A printed circuit board drilling machine main shaft wear degree monitoring method under big data management

Abstract

The application provides a printed circuit board drilling machine main shaft wear degree monitoring method under big data management, collects vibration, temperature, pressure and axial displacement parameter data when the main shaft runs, processes the collected parameters, establishes a main shaft wear degree monitoring model, and judges the wear state of the main shaft by inputting corresponding data, so that production efficiency is improved, maintenance cost is reduced, product quality is improved, safety is improved, and maintenance strategy is optimized.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, and in particular to a method for monitoring the wear of a printed circuit board drilling machine spindle under big data management. Background Technology

[0002] During high-speed rotation, friction occurs between the spindle and components such as the drill bit and clamping device. Prolonged friction can lead to wear on the spindle surface. During PCB drilling, metal shavings, glass fibers, and other particles are generated. These particles come into contact with the spindle surface and cause wear. Long-term exposure to drilling loads can cause wear on the spindle surface, especially when machining harder materials or in high-volume processing. Improperly designed or used spindle lubrication systems can lead to poor lubrication, increasing spindle friction and wear.

[0003] Traditional monitoring methods, when assessing spindle wear, often only collect a limited number of parameters, such as temperature and pressure, failing to provide sufficiently comprehensive information. Furthermore, traditional methods offer only limited real-time monitoring and early warning capabilities, making accurate real-time predictions difficult and prone to equipment failure and production interruptions. Maintenance is typically based on fixed cycles and experience, lacking a deep understanding of the equipment's actual condition, potentially leading to unnecessary maintenance operations or missed critical maintenance opportunities. Moreover, they cannot fully utilize data collected by various sensors, hindering in-depth data analysis and processing, and preventing the analysis of historical and real-time data. This makes it difficult to identify wear patterns and trends, accurately predict wear status, and identify potential failure risks. Finally, traditional methods cannot visualize monitoring data for users, affecting their ability to understand equipment status promptly and optimize production plans and maintenance strategies.

[0004] Therefore, big data-based monitoring methods can compensate for the shortcomings of traditional methods, improve the accuracy and precision of monitoring, thereby effectively reducing the risk of equipment failure and improving production efficiency and product quality. Summary of the Invention

[0005] The purpose of this invention is to provide a method for monitoring the wear of the spindle of a printed circuit board drilling machine under big data management.

[0006] The problem this invention aims to solve is to achieve real-time monitoring, prediction, and maintenance of the spindle status by utilizing big data technology and data analysis, thereby addressing issues such as inadequate monitoring of spindle wear and unclear maintenance details, and resolving the problem of severe spindle damage and shortened lifespan caused by untimely maintenance, thus improving equipment reliability and production efficiency.

[0007] A method for monitoring the wear of a printed circuit board drilling machine spindle under big data management includes:

[0008] S1: Install various sensors on the printed circuit board equipment, including vibration sensors, temperature sensors, pressure sensors, and displacement sensors, to collect vibration, temperature, pressure, and axial displacement parameter data during the operation of the spindle. The sensors transmit the collected data to the data center for storage and processing via the Internet of Things. During the transmission process, the data is checked using Cyclic Redundancy Check (CRC) detection.

[0009] S2: Establish a big data storage system in the data center using cloud storage services to store, clean, integrate, and perform feature engineering on the data collected by the sensors;

[0010] S3: Utilizing historical and real-time data, and employing big data analytics combined with data mining techniques, the data is analyzed and processed in depth to extract information and features for establishing a spindle wear monitoring model. This is a spindle wear monitoring model, where Vibration represents the spindle vibration change data after feature engineering extraction, Temperature represents the spindle temperature change data after feature engineering extraction, Pressure represents the spindle pressure change data after feature engineering extraction, and Displacement represents the spindle axial displacement change data after feature engineering extraction. , , , and These are the weighting coefficients of the model. By analyzing the spindle operating parameter data, the wear patterns and trends are discovered, the wear status of the spindle is predicted, and potential failure risks are identified.

