Flange machining control system based on Internet of Things

Abstract

The invention discloses a flange machining control system based on the Internet of Things, relates to the technical field of flange machining control, and solves the technical problems that in the machining process, data acquisition and analysis means are limited, a systematic analysis matching mechanism is lacked, and consequently the product quality does not meet the requirement. Through cooperative work of the machining information acquisition module and the preliminary machining analysis module, a flange material can be scientifically determined according to machining requirements, proper cutting technology and equipment are used for processing, and key data such as flatness and roughness are acquired in real time in the machining process by utilizing the Internet of Things technology; through an accurate measurement means and a comparative analysis mechanism, it is ensured that a machined flange product meets the strict quality standard, the product quality stability is improved, a machining scheme selection module conducts deep analysis on equipment operation data by means of a time sequence analysis method and other advanced means, similar machining records conforming to the current machining requirement can be quickly found, and the machining efficiency is improved. And accurate control information is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of flange processing control, and specifically to an Internet of Things-based flange processing control system. Background Art

[0002] With the development of science and technology, the product production line has gradually realized automated control to facilitate enterprises to save labor and material resources. Similarly, the processing of flanges has also gradually become automated control.

[0003] In the traditional flange processing field, the processing process often relies on manual experience and relatively isolated equipment operations. The response to processing requirements is not accurate and efficient enough. There is a lack of a systematic analysis and matching mechanism when determining flange materials, and it mostly relies on the past experience of operators to judge. In the cutting process and subsequent processing links, the parameter setting lacks a scientific basis, making it difficult to ensure the consistency of product quality. At the same time, the means of data acquisition and analysis during the processing process are limited, and it is impossible to monitor key quality indicators such as flatness and roughness in real time and comprehensively, resulting in the product quality being difficult to meet the increasingly stringent industrial standards. In terms of equipment management and processing plan selection, there is a lack of in-depth mining and effective utilization of historical data, and it is difficult to quickly screen out the optimal processing equipment and plan according to different processing requirements. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an Internet of Things-based flange processing control system, which solves the problems of limited data acquisition and analysis means during the processing process and the lack of a systematic analysis and matching mechanism, resulting in the product quality not meeting the requirements.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An Internet of Things-based flange processing control system, including: A processing information acquisition module, which transmits the current processing requirements to the preliminary processing analysis module; A preliminary processing analysis module, which conducts processing analysis according to the acquired processing requirements. First, it determines the corresponding processing materials, performs cutting to obtain semi-finished products, and combines the processing requirements for processing. At the same time, it acquires the flatness and roughness during the processing process, compares them with the processing standards, and generates parameter adjustment signals or normal monitoring signals; Analyze the parameter adjustment signals, obtain the parameters to be adjusted, and adjust them according to the processing standards to generate normal monitoring signals, and at the same time transmit them to the processing plan selection module; A processing plan selection module, which analyzes the normal monitoring signals, screens the historical data to obtain similar processing records, analyzes the product quality corresponding to their processing data, screens the equipment to be analyzed, and at the same time obtains the standard equipment according to the equipment efficiency; Screen for similar processing records of standard equipment that match the current processing requirements to generate control information. Conversely, generate a secondary analysis signal and perform analysis. Obtain segmented processing steps by segmenting the processing process, and generate control information based on the optimal processing parameters of the corresponding segmented process, and simultaneously transmit it to the processing control information display module; The processing control information display module transmits the control information to the control terminal.

[0006] As a further solution of the present invention, the specific method for the preliminary processing analysis module to obtain the flatness during the processing process is as follows: Obtain the semi-finished product during the processing process, obtain the measurement points corresponding to the surface of the semi-finished product, label them as i, and i = 1, 2,..., j, where j represents the number of measurement points, and represent the coordinates of the measurement point i as (x i , y i , z i ). Then determine two parallel plane equations, denoted as Ax + By + Cz + D1 = 0 and Ax + By + Cz + D2 = 0 respectively, and calculate the distance between the two planes. According to the formula Calculate to obtain the distance d, and d is the minimum distance, which is also the flatness of the flange.

