CN117452889A - Intelligent tire equipment platform system and control method - Google Patents
Intelligent tire equipment platform system and control method Download PDFInfo
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- CN117452889A CN117452889A CN202311374818.5A CN202311374818A CN117452889A CN 117452889 A CN117452889 A CN 117452889A CN 202311374818 A CN202311374818 A CN 202311374818A CN 117452889 A CN117452889 A CN 117452889A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses an intelligent tire equipment platform system and a control method, which belong to the field of tire production, wherein the intelligent tire equipment platform system can realize data cloud sharing; the state of the equipment can be monitored remotely, and the data algorithm model is optimized; the dependence on manpower is reduced; and preventive maintenance is realized. The intelligent control system mainly comprises a tire equipment production line, a sensing system and an edge intelligent control platform, wherein internal communication is realized among the tire equipment production line, the sensing system and the edge intelligent control platform through communication interfaces, the edge intelligent control platform is connected with an Internet cloud platform through a network interface, the Internet cloud platform comprises a customized service module, the customized service module comprises an infrastructure as-a-service, a platform as-a-service and software as-a-service, an accumulated data support, an expert system diagnosis module without delay requirements, a remote operation and maintenance module and a preventive maintenance module, and the edge intelligent control platform comprises a data acquisition module, a data analysis and processing module and a data storage module. The invention is mainly used for tyre production.
Description
Technical Field
The invention relates to a tire production system, in particular to an intelligent tire equipment platform system and a control method.
Background
1. The current state of the existing tire production system:
the existing tire production system mainly adopts single machine production, and is characterized in that the existing tire production system is lack of effective information intercommunication between production upstream and downstream ring joints, production equipment of the same type, production links and production elements such as a production system, a production system and a material supplier, and the like, and is mainly expressed as follows:
1) The device cannot cloud up: related data such as equipment, production and the like cannot be accumulated for a long time; the self-optimization of the production process and the like cannot be realized for large data mining application; the intellectualization cannot be realized. The single machine equipment in the situation can not realize data acquisition, analysis and storage, and can bring about poor and unstable product quality; the problems that the equipment state and the production process data cannot be known accurately in depth, the equipment OEE is poor and the like;
2) Unable remote monitoring equipment: the related personnel and the system can not see the real-time working state of the equipment, the situation needs to be stared by people on site, the data can not be monitored in real time, the general algorithm model can not be carried out to obtain the optimal production state of the equipment, the production cost is reduced, the production efficiency is improved,
3) Strong dependence on man-made: the automation and the intelligent degree of the production process are low, and factory informatization systems such as MES and the like cannot be effectively integrated. The current situation brings information island, and each link of the factory lacks effective information intercommunication; strong dependence on manpower, high cost and poor quality stability; and the problems of long culture period, instability and the like of high-level technicians due to barren labor.
4) Pre-maintenance + Intelligent MRO cannot be achieved: predictive maintenance cannot be achieved; the stock of spare parts cannot be effectively reduced; the device on-rate cannot be improved. This situation causes problems of high spare part management cost, poor equipment OEE, and the like.
5) The device cannot manage the full life cycle: the actual operating state of the device after delivery to the customer cannot be tracked for a long period of time. This situation causes the problems that the equipment lacks full life cycle management, and the equipment lacks technical support points for iterative upgrade.
6) Device manufacturers cannot actively service customers in time: the equipment cannot be found in time after the problem occurs, and only the problem can be passively processed. This situation provides continuous value added service to the customer, lacking customer adhesion.
7) The device manufacturer cannot realize remote debugging, and cannot carry out remote debugging and technical service. This results in problems such as debugging, after-sales problem handling (no matter the size of the problem), having to go to site by engineers, high financial cost, time cost, poor after-sales service timeliness, etc.
8) The construction of an equipment expert system cannot be realized: various alarms and operational errors during the operation of the equipment do not lead to reproducible operational experience. This does not allow for a rapid and efficient construction of the equipment expert system and does not provide for experience data support and experience accumulation in the performance enhancement of fool-proof and error-proof equipment.
2. The strategic direction of development at home and abroad:
in particular, in industrial production, a tire intelligent equipment platform system and a control method with digitization and intellectualization as cores are urgent needs at present.
Disclosure of Invention
The invention aims to provide an intelligent tire equipment platform system and a control method, which are used for overcoming the defects in the background technology.
The intelligent tire equipment platform system comprises a mechanical body of intelligent tire equipment, a sensing system and an intelligent edge control platform, wherein internal communication is realized among the mechanical body of intelligent tire equipment, the sensing system and the intelligent edge control platform through communication interfaces, the intelligent edge control platform is connected with an Internet cloud platform through a network interface, the Internet cloud platform comprises a customized service module, the customized service module comprises an infrastructure as-a-service, a platform as-a-service and software as-a-service and accumulated data support, an expert system diagnosis module without delay requirements, a remote operation and maintenance module and a preventive maintenance module, the intelligent edge control platform comprises a data acquisition module, a data analysis processing module and a data storage module, the data analysis processing module comprises a general model algorithm, a process model algorithm, and the general model algorithm comprises a joint precision control algorithm, an equipment health management algorithm, an AI model algorithm, a tracking model algorithm and a big data model algorithm.
Further, the general model algorithm includes: joint accuracy control algorithm, equipment health management algorithm, AI model algorithm, tracking model algorithm, and big data model algorithm.