[0011] S4: Based on the established spindle wear model, design and implement a spindle wear early warning system. Once the spindle wear exceeds the preset threshold, the system will automatically issue an alarm and prompt maintenance personnel to perform maintenance and upkeep to avoid equipment failure and production interruption.

[0012] S5: Through the big data management platform, monitoring data is presented to users in a visual form, including spindle wear trends, early warning information, maintenance suggestions, and regular reports to help users understand equipment status and optimize production plans and maintenance strategies.

[0013] Furthermore, in step S1, various sensors are installed on the printed circuit board equipment, including vibration sensors, temperature sensors, pressure sensors, and displacement sensors, to collect and monitor the spindle operating parameters, including:

[0014] Vibration sensors are installed on the machining parts of the drilling machine to monitor the vibration of the spindle. The vibration sensors output analog electrical signals, which are then converted into digital signals using an analog-to-digital converter.

[0015] An infrared temperature sensor is installed on the drilling machine to monitor the temperature of the spindle during machining. The temperature sensor outputs an analog electrical signal, which is then converted into a digital signal using an analog-to-digital converter.

[0016] A pressure sensor is installed on the machining part of the drilling machine to monitor the pressure change of the spindle. The pressure sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter.

[0017] A displacement sensor is installed on the spindle support structure to monitor the axial displacement of the spindle. The displacement sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter.

[0018] Furthermore, in step S1, the data is subjected to cyclic redundancy check (CRC) detection during transmission, including:

[0019] S11: Select a CRC generator polynomial, represented by a binary number;

[0020] S12: Use the data to be sent and zero padding as the dividend, generate a polynomial as the divisor, and perform polynomial division.

[0021] The remainder is used as the CRC checksum, and the CRC checksum is appended to the data packet and sent together.

[0022] S13: After receiving the data packet, the receiving end also uses the data and the same amount of zero padding as the dividend, and performs polynomial division using the same generator polynomial.

[0023] If the remainder calculated by the receiving end is zero, the data packet is considered correct; otherwise, the data packet contains an error.

[0024] S14: The sending and receiving ends agree on a generator polynomial and use the same generator polynomial for CRC calculation during communication;

[0025] If the CRC checksum calculated by the receiving end is inconsistent with the received CRC checksum, it indicates that there is an error in the data packet, and appropriate processing is required, such as requesting a retransmission of the data packet.

[0026] Furthermore, S2 stores, cleans, and integrates the data collected by the sensor, including:

[0027] S21: Through statistical analysis, detect abnormal data caused by sensor malfunction or human error, and delete such data.

[0028] S22: Use linear interpolation to fill in missing values ​​caused by sensor failure and communication problems. For missing values, select the average of four data points before and after the missing value as the filling value.

[0029] S23: When integrating data, the different data formats collected by different sensors are converted and unified into the same time series data format;

[0030] S24: After integrating the data, the feature engineering step is to extract useful features from the original data. For spindle monitoring data, the frequency, amplitude, and peak statistical characteristics of vibration, the mean, maximum, and minimum characteristics of temperature and pressure, and the fluctuation range and trend characteristics of axial displacement parameters can be calculated.

[0031] S25: Integrate the data collected by each sensor after the statistical characteristics to form a complete main shaft monitoring dataset, and integrate them according to the time series.

[0032] Furthermore, based on the established spindle wear model, a spindle wear early warning system is designed and implemented, including:

[0033] S41: Vibration, temperature, pressure and axial displacement parameters of the spindle during operation are collected through vibration sensors, temperature sensors, pressure sensors and displacement sensors;

[0034] S42: Process the collected data using the steps described in S1-S2, and then input the processed data into the spindle wear monitoring model established in S3. Calculate the spindle wear condition;

[0035] S43: Spindle wear condition less than 0.8 indicates low spindle wear and no maintenance required; spindle wear condition between 0.8 and 2 indicates medium spindle wear and should be added to the monitoring and maintenance list; spindle wear condition greater than 2 indicates severe spindle wear and requires immediate maintenance.

