Self-adaptive shaft power intelligent optimization method and system based on gantry machining

Abstract

The invention relates to the technical field of automation control, in particular to a self-adaptive shaft power intelligent optimization method and system based on gantry machining, and the method comprises the following steps: collecting rotating speed, cutting force and temperature data, calculating initial machining parameters according to the rotating speed, the cutting force and the temperature data, and executing parameter adjustment based on the initial machining parameters. And dynamically optimizing the power output of the main shaft to obtain adjusted power output. The rotating speed, cutting force and temperature data are collected and analyzed in real time, machining parameters are allowed to be adjusted to adapt to the actual machining environment, machining errors and machine tool abrasion caused by parameter mismatching are reduced, the production efficiency and the product quality are improved, power output of the main shaft is dynamically optimized, it is ensured that energy consumption in the machining process is minimized, and the machining efficiency is improved. And meanwhile, the optimal machining speed and precision are kept, a real-time feedback mechanism timely finds and corrects the problem of deviating from the preset machining standard, the rejection rate of finished products is reduced, and the reliability of the machine tool is improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and particularly to an intelligent optimization method and system for adaptive axis power based on gantry machining. Background Art

[0002] The field of automation control technology is a crucial branch in engineering and technology, which involves using control systems and information technology to manage machines and processes, thereby reducing manual intervention. This field includes, but is not limited to, industrial automation, robotics, intelligent systems, and network control systems, etc. The core goal of automation control is to improve production efficiency, quality, reliability, and reduce production costs.

[0003] The existing technologies often seem inadequate in the face of complex and changeable machining conditions. Because relying on preset parameters, the response to sudden changes is often lagged, which may lead to insufficient machining accuracy and machine damage. In addition, the deficiencies in real-time data processing and immediate feedback result in blind spots in machine tool status monitoring and maintenance. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an intelligent optimization method and system for adaptive axis power based on gantry machining.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions. An intelligent optimization method for adaptive axis power based on gantry machining includes the following steps: Collect speed, cutting force, and temperature data, calculate initial machining parameters based on the speed, cutting force, and temperature data, perform parameter adjustment based on the initial machining parameters, dynamically optimize the spindle power output, and obtain the adjusted power output; Based on the adjusted power output, monitor the parameter changes during the continuous machining process, perform real-time feedback, and obtain the feedback adjustment result; analyze the feedback adjustment result to identify the situations deviating from the predetermined machining standards, and obtain the deviation result; Receive the deviation result, continuously compare the set parameters, detect the deviation, calculate the correction value, obtain the correction instruction and apply it; Conduct a stability test, judge the performance of the machine tool spindle under each working condition, and adjust the machine tool settings according to the performance to obtain an optimization plan.

[0006] Preferably, the obtaining step of the initial machining parameters is as follows: Collect speed, cutting force, and temperature data, and summarize them to obtain a data set; Based on the data set, calculate the work efficiency index; Based on the work efficiency index, combine the material removal rate and tool wear to obtain the initial machining parameters.

[0007] Preferably, the steps for obtaining the adjusted power output are as follows: Based on the initial processing parameters, adjust the spindle speed and feed rate to match different cutting force and temperature conditions; Continuously track the adjustment effect of the spindle speed and feed rate, analyze the stability and efficiency of the power output, check whether the power output meets the processing requirements, and obtain the real-time monitoring results; According to the real-time monitoring results, adjust the spindle power output, optimize the adjustment strategy, and obtain the adjusted power output.

[0008] Preferably, the steps for obtaining the feedback adjustment result are as follows: Based on the adjusted power output, continuously collect spindle power output data; Based on the power output data, calculate the dynamic response index; According to the dynamic response index, analyze the performance stability and processing efficiency of the spindle, and generate the feedback adjustment result.

[0009] Preferably, the steps for obtaining the deviation result are as follows: Based on the feedback adjustment result, calculate the deviation index ; According to the deviation index, determine whether a deviation occurs and generate the deviation result.

