fig. 2, the parameter data of the injection molding machine includes: injection molding process parameters, equipment operating signals, and environmental parameters; the parameter data of the product comprises: product defect information.

Injection molding process parameters: and processing information of the inner cavity of the die in the injection molding process is acquired in real time through equipment such as a temperature sensor, a pressure sensor, a flow sensor and the like. For example, temperature, pressure and flow rate changes during injection molding are recorded for subsequent analysis and optimization.

Device operation signal: by means of a sensor or monitoring device, the working signals of equipment components (such as a motor, a pump, a heating bin and the like) are collected. For example, information such as the on-off state of the electric signal, the position and speed of the movement of the component, etc. is recorded in order to judge the normal operation condition and the abnormal condition of the apparatus.

Environmental parameters: and acquiring information such as temperature, humidity and the like of the surrounding environment of the injection molding machine through equipment such as a temperature sensor, a humidity sensor and the like. These environmental parameters may have an impact on the injection molding process and product quality, so monitoring and recording them can help analyze changes and problems in the production process.

Product defect information: and acquiring defect information possibly existing in the produced product through equipment such as a camera or a visual sensor. For example, the appearance defects (such as cracking, blisters, buckling deformation, discoloration, etc.) of the product are detected and their location and severity are recorded for subsequent quality analysis and problem localization.

In some embodiments, the data analysis module is further configured to pre-process the parameter data, the pre-processing including: data cleansing, outlier removal, missing value removal, and/or data normalization.

As shown in fig. 2, the data analysis module in this embodiment may further perform data preprocessing: and preprocessing the collected original parameter data. Including data cleaning, outlier removal, missing value removal, data normalization, etc., to ensure quality and consistency of the data. The pretreatment process is helpful to improve training effect and generalization capability of the model.

As shown in fig. 2, after the data preprocessing, the data analysis module in this embodiment may further perform feature extraction and selection: key features are extracted from the acquired dataset. The key features include temperature, pressure, speed, etc. of the injection molding machine, as well as other features related to the operation of the injection molding machine and the quality of the product. In this process, feature selection algorithms can be used to screen out the features that are most representative and relevant to reduce the complexity of model training and improve prediction accuracy.

In some embodiments, the artificial intelligence model described above includes: support vector machines, decision trees, random forests, or neural networks.

As shown in fig. 2, the data analysis module in this embodiment may further perform artificial intelligence model selection and configuration: an appropriate machine learning or deep learning model is selected for training. Common models include support vector machines (SVM, support Vector Machine), decision trees, random forests, neural networks, and the like. And according to actual requirements and data characteristics, adjusting configuration parameters of the model to achieve better performance.

In some embodiments, the artificial intelligence model is used for training based on a supervised learning approach, and optimizing the artificial intelligence model based on a cross-validation and regularization approach.

As shown in fig. 2, the data analysis module in this embodiment may also perform model training and optimization: training of the artificial intelligence model is performed by inputting the preprocessed data into the selected artificial intelligence model. In the training process, a supervised learning method is used for matching the input of historical data with corresponding output, so that the model can learn and understand the relevance between the running state and faults of the injection molding machine and the relevance between the state parameters and product defects of the injection molding machine, and a trained artificial intelligent model is obtained. Meanwhile, in the training process, cross-validation, regularization and other technologies are used for optimizing the performance of the model and preventing the occurrence of the over-fitting phenomenon.

In some embodiments, the artificial intelligence model is further configured to determine performance metrics of the artificial intelligence model based on the test data set, and adjust parameter settings, feature combinations, or model structures of the artificial intelligence model based on the performance metrics; the performance indexes comprise: accuracy, recall, and/or precision.

As shown in fig. 2, the data analysis module in this embodiment may further perform model evaluation and tuning: after training of the artificial intelligence model is completed, the performance and accuracy of the model need to be evaluated. By evaluating the model by using the test data set, performance indexes such as accuracy, recall, precision and the like of the model are calculated. If the performance index of the model does not reach the standard, the model can be optimized, and different modes such as parameter setting, characteristic combination or model structure change are tried to improve the performance of the artificial intelligent model.

