Production recording system applied to composite material forming hydraulic machine

Abstract

The invention relates to the field of industrial automation control, in particular to a production recording system applied to a composite material forming hydraulic machine, which comprises a production data acquisition system, a PLC (Programmable Logic Controller) control system, a touch screen, printing equipment, a mobile storage device, a desktop computer and a temperature control system, the working process method of the system comprises the following steps of: 1, acquiring data such as main cylinder pressure, main cylinder position, heating temperature, mold temperature and product temperature of the hydraulic machine in real time through the production data acquisition system; 2, transmitting the acquired data to a PLC (Programmable Logic Controller) system for primary processing; 3, the touch screen system records and arranges the data to generate a production curve and a data report; 4, the touch screen system prints a data report in real time through printing equipment, and paper filing is carried out; and 5, the production data are simultaneously exported into a mobile storage device, and electronic archiving and data analysis are carried out through a desktop computer, so that a user can conveniently and quickly carry out related data analysis or production tracing in the later period.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial automation control, in particular to a production record system applied to a composite material forming hydraulic machine. BACKGROUND

[0002] In the traditional hydraulic machine control system, PLC and touch screen are mainly used for related industrial control. If production data is collected, 1) manual recording is required, and workers record device data on paper one by one, and finally the paper records are submitted together with the products for process checking; 2) additional software development is required, and the collected data needs to be sorted, classified, compared, archived and processed by an additional software system; 3) PLC collects related data, performs preliminary data sorting, transmits device and production data, and submits to the Internet of Things system for related digital processing, and the digital system for data display and storage.

[0003] The above method has the problem of additional introduction of an MES system or secondary development. For small and micro enterprises, this means high software costs and later maintenance costs. In view of this situation, the present application aims to add a small amount of additional hardware devices, such as a printer, a USB flash drive and a computer, to the traditional and commonly used PLC + touch screen control system, so as to collect and process device production data. Users can easily and quickly analyze related data or trace production. SUMMARY

[0004] The present application aims to solve the problems in the prior art and provides a control system and control method for a hydraulic machine using an accumulator.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: A production record system applied to a composite material forming hydraulic machine, comprising a production data collection system, a PLC control system, a touch screen, a printing device, a mobile storage device, a desktop computer and a temperature control system, characterized in that the working process of the system comprises: S1, collecting the main cylinder pressure, main cylinder position, heating temperature, mold temperature, product temperature and other data of the hydraulic machine in real time through the production data collection system; S2, transmitting the collected data to the PLC system for preliminary processing; S3, the touch screen system records and sorts the data, generates production curves and data reports; S4, the touch screen system prints the data report in real time through the printing device, and performs paper archiving; S5, the production data is simultaneously exported to the mobile storage device, and the electronic archiving and data analysis are performed through the desktop computer.

[0006] Further, in the S1, the pressure of the master cylinder is set as p(t), the master cylinder position is set as L(t), the heating temperature is set as Th(t), the mold temperature is set as Tm(t), and the product temperature is set as Tp(t); the average value of the master cylinder pressure is set as , which is expressed by a calculation formula as follows: The average value of the heating temperature is set as , which is expressed by a calculation formula as follows: The average value of the mold temperature is set as , which is expressed by a calculation formula as follows: The average value of the product temperature is set as , which is expressed by a calculation formula as follows: , wherein N represents the number of collected data points, and ti is the time of collecting the ith data point.

[0007] Further, the maximum value of the master cylinder pressure is set as pmax, and the minimum value is set as pmin; the ; the The maximum value of the heating temperature is set as Thmax, and the minimum value is set as Thmin, the ; the maximum value of the mold temperature is set as T mmax , and the minimum value is set as T mmin , the , .

[0008] Further, the maximum value of the product temperature is set as T pmax , and the minimum value is set as T pmin , the , the .

[0009] Further, in the S2, the PLC system detects whether the data parameter value is within the expected range, and identifies the abnormal value, wherein the step of identifying the abnormal value comprises S21, defining the expected range: based on the historical data, determining the normal working range or expected value of the data; S22, collecting real-time or batch data from the sensor or data source; S23, data cleaning; S24, descriptive statistical analysis; S25, establishing control limit; S26, visual data analysis; S27, calculating the abnormal value index; S28, identifying and verifying the abnormal value; S29, recording and reporting; S30, feedback and adjustment; S31, continuous monitoring; S32, automation and integration; S33, review and optimization.