[0010] Preferably, the steps for obtaining the correction instruction are as follows: Based on the deviation result, continuously compare the set processing parameters and calculate the correction value for each parameter; According to the average correction value, generate the correction instruction and apply the correction instruction to adjust the settings of the machine tool.

[0011] Preferably, the steps for obtaining the optimization scheme are as follows: Run the machine tool spindle under different working conditions, and record the rotational speed stability, vibration data, and temperature records of the spindle; Analyze the rotational speed stability, vibration data, and temperature records, and analyze the performance of the machine tool spindle under each working condition to obtain the analysis results; Based on the analysis results, change the rotational speed setting or adjust the feed rate to obtain the optimization scheme.

[0012] The present invention provides an adaptive axis power intelligent optimization system, including: An initial parameter calculation module, which collects rotational speed, cutting force, and temperature data, combines the rotational speed, cutting force, and temperature data, performs the calculation of the power output of the initial state of the spindle, and obtains the initial power parameters; The dynamic optimization and adjustment module performs real-time adjustment of the spindle power based on the initial power parameters, compares with the set parameter standards, detects deviations, calculates correction values and applies them to obtain the power output adjustment result; The stability analysis module receives the power output adjustment result, monitors the continuous parameter changes during the machining process, conducts stability tests, adjusts the machine tool settings according to the test feedback, optimizes the overall machining performance, and obtains the optimized solution result.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By collecting and analyzing the rotational speed, cutting force and temperature data in real time, the present invention allows for the adjustment of machining parameters to adapt to the actual machining environment, reduces machining errors and machine tool wear caused by parameter mismatches, improves production efficiency and product quality, while dynamically optimizing the spindle power output ensures the minimization of energy consumption during the machining process, and at the same time maintains the optimal machining speed and accuracy. The real-time feedback mechanism promptly discovers and corrects problems deviating from the predetermined machining standards, reduces the rejection rate of finished products, improves the reliability of the machine tool, and the stability test and continuous parameter comparison improve the overall performance of the machine tool spindle, making the machine tool settings more refined, further enhancing the adaptability and stability of the machining process. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the steps of the present invention. Detailed Embodiment

[0015] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] Please refer to Figure 1 , the present invention provides a technical solution, an adaptive axis power intelligent optimization method based on gantry machining, including the following steps: Collect rotational speed, cutting force and temperature data, calculate the initial machining parameters according to the rotational speed, cutting force and temperature data, perform parameter adjustment based on the initial machining parameters, dynamically optimize the spindle power output, and obtain the adjusted power output; Based on the adjusted power output, monitor the parameter changes during the continuous machining process, perform real-time feedback, and obtain the feedback adjustment result; analyze the feedback adjustment result to identify the situations deviating from the predetermined machining standards, and obtain the deviation result; Receive the deviation result, continuously compare the set parameters, detect deviations, and calculate correction values to obtain the correction instruction and apply it; Conduct a stability test, judge the performance of the machine tool spindle under each working condition, and adjust the machine tool settings according to the performance to obtain an optimized solution.

[0017] Specifically, by collecting and analyzing the rotational speed, cutting force, and temperature data in real time, it is allowed to adjust the machining parameters to adapt to the actual machining environment, reduce machining errors and tool wear caused by parameter mismatches, improve production efficiency and product quality, while dynamically optimizing the spindle power output to ensure the lowest energy consumption during the machining process, and at the same time maintain the optimal machining speed and accuracy. The real-time feedback mechanism can promptly detect and correct problems deviating from the predetermined machining standards, reduce the rejection rate of finished products, and improve the reliability of the machine tool. The stability test and continuous parameter comparison improve the overall performance of the machine tool spindle, make the machine tool settings more refined, and further enhance the adaptability and stability of the machining process.

[0018] The steps for obtaining the initial machining parameters are as follows: Collect rotational speed, cutting force, and temperature data, and summarize to obtain a data set; Based on the data set, calculate the work efficiency index, and the calculation formula is: ; Wherein, is the work efficiency index, is the average temperature, is the average cutting force, is the average rotational speed; Based on the work efficiency index, combined with the material removal rate and tool wear, obtain the initial machining parameters.

