Flange processing control system based on the Internet of Things

Abstract

The present invention discloses a flange processing control system based on the Internet of Things. The present invention relates to the field of flange processing control technology and solves the technical problem that data acquisition and analysis means are limited in the processing process, there is a lack of a systematic analysis and matching mechanism, and the product quality does not meet the requirements. The present invention can scientifically determine the flange material according to the processing requirements through the collaborative work of the processing information acquisition module and the preliminary processing analysis module, and use appropriate cutting technology and equipment for processing. By utilizing the Internet of Things technology, key data such as flatness and roughness can be obtained in real time during the processing process. Through precise measurement means and comparative analysis mechanisms, it is ensured that the processed flange products meet strict quality standards and improve product quality stability. The processing plan selection module uses advanced means such as time series analysis to conduct in-depth analysis of equipment operation data, and can quickly find similar processing records that meet current processing requirements and generate accurate control information.

Description

Technical Field

[0001] The present invention relates to the technical field of flange processing control, and in particular to a flange processing control system based on the Internet of Things. Background Art

[0002] With the development of science and technology, product production lines have gradually realized automated control, so that enterprises can save manpower and material resources. Similarly, the processing of flanges has also gradually become automated.

[0003] In the traditional field of flange processing, the processing process often relies on manual experience and relatively isolated equipment operation. The response to processing needs is not accurate and efficient enough. There is a lack of a systematic analysis and matching mechanism when determining flange materials, and most of the judgment is based on the operator's past experience. 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 in the processing process are limited, and key quality indicators such as flatness and roughness cannot be monitored in real time and comprehensively, resulting in product quality that is difficult to meet increasingly stringent industrial standards. In terms of equipment management and processing solution selection, there is a lack of in-depth mining and effective use of historical data, making it difficult to quickly screen the optimal processing equipment and solutions based on different processing needs. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a flange processing control system based on the Internet of Things, which solves the problem that the data acquisition and analysis methods in the processing process are limited and there is a lack of a systematic analysis and matching mechanism, resulting in product quality that does not meet the requirements.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a flange processing control system based on the Internet of Things, comprising:

[0006] The processing information acquisition module transmits the current processing requirements to the preliminary processing analysis module;

[0007] The preliminary processing analysis module performs processing analysis based on the obtained processing requirements. First, the corresponding processing materials are determined and cut to obtain the primary processed products. Processing is carried out in accordance with the processing requirements. At the same time, the flatness and roughness during the processing are obtained and compared with the processing standards to generate parameter adjustment signals or normal monitoring signals.

[0008] Analyze the parameter adjustment signal, obtain the parameters to be adjusted, adjust them according to the processing standards, generate normal monitoring signals, and transmit them to the processing plan selection module at the same time;

[0009] The processing plan selection module analyzes normal monitoring signals, filters historical data to obtain similar processing records, analyzes the product quality corresponding to the processing data, selects the equipment to be analyzed, and obtains standard equipment based on equipment efficiency;

[0010] Screen similar processing records of standard equipment that match the current processing requirements to generate control information. Otherwise, generate secondary analysis signals and analyze them. By segmenting the processing process to obtain segmented processing steps, and using the optimal processing parameters of the corresponding segmented process as the standard, generate control information and transmit it to the processing control information display module at the same time;

[0011] The processing control information display module transmits the control information to the control terminal.

[0012] As a further solution of the present invention, the specific method for the preliminary processing analysis module to obtain the flatness during the processing is:

[0013] Obtain the primary processed product during the processing, obtain the corresponding measurement points on the surface of the primary processed product, and mark them as i, where i=1, 2, ..., j, where j represents the number of measurement points, and the coordinates of the measurement point i are expressed as (x i ,y i , z i ), then determine the equations of the two parallel planes as Ax+By+Cz+D1=0 and Ax+By+Cz+D2=0, and calculate the distance between the two planes according to the formula The distance d is calculated and is the minimum distance and also the flatness of the flange.