[0036] The beneficial effects of this invention are: improved production efficiency; through real-time monitoring and early warning, equipment failures and production interruptions can be reduced, thereby improving production efficiency.

[0037] Reduce maintenance costs by monitoring and alerting equipment in real time, allowing for maintenance and upkeep before equipment malfunctions occur, thus avoiding additional repair costs due to equipment failure;

[0038] Improving product quality: Real-time monitoring of equipment status can promptly detect equipment abnormalities and prevent product quality issues caused by equipment malfunctions.

[0039] Improving safety by monitoring data collected by sensors can prevent workplace safety accidents caused by equipment malfunctions.

[0040] Optimizing maintenance strategies, through big data analytics, allows for a deeper understanding of equipment wear patterns and trends, thereby optimizing maintenance strategies, reducing unnecessary maintenance operations, and improving maintenance efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart of a method for monitoring the wear of a printed circuit board drilling machine spindle under big data management, according to the present invention. Detailed Implementation

[0042] The present invention will be further described clearly and completely below, but the scope of protection of the present invention is not limited thereto.

[0043] A method for monitoring the wear of a printed circuit board drilling machine spindle under big data management includes:

[0044] S1: Install various sensors on the printed circuit board equipment, including vibration sensors, temperature sensors, pressure sensors, and displacement sensors, to collect vibration, temperature, pressure, and axial displacement parameter data during the operation of the spindle. The sensors transmit the collected data to the data center for storage and processing via the Internet of Things. During the transmission process, the data is checked using Cyclic Redundancy Check (CRC) detection.

[0045] S2: Establish a big data storage system in the data center using cloud storage services to store, clean, integrate, and perform feature engineering on the data collected by the sensors;

[0046] S3: Utilizing historical and real-time data, and employing big data analytics combined with data mining techniques, the data is analyzed and processed in depth to extract information and features for establishing a spindle wear monitoring model. This is a spindle wear monitoring model, where Vibration represents the spindle vibration change data after feature engineering extraction, Temperature represents the spindle temperature change data after feature engineering extraction, Pressure represents the spindle pressure change data after feature engineering extraction, and Displacement represents the spindle axial displacement change data after feature engineering extraction. , , , and These are the weighting coefficients of the model. By analyzing the spindle operating parameter data, the wear patterns and trends are discovered, the wear status of the spindle is predicted, and potential failure risks are identified.

[0047] In this embodiment, , , , and The values ​​were 0.2468, 0.7890, 0.1357, 0.8765 and 0.5432 respectively.

[0048] S4: Based on the established spindle wear model, design and implement a spindle wear early warning system. Once the spindle wear exceeds the preset threshold, the system will automatically issue an alarm and prompt maintenance personnel to perform maintenance and upkeep to avoid equipment failure and production interruption.

[0049] S5: Through the big data management platform, monitoring data is presented to users in a visual form, including spindle wear trends, early warning information, maintenance suggestions, and regular reports to help users understand equipment status and optimize production plans and maintenance strategies.

[0050] refer to Figure 1 The above is a flowchart of a method for monitoring the wear of a printed circuit board drilling machine spindle under big data management.

[0051] Furthermore, in step S1, various sensors are installed on the printed circuit board equipment, including vibration sensors, temperature sensors, pressure sensors, and displacement sensors, to collect and monitor the spindle operating parameters, including:

[0052] Vibration sensors are installed on the machining parts of the drilling machine to monitor the vibration of the spindle. The vibration sensors output analog electrical signals, which are then converted into digital signals using an analog-to-digital converter.

[0053] An infrared temperature sensor is installed on the drilling machine to monitor the temperature of the spindle during machining. The temperature sensor outputs an analog electrical signal, which is then converted into a digital signal using an analog-to-digital converter.