Further, the joint accuracy control algorithm and the equipment health management algorithm specifically include the following:
joint accuracy control algorithm:
collecting front-back relation data and left-right relation data of joint materials in the production process of the tire semi-finished products by adopting machine vision; the driving parameters of the related conveyor belt and the correction parameters of the conveyor belt are controlled by the equipment control system;
invoking a decision tree algorithm of a system general model algorithm, splitting the acquired driving parameter data through a series of judging conditions, constructing a tree structure, and making predictions based on the characteristic values with excellent joint quality;
analyzing the predicted driving optimization parameters, feeding back to the control system, and performing closed-loop control on each driving parameter to keep the joint precision at a higher output quality and output rate;
equipment health management algorithm:
through the internal sensing of the equipment, the working state, life cycle and maintenance cycle parameters of various sensors, drivers and execution components in the system are adopted;
The method comprises the steps of calling a random algorithm and a decision tree algorithm of a system general model algorithm, carrying out voting or averaging on predictions of a plurality of decision trees to obtain a final result, and managing maintenance periods and replacement periods in each period in a system;
and the maintenance period and the replacement period in the management system are fed back to the equipment operation and maintenance department and the spare part library, so that the spare part time, the replacement time and the replacement timeliness are shortened, the outage rate is reduced, and the production efficiency in the life cycle of equipment is improved.
Further, the process model algorithm refers to an algorithm model for optimizing a tire manufacturing process, wherein the tire manufacturing process comprises: a sizing material formula process, a rubber mixing process, a tire molding process, a mold design process, a tire body building process and a tire body vulcanization process.
Further, the tire building process algorithm model comprises the following steps:
(1) Solution of unqualified width, thickness and lace of laminating drum laminating material
Visual inspection is carried out on the out-of-roundness and the diameter of the bonding drum before the bonding of the carcass drum, if a semi-finished product is unqualified, a red alarm lamp of equipment alarms, the equipment stops working, and the next action can be carried out after the qualified bonding is carried out again; the intelligent tire equipment platform system simultaneously checks the process production information on semi-finished products, seals up and stores the same batch of products in the three-dimensional warehouse, and gives an alarm and stops the other machines in use in time; reconstructing a 3D model of the lamination process in real time during normal lamination, and evaluating a embryo weight model and a dynamic balance model in real time;
(2) Solving the problem of skew and non-concentricity of laminating material of laminating drum
The visual system detects the laminating effect in real time during lamination, and the tire intelligent equipment platform system automatically adjusts the feeding deviation correcting system according to the offset at the same time when the lamination material is out of concentricity or the lamination is askew, so that the next step can be carried out until the lamination qualified party;
(3) Solution of unqualified width, thickness and lace of belt drum attaching material
The method is applicable to materials and product types only, which are different from the method (1);
(4) Solution of problem of skew and non-concentricity of belt drum attaching material
The same as the above (2) is applicable only to materials and product types.
Further, the tire building process algorithm model further comprises the following steps:
(1) Bubble problem solving
For bubbles that occur during bonding: the vision system detects the laminating rolling effect in real time, and when bubbles appear, the intelligent tire equipment platform system automatically checks whether the production date of semi-product materials is qualified or not; secondly, automatically adjusting the pressure of the laminating press roller;
for bubbles that appear during rolling: firstly, checking whether a semi-finished product is qualified or not by the intelligent tire equipment platform system; secondly, judging whether the actual rolling parameters are matched with the set pressure or not, and if the actual rolling parameters are inconsistent, automatically adjusting;
(2) Solving problem of full-automatic lamination of semi-finished products
The vision system automatically detects the size of the material joint, and alarms and stops for the condition of exceeding the standard; the tire intelligent equipment platform system automatically adjusts the cutting length, the laminating speed ratio and the laminating pressure according to the size of the joint, dynamically adjusts the size of the joint, and realizes full-automatic laminating of semi-products;
(3) Solution of unqualified problem of tyre taper
The taper caused by the disqualification of the positioning precision of the transfer ring is disqualified: when the belt layer transfer ring and the carcass transfer ring are positioned, the vision system detects the actual positioning position of the transfer ring in real time, and when positioning errors occur, the tire intelligent equipment platform system automatically adjusts the positioning position of the transfer ring;
the taper caused by asymmetric left and right sidewall turn-up heights or discomforts: in the process of unpacking, a visual system detects the unpacking height in real time, and when the height difference of two sides exceeds a threshold value, the tire intelligent equipment platform system prompts the inspection of the pressure of the formed unpacking capsule and the precision of the transfer ring clamping claw.
Furthermore, the data acquisition module acquires data in a visual detection mode, a length detection mode, a driving parameter online reading mode and the like, a production strategy capable of optimizing the tire molding feeding length is formed on the platform through big data processing and an AI algorithm, and the tire molding gradually approaches to an optimal solution through repeated iteration updating of the control strategy.
The invention relates to a control method of a tire intelligent equipment platform system, which comprises the following steps:
s1: before the carcass drum is attached, driving basic parameters such as the speed, the feeding length and the like of a semi-product part and an equipment curtain cloth feeding conveyor belt are collected by visually checking the out-of-roundness, the diameter, the rotating speed and the attaching process of the attaching drum; if the lamination length of the semi-finished product is not matched with the circumference of the lamination drum, a red alarm lamp of the equipment alarms, the equipment stops working, the next action can be carried out after the semi-finished product is qualified again, and the abnormal one-time labeling of the materials is carried out;
s2: during normal lamination, reconstructing a lamination process 3D model in real time, and evaluating a embryo weight model and a dynamic balance model in real time;
s3: judging whether the molding equipment continues to operate or not according to the evaluation result; the data are accumulated and continuously collected for three times, production information collected in the working procedures on semi-products is checked at the same time in the system, products in the same batch of the three-dimensional warehouse are sealed, and other machines in use are timely alarmed and stopped;
s4: performing data cleaning, denoising and outlier processing on semi-finished product part production information data, and ensuring the accuracy and reliability of the data;
s5: according to the preprocessed data, a mathematical model of the semi-manufactured part production process is established, and a statistical model based on the data is adopted, wherein the purpose of establishing the model is to describe the relation between the length parameter and the operation parameter in the semi-manufactured part production process;
S6: performing model verification, namely inputting data into the model, and comparing and verifying the data with actual performance data;
s7: generating relevant parameters for optimizing the semi-product production process by using a current planning algorithm of the feeding length;
s8: acquiring the data of the forming step sequence, reconstructing a 3D model in the laminating process in real time, evaluating a embryo weight model and a dynamic balance model in real time, and comparing the embryo weight model with production requirements and standards;
s9: and feeding back the determined feasible optimization parameters to corresponding execution mechanisms of the semi-product production equipment in the upper working procedure through an internal network of the factory, adjusting the semi-product production process, and monitoring and feeding back the production process.