[0014] As a further solution of the present invention, the specific method for the preliminary processing analysis module to obtain the roughness during the processing is:

[0015] Obtain the surface profile curve data of the primary processed product and process it to obtain the sampling length l in the profile curve data, and at the same time obtain the profile offset k (a). The profile offset k (a) is obtained by an optical profiler. Further according to the formula Calculate Ra.

[0016] As a further solution of the present invention, the specific manner in which the preliminary processing analysis module generates the parameter adjustment signal or the normal monitoring signal is:

[0017] The obtained flatness and roughness are compared 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, a normal monitoring signal is generated. Otherwise, if either set does not meet the processing standards, a parameter adjustment signal is generated.

[0018] As a further solution of the present invention, the specific manner in which the preliminary processing and analysis module analyzes the parameter adjustment signal to generate a normal monitoring signal is as follows:

[0019] The processing parameters different from the processing standards are obtained and recorded as parameters to be adjusted, and the parameters to be adjusted are adjusted according to the processing standards. Then, the adjusted processing process is monitored and a normal monitoring signal is generated.

[0020] As a further solution of the present invention, the specific method in which the processing solution selection module analyzes the normal monitoring signal is as follows:

[0021] Obtain historical data and filter it based on current processing requirements to obtain similar processing records. At the same time, obtain the processing data corresponding to similar processing records and analyze the product quality in the processing data.

[0022] Obtain flange finished products corresponding to the same batch, compare the data of flanges of the same specifications and materials processed by different equipment, analyze the differences in processing quality of different equipment under the same process, and sort all processing equipment from small to large according to the processing quality differences, generate equipment sorting information, and analyze it.

[0023] As a further solution of the present invention, the specific manner in which the processing solution selection module analyzes the equipment sorting information is as follows:

[0024] Obtain equipment sorting information and perform time series analysis to screen out stably operating equipment, record them as the equipment to be analyzed, and label them as n, where 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 select the equipment to be analyzed with the highest production efficiency as the standard, record them as the standard equipment, and analyze the standard equipment to generate control information.

[0025] As a further solution of the present invention, the specific manner in which the processing plan selection module analyzes the standard equipment and generates control information is as follows:

[0026] Obtain standard equipment and 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 use their processing parameters as the standard to generate control information. Otherwise, if they do not match, further analyze and generate secondary analysis signals, and analyze them to generate control information.

[0027] As a further solution of the present invention, the specific manner in which the processing scheme selection module analyzes the secondary analysis signal to generate control information is as follows:

[0028] Obtain standard equipment and all historical data at the same time, and segment the processing process according to the current processing requirements to obtain segmented processing steps and 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.

[0029] The present invention provides a flange processing control system based on the Internet of Things. Compared with the existing technology, it has the following advantages:

[0030] Through the collaborative work of the processing information acquisition module and the preliminary processing analysis module, this invention can scientifically determine the flange material based on processing requirements and apply appropriate cutting techniques and equipment. Leveraging Internet of Things technology, key data such as flatness and roughness can be acquired in real time during the processing process. Through precise measurement methods and comparative analysis mechanisms, when data does not meet processing standards, parameter adjustment signals can be quickly generated and intelligent adjustments can be made, ensuring that the processed flange products meet strict quality standards and improving product quality stability. The processing solution selection module uses advanced methods such as time series analysis to conduct in-depth analysis of equipment operation data, quickly identifying similar processing records that meet current processing requirements and generating accurate control information. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] Example 1

[0034] See also 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 plan selection module and a processing control information display module, combined with Figure 1 It can be known that the functional modules are electrically connected in a unidirectional manner.

[0035] The processing information acquisition module is used to obtain the current processing requirements and transmit the processing requirements to the preliminary processing analysis module. The processing requirements include processing materials and processing parameters.

[0036] 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 the preliminary processed product. Then, the preliminary processed product is processed according to the processing parameters in the processing requirements and real-time data is obtained during the processing process, and the real-time data includes flatness and roughness;

[0037] First, determine the appropriate flange material based on the processing requirements. For example, if the processing requirement is for chemical pipeline connection, considering the corrosive nature of the medium, corrosion-resistant stainless steel, such as 316L stainless steel, is usually selected. After the material is selected, it is cut. This step requires the use of appropriate cutting equipment and processes based on the target flange dimensions.