[0007] As a further solution of the present invention, the specific method for the preliminary processing analysis module to obtain the roughness during the processing process is as follows: Obtain the surface profile curve data of the semi-finished product and process it to obtain the sampling length l in the profile curve data. At the same time, obtain the profile offset k(a), and the profile offset k(a) here is obtained by an optical profiler. Further, according to the formula Calculate to obtain Ra.

[0008] As a further solution of the present invention, the specific method for the preliminary processing analysis module to generate a parameter adjustment signal or a normal monitoring signal is as follows: Compare the obtained flatness and roughness with the processing standard, and the specific value of the processing standard is set by the operator according to the current processing requirements. If both the flatness and roughness meet the processing standard, generate a normal monitoring signal. Conversely, if either of them does not meet the processing standard, generate a parameter adjustment signal.

[0009] As a further solution of the present invention, the specific method for the preliminary processing analysis module to analyze the parameter adjustment signal to generate a normal monitoring signal is as follows: Obtain the processing parameters different from the processing standard, denoted as the parameters to be adjusted, and adjust the parameters to be adjusted according to the processing standard. Then monitor the adjusted processing process and generate a normal monitoring signal.

[0010] As a further solution of the present invention, the specific manner in which the processing plan selection module analyzes the normal monitoring signal is as follows: Obtain historical data, screen the historical data based on the current processing requirements to obtain similar processing records, and at the same time obtain the processing data corresponding to the similar processing records, and analyze the product quality in the processing data; Obtain the flange finished products corresponding to the same batch, compare the data of flanges with the same specifications and materials processed by different equipment, analyze the processing quality differences of different equipment under the same process, sort all the processing equipment from small to large according to the processing quality differences to generate equipment sorting information, and analyze it.

[0011] As a further solution of the present invention, the specific manner in which the processing plan selection module analyzes the equipment sorting information is as follows: Obtain the equipment sorting information and perform time series analysis, screen out the equipment that runs stably, denoted as the equipment to be analyzed, and label it as n, and n = 1, 2,..., m, where m represents the number of equipment to be analyzed. At the same time, obtain the production efficiency corresponding to the equipment to be analyzed n, and screen the equipment to be analyzed with the highest production efficiency as the standard, denoted as the standard equipment, and analyze the standard equipment to generate control information.

[0012] As a further solution of the present invention, the specific manner in which the processing plan selection module analyzes the standard equipment to generate control information is as follows: Obtain the standard equipment and the corresponding similar processing records, and at the same time obtain the processing requirements corresponding to the similar processing records, and determine whether they match the current processing requirements. If they match, obtain the corresponding similar processing records, and generate control information based on their processing parameters as the standard. On the contrary, if they do not match, further analyze, generate a secondary analysis signal, and analyze it to generate control information.

[0013] As a further solution of the present invention, the specific manner in which the processing plan selection module analyzes the secondary analysis signal to generate control information is as follows: Obtain the standard equipment, and at the same time obtain all the historical data, segment the processing process according to the current processing requirements to obtain segmented processing steps, obtain the processing parameters corresponding to the segmented processing steps, select the optimal processing parameters to generate parameter standards, then integrate all the obtained parameter standards to generate control information, and finally transmit the control information to the processing control information display module.

[0014] The present invention provides a flange processing control system based on the Internet of Things. Compared with the prior art, it has the following beneficial effects: Through the collaborative work of the processing information acquisition module and the preliminary processing analysis module, the present invention can scientifically determine the flange material according to the processing requirements, and use appropriate cutting processes and equipment for processing. By using the Internet of Things technology, key data such as flatness and roughness can be obtained in real time during the processing. Through accurate measurement means and comparative analysis mechanisms, when the data does not meet the processing standards, parameter adjustment signals can be quickly generated and intelligently adjusted to ensure that the processed flange products meet strict quality standards, improve the quality stability of products. The processing scheme selection module uses advanced means such as time series analysis to deeply analyze the equipment operation data, can quickly find similar processing records that match the current processing requirements, and generate accurate control information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1 Please refer to Figure 1 , this application provides a flange processing control system based on the Internet of Things, including a processing information acquisition module, a preliminary processing analysis module, a processing scheme selection module, and a processing control information display module. It can be known from Figure 1 that the above functional modules are connected in a one-way electrical connection.