As shown in fig. 2, the fault diagnosis and removal module in this embodiment may perform automatic fault detection: and monitoring operation data of the injection molding machine in real time, and automatically detecting possible fault conditions through analysis of an artificial intelligence model trained by the AI according to a preset fault mode and rules. For example, by analyzing the changes of parameters such as temperature, pressure, flow rate, etc., it is determined whether a failure phenomenon such as an excessive temperature, pressure abnormality, flow rate fluctuation, etc., has occurred.

In some embodiments, the fault diagnosis and removal module is further configured to locate a position of a fault, display a three-dimensional model of the injection molding machine on the operation interface, and mark the position of the fault in the three-dimensional model; the fault diagnosis and elimination module is also used for determining elimination suggestions of faults based on the knowledge base; the fault diagnosis and removal module is also used for recording and saving faults.

As shown in fig. 2, the fault diagnosis and removal module in this embodiment may also perform fault diagnosis and location: when a potential fault is detected, the system can diagnose and position the fault according to the collected data and the existing knowledge base. The method can analyze the data relation of a plurality of sensors, distinguish the cause of the fault, display the three-dimensional model of the injection molding machine on an operation interface, and remind the model of the fault by marking red and highlighting bright.

As shown in fig. 2, the fault diagnosis and removal module in this embodiment may further perform fault removal suggestion: for detected and diagnosed faults, the system provides corresponding troubleshooting suggestions. The user may be provided with instructions and suggested steps to repair the fault based on existing fault solutions and best practices.

As shown in fig. 2, the fault diagnosis and removal module in this embodiment may further perform fault history recording and analysis: the system records and saves the historical fault data and performs fault statistics and analysis. Through analysis of the historical data, common fault modes can be identified and predicted, and a user is helped to make fault prevention and maintenance plans.

In some embodiments, the fault diagnosis and elimination module is further configured to resolve a fault; the fault diagnosis and elimination module is also used for adjusting elimination suggestions based on feedback of the user and updating the knowledge base.

As shown in fig. 2, the fault diagnosis and elimination module in this embodiment may further perform user feedback and knowledge update: if the system fails to automatically resolve the fault, the system can interact with the user, collect more information and provide more accurate troubleshooting advice (i.e., feedback from the user). Meanwhile, the faults and solutions fed back by the user can also be used for updating the knowledge base of the system, so that the accuracy and efficiency of fault diagnosis and elimination are improved.

The product analysis and reporting module in this embodiment may perform defect detection and classification: the product analysis and reporting module can detect and classify appearance defects of the injection molding product through image processing and combining with an artificial intelligent model. Common defects such as cracks, warpage and bubbles on the product can be automatically identified and positioned, and the defect causes are analyzed and judged, so that references are provided for subsequent process improvement and quality control, and the production quality and efficiency of the injection molding machine are improved.

In some embodiments, the product analysis and reporting module is further configured to obtain defects of the plurality of products, determine statistical data of the plurality of products based on the defects of the plurality of products; the statistical data comprise defective rate and quality trend; the product analysis and reporting module is also used for generating a product analysis report based on the statistical data; the product analysis and reporting module is also used for visually displaying the product analysis report.

As shown in fig. 2, the product analysis and reporting module in this embodiment may also perform statistical analysis: the product analysis and reporting module may analyze statistical data of the quality of the injection molded product. The method can calculate the defective rate, quality trend and the like of the production batch and generate a corresponding statistical chart. Through statistical analysis, a user can know the overall condition of the product quality, find abnormal conditions and rules, and provide a reference basis for quality management.

As shown in fig. 2, the product analysis and reporting module in this embodiment may also perform report generation: based on the analysis results, the system may automatically generate detailed product analysis reports. Helping users to fully understand the quality condition of products, quickly locate problems and take improvement measures. The user may also customize the configuration of the report as needed to meet specific needs.