[0010] Furthermore, S3 includes S31, data preprocessing; S32, data organization; S33, generating time series; S34, drawing production curves; S35, calculating statistical indicators; S36, generating data reports and formatting reports; S37, data review; S38, data review; S39, user interface display; S40, interactive function implementation and data export.

[0011] Furthermore, in S25, the control limits are established by setting control limits based on standard deviation, which can be expressed by the calculation formula as follows: Where PLCL represents the lower control limit of the data, pucL represents the lower control limit of the data, μ represents the mean or expected value of the dataset, σ represents the standard deviation of the dataset, used to measure the dispersion of the data, and κ represents a constant used to determine the location of the control limits.

[0012] Furthermore, in S27, the outlier index Z-score is calculated as follows: Where Z: Z-score represents the standard score, which is a statistic; X: usually represents the value of a single data point; μ: mean of the dataset; σ: standard deviation of the dataset, representing the dispersion of the data points.

[0013] Compared with existing technologies, the advantages of this invention are: 1. Real-time monitoring of the production process and accurate recording of data have been achieved; 2. Through integrated design, the ease of operation and the efficiency of data recording are improved; 3. The dual protection of paper and electronic archiving enhances data security and traceability; 4. On-site printing and electronic archiving facilitate the rapid detection of production anomalies and process adjustments.

[0014] This invention proposes a production recording system for a composite material molding hydraulic press. It is a production data acquisition system that integrates an Internet of Things (IoT) system. By adding a few additional hardware devices such as a printer, USB flash drive, and computer to the traditionally used PLC + touch screen control system, production data can be collected and processed. Users can then conveniently and quickly perform relevant data analysis or production traceability. Attached Figure Description

[0015] Figure 1 This is a flowchart of the control system in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1, A production recording system for a composite material molding hydraulic press, see attached diagram in the specification. Figure 1 The system includes a production data acquisition system, a PLC control system, a touch screen, printing equipment, a mobile storage device, a desktop computer, and a temperature control system. Its distinguishing feature is that the system's operational process includes: S1. Real-time data such as main cylinder pressure, main cylinder position, heating temperature, mold temperature, and product temperature of the hydraulic press are collected through the production data acquisition system; S2. The collected data is transmitted to the PLC system for preliminary processing; S3, the touch screen system records and organizes data, generating production curves and data reports; S4. The touch screen system prints data reports in real time through the printing device for paper archiving. S5. Production data is simultaneously exported to a mobile storage device for electronic archiving and data analysis via a desktop computer.

[0018] Example 2: Based on the above examples, in S1, the pressure of the main cylinder is set to p(t), the position of the main cylinder is set to L(t), and the heating temperature is set to T. h (t), mold temperature is T m (t), Product temperature is T p (t); In this embodiment, the pressure of the master cylinder is collected multiple times by the production data system, and the average value of the collected pressure values ​​is calculated. In this embodiment, the average value of the master cylinder pressure is set to... The calculation formula is expressed as follows: Where N represents the number of data points collected, and ti is the time of collection for the i-th data point; based on the above embodiment, This represents the average pressure of the master cylinder, where p(ti) indicates the pressure value of the master cylinder being collected at time ti, and ∑ represents the summation sign. The implementation process is as follows: Assuming we are in a production process and collect master cylinder pressure data at regular intervals, we want to calculate the average pressure of the master cylinder over a period of time. The following are the implementation steps: 1. Data Acquisition: Collect the pressure value of the main cylinder at fixed intervals, such as every minute; 2. Record data: Record the pressure value p(ti) corresponding to each time point ti; 3. Determine the number of data points: Determine the total number of data points N collected during the analysis period; 4. Calculate the sum: Sum all the collected pressure values ​​p(ti); 5. Calculate the average value: Divide the sum of the pressure values ​​by the number of data points N to obtain the average pressure. ; 6. Analysis Results: Analyze and calculate the average pressure value to ensure it is within the expected operating range.