Compared with the prior art, the invention has the beneficial effects that:
1. intelligent equipment platform system capable of realizing data cloud sharing
Through intelligent equipment platform system, the customer can collect a large amount of industrial data in real time, including real-time humiture, vibration, monitored data such as voltage, and these data can be used for data analysis and prediction, helps the customer to know bottleneck and the difficulty in the production process, and then optimizes production flow, improves production efficiency and quality.
The intelligent equipment platform system can provide self-research high-performance high-concurrency cluster type MQTT access machine, support distributed deployment, support transverse expansion, support distributed release subscription and support more than 10 ten thousand device accesses by a single server. The high-performance and high-concurrency access capability enables the intelligent equipment platform system to process a large amount of data, thereby providing a basis for data accumulation and algorithm optimization.
The intelligent equipment platform system can also provide a high-performance stream calculation engine capable of arranging steps and programming the steps, support clients to configure the steps and functions of stream calculation according to own requirements, support the arrangement of the stream calculation steps by adopting a graphical interface, support a stream calculation programming environment and program specific operation of stream calculation in the programming environment. The result of this stream calculation can be queried, and an alarm can be generated, mail sent or a short message alarm sent. This powerful stream computation capability allows the intelligent equipment platform system to process and analyze data in real time, thus providing the possibility of data algorithm optimization.
2. The intelligent equipment platform system can remotely monitor the state of the equipment and optimize the data algorithm model.
In practical application, data acquisition and transmission are central links of distributed intelligent manufacturing. The system integrates the functions of equipment data acquisition, remote monitoring, wireless transmission, data analysis, fault alarm, remote control, remote maintenance and the like, and can realize the data acquisition and the on-line monitoring of equipment such as a PLC, an instrument and meter, an industrial robot, a sensor and the like.
The collected data needs to be cleaned and preprocessed to remove noise and abnormal values, so that the data quality is improved. In processing data, appropriate algorithms and tools are selected for processing and analysis, taking into account the dimensions and magnitudes of the data.
3. The intelligent equipment platform system may reduce the dependency on the human by:
automated data processing and analysis: the intelligent equipment platform system can collect and process a large amount of production data, and automatically conduct data analysis and mining through algorithms and data analysis tools. This can reduce the cost and time of manually processing the data while improving the accuracy and reliability of the data.
Intelligent decision support: the intelligent equipment platform system can provide intelligent decision support based on device data analysis and AI self-learning technology. For example, through predictive analysis, equipment failures can be predicted, production flows can be optimized, etc., thereby reducing the cost and time of manual decisions.
And (3) automatic production flow control: the intelligent equipment platform system can realize the automatic control of the production flow through integration with production equipment. The method can reduce the requirement of manual intervention on the production flow, and improve the production efficiency and the product quality.
The manual operation links are reduced: the intelligent equipment platform system can reduce manual operation links by integrating various production and management links. For example, by means of automatic scheduling, automatic order processing, etc. of simulation algorithms, the time and cost of manual operations can be reduced.
4. The intelligent equipment platform system may achieve preventative maintenance by:
real-time monitoring and data acquisition: through the integration with production facility, intelligent equipment platform system can real-time supervision equipment's running state, gathers the key parameter of equipment, like temperature, pressure, rotational speed etc.. Through analysis of the data, potential problems and faults of the equipment can be found in time.
Data analysis and prediction: based on the data analysis, the intelligent equipment platform system can analyze the collected equipment data and predict the possible problems of the equipment. For example, by analyzing the operational data of the equipment, it is possible to predict when the equipment may need to be replaced or serviced, thereby making plans and preparations ahead of time.
And (3) making a maintenance plan and an alarm system: according to the predicted fault condition and the historical maintenance record of the equipment, the intelligent equipment platform system can make a reasonable maintenance plan including maintenance time, personnel, required spare parts and the like through algorithmic archiving. Meanwhile, an alarm system can be arranged, and when the equipment parameters exceed the normal range or the fault is predicted to be happened, maintenance personnel are informed to take corresponding measures in time.
Remote collaboration and troubleshooting: through the remote monitoring and cooperation tool of the intelligent equipment platform system, maintenance personnel can check the running state of equipment on line, cooperate with production personnel, engineers or other specialists, and rapidly locate and remove faults. This can greatly shorten the time and cost of troubleshooting.
Maintenance recording and analysis: the intelligent equipment platform system can record maintenance history and related data of the equipment for analysis and summarization. This helps to understand the actual condition and life of the equipment and provides a reference for future preventive maintenance programs.
5. The intelligent equipment platform system can comprehensively manage the life cycle of the equipment, and can comprehensively manage and control all stages from the design, purchase, installation, debugging, use, maintenance, repair, reconstruction to scrapping and the like of the equipment.
In the equipment design stage, the intelligent equipment platform system can provide various tools and resources, so that a designer is helped to conduct efficient model design and simulation test, and the equipment is ensured to be perfect as much as possible before being put into production.
In the equipment purchasing stage, the intelligent equipment platform system can realize transparent and standardized purchasing flow through tools such as an electronic bidding system and the like, and ensure that the quality and price of equipment are reasonably controlled.
In the installation, debugging and use stages of equipment, the intelligent equipment platform system can discover and solve the problems possibly occurring in the equipment in time through tools such as remote monitoring, fault diagnosis and the like, and ensures the stability and the safety of the equipment.
In the maintenance, repair and transformation stages of equipment, the intelligent equipment platform system can predict and prevent possible problems of the equipment through strategies such as preventive maintenance and the like by means of algorithms through collected equipment data, and the downtime and maintenance cost of the equipment are reduced.
In the scrapping stage of the equipment, the intelligent equipment platform system can provide tools such as equipment scrapping evaluation and the like to comprehensively evaluate and analyze the equipment, so that the equipment can be reasonably processed and utilized after scrapping.