[0038] Taking the cutting of plate-like stainless steel materials to prepare small flange blanks as an example, laser cutting technology can be used, which can achieve high-precision cutting, smooth incisions, small heat-affected zones, and high dimensional accuracy of the primary processed products obtained after cutting, laying a good foundation for subsequent processing.

[0039] Next, the pre-processed product undergoes further processing according to the detailed machining parameters specified in the machining requirements. For example, if the machining parameters call for external turning of the flange blank on a lathe, precise settings such as the lathe speed, feed rate, and depth of cut are required. During the machining process, the IoT system is used to capture key data in real time, with flatness and roughness being key indicators of machining quality.

[0040] Taking flatness as an example, installing high-precision displacement sensors on processing equipment, combined with specialized algorithms, allows real-time monitoring of the deviation of the machined surface from the ideal plane. For example, when milling a flange sealing surface, the sensor continuously collects data, which, after system analysis and processing, provides instant feedback on the sealing surface's flatness. For roughness, real-time measurement can be performed using a stylus or optical profilometer.

[0041] For example, the diamond stylus of a stylus profilometer slides across the machined surface, converting minute surface fluctuations into electrical signals. These signals are then converted and calculated to produce roughness parameters. By continuously monitoring and analyzing this real-time data, machining parameters can be adjusted promptly to ensure that the finished flange meets stringent quality standards.

[0042] The flatness is obtained by obtaining the pre-processed product during the processing and calculating it using the minimum area method. The corresponding measurement points on the surface of the pre-processed product are obtained and labeled as i, where i=1, 2, ..., j, where j represents the number of measurement points, and the coordinates of the measurement point i are expressed as (x i ,y i , z i), then determine the equations of two parallel planes and record them as Ax+By+Cz+D1=0 and Ax+By+Cz+D2=0 respectively, and the two planes determined include all the measurement points, and calculate the distance between the two planes at the same time, according to the formula The distance d is calculated and is the minimum distance and also the flatness of the flange;

[0043] The roughness is obtained by analyzing the arithmetic mean deviation of the profile, obtaining the surface profile curve data of the primary processed product, processing it, obtaining the sampling length l in the profile curve data, and obtaining the profile offset k (a). The profile offset k (a) is obtained by an optical profiler. Specifically, the instrument emits a beam of coherent light. After the light hits the flange surface, the reflected light interferes with the reference light. Due to the microscopic roughness 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, the contour curve is discretized and the integral value is approximated by the numerical integration method to obtain the Ra value.

[0044] The obtained flatness and roughness are compared with the processing standard, and the processing standard includes the flatness and roughness obtained by 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 standards, a normal monitoring signal is generated. On the contrary, if either of the two does not meet the processing standards, a parameter adjustment signal is generated.

[0045] For example, when processing flanges used to connect high-precision chemical equipment, operators may set the flatness standard to ±0.02mm and the roughness standard to Ra0.8μm in view of the equipment's extremely high requirements for sealing performance. In a certain flange processing, the measured flatness is ±0.015mm and the roughness is Ra0.7μm, both of which meet the pre-set standards, and a normal monitoring signal is generated. Conversely, if the measured flatness is ±0.023mm and the roughness is Ra0.83μm, which do not meet the pre-set standards, a parameter adjustment signal is generated.

[0046] At the same time, the generated parameter adjustment signal is analyzed, and the processing parameters that are different from the processing standards are recorded as parameters to be adjusted, and the parameters to be adjusted are adjusted according to the processing standards. Then, the adjusted processing process is monitored, and a normal monitoring signal is generated and transmitted to the processing plan selection module.