[0019] Specifically, collect rotational speed, cutting force, and temperature data, and use high-precision sensors to monitor the rotational speed of the machine tool spindle, the force at the cutting edge, and the temperature of the entire machining area in real time. The accurate acquisition of these data is crucial because they directly affect subsequent data analysis and parameter adjustment. The data collected by the sensors first undergoes preliminary filtering to exclude environmental noise and the interference of potential non-working state data. After that, the data is formatted to ensure the unity and efficiency of subsequent processing, and a data set is obtained.

[0020] The formula is beneficial in that by combining three main machining parameters: temperature (T), cutting force (F), and rotational speed (Z), the work efficiency can be evaluated. This method is simple and direct, can quickly reflect the machining state of the machine tool, and at the same time helps to optimize the machining parameters and improve production efficiency.

[0021] Calculation process: In a specific monitoring period, the average temperature degrees Celsius, the average cutting force Newtons, and the average rotational speed revolutions per minute. Then the calculation process of the work efficiency index is as follows: ; This result indicates that the work efficiency index is 90,000, showing that the machine tool has a high work efficiency in this state, which is beneficial for the next adjustment of machining parameters.

[0022] Based on the work efficiency index, further refine the calculation process of machining parameters. Considering the material removal rate and tool wear, first adjust the removal rate according to the material properties, and then evaluate the wear of the cutting tool in combination with the tool life. The adjustment of these parameters is based on engineering calculations and historical data analysis to ensure the high efficiency and precision of the machining process. Then, adjust the working parameters in real time to cope with the actual material removal efficiency and tool wear rate. Finally, the output initial machining parameters provide an accurate reference for the machine tool machining control system to ensure the maximization of machining quality and efficiency, and obtain the initial machining parameters.

[0023] The steps to obtain the adjusted power output are as follows: Based on the initial machining parameters, adjust the speed and feed rate of the spindle to match different cutting force and temperature conditions; Continuously track the adjustment effect of the spindle speed and feed rate, analyze the stability and efficiency of the power output, and check whether the power output meets the machining requirements to obtain the real-time monitoring results; According to the real-time monitoring results, adjust the spindle power output and optimize the adjustment strategy to obtain the adjusted power output.

[0024] Specifically, the process of adjusting the spindle speed and feed rate starts with collecting data from the cutting force and temperature sensors on site. The sensors are fixed at the positions close to the spindle and the tool, and monitor the operating state in real time. The obtained data is directly sent to the main control unit. According to the initial machining parameter settings, the spindle speed and feed rate are automatically adjusted to ensure that each adjustment matches the current cutting conditions. The adjustment frequency is set according to the workpiece material and cutting depth, so as to ensure the maximization of the material removal efficiency during the machining process and obtain the adjustment data.

[0025] Continue to track the adjustment effect of the spindle speed and feed rate. This process involves using vibration and temperature sensors installed near the spindle. These sensors collect data multiple times per second. The data is transmitted to the monitoring system in the control room through a wired connection, and the operating state of the spindle is displayed in real time. And automatically compare the current data through the set performance standards. If it is found that the data exceeds the normal range, record the time and conditions when it occurs. Through this monitoring, it is ensured that the machine tool can meet the machining requirements at any given time and obtain the real-time monitoring results.

[0026] Considering the recorded spindle speed, feed rate, and temperature changes, perform mathematical averaging and difference analysis on the data to determine whether the spindle power output needs to be adjusted. The adjustment is made through manual intervention, where the operator technician inputs new speed and feed rate parameters via the control panel. At the same time, the adjustment strategy includes considering the current cutting depth and the expected surface roughness. Based on the feedback of these parameters, the operator technician fine-tunes the spindle settings to improve production efficiency while ensuring accuracy, resulting in an adjusted power output.

[0027] The steps for obtaining the feedback adjustment result are as follows: Based on the adjusted power output, continuously collect spindle power output data; Based on the power output data, calculate the dynamic response index, and the calculation formula is: ; where, is the dynamic response index, is the power output value of the th data point, is the average value of the power output, is the standard deviation of the power output value, is the total number of data points; According to the dynamic response index, analyze the performance stability and processing efficiency of the spindle to generate the feedback adjustment result.