[0019] Example as follows: Suppose we collect the following pressure data at four time points: At t1 = 0 minutes, p(t1) = 100 psi; At t2 = 1 minute, p(t2) = 110 psi; At t3 = 2 minutes, p(t3) = 95 psi; At t4 = 3 minutes, p(t4) = 105 psi; Based on the above data, we calculate the average pressure. as follows: P = 100 + 110 + 95 + 105 / 4 = 410 / 4 = 102.5 psi. This average value can be used for further analysis or as a setpoint for the control system. The actual formula and implementation may need to be adjusted according to the specific application scenario and requirements.

[0020] In this embodiment, the heating temperature is collected multiple times, and each heating temperature is different. The average heating temperature of the system can be calculated from the multiple heating temperature values, and the average heating temperature is set as [value missing]. The formula is expressed as follows: Where N represents the number of data points collected, t i T is the time of data collection for the i-th data point. h (t i ) is at time t i The collected heating temperature values, ∑ represents the T values ​​for all i from 1 to N. h (t i In this embodiment, we monitor and calculate the average heating temperature of the composite material hydraulic press over different time periods. The relevant implementation steps are as follows: 1. Data Acquisition: The heating temperature is collected using a temperature sensor within a preset time interval; 2. Record data: Record the heating temperature Th(ti) corresponding to each time point ti; 3. Determine the number of data points: Count the total number of temperature data points N collected during the observation period; 4. Calculate the sum: Sum all the collected heating temperature values ​​Th(ti).

[0021] 5. Calculate the average value: Divide the sum of the heating temperature values ​​by the number of data points N to obtain the average heating temperature.

[0022] 6. Analysis Results: Analyze and calculate the average heating temperature to ensure it meets the process requirements or make necessary adjustments.

[0023] Example: The following heating temperature data were collected at 5 different time points: At t1 = 0 minutes, Th(t1) = 200°C; At t2 = 1 minute, Th(t2) ​​= 210°C; At t3 = 2 minutes, Th(t3) = 195°C; At t4 = 3 minutes, Th(t4) = 205°C; At t5 = 4 minutes, Th(t5) = 215°C; Based on the data above, we calculate the average heating temperature. as follows: Tˉ=200+210+195+205+215 / 5=1025 / 5=205°C, Tˉ=200+210+195+205+215 / 5=1025 / 5=205°C Example 3: Continuing from the previous example, the system repeatedly collects mold temperature data. The average mold temperature can be calculated from these multiple data collections. The average mold temperature is then set to [value missing]. The formula is expressed as follows: Where N represents the number of data points collected, t i This refers to the time of data collection for the i-th data point. In this embodiment, similarly, the system collects product temperature values ​​multiple times. The corresponding product temperature values ​​are different in each time period. The average product temperature can be set to... The formula is expressed as follows: Where N represents the number of data points collected, t i It is the time of data collection for the i-th data point.

[0024] Example 4: Based on the above examples, the maximum value of the main cylinder pressure is set to p based on the comparison of the collected main cylinder pressure values. max The minimum value is p min The The The maximum heating temperature is set to T. hmax The minimum value is T hmin The Set the maximum mold temperature to T. mmax The minimum value is T mmin The The The relevant implementation method is as follows: Suppose we are monitoring the pressure P(t) in a hydraulic system, and we want to find the lowest pressure point within a certain time period. The following are the implementation steps: Data Acquisition: Within a specified time period, use sensors and a data acquisition system to attempt to detect the pressure P(t); Data recording: The collected pressure data is recorded in a time series; Define the time range: Set the time range for the analysis, for example, from t1 to t2; Calculate the minimum value: Within the time range t1-t2, find the minimum value among all values ​​of P(t); Analysis results: Analyze whether the minimum pressure value min(P(t)) is within the safe operating range, or whether further adjustment is needed; Example: The following is a record of stress data: At t1 = 0 minutes, P(t1) = 100 psi; At t2 = 1 minute, P(t2) = 90 psi; At t3 = 2 minutes, P(t3) = 80 psi; At t4 = 3 minutes, P(t4) = 70 psi. In this embodiment, min(P(t)) will be P(t4) = 70 psi, as this is the lowest pressure value during the observation period. This calculation can be performed manually or programmatically, especially when there are many data points. In automated systems, this calculation can be performed in real time to ensure that the system operates within safe and effective parameter ranges.