[0054] A pressure sensor is installed on the machining part of the drilling machine to monitor the pressure change of the spindle. The pressure sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter.

[0055] A displacement sensor is installed on the spindle support structure to monitor the axial displacement of the spindle. The displacement sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter.

[0056] Furthermore, in step S1, the data is subjected to cyclic redundancy check (CRC) detection during transmission, including:

[0057] S11: Select a CRC generator polynomial, represented by a binary number;

[0058] S12: Use the data to be sent and zero padding as the dividend, generate a polynomial as the divisor, and perform polynomial division.

[0059] The remainder is used as the CRC checksum, and the CRC checksum is appended to the data packet and sent together.

[0060] S13: After receiving the data packet, the receiving end also uses the data and the same amount of zero padding as the dividend, and performs polynomial division using the same generator polynomial.

[0061] If the remainder calculated by the receiving end is zero, the data packet is considered correct; otherwise, the data packet contains an error.

[0062] S14: The sending and receiving ends agree on a generator polynomial and use the same generator polynomial for CRC calculation during communication;

[0063] If the CRC checksum calculated by the receiving end is inconsistent with the received CRC checksum, it indicates that there is an error in the data packet, and appropriate processing is required, such as requesting a retransmission of the data packet.

[0064] Furthermore, S2 stores, cleans, and integrates the data collected by the sensor, including:

[0065] S21: Through statistical analysis, detect abnormal data caused by sensor malfunction or human error, and delete such data.

[0066] S22: Use linear interpolation to fill in missing values ​​caused by sensor failure and communication problems. For missing values, select the average of four data points before and after the missing value as the filling value.

[0067] S23: When integrating data, the different data formats collected by different sensors are converted and unified into the same time series data format;

[0068] S24: After integrating the data, the feature engineering step is to extract useful features from the original data. For spindle monitoring data, the frequency, amplitude, and peak statistical characteristics of vibration, the mean, maximum, and minimum characteristics of temperature and pressure, and the fluctuation range and trend characteristics of axial displacement parameters can be calculated.

[0069] S25: Integrate the data collected by each sensor after the statistical characteristics to form a complete main shaft monitoring dataset, and integrate them according to the time series.

[0070] Furthermore, based on the established spindle wear model, a spindle wear early warning system is designed and implemented, including:

[0071] S41: Vibration, temperature, pressure and axial displacement parameters of the spindle during operation are collected through vibration sensors, temperature sensors, pressure sensors and displacement sensors;

[0072] S42: Process the collected data using the steps described in S1-S2, and then input the processed data into the spindle wear monitoring model established in S3. Calculate the spindle wear condition;

[0073] S43: Spindle wear condition less than 0.8 indicates low spindle wear and no maintenance required; spindle wear condition between 0.8 and 2 indicates medium spindle wear and should be added to the monitoring and maintenance list; spindle wear condition greater than 2 indicates severe spindle wear and requires immediate maintenance.

[0074] This invention provides a method for monitoring the spindle wear of a printed circuit board drilling machine under big data management. It collects vibration, temperature, pressure, and axial displacement parameter data during spindle operation, processes these parameters, and establishes a spindle wear monitoring model. By inputting relevant data, the wear condition of the spindle can be determined, thereby improving production efficiency, reducing maintenance costs, improving product quality, enhancing safety, and optimizing maintenance strategies.