[0028] Specifically, is the dynamic response index, is the power output value of the th data point, is the average value of the power output, is the standard deviation of the power output value, is the total number of data points.

[0029] To calculate , first collect sufficient power output data . The average value is obtained by taking the arithmetic mean of all , while the standard deviation is obtained by calculating the square root of the average value of the squares of the differences between and .

[0030] If , then , , and the calculation gives .

[0031] The benefit of the formula is that it helps to judge the performance stability and processing efficiency of the main shaft by quantifying the volatility of the power output value, and the result shows that the power output of the main shaft is relatively stable.

[0032] From the adjusted power output, continuously collect the power output data of the main shaft. By installing multiple sensors in the main shaft processing unit, data on the main shaft speed, torque, and temperature are captured in real time. These data are directly sent to the central processing unit for preliminary data cleaning and formatting to ensure the quality and consistency of the data. Through data collection, continuous monitoring and analysis of the main shaft performance can be ensured.

[0033] According to the dynamic response index, analyze the performance stability and processing efficiency of the main shaft. By comparing the calculated dynamic response index with the set threshold, judge whether the operation of the main shaft meets the performance standard. If abnormal indicators are found, immediately adjust the processing parameters or perform maintenance to ensure the continuity and quality of the processing process. This analysis process not only improves the processing accuracy but also reduces the downtime caused by equipment failures.

[0034] The steps for obtaining the deviation result are as follows: Based on the feedback adjustment result, calculate the deviation index , and the calculation formula is: ; Where is the deviation index, is the th processing parameter value, is the th predetermined processing standard value, is the total number of parameter values;

[0035] According to the deviation index, judge whether a deviation occurs and generate a deviation result.

[0036] Specifically, based on the feedback adjustment result obtained from the previous process, continue to track and monitor the power output of the main shaft, perform real-time analysis on the received data, identify deviations by comparing the difference between the actual output and the predetermined standard, ensure the accuracy of the analysis, and record the discovered deviations in detail. The recorded deviation data will be used for the next step of processing.

[0037] The benefit of formula is that it accumulates each deviation by summing the squares and then quantifies the overall deviation degree by taking the square root. This helps to make a more overall evaluation of the deviation and thus more precisely guide the adjustment of the production process.

[0038] Calculation process: The monitored actual parameter value is [105, 95, 110], the predetermined standard value is [100, 100, 100], then the deviation index is calculated as follows:

[0039] This result indicates that the deviation index is 12.25, which reflects the overall deviation degree relative to the predetermined standard. The larger the deviation value, the farther the parameters in the actual production process deviate from the predetermined standard and need to be adjusted.

[0040] According to the calculated deviation index, determine whether there is a significant deviation, and accordingly adjust the processing parameters to be closer to the predetermined standard to generate the final deviation result. This process involves further analysis of the deviation index. By setting a deviation threshold to determine whether adjustment is needed, the setting of the deviation threshold is based on historical data analysis and process requirement standards to ensure that the adjusted parameters can maximize product quality and production efficiency, thereby achieving the purpose of optimizing production.

[0041] The steps for obtaining the correction instruction are as follows: Based on the deviation result, continuously compare the set processing parameters, and use the following formula to calculate the correction value of each parameter: ; where, is the average correction value, is the th parameter value, is the th set parameter value, is the number of parameters; Generate a correction instruction according to the average correction value, and apply the correction instruction to adjust the settings of the machine tool.

[0042] Specifically, the beneficial aspect of the formula is that it can quantify the deviation of the overall parameters and provide a unified correction benchmark based on this, which helps to optimize the performance of the machine tool and gradually approach the predetermined processing standard; Calculation process: In a specific processing, there are five parameter values ( ), the set parameter values are respectively , and the actually measured parameter values are . Substitute these values into the formula to calculate the average correction value :