6. The intelligent equipment platform system can help manufacturers to better serve their customers:
customer satisfaction is improved: through intelligent equipment platform system, the producer can know customer's demand and feedback in real time, and then provides timely, effectual service. The manufacturer can receive complaints and suggestions of the clients through the cloud platform, and then improve products and services according to the feedback so as to improve the satisfaction degree of the clients.
Enhancing customer loyalty: through intelligent equipment platform system, the producer can establish more closely with its customer, develop more efficient cooperation, can increase customer's viscosity through the platform as customer and manufacturer's tie.
The client value is improved: through the intelligent equipment platform system, manufacturers can analyze the purchasing behavior and the demands of clients so as to provide more accurate marketing and services. The manufacturer can analyze the purchase records and preferences of the clients through the cloud platform, and provide personalized product and service schemes for the clients so as to improve the value of the clients.
7. Remote debugging equipment can be realized through the intelligent equipment platform system:
web-based remote debugging: the intelligent equipment platform system can provide a remote debugging interface through a Web browser, a user can debug on a local operation interface, and meanwhile, the intelligent equipment platform system is remotely connected to equipment for real-time debugging.
Audio and video remote debugging: the intelligent equipment platform system can provide audio and video remote debugging functions, and can transmit the field pictures and sound of the equipment to a screen of a remote debugging person in real time, so that the debugging person can more intuitively know the field situation and perform fault detection and problem solving better.
And (3) online debugging: the intelligent equipment platform system may provide online debugging functionality, allowing a user to run a device program on a remote server. This approach may help the commissioning personnel better discover and resolve problems while reducing impact on the field devices.
Collaborative debugging: the intelligent equipment platform system can provide a multi-person collaborative debugging function, allows a plurality of debugging personnel to operate and communicate on the same interface, and solves the fault problem of the same equipment in a collaborative manner. The method can improve the debugging efficiency and reduce the time and cost for solving the problem.
8. The intelligent equipment platform system realizes equipment expert system building:
training platform: the intelligent equipment platform system can provide a training education platform, integrates various education resources, equipment fault analysis, expert evaluation, equipment abnormal faults obtained by algorithm analysis and the like, and is convenient for users to learn online. Through the platform, a user can learn equipment programs and fault processing methods at any time, and the professional literacy of the user is improved.
And (3) a training platform: the intelligent equipment platform system can provide a practical training platform, and can provide various simulated industrial equipment and scenes in combination with an actual industrial field. The user can perform simulation operation and experiments on the platform, master actual operation skills and solve actual problems, better adapt to work demands and build a customer's own equipment expert system.
Drawings
FIG. 1 is a diagram of an overall architecture in an intelligent equipment platform system of the present invention;
FIG. 2 is a flow chart of model algorithm construction and workflow in the intelligent equipment platform system of the present invention;
FIG. 3 is a diagram of the intelligent equipment data analysis and equipment operating condition monitoring of the tire in the intelligent equipment platform system of the present invention;
FIG. 4 is a diagram of feedback control of a tire intelligent equipment in the intelligent equipment platform system of the present invention;
FIG. 5 is a diagram of a factory implementation of intelligent tire equipment in an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Example 1:
as shown in fig. 1, the tire intelligent equipment platform system comprises a tire intelligent equipment mechanical body, a sensing system and an edge intelligent control platform, wherein internal communication is realized among the tire intelligent equipment mechanical body, the sensing system and the edge intelligent control platform through communication interfaces, the edge intelligent control platform is connected with an internet cloud platform through a network interface, the internet cloud platform comprises a customized service module, the customized service module comprises an infrastructure, a platform, a software and an expert system diagnosis module, a remote operation and maintenance module and a preventive maintenance module, the infrastructure, the platform, the software and the service are integrated with data support, the expert system diagnosis module has no time delay requirement, the remote operation and maintenance module and the preventive maintenance module, the edge intelligent control platform comprises a data acquisition module, a data analysis processing module and a data storage module, the data analysis processing module comprises a general model algorithm and a process model algorithm, and the general model algorithm comprises a joint precision control algorithm, an equipment health management algorithm, an AI model algorithm, a tracking model algorithm and a big data model algorithm.
Sensing system
The sensing system gathers the information of the traditional sensor and the intelligent sensor of the intelligent equipment to the edge intelligent control platform through internal communication or network communication, and completes the data acquisition of each part of the intelligent equipment.
Sensor main type
The sensors are classified into two types, a conventional sensor and an intelligent sensor:
a) Conventional sensors: photoelectric sensors, proximity sensors, pressure sensors, temperature sensors, encoders, displacement sensors, flow sensors, etc.
b) An intelligent sensor; visual detection system, vibration sensor, noise sensor, behavior management and control system etc.
Type of primary information of sensing system
The sensing system mainly comprises the following four kinds of information:
a) Digital quantity information;
b) Analog quantity information;
c) Image information;
d) Audio information.
Peripheral auxiliary system
The peripheral auxiliary system is mainly communicated with the intelligent equipment through internal communication or network communication and is cooperated with the intelligent equipment to finish production tasks, and mainly comprises the following contents:
a) A robot;
b)AGV\RGV；
c)EMS；
d) Truss;
e) A conveying line;
f) And a lifter.
Factory informatization interface system
The factory informatization system interface is the sum of data interfaces between the intelligent equipment and the factory informatization system, including but not limited to an MES interface, an ERP interface, a PLM interface and the like, and has the following main functions:
a) The intelligent equipment realizes the information intercommunication of the related upstream and downstream ring-segment equipment through the factory informatization interface;
b) The intelligent equipment obtains related information of production raw materials and products in real time through a factory informatization interface;
c) The intelligent equipment receives tasks and plans of the PLM system through a factory informatization interface;
d) The intelligent equipment receives production parameters from technical departments through a factory informatization interface;
e) Through the factory informatization interface, the intelligent equipment receives instructions from functional departments such as security, manpower and the like.