[0018] The processing information acquisition module is used to obtain the current processing requirements and transmit the processing requirements to the preliminary processing analysis module, and the processing requirements include processing materials and processing parameters.

[0019] The preliminary processing analysis module is used to perform corresponding processing analysis according to the processing requirements. First, the corresponding flange material is determined according to the processing requirements, and the obtained flange material is cut to obtain a preliminary processed product. Then, the preliminary processed product is processed according to the processing parameters in the processing requirements, and real-time data during the processing is obtained, and the real-time data includes flatness and roughness; First, determine the suitable flange material according to the processing requirements. For example, if the processing requirement is for chemical pipeline connection, considering the corrosiveness of the medium, stainless steel materials with good corrosion resistance, such as 316L stainless steel, are usually selected. After the material is selected, perform cutting on it. This step requires using appropriate cutting equipment and processes according to the size specifications of the target flange.

[0020] Taking the preparation of small flange blanks by cutting sheet-shaped stainless steel materials as an example, laser cutting technology can be used. It can achieve high-precision cutting, with smooth cut surfaces and small heat-affected zones. The size accuracy of the semi-finished products obtained after cutting is high, laying a good foundation for subsequent processing. Next, further process the semi-finished products according to the detailed processing parameters in the processing requirements. Suppose the processing parameters require turning the outer diameter of the flange blank on a lathe. Parameters such as the rotational speed, feed rate, and cutting depth of the lathe need to be accurately set. During the processing, use the Internet of Things system to obtain key data in real time. Flatness and roughness are important indicators to measure the processing quality.

[0021] Taking flatness as an example, by installing high-precision displacement sensors on the processing equipment and combining specific algorithms, the change amount of the processed surface relative to the ideal plane can be monitored in real time. For example, when milling the flange sealing surface, the sensor continuously collects data. After being analyzed and processed by the system, the flatness of the sealing surface can be immediately feedback. For roughness, a stylus profilometer or an optical profilometer can be used for real-time measurement.

[0022] For example, the diamond stylus of the stylus profilometer slides on the processed surface, converting the minute undulations on the surface into electrical signals, and obtaining the roughness parameters through conversion and calculation. By continuously monitoring and analyzing these real-time data, the processing parameters can be adjusted in a timely manner to ensure that the processed flange products meet strict quality standards.