Claims

1. A method for monitoring the wear of a printed circuit board drilling machine spindle under big data management, characterized in that, include: S1: Install vibration sensors, temperature sensors, pressure sensors and displacement sensors on the printed circuit board equipment to collect vibration, temperature, pressure and axial displacement parameter data of the spindle during operation. The sensors transmit the collected data to the data center for storage and processing through the Internet of Things. During the transmission process, the data is checked by cyclic redundancy check (CRC). The method of performing Cyclic Redundancy Check (CRC) detection on data during transmission includes: S11: Select a CRC generator polynomial, represented by a binary number; S12: Use the data to be sent and zero padding as the dividend, generate a polynomial as the divisor, and perform polynomial division. The remainder is used as the CRC checksum, and the CRC checksum is appended to the data packet and sent together. S13: After receiving the data packet, the receiving end also uses the data and the same amount of zero padding as the dividend, and performs polynomial division using the same generator polynomial. If the remainder calculated by the receiving end is zero, the data packet is considered correct; otherwise, the data packet contains an error. S14: The sending and receiving ends agree on a generator polynomial and use the same generator polynomial for CRC calculation during communication; If the CRC checksum calculated by the receiving end is inconsistent with the received CRC checksum, it indicates that there is an error in the data packet, and appropriate processing is required, such as requesting a retransmission of the data packet. S2: Establish a big data storage system in the data center using cloud storage services to store, clean, integrate, and perform feature engineering on the data collected by the sensors; S3: Utilizing historical and real-time data, and employing big data analytics combined with data mining techniques, the data is analyzed and processed in depth to extract information and features for establishing a spindle wear monitoring model. This is a spindle wear monitoring model, where Vibration represents the spindle vibration change data after feature engineering extraction, Temperature represents the spindle temperature change data after feature engineering extraction, Pressure represents the spindle pressure change data after feature engineering extraction, and Displacement represents the spindle axial displacement change data after feature engineering extraction. , , , and These are the weighting coefficients of the model. By analyzing the spindle operating parameter data, the wear patterns and trends are discovered, the wear status of the spindle is predicted, and potential failure risks are identified. S4: Based on the established spindle wear model, design and implement a spindle wear early warning system. Once the spindle wear exceeds the preset threshold, the system will automatically issue an alarm and prompt maintenance personnel to perform maintenance and upkeep to avoid equipment failure and production interruption. S5: Through the big data management platform, monitoring data is presented to users in a visual form, including spindle wear trends, early warning information, maintenance suggestions, and regular reports to help users understand equipment status and optimize production plans and maintenance strategies.

2. The method for monitoring the wear of a printed circuit board drilling machine spindle under big data management as described in claim 1, characterized in that, In step S1, vibration sensors, temperature sensors, pressure sensors, and displacement sensors are installed on the printed circuit board equipment to collect and monitor the spindle operating parameters, including: Vibration sensors are installed on the machining parts of the drilling machine to monitor the vibration of the spindle. The vibration sensors output analog electrical signals, which are then converted into digital signals using an analog-to-digital converter. An infrared temperature sensor is installed on the drilling machine to monitor the temperature of the spindle during machining. The temperature sensor outputs an analog electrical signal, which is then converted into a digital signal using an analog-to-digital converter. A pressure sensor is installed on the machining part of the drilling machine to monitor the pressure change of the spindle. The pressure sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter. A displacement sensor is installed on the spindle support structure to monitor the axial displacement of the spindle. The displacement sensor outputs an analog electrical signal, which is converted into a digital signal using an analog-to-digital converter.

3. The method for monitoring the wear of a printed circuit board drilling machine spindle under big data management as described in claim 1, characterized in that, S2 stores, cleans, and integrates the data collected by the sensor, including: S21: Through statistical analysis, detect abnormal data caused by sensor malfunction or human error, and delete such data. S22: Use linear interpolation to fill in missing values ​​caused by sensor failure and communication problems. For missing values, select the average of four data points before and after the missing value as the filling value. S23: When integrating data, the different data formats collected by different sensors are converted and unified into the same time series data format; S24: After integrating the data, the feature engineering step is to extract useful features from the original data. For spindle monitoring data, calculate the frequency, amplitude, and peak statistical characteristics of vibration, the mean, maximum, and minimum characteristics of temperature and pressure, and the fluctuation range and trend characteristics of axial displacement parameters. S25: Integrate the data collected by each sensor after the statistical characteristics to form a complete main shaft monitoring dataset, and integrate them according to the time series.

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