For factories without factory informatization systems, intelligent equipment can communicate with other equipment through an edge intelligent control platform, so that information intercommunication and mutual cooperation among equipment are realized.
Example 2:
on the basis of embodiment 1, the generic model algorithm comprises: joint accuracy control algorithm, equipment health management algorithm, AI model algorithm, tracking model algorithm, and big data model algorithm.
2.1 general model algorithm:
the generic algorithm model refers to an algorithm model that can be adapted to a variety of different types of problems. It is a basic algorithm framework or template that can be modified and adjusted appropriately according to the requirements of the actual problem. Common general algorithm models in systems are: joint accuracy control algorithms, equipment health management algorithms, AI model algorithms, tracking model algorithms, big data model algorithms, etc., such as:
1) Joint accuracy control algorithm:
the front-back relation data and the left-right relation data of the joint materials in the production process of the tire semi-finished products are collected by adopting machine vision; the driving parameters of the related conveyor belt, the correction parameters of the conveyor belt and the like are controlled by the equipment;
and calling a decision tree algorithm of a system general model algorithm, splitting the acquired driving parameter data through a series of judging conditions, constructing a tree structure, and making predictions based on the characteristic values with excellent joint quality.
-analyzing the predicted driving optimization parameters, feeding back to the control system, closed-loop controlling the driving parameters, maintaining the joint accuracy at a better yield quality and yield.
2) Equipment health management algorithm
Through the internal sensing of the equipment, the parameters such as the working states, the life cycle, the maintenance cycle and the like of various sensors, drivers and execution components in the system are adopted;
the integrated method formed by combining a plurality of decision trees is used for managing maintenance period and replacement period in each period in the system by voting or averaging predictions of the plurality of decision trees to obtain a final result.
And the maintenance period and the replacement period in the management system are fed back to the equipment operation and maintenance department and the spare part library, so that the spare part time, the replacement time and the replacement timeliness are shortened, the outage rate is reduced, and the production efficiency in the equipment life cycle is improved.
The process model algorithm refers to an algorithm model for optimizing the tire manufacturing process. Tire manufacturing involves a number of process steps such as compound formulation, rubber compounding, tire building, mold design, carcass building, carcass curing, and the like. Optimizing these process links can improve the performance, quality and production efficiency of the tire.
3.1 tire building process algorithm model:
1) Solving the problems of unqualified width, thickness, lace and the like, fold, quality consistency or other unqualified materials of the laminating drum
Visual inspection is carried out on the out-of-roundness and the diameter of the bonding drum before the carcass drum is bonded, if a semi-finished product is unqualified, a red alarm lamp of the equipment alarms, the equipment stops working, and the next action can be carried out after the semi-finished product is qualified again; the intelligent tire equipment platform system simultaneously checks the process production information on semi-finished products, seals up and stores the same batch of products in the three-dimensional warehouse, and gives an alarm and stops the other machines in use in time; and during normal fitting, reconstructing a fitting process 3D model (digital twin) in real time, and evaluating a embryo weight model and a dynamic balance model in real time.
2) Solving the problem of skew and non-concentricity of laminating material of laminating drum
The visual system detects the laminating effect (identifies the color of the red light mark and the sizing material) in real time during lamination, and alarms and stops when the non-concentricity of the laminating materials (PA, child openings) exceeds the standard or the lamination is askew, and the tire intelligent equipment platform system automatically adjusts the feeding deviation correcting system according to the offset at the same time until the qualified lamination party can enter the next step.
3) Disqualification of belt drum laminating material width, thickness, lace, etc., wrinkling, quality uniformity or other disqualification
3 and 1, but with different materials and product types.
4) The belt drum is inclined and non-concentric with the material
4 and 1, but with different materials and product types.
Note that: 1. 2, 3, 4 can be integrally embodied, 1 and 2 are for the same product, 3 and 4 are for one product, 1 and 3 are a technology, 2 and 4 are a technology, and four points can be integrally embodied.
5) Bubble problem solving
-for the bubbles that occur at the time of bonding: the vision system detects the laminating rolling effect in real time, and when bubbles appear, the intelligent tire equipment platform system automatically checks whether the production date of semi-product materials is qualified or not; and secondly, the pressure of the laminating press roller is automatically adjusted, so that the problem of bubbles is solved.
-for the bubbles that appear during rolling: firstly, checking whether a semi-finished product is qualified or not by the intelligent tire equipment platform system; and judging whether the actual rolling parameters (rolling pressure, drum inflation pressure and the like) are matched with the set pressure, and if the actual rolling parameters are inconsistent, automatically adjusting the related parameters.
6) Semi-finished product full-automatic lamination (belted layer, composite piece)
The vision system automatically detects the size of the material joint, and the machine stops when the material joint exceeds the standard; the tire intelligent equipment platform system automatically adjusts cutting length, laminating speed ratio and laminating pressure according to the size of the joint, dynamically adjusts the size of the joint, and achieves full-automatic laminating of semi-finished products.
7) Solving the unqualified taper of the tire
The taper caused by the disqualification of the positioning precision of the transfer ring is disqualified: when the belt layer transfer ring and the carcass transfer ring are positioned, the vision system detects the actual position of the transfer ring in real time, and when positioning errors occur, the tire intelligent equipment platform system automatically adjusts the positioning position of the transfer ring.
The taper caused by asymmetric left and right sidewall turn-up heights or discounting is disqualified: in the process of unpacking, a visual system detects the unpacking height in real time, and when the height difference of two sides exceeds 5mm, an alarm is stopped, and a tire intelligent equipment platform system prompts the inspection of the pressure of the formed unpacking capsule and the precision of a transfer ring clamping claw.
And (2) solving the problem that the conicity caused by the deflection and the non-concentricity of the laminating drum laminating material is unqualified.
8) Solving the problem of out-of-roundness, over-standard dynamic balance and radial runout of the tire by online visual detection of dynamic balance, taper and dynamic balance exceeding embryo
The vision system detects out-of-roundness, taper and the like in real time when the embryo is rolled, and the machine stops when exceeding the standard; and meanwhile, reversely checking the digital twin embryo, checking the joint position and the material characteristic in the attaching process, and automatically optimizing.