[0047] The processing plan selection module is used to analyze the normal monitoring signals obtained, obtain historical data, and filter the historical data based on the current processing requirements to obtain similar processing records. At the same time, it obtains the processing data corresponding to the similar processing records. The processing data includes processing industry parameters, equipment operating parameters and product quality data, and analyzes the product quality in the processing data. The specific analysis method is as follows:

[0048] Obtain flange products corresponding to the same batch, compare data on flanges of the same specifications and materials processed by different equipment, and analyze the differences in processing quality between different equipment under the same process. For example, compare the dimensional accuracy and surface roughness of carbon steel flanges processed by two lathes. Then sort all processing equipment from smallest to largest according to processing quality differences and generate equipment sorting information;

[0049] Next, equipment ranking information is obtained and time series analysis is used to analyze all equipment. The specific time series analysis process involves preprocessing the data, which includes data cleaning and organization. A time series model is then selected. Common time series models include the autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA). For example, if the equipment operating data exhibits significant cyclical fluctuations with relatively stable amplitudes, an ARIMA model can be considered. For example, the spindle speed data for a piece of processing equipment exhibits cyclical fluctuations within a production shift. Through analysis of historical data and evaluation of model fit, the ARIMA (p, d, q) model was selected, where p, d, and q represent the autoregressive order, differencing order, and moving average order, respectively. The model was then constructed accordingly. After determining the model type, the model parameters were estimated using the collected historical data. For the ARIMA model, the values ​​of p, d, and q can be estimated using methods such as maximum likelihood estimation or least squares. By continuously adjusting the parameters, the model's fitting error to historical data is minimized. For example, through multiple experiments and calculations, the ARIMA model parameters for the spindle speed data of a certain device are determined to be ARIMA(2,1,1), that is, the autoregressive order p is 2, the differential order d is 1, and the moving average order q is 1. Further, based on the obtained model, equipment prediction is performed, and stable operating equipment is screened. Here, stable operating equipment refers to equipment whose predicted values ​​obtained after analysis are within a reasonable range of deviation from the actual measured values. These equipment are recorded as the equipment to be analyzed and labeled n, with n=1, 2, ..., m, where m represents the number of equipment to be analyzed. At the same time, the production efficiency corresponding to the equipment to be analyzed n is obtained, and the equipment to be analyzed with the highest production efficiency is selected as the standard, recorded as the standard equipment.

[0050] Obtain standard equipment and 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 use their processing parameters as the standard to generate control information. Otherwise, if they do not match, further analysis is performed and a secondary analysis signal is generated; and the match here means that the corresponding processing parameters of the two meet each other's requirements.

[0051] The generated secondary analysis signal is then processed to obtain standard equipment and all historical data. The machining process is then segmented according to current machining requirements. This segmentation specifically involves identifying different machining steps, obtaining corresponding machining parameters, and obtaining the corresponding machining parameters. For example, in the machining of carbon steel butt-weld flanges, the machining process can be divided into multiple steps: stock preparation, cutting (turning, milling, drilling, etc.), welding, heat treatment, and surface treatment. For each segmented machining step, the system extracts the corresponding machining parameters from historical data. For example, in the stock preparation stage, the appropriate blanking dimensions and cutting process parameters are obtained. In the turning step, parameters such as lathe speed, feed rate, and depth of cut corresponding to different turning operations (external turning, internal turning, etc.) are obtained. The optimal machining parameters are then selected to generate parameter standards. All these obtained parameter standards are then integrated to generate control information, which is then transmitted to the machining control information display module.

[0052] After obtaining numerous machining parameters for each segmented machining step, the system applies specific algorithms and evaluation mechanisms to select the optimal parameters from these parameters, thereby generating parameter standards for each segmented step. For example, by analyzing and comparing historical data, combined with current equipment status and machining requirements, the system determines that under current conditions, the optimal lathe speed for turning is 1200 rpm, a feed rate of 0.15 mm / rev, and a depth of cut of 2 mm. These parameters are then used as the standard parameters for the turning step. Finally, the system integrates the parameter standards generated for each segmented machining step to form a complete set of control information.

[0053] The processing control information display module is used to transmit the acquired control information to the corresponding control terminal, and the control terminal generates parameter control instructions for regulation.