[0025] Example 5: Set the maximum product temperature to T. pmax The minimum value is T pmin , The , representing the product temperature T collected at all time points t. P The maximum value in (t), where the max function is used to find the maximum value in a set of data. , representing the minimum value of temperature T(t) collected at all time points t. The min function is used to find the minimum value in a set of data. In this embodiment, the temperature of the product during the production process needs to be monitored, and the highest and lowest temperatures of the product within a specific time period need to be determined. The following are the implementation steps: Data acquisition: The product's temperature T(t) is monitored in real-time using a temperature sensor within a specified time period. Record the product temperature TP(t) at each time point t; Define the time range: Set the analysis time range from t1 to tN; Calculate the maximum and minimum values: Within the time range t1 to tN, find the maximum value Tpmax of all TP(t) and the minimum value Tpmin of all T(t); Analysis results: Analyze whether Tpmax and Tpmin are within the range required by the process, or whether there are any abnormalities; Example: The following product temperature data were collected at different time points: At t1=0 minutes, T P (t1) = 300°C; At t2 = 1 minute, T P (t2) = 310°C; At t3 = 2 minutes, T P (t3) = 295°C; At t4 = 3 minutes, T P (t4) = 305°C; In this embodiment, T pmax =max(300,310,295,305)=310°C;T pmin =min(300,310,295,305)=295°C means that during the observation period, the highest product temperature is 310°C and the lowest is 295°C. These extreme values ​​can be used for quality control, process optimization, or safety monitoring. In automated systems, the calculation of such extreme values ​​can be performed in real time to ensure the stability of the production process and product quality.

[0026] Example 6, step S2 includes checking whether the PLC system detects data parameter values ​​within the expected range and identifying outliers. The step of identifying outliers includes S21, defining the expected range: determining the normal working range or expected value of the data based on historical data; S22, collecting real-time or batch data from sensors or data sources; S23, data cleaning; S24, descriptive statistical analysis; S25, establishing control limits; S26, visual data analysis; S27, calculating outlier indicators; S28, identifying and verifying outliers; S29, recording and reporting; S30, feedback and adjustment; S31, continuous monitoring; S32, automation and integration; and S33, review and optimization.

[0027] Example 7, S3 includes S31, data preprocessing; S32, data organization; S33, generating time series; S34, drawing production curves; S35, calculating statistical indicators; S36, generating data reports and formatting reports; S37, data review; S38, data review; S39, user interface display; S40, interactive function implementation and data export.

[0028] In Example 8, the control limits established in S25 are set based on standard deviation, which can be expressed by the calculation formula as follows: Where PLCL represents the lower control limit of the data, and the calculation formula is: pucL represents the lower control limit of the data, and its calculation formula is: In this embodiment, μ represents the mean or expected value of the dataset, indicating the central location of the data; σ represents the standard deviation of the dataset, used to measure the dispersion or variability of the data; and κ represents a constant used to determine the location of control limits. Commonly used k values ​​are 2 or 3, depending on the tolerance for process variability. The purpose of these control limits is to set a statistical boundary to monitor whether the process is operating within the expected range. If process data points exceed these control limits, it may indicate an abnormal or non-random change in the process. The embodiment further includes: 1. Data collection: collecting a certain amount of product weight data; 2. Calculate the mean: Calculate the mean μ of all product weight data; 3. Calculate the standard deviation: Calculate the standard deviation σ of the product weight data; 4. Determine the constant k: Select a constant k to set the control limits, for example, choose k=2 or k=3; 5. Calculate control limits: Using the selected k value, calculate the lower process control limit pLCL and the upper process control limit PUCL; 6. Monitoring process: During the production process, the product weight is monitored in real time and compared with the calculated control limits; 7. Analysis Results: If the product weight exceeds the control limit, further investigation and adjustments will be made. Example 9

[0029] In S27, the outlier index Z-score is calculated as follows: Where Z: Z-score represents the standard score, which is a statistic; X: usually represents the value of a single data point; μ: mean of the dataset; σ: standard deviation of the dataset, representing the dispersion of the data points. In this embodiment, if the Z-score is close to 0, it means that the data point is close to the mean; if the absolute value of the Z-score is large, it means that the data point is far from the mean and may be an outlier.