For the dynamic balance and out-of-roundness exceeding caused by uneven joint distribution: the intelligent tire equipment platform system automatically optimizes the angle position of the semi-product joint according to the distribution and material characteristics of the digital embryo joint of the digital twin during the semi-product lamination, and realizes the best uniformity of dynamic balance of the tire.
For the dynamic balance and out-of-roundness exceeding caused by the exceeding of the joint: the vision system automatically detects the size of the material joint, and alarms and stops for the condition of exceeding the standard; the tire intelligent equipment platform system automatically adjusts the cutting length, the laminating speed ratio and the laminating pressure according to the size of the joint, and dynamically adjusts the size of the joint.
For the exceeding of dynamic balance and uniformity caused by the disqualification of semi-finished products, the processing measures of the intelligent tire equipment platform system are referred to as 1 and 3.
9) Solves the problem that the weight of the embryo exceeds the standard (embryo model: 12R22.5 (DSR 266N)
Weighing, and if overweight, tracing the digital twin embryo. And (4) re-checking links 1 and 2, determining whether the length, the thickness, the width and the like are qualified, automatically optimizing the equipment of the intelligent tire equipment platform system under the process permission condition, and alarming and stopping when the equipment exceeds the process range.
10 Solves the problem of exceeding the circumference of the embryo
For the exceeding of the perimeter of the embryo, the intelligent tire equipment platform system firstly traces back the digital twin embryo, rechecks links 1 and 3, determines whether the length, the thickness, the width and the like are qualified, and automatically optimizes the equipment under the process permission condition; secondly, checking various pressures during rolling of the embryo; finally, judging relevant parameters of the forming drum through the vision system data.
11 Person authentication and behavior management
-personnel behavior management: the tire intelligent equipment platform system analyzes the operation actions of operators corresponding to the class A products with better quality according to final inspection big data analysis, prefers the personnel with good operation and fast operation, and trains and controls other operators.
12 Parameter optimization)
And the tire intelligent equipment platform system carries out big data processing on equipment parameters corresponding to the class A product tire, analyzes optimal parameters and reversely guides the optimal parameters into equipment production, and finally ensures that the equipment products are all or are close to class A products.
13 Automatic evolution of devices
When equipment starts to age, performance is reduced, the tire intelligent equipment platform system can analyze the state of the equipment, upgrade and reconstruction suggestions are provided for weak links of the equipment, so that continuous breakthrough and evolution are realized, the quality of products and the production efficiency are continuously improved, and meanwhile, the service life of the equipment is greatly prolonged.
These tire process algorithm models can help tire manufacturers improve product quality and performance and optimize production efficiency, thereby reducing manufacturing costs. By combining with actual production data, these algorithmic models may be customized for application to different types and specifications of tire process optimization. The model algorithm is an application program with an AI function, which abstracts a problem to be solved by intelligent equipment in the tire production process into a mathematical model and then solves the mathematical model. The model algorithm can be transplanted to each other between the edge intelligent control systems, and can also be transplanted to each other between the edge intelligent control systems and the cloud platform. The model algorithm construction and workflow is shown in fig. 2.
In this embodiment, the functions that the tire intelligent equipment platform system of the present invention can realize include:
system datapath
The equipment system data path includes the following seven types:
a) The body is communicated with the inside of the sensing system;
b) The body is in network communication with the edge intelligent control platform;
c) The body communicates with the cloud platform IOT;
d) Edge intelligent control platform and factory informatization system network communication
e) The cloud platform is in network communication with the factory informatization system;
f) The edge intelligent control platform is in network communication with the cloud platform;
g) The edge intelligent control platform is in network communication with a peripheral auxiliary system.
Operation of the system
The workflow of the intelligent equipment comprises: data acquisition, data analysis and equipment operation state monitoring, feedback control, as shown in figure 3,
data acquisition
The SCADA system mainly collects field data to the MES system, and then performs centralized monitoring and other advanced applications; the intelligent tyre equipment collects data related to the equipment to an intelligent equipment software system or an industrial internet platform, and then analyzes and processes the data.
Data analysis and device operational status monitoring
The data is collected on an intelligent equipment software platform or a tire industry internet cloud platform, a strategy capable of optimizing production is formed on the platform through big data processing and AI algorithm, meanwhile, the running state of the intelligent equipment is monitored and alarmed through software and hardware,
as shown in fig. 4, the feedback control is to reversely optimize the operation of the equipment by using the control strategy iterated by the platform AI algorithm model, so as to realize the self-decision and self-adaption of the equipment, achieve the purpose of the equipment intellectualization,
The AI policy automatic import intelligence is equipped with two types:
a) Semi-automatic importing: and after the model optimizes the parameters, the upper computer gives an alarm prompt, and technicians confirm the parameter results given by the strategy one by one and download after modifying the parameter results.
b) Full-automatic importing: and when the accuracy rate of the output strategy reaches the set value, intelligent equipment can be automatically imported, and the operation parameters are optimized. In addition, technicians need to spot check parameters on line on a regular basis.
Automatic import of AI policies into intelligent equipment notes
The AI policy automatic import intelligent equipment should take care of the following three points:
a) The automatically imported parameter values must be within the upper and lower limit value intervals of the parameters;
b) The AI algorithm before automatic leading-in invokes the current relevant operation parameters and processes the abnormal data;
c) The AI algorithm evaluates the effect after each automatic import, and when the accuracy is lower than 95%, the automatic import mode is off-line, the equipment alarms, and relevant personnel are notified through a platform.