[0054] Some of the data in the above formulas are calculated based on their numerical values ​​and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0055] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents 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 by: include: The processing information acquisition module transmits the current processing requirements to the preliminary processing analysis module; The preliminary processing analysis module performs processing analysis based on the obtained processing requirements. First, the corresponding processing materials are determined and cut to obtain the primary processed products. Processing is carried out in accordance with the processing requirements. At the same time, the flatness and roughness during the processing are obtained and compared with the processing standards to generate parameter adjustment signals or normal monitoring signals. Analyze the parameter adjustment signal, obtain the parameters to be adjusted, adjust them according to the processing standards, generate normal monitoring signals, and transmit them to the processing plan selection module at the same time; The processing plan selection module analyzes normal monitoring signals, filters historical data to obtain similar processing records, analyzes the product quality corresponding to the processing data, selects the equipment to be analyzed, and obtains standard equipment based on equipment efficiency; Screening similar processing records of standard equipment that match the current processing requirements to generate control information; otherwise, generating and analyzing secondary analysis signals if there is no match. By segmenting the processing process to obtain segmented processing steps, and using the optimal processing parameters of the corresponding segmented process as the standard, generating control information, and transmitting it to the processing control information display module at the same time; The processing control information display module transmits the control information to the control terminal.

2. The flange processing control system based on the Internet of Things according to claim 1 is characterized in that: The specific method for the preliminary processing analysis module to obtain the flatness during the processing is: Obtain the primary processed product during the processing, obtain the corresponding measurement points on the surface of the primary processed product, and mark them as i, where i=1, 2, ..., j, where j represents the number of measurement points, and the coordinates of the measurement point i are expressed as (x i ,y i , z i ), then determine the equations of the two parallel planes as Ax+By+Cz+D1=0 and Ax+By+Cz+D2=0, and calculate the distance between the two planes according to the formula The distance d is calculated and is the minimum distance and also the flatness of the flange.

3. The flange processing control system based on the Internet of Things according to claim 1 is characterized in that: The specific method for the preliminary processing analysis module to obtain the roughness during the processing is: Obtain the surface profile curve data of the primary processed product and process it to obtain the sampling length l in the profile curve data, and at the same time obtain the profile offset k (a). 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 is characterized in that: The specific method for the preliminary processing and analysis module to generate the parameter adjustment signal or the normal monitoring signal is: The obtained flatness and roughness are compared 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, a normal monitoring signal is generated. Otherwise, if either set does not meet the processing standards, a parameter adjustment signal is generated.

5. The flange processing control system based on the Internet of Things according to claim 1 is characterized in that: The specific method in which the preliminary processing and analysis module analyzes the parameter adjustment signal to generate a normal monitoring signal is: The processing parameters different from the processing standards are obtained and recorded as parameters to be adjusted, and the parameters to be adjusted are adjusted according to the processing standards. Then, the adjusted processing process is monitored and a normal monitoring signal is generated.

6. The flange processing control system based on the Internet of Things according to claim 1 is characterized in that: The specific method in which the processing scheme selection module analyzes the normal monitoring signal is as follows: Obtain historical data and filter it based on current processing requirements to obtain similar processing records. At the same time, obtain the processing data corresponding to similar processing records and analyze the product quality in the processing data. Obtain flange finished products corresponding to the same batch, compare the data of flanges of the same specifications and materials processed by different equipment, analyze the differences in processing quality of different equipment under the same process, and sort all 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 is characterized in that: The specific method in which the processing plan selection module analyzes the equipment sorting information is as follows: Obtain equipment sorting information and perform time series analysis to screen out stably operating equipment, record them as the equipment to be analyzed, and label them as n, where 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 select the equipment to be analyzed with the highest production efficiency as the standard, record them as the standard equipment, and analyze the standard equipment to generate control information.

8. The flange processing control system based on the Internet of Things according to claim 7 is characterized in that: The specific method for the processing scheme selection module to analyze the standard equipment and generate control information is as follows: Obtain standard equipment and 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 use their processing parameters as the standard to generate control information. Otherwise, if they do not match, further analyze and generate secondary analysis signals, and analyze them to generate control information.

9. The flange processing control system based on Internet of Things according to claim 7 is characterized in that: The specific method in which the processing scheme selection module analyzes the secondary analysis signal and generates control information is as follows: Obtain standard equipment and all historical data at the same time, and segment the processing process according to the current processing requirements to obtain segmented processing steps and 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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