[0023] The method for obtaining flatness is as follows: Obtain the semi-finished products during the processing, and use the minimum zone method for calculation. Obtain the measurement points corresponding to the surface of the semi-finished products, labeled as i, and i = 1, 2,..., j, where j represents the number of measurement points, and represent the coordinates of the measurement point i as (x i , y i , z i ). Then determine two parallel plane equations, denoted as Ax + By + Cz + D1 = 0 and Ax + By + Cz + D2 = 0 respectively, and the two determined planes include all the measurement points. At the same time, calculate the distance between the two planes. According to the formula Calculate the distance d, and d is the minimum distance, which is also the flatness of the flange; Among them, the way to obtain the roughness is (to analyze and obtain the roughness through the arithmetic mean deviation of the profile). Obtain the surface profile curve data of the semi-finished product, process it, obtain the sampling length l in the profile curve data, and at the same time obtain the profile offset k(a). And here, the way to obtain the profile offset k(a) is through an optical profiler. Specifically, the instrument emits a beam of coherent light. After this light irradiates the flange surface, the reflected light interferes with the reference light. Due to the microscopic unevenness of the surface, the optical path of the reflected light at different positions is different, and the interference fringes will produce corresponding deformations. By analyzing the shape and distribution of the interference fringes, the height difference of each point on the surface relative to the reference plane can be calculated using relevant optical algorithms. This height difference is the profile offset. Further, according to the formula calculate Ra. In the actual process, discretize the profile curve and approximately calculate the integral value by numerical integration method to obtain the Ra value; Compare the obtained flatness and roughness with the processing standard. And the processing standard includes the flatness and roughness obtained from the above analysis. The specific values are set by the operator according to the current processing requirements. If both the flatness and roughness meet the processing standard, generate a normal monitoring signal. On the contrary, if any one of the two does not meet the processing standard, generate a parameter adjustment signal; For example, when processing a flange used for connecting high-precision chemical equipment, considering the extremely high requirements of the equipment for sealing performance, the operator may set the flatness standard to ±0.02 mm and the roughness standard to Ra 0.8 μm. In a certain flange processing, the measured flatness is ±0.015 mm and the roughness is Ra 0.7 μm, both of which meet the pre-set standards, so a normal monitoring signal is generated. On the contrary, if the measured flatness is ±0.023 mm and the roughness is Ra 0.83 μm, which do not meet the pre-set standards, a parameter adjustment signal is generated.

[0024] At the same time, analyze the generated parameter adjustment signal, obtain the processing parameters different from the processing standard and record them as the parameters to be adjusted, adjust the parameters to be adjusted according to the processing standard, then monitor the adjusted processing process, generate a normal monitoring signal, and transmit it to the processing plan selection module at the same time.

[0025] The processing plan selection module is used to analyze the obtained normal monitoring signal. By obtaining historical data and screening the historical data based on the current processing requirements, obtain similar processing records. At the same time, obtain the processing data corresponding to the similar processing records. And the processing data includes processing industrial parameters, equipment operation parameters and product quality data, and analyze the product quality in the processing data. And the specific analysis method is as follows: Obtain the corresponding finished flanges of the same batch, compare the data of flanges with the same specifications and materials processed by different equipment, analyze the processing quality differences of different equipment under the same process. For example, compare the dimensional accuracy and surface roughness of carbon steel flanges processed by two lathes, and sort all processing equipment from smallest to largest according to the processing quality differences to generate equipment sorting information; Then obtain the equipment sorting information and analyze all equipment using time series analysis method. The specific process of the time series analysis method is as follows: First, preprocess the obtained data, and the preprocessing operations include data cleaning and sorting. Then select a time series model. Common time series models include autoregressive model (AR), moving average model (MA), autoregressive moving average model (ARMA), and autoregressive integrated moving average model (ARIMA), etc. For example, if the equipment operation data shows obvious periodic fluctuations and the fluctuation amplitude is relatively stable, the ARIMA model can be considered. Taking the spindle speed data of a certain processing equipment as an example, it shows periodic rising and falling changes within a production shift. Through the analysis of historical data and the evaluation of the model fitting effect, it is determined to adopt the ARIMA(p,d,q) model, where p, d, and q are the autoregressive order, differencing order, and moving average order respectively, and perform the corresponding model construction. After determining the model type, use the collected historical data to estimate the model parameters. Taking the ARIMA model as an example, methods such as maximum likelihood estimation method or least squares method can be used to estimate the values of p, d, and q. By continuously adjusting the parameters, minimize the fitting error of the model to historical data. For example, through multiple tests and calculations, it is determined that the ARIMA model parameters for the spindle speed data of a certain equipment are ARIMA(2,1,1), that is, the autoregressive order p is 2, the differencing order d is 1, and the moving average order q is 1. Further, perform equipment prediction based on the obtained model, and screen out the equipment with stable operation. And here, the equipment with stable operation means that the deviation between the predicted value obtained through analysis and the actual measured value is within a reasonable range, denoted as the equipment to be analyzed, and labeled as n, and n = 1, 2, …, m, where m represents the number of equipment to be analyzed. At the same time, obtain the production efficiency corresponding to the equipment to be analyzed n, and screen out the equipment to be analyzed with the highest production efficiency as the standard, denoted as the standard equipment; Obtain the standard equipment and the corresponding similar processing records, and at the same time obtain the processing requirements corresponding to the similar processing records, and judge whether they match the current processing requirements. If they match, obtain the corresponding similar processing records and generate control information based on their processing parameters. Otherwise, if they do not match, further analyze and generate a secondary analysis signal; And here, the match means that the corresponding processing parameters of the two satisfy each other's requirements.