[0030] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A production record system applied to a composite material forming hydraulic machine, comprising a production data acquisition system, a PLC control system, a touch screen, a printing device, a mobile storage device, a desktop computer and a temperature control system, characterized in that, The flow method of the system working includes: S1, collecting the data of the main cylinder pressure, the main cylinder position, the heating temperature, the mold temperature, the product temperature and the like of the hydraulic machine through the production data acquisition system in real time; S2, transmitting the collected data to the PLC system for preliminary processing; S3, recording and arranging the data by the touch screen system, generating the production curve and the data report; S4, printing the data report by the touch screen system through the printing equipment, and performing paper archiving; S5, exporting the production data to the mobile storage device, and performing electronic archiving and data analysis through the desktop computer.

2. The production recording system for a composite material molding hydraulic press according to claim 1, wherein In the S1, the pressure of the master cylinder is set as p(t), the master cylinder position is set as L(t), the heating temperature is set as Th(t), the mold temperature is set as Tm(t), and the product temperature is set as Tp(t); the average value of the master cylinder pressure is set as , and is expressed by a calculation formula as ; the average value of the heating temperature is set as , and is expressed by a calculation formula as ; the average value of the mold temperature is set as , and is expressed by a calculation formula as ; and the average value of the product temperature is set as , and is expressed by a calculation formula as , wherein N represents the number of collected data points, and ti is the time at which the ith data point is collected.

3. A production recording system for a composite material molding hydraulic press according to claim 2, characterized in that: The maximum value of the main cylinder pressure is pmax and the minimum value is pmin; the ; the maximum value of the heating temperature is Thmax and the minimum value is Thmin, the ; the maximum value of the mold temperature is T mmax and the minimum value is T mmin ; the , .

4. The production recording system for a composite material molding hydraulic press according to claim 2, characterized by: The maximum value of the product temperature is set to T pmax and the minimum value is set to T pmin , said , said .

5. The production recording system for a hydraulic press for molding a composite material according to claim 1, wherein: The S2 includes that the PLC system detects whether the data parameter value is in the expected range, and identifies the abnormal value, and the step of identifying the abnormal value includes S21, defining the expected range: determining the normal working range or the expected value of the data based on the historical data; S22, collecting real-time or batch data from the sensor or the data source; S23, data cleaning; S24, descriptive statistical analysis, S25, establishing control limit; S26, visual data analysis; S27, calculating the abnormal value index; S28, identifying and verifying the abnormal value; S29, recording and reporting; S30, feedback and adjustment; S31, continuous monitoring; S32, automation and integration; S33, auditing and optimization.

6. The production recording system for a hydraulic press for molding a composite material according to claim 1, wherein: In the S3, S31, data preprocessing; S32, data arrangement; S33, generating time sequence; S34, drawing production curve; S35, calculating statistical index; S36, generating data report and report formatting; S37, data auditing; S38, data auditing; S39, user interface display; S40, interactive function implementation and data export are included.

7. The production recording system for a hydraulic press for molding a composite material according to claim 5, wherein: The control limit established in the S25 is a control limit set based on a standard deviation, which is expressed by a calculation formula as follows: wherein, PLCL represents a control lower limit of data, pucL represents a control lower limit of data, μ represents a mean or an expected value of a data set, σ represents a standard deviation of a data set, which is used to measure a degree of dispersion of data, and κ represents a constant, which is used to determine a position of the control limit.

8. The production recording system for a hydraulic machine for molding a composite material according to claim 5, characterized in that: The Z-score of the calculation of the abnormal value in the S27 is Wherein Z: Z-score represents standard score, which is a statistical quantity, X: usually represents the value of a single data point; μ: the mean value of the data set (average); σ: the standard deviation of the data set, indicating the degree of dispersion of the data points.

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