As shown in fig. 2, the product analysis and reporting module in this embodiment may also perform data visualization: the product analysis and reporting module visually displays the product quality data in the form of charts, trend charts and the like. So that the user can more intuitively observe and understand the data information. Through the interactive chart, the user can flexibly select the type and the time range of the data to be displayed, and data comparison and screening are performed, so that the user is helped to analyze the product quality more deeply.

Embodiment III:

an embodiment of the present invention provides an electronic device, referring to a schematic structural diagram of an electronic device shown in fig. 3, where the electronic device includes: the injection molding machine data analysis system provided in the foregoing embodiment.

It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device described above may refer to the corresponding process in the foregoing embodiment of the data analysis system of the injection molding machine, which is not described herein again.

In addition, in the description of embodiments of the present invention, unless explicitly stated and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An injection molding machine data analysis system, the injection molding machine data analysis system comprising: the system comprises a data acquisition module, a data analysis module, a fault diagnosis and elimination module and a product analysis and reporting module; the data acquisition module, the fault diagnosis and elimination module and the product analysis and report module are all connected with the data analysis module;

the data acquisition module is used for acquiring parameter data of the injection molding machine and the product based on the sensor and the camera, and sending the parameter data to the data analysis module;

the data analysis module is used for receiving the parameter data, outputting a prediction result of the parameter data based on an artificial intelligent model which is trained in advance, and sending the prediction result to the fault diagnosis and elimination module and the product analysis and reporting module; the prediction result comprises: injection molding machine parameter characteristics, injection molding machine operating characteristics and product quality characteristics, the injection molding machine parameter characteristics include: temperature, pressure and speed;

the fault diagnosis and elimination module is used for determining faults of the injection molding machine based on the prediction result;

the product analysis and reporting module is configured to determine defects of the product based on the prediction.

2. The injection molding machine data analysis system of claim 1, wherein the injection molding machine parameter data comprises: injection molding process parameters, equipment operating signals, and environmental parameters; the parameter data of the product comprises: product defect information.

3. The injection molding machine data analysis system of claim 1, wherein the data analysis module is further configured to pre-process the parameter data, the pre-processing comprising: data cleansing, outlier removal, missing value removal, and/or data normalization.

4. The injection molding machine data analysis system of claim 1, wherein the artificial intelligence model comprises: support vector machines, decision trees, random forests, or neural networks.

5. The injection molding machine data analysis system of claim 1, wherein the artificial intelligence model is configured to train based on a supervised learning approach and optimize the artificial intelligence model based on a cross-validation and regularization approach.

6. The injection molding machine data analysis system of claim 1, wherein the artificial intelligence model is further configured to determine performance metrics of the artificial intelligence model based on a test data set, adjust parameter settings, feature combinations, or model structures of the artificial intelligence model based on the performance metrics; the performance index comprises: accuracy, recall, and/or precision.

7. The injection molding machine data analysis system of claim 1, wherein the fault diagnosis and removal module is further configured to locate the location of the fault, and display a three-dimensional model of the injection molding machine in an operation interface, wherein the location of the fault is marked in the three-dimensional model;

the fault diagnosis and elimination module is further used for determining elimination suggestions of the faults based on a knowledge base;

the fault diagnosis and removal module is also used for recording and storing the faults.

8. The injection molding machine data analysis system of claim 7, wherein the fault diagnosis and elimination module is further configured to address the fault;

the fault diagnosis and elimination module is further used for adjusting the elimination advice based on feedback of a user and updating the knowledge base.

9. The injection molding machine data analysis system of claim 1, wherein the product analysis and reporting module is further configured to obtain defects of a plurality of the products, determine statistical data of a plurality of the products based on the defects of the plurality of the products; the statistical data comprise defective rate and quality trend;

the product analysis and reporting module is further configured to generate a product analysis report based on the statistics;

the product analysis and reporting module is also configured to visually display the product analysis report.

10. An electronic device comprising the injection molding machine data analysis system of any one of claims 1-9.