[0026] Next, the generated secondary analysis signal is processed to obtain a standard device. At the same time, all historical data is obtained, and the processing process is segmented according to the current processing requirements. Here, the segmentation process specifically refers to corresponding different processing steps to obtain segmented processing steps, and the processing parameters corresponding to the segmented processing steps are obtained. Taking the processing of carbon steel butt welding flanges as an example, the processing process can be divided into multiple steps such as blank preparation, cutting processing (turning, milling, drilling, etc.), welding, heat treatment, surface treatment, etc. For each segmented processing step, the system obtains the corresponding processing parameters from the historical data. For example, in the blank preparation stage, the appropriate blanking size and cutting process parameters are obtained; in the turning processing step, the lathe speeds, feed rates, and cutting depths corresponding to different turning operations (outer diameter turning, inner hole turning, etc.) are obtained. At the same time, the optimal processing parameters are selected to generate parameter standards, and then all the obtained parameter standards are integrated to generate control information, and finally the control information is transmitted to the processing control information display module.

[0027] After obtaining numerous processing parameters for each segmented processing step, the system uses specific algorithms and evaluation mechanisms to select the optimal processing parameters from these parameters, and then generates parameter standards for each segmented step. For example, through the analysis and comparison of historical data, combined with the current equipment status and processing requirements, it is determined that under the current conditions, the optimal lathe speed for turning processing is 1200 revolutions per minute, the feed rate is 0.15 millimeters per revolution, and the cutting depth is 2 millimeters, which is used as the parameter standard for the turning step. Finally, the system integrates the parameter standards generated by each segmented processing step to form a complete set of control information.

[0028] The processing control information display module is used to transmit the obtained control information to the corresponding control terminal, and the control terminal generates parameter control instructions for regulation.

[0029] Some of the data in the above formula are only taken for numerical calculation without substituting parameter units for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0030] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The flange processing control system based on the Internet of Things is characterized in that Including: A processing information acquisition module that transmits the current processing requirements to the preliminary processing analysis module; A preliminary processing analysis module that conducts processing analysis based on the acquired processing requirements. First, it determines the corresponding processing material, cuts it to obtain a preliminary processed product, combines the processing requirements for processing, and simultaneously acquires the flatness and roughness during the processing, compares them with the processing standards, and generates a parameter adjustment signal or a normal monitoring signal; Analyze the parameter adjustment signal, obtain the parameters to be adjusted, adjust them according to the processing standards, generate a normal monitoring signal, and simultaneously transmit it to the processing plan selection module; A processing plan selection module that analyzes the normal monitoring signal, screens historical data to obtain similar processing records, analyzes the product quality corresponding to the processing data thereof, screens the equipment to be analyzed, and simultaneously obtains the standard equipment according to the equipment efficiency; Screen the similar processing records of the standard equipment that match the current processing requirements to generate control information. On the contrary, generate a secondary analysis signal and conduct analysis. Obtain the segmented processing steps by segmenting the processing process, and generate control information based on the optimal processing parameters of the corresponding segmented process, and simultaneously transmit it to the processing control information display module; A processing control information display module that transmits the control information to the control terminal.