Example 3:
the invention relates to a control method of a tire intelligent equipment platform system, which comprises the following steps:
s1: before the carcass drum is attached, driving basic parameters such as the speed, the feeding length and the like of a semi-product part and an equipment curtain cloth feeding conveyor belt are collected by visually checking the out-of-roundness, the diameter, the rotating speed and the attaching process of the attaching drum; if the lamination length of the semi-finished product is not matched with the circumference of the lamination drum, a red alarm lamp of the equipment alarms, the equipment stops working, the next action can be carried out after the semi-finished product is qualified again, and the abnormal one-time labeling of the materials is carried out;
S2: during normal lamination, reconstructing a lamination process 3D model in real time, and evaluating a embryo weight model and a dynamic balance model in real time;
s3: judging whether the molding equipment continues to operate or not according to the evaluation result; the data are accumulated and continuously collected for three times, production information collected in the working procedures on semi-products is checked at the same time in the system, products in the same batch of the three-dimensional warehouse are sealed, and other machines in use are timely alarmed and stopped;
s4: performing data cleaning, denoising and outlier processing on semi-finished product part production information data, and ensuring the accuracy and reliability of the data;
s5: according to the preprocessed data, a mathematical model of the semi-manufactured part production process is established, and a statistical model based on the data is adopted, wherein the purpose of establishing the model is to describe the relation between the length parameter and the operation parameter in the semi-manufactured part production process;
s6: performing model verification, namely inputting data into the model, and comparing and verifying the data with actual performance data;
s7: generating relevant parameters for optimizing the semi-product production process by using a current planning algorithm of the feeding length;
s8: acquiring the data of the forming step sequence, reconstructing a 3D model in the laminating process in real time, evaluating a embryo weight model and a dynamic balance model in real time, and comparing the embryo weight model with production requirements and standards;
S9: and feeding back the determined feasible optimization parameters to corresponding execution mechanisms of the semi-product production equipment in the upper working procedure through an internal network of the factory, adjusting the semi-product production process, and monitoring and feeding back the production process.
According to the control method, data are collected through visual detection, length detection, on-line reading of driving parameters and the like, a production strategy capable of optimizing the tire forming feeding length is formed on a platform through big data processing and an AI algorithm, and the control strategy is updated through repeated iterations, so that the tire forming gradually approaches to an optimal solution.
Example 4:
on the basis of embodiment 1, embodiment 2 and embodiment 3, the implementation of the present invention on a new plant mainly includes 5 steps of plant overall planning, intelligent equipment scheme planning, intelligent equipment implementation preparation, intelligent equipment implementation, intelligent equipment on-line operation, and the like, as shown in fig. 5.
Firstly, the important goal of intellectualization of the bottom layer equipment must be realized; secondly, process intellectualization and workshop intellectualization are realized; and then combining with a factory informatization system to finally realize intelligent production of the whole factory. Meanwhile, each process link device needs to be provided with a predictive maintenance system, an energy management new technology and a unified system communication interface through the unified communication interface of the basic intelligent device, so that the island effect of the system is broken, the application of the industrial Internet of things technology for interconnection and intercommunication of each system interface is realized, the real-time data acquisition, analysis and feedback of each link device are realized, the data support is provided for the full life cycle management of the device, and the OEE of the device is improved to the maximum extent.
For the implementation of the tyre factory introduction part or single intelligent equipment, two aspects should be noted:
a) For the intelligent equipment, the edge intelligent control platform can be purchased according to the needs. Before signing a contract or a technical agreement, firstly, confirming the purchase quantity and content of an AI algorithm; and secondly, confirming the iterative cycle of the algorithm library and the accuracy of the derived strategy.
b) For the outside of the intelligent equipment, the communication mode, the interface type, the data quantity and the like of other systems need to be confirmed.
Upgrading and reforming stock equipment of tire plant
And designing an upgrading and reconstruction scheme according to the intelligent upgrading requirement and the cost requirement.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The utility model provides a platform system is equipped to tire intelligence, includes that tire intelligence is equipped mechanical body, sensing system and edge intelligence and is controlled the platform, and tire intelligence is equipped mechanical body, sensing system and edge intelligence and is controlled between the platform and realize internal communication through communication interface, and edge intelligence is controlled the platform and is passed through network interface and connect internet cloud platform, its characterized in that: the Internet cloud platform comprises a customized service module, wherein the customized service module comprises an infrastructure-as-a-service module, a platform-as-a-service module, a software-as-a-service module, an accumulated data support module, an expert system diagnosis module without time delay requirements, a remote operation and maintenance module and a preventive maintenance module, the edge intelligent control platform comprises a data acquisition module, a data analysis processing module and a data storage module, the data analysis processing module comprises a general model algorithm and a process model algorithm, and the general model algorithm comprises a joint precision control algorithm, an equipment health management algorithm, an AI model algorithm, a tracking model algorithm and a big data model algorithm.
2. The tire intelligence equipment platform system of claim 1, wherein: the general model algorithm comprises the following steps: joint accuracy control algorithm, equipment health management algorithm, AI model algorithm, tracking model algorithm, and big data model algorithm.
3. The tire intelligence equipment platform system of claim 2, wherein: the joint precision control algorithm and the equipment health management algorithm specifically comprise the following steps:
joint accuracy control algorithm:
collecting front-back relation data and left-right relation data of joint materials in the production process of the tire semi-finished products by adopting machine vision; the driving parameters of the related conveyor belt and the correction parameters of the conveyor belt are controlled by the equipment control system;
invoking a decision tree algorithm of a system general model algorithm, splitting the acquired driving parameter data through a series of judging conditions, constructing a tree structure, and making predictions based on the characteristic values with excellent joint quality;
analyzing the predicted driving optimization parameters, feeding back to the control system, and performing closed-loop control on each driving parameter to keep the joint precision at a higher output quality and output rate;
equipment health management algorithm:
Through the internal sensing of the equipment, the working state, life cycle and maintenance cycle parameters of various sensors, drivers and execution components in the system are adopted;
the method comprises the steps of calling a random algorithm and a decision tree algorithm of a system general model algorithm, carrying out voting or averaging on predictions of a plurality of decision trees to obtain a final result, and managing maintenance periods and replacement periods in each period in a system;
and the maintenance period and the replacement period in the management system are fed back to the equipment operation and maintenance department and the spare part library, so that the spare part time, the replacement time and the replacement timeliness are shortened, the outage rate is reduced, and the production efficiency in the life cycle of equipment is improved.