2. The flange processing control system based on the Internet of Things according to claim 1, characterized in that The specific method for the preliminary processing analysis module to acquire the flatness during the processing is: Obtain the semi-finished product during the processing, obtain the measurement points corresponding to the surface of the semi-finished product, label them as i, and i = 1, 2, …, j, where j represents the number of measurement points, and represent the coordinates of the measurement point i as (x i , y i , z i ). Then determine two parallel plane equations, denoted as Ax + By + Cz + D1 = 0 and Ax + By + Cz + D2 = 0 respectively. At the same time, calculate the distance between the two planes. According to the formula calculate to obtain the distance d, and d is the minimum distance, which is also the flatness of the flange.

3. The flange processing control system based on the Internet of Things according to claim 1, characterized in that The specific method for the preliminary processing analysis module to acquire the roughness during the processing is: Obtain the surface profile curve data of the semi-finished product, process it, obtain the sampling length l in the profile curve data, and at the same time obtain the profile offset k(a). Here, the profile offset k(a) is obtained by an optical profiler. Further, according to the formula calculate Ra.

4. The flange processing control system based on the Internet of Things according to claim 1, wherein, The specific method for the preliminary processing analysis module to generate a parameter adjustment signal or a normal monitoring signal is: Compare the obtained flatness and roughness with the processing standards, and the specific values of the processing standards are set by the operator according to the current processing requirements. If both the flatness and roughness meet the processing standards, generate a normal monitoring signal. On the contrary, if any one of the two does not meet the processing standards, generate a parameter adjustment signal.

5. The flange processing control system based on the Internet of Things according to claim 1, characterized in that The specific method for the preliminary processing analysis module to analyze the parameter adjustment signal and generate a normal monitoring signal is: Obtain the processing parameters different from the processing standards as the parameters to be adjusted, adjust the parameters to be adjusted according to the processing standards, then monitor the adjusted processing process, and generate a normal monitoring signal.

6. The flange processing control system based on the Internet of Things according to claim 1, wherein The specific method for the processing plan selection module to analyze the normal monitoring signal is: Obtain historical data, screen the historical data according to the current processing requirements as the standard to obtain similar processing records, and simultaneously obtain the processing data corresponding to the similar processing records and analyze the product quality in the processing data; Obtain the flange finished products corresponding to the same batch, compare the data of flanges with the same specifications and materials processed by different equipment, analyze the processing quality differences of different equipment under the same process, sort all the processing equipment from small to large according to the processing quality differences, generate equipment sorting information, and analyze it.

7. The flange processing control system based on the Internet of Things according to claim 6, characterized in that, The specific method for the processing plan selection module to analyze the equipment sorting information is: Obtain the device sorting information and perform time series analysis to screen out the devices that are running stably, denoted as the devices to be analyzed, and labeled as n, where n = 1, 2, …, m, and m represents the number of devices to be analyzed. At the same time, obtain the production efficiency corresponding to the device to be analyzed n, and screen out the device to be analyzed with the maximum production efficiency as the standard, denoted as the standard device, and analyze the standard device to generate control information.

8. The flange processing control system based on the Internet of Things according to claim 7, wherein, The specific method for the processing plan selection module to analyze the standard device and generate control information is as follows: Obtain the standard device and the corresponding similar processing records. At the same time, obtain the processing requirements corresponding to the similar processing records, and judge whether they match the current processing requirements. If they match, obtain the corresponding similar processing records, and use their processing parameters as the standard to generate control information. On the contrary, if they do not match, further analyze and generate a secondary analysis signal, and at the same time analyze it to generate control information.

9. The flange processing control system based on the Internet of Things according to claim 7, characterized in that, The specific method for the processing plan selection module to analyze the secondary analysis signal and generate control information is as follows: Obtain the standard device, and at the same time obtain all historical data. Segment the processing process according to the current processing requirements to obtain segmented processing steps, and obtain the processing parameters corresponding to the segmented processing steps. At the same time, select the optimal processing parameters to generate parameter standards, then integrate all the obtained parameter standards to generate control information, and finally transmit the control information to the processing control information display module.

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