4. The tire intelligence equipment platform system of claim 1, wherein: the process model algorithm refers to an algorithm model for optimizing a tire manufacturing process, wherein the tire manufacturing process comprises: a sizing material formula process, a rubber mixing process, a carcass molding process, a mold design process, a carcass building process and a carcass vulcanization process.
5. The tire intelligence equipment platform system of claim 4, wherein: the carcass building process algorithm model comprises the following steps:
(1) Solution of unqualified width, thickness and lace of laminating drum laminating material
Visual inspection is carried out on the out-of-roundness and the diameter of the bonding drum before the bonding of the carcass drum, if a semi-finished product is unqualified, a red alarm lamp of equipment alarms, the equipment stops working, and the next action can be carried out after the qualified bonding is carried out again; the intelligent tire equipment platform system simultaneously checks the process production information on semi-finished products, seals up and stores the same batch of products in the three-dimensional warehouse, and gives an alarm and stops the other machines in use in time; reconstructing a 3D model of the lamination process in real time during normal lamination, and evaluating a embryo weight model and a dynamic balance model in real time;
(2) Solving the problem of skew and non-concentricity of laminating material of laminating drum
The visual system detects the laminating effect in real time during lamination, and the tire intelligent equipment platform system automatically adjusts the feeding deviation correcting system according to the offset at the same time when the lamination material is out of concentricity or the lamination is askew, so that the next step can be carried out until the lamination qualified party;
(3) Solution of unqualified width, thickness and lace of belt drum attaching material
The method is applicable to materials and product types only, which are different from the method (1);
(4) Solution of problem of skew and non-concentricity of belt drum attaching material
The same as the above (2) is applicable only to materials and product types.
6. The tire intelligence equipment platform system of claim 5, wherein: the carcass building process algorithm model also comprises the following steps:
(1) Bubble problem solving
For bubbles that occur during bonding: the vision system detects the laminating rolling effect in real time, and when bubbles appear, the intelligent tire equipment platform system automatically checks whether the production date of semi-product materials is qualified or not; secondly, automatically adjusting the pressure of the laminating press roller;
for bubbles that appear during rolling: firstly, checking whether a semi-finished product is qualified or not by the intelligent tire equipment platform system; secondly, judging whether the actual rolling parameters are matched with the set pressure or not, and if the actual rolling parameters are inconsistent, automatically adjusting;
(1) Solving problem of full-automatic lamination of semi-finished products
The vision system automatically detects the size of the material joint, and alarms and stops for the condition of exceeding the standard; the tire intelligent equipment platform system automatically adjusts the cutting length, the laminating speed ratio and the laminating pressure according to the size of the joint, dynamically adjusts the size of the joint, and realizes full-automatic laminating of semi-products;
(1) Solution of unqualified problem of tyre taper
The taper caused by the disqualification of the positioning precision of the transfer ring is disqualified: when the belt layer transfer ring and the carcass transfer ring are positioned, the vision system detects the actual positioning position of the transfer ring in real time, and when positioning errors occur, the tire intelligent equipment platform system automatically adjusts the positioning position of the transfer ring;
The taper caused by asymmetric left and right sidewall turn-up heights or discomforts: in the process of unpacking, a visual system detects the unpacking height in real time, and when the height difference of two sides exceeds a threshold value, the tire intelligent equipment platform system prompts the inspection of the pressure of the formed unpacking capsule and the precision of the transfer ring clamping claw.
7. The tire intelligence equipment platform system of claim 1, wherein: the data acquisition module acquires data through visual detection, length detection, on-line reading of driving parameters and other modes, a production strategy capable of optimizing the length of the carcass shaping feeding is formed on the platform through big data processing and an AI algorithm, and the control strategy is updated through repeated iteration, so that the carcass shaping gradually approaches to an optimal solution.
8. A control method based on the intelligent tire equipment platform system according to any one of claims 1 to 7, characterized in that: the method comprises the following steps:
s1: before the carcass drum is attached, driving basic parameters such as the speed, the feeding length and the like of a semi-product part and an equipment curtain cloth feeding conveyor belt are collected by visually checking the out-of-roundness, the diameter, the rotating speed and the attaching process of the attaching drum; if the lamination length of the semi-finished product is not matched with the circumference of the lamination drum, a red alarm lamp of the equipment alarms, the equipment stops working, the next action can be carried out after the semi-finished product is qualified again, and the abnormal one-time labeling of the materials is carried out;
S2: during normal lamination, reconstructing a lamination process 3D model in real time, and evaluating a embryo weight model and a dynamic balance model in real time;
s3: judging whether the molding equipment continues to operate or not according to the evaluation result; the data are accumulated and continuously collected for three times, production information collected in the working procedures on semi-products is checked at the same time in the system, products in the same batch of the three-dimensional warehouse are sealed, and other machines in use are timely alarmed and stopped;
s4: performing data cleaning, denoising and outlier processing on semi-finished product part production information data, and ensuring the accuracy and reliability of the data;
s5: according to the preprocessed data, a mathematical model of the semi-manufactured part production process is established, and a statistical model based on the data is adopted, wherein the purpose of establishing the model is to describe the relation between the length parameter and the operation parameter in the semi-manufactured part production process;
s6: performing model verification, namely inputting data into the model, and comparing and verifying the data with actual performance data;
s7: generating relevant parameters for optimizing the semi-product production process by using a current planning algorithm of the feeding length;
s8: acquiring the data of the forming step sequence, reconstructing a 3D model in the laminating process in real time, evaluating a embryo weight model and a dynamic balance model in real time, and comparing the embryo weight model with production requirements and standards;
S9: and feeding back the determined feasible optimization parameters to corresponding execution mechanisms of the semi-product production equipment in the upper working procedure through an internal network of the factory, adjusting the semi-product production process, and monitoring and feeding back the production process.
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