[0043] This result indicates that the average correction value is 2.45, which points out the average deviation degree between the processing parameters and the set values. If the value is too large, it indicates that the need for parameter adjustment is more urgent, and a smaller The value indicates that the current processing state is relatively close to the predetermined standard; Based on the received deviation results, real-time data is collected and analyzed. The data reflects the specific differences between the parameters and the predetermined standard during the processing. By comparing the actual processing parameters with the set standard, the key parameters that need to be adjusted are identified. This not only provides an immediate view of the parameter deviation but also makes the process of formulating correction instructions more precise. After adjustment, correction instructions are generated. These instructions adjust various settings of the machine tool according to the deviation results, thereby ensuring that the processing parameters are closer to or meet the predetermined processing standards, ultimately ensuring the improvement of processing quality and efficiency and achieving the purpose of optimizing the production process; According to the calculated average correction value, the settings of the machine tool are adjusted to adapt to the actual processing requirements. This step involves the adjustment of the machine tool control system. By modifying the operating parameters of the machine tool, it adapts to the actual production conditions. The application of the correction instructions ensures the maximum approximation between the processing parameters and the predetermined standard. Through this dynamic adjustment, the processing accuracy and efficiency can be improved. The final correction instructions are based on the comprehensive analysis results of the actual processing data and the predetermined standard. These correction measures are implemented in the subsequent production. In actual operation, the implementation effect of the monitoring feedback adjustment results is further optimized for the processing parameter settings.

[0044] The steps for obtaining the optimization plan are as follows: Run the machine tool spindle under different working conditions and record the rotational speed stability, vibration data, and temperature records of the spindle; Analyze the rotational speed stability, vibration data, and temperature records, and analyze the performance of the machine tool spindle under each working condition to obtain the analysis results; Based on the analysis results, change the rotational speed setting or adjust the feed rate to obtain the optimization plan.

[0045] Specifically, stability tests are carried out under various working conditions, including recording the rotational speed stability, vibration data, and temperature of the machine tool spindle. Sensors are used for data recording. These sensors continuously monitor and capture every change during the spindle operation, thus forming a data set. Each data point in the data set is measured and reflects the real-time performance of the spindle under specific working conditions, which is crucial for identifying potential performance problems of the machine tool spindle. For example, the vibration frequency and amplitude captured by a vibration analyzer, and the temperature peak recorded by a temperature sensor. These data together form the basis for evaluating the spindle stability.

[0046] Conduct a detailed analysis using the collected performance data, especially comparing and contrasting the stability of rotational speed, vibration frequency, and temperature peaks, performing data classification and trend analysis to reveal the performance differences of the spindle under different working conditions. This is crucial for identifying abnormal performances and potential risks in the data. For example, determining the abnormal frequency region through spectral analysis of vibration data, or finding the specific range of abnormal temperature through statistical methods. Each piece of data is evaluated and recorded in the form of tables and graphs, ensuring the comprehensiveness and accuracy of the analysis.

[0047] According to the analysis results of the first two steps, adjust the machine tool settings, including optimizing the spindle speed, improving the cooling system, and adjusting the feed rate. These adjustments are based on the data analysis results and are implemented through the control system. After receiving the adjustment instructions, the control system adjusts the relevant parameters of the machine tool to achieve the best machining effect. The adjusted machine tool settings will be tested again to verify the adjustment effect and ensure that the spindle can exhibit the best performance under all predetermined working conditions, thus forming a comprehensive machine tool optimization plan.

[0048] The present invention provides an adaptive axis power intelligent optimization system, including: An initial parameter calculation module that collects rotational speed, cutting force, and temperature data, combines the rotational speed, cutting force, and temperature data, and performs the calculation of the power output of the initial state of the spindle to obtain the initial power parameters; A dynamic optimization adjustment module that, based on the initial power parameters, performs real-time adjustment of the spindle power, compares the set parameter standards, detects deviations, calculates correction values and applies them to obtain the power output adjustment result; A stability analysis module that receives the power output adjustment result, monitors the continuous parameter changes during the machining process, conducts stability tests, and adjusts the machine tool settings according to the test feedback to optimize the overall machining performance and obtain the optimized plan result.

[0049] The above are only the preferred embodiments of the present invention and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An adaptive axis power intelligent optimization method based on gantry machining, characterized in that: The following steps are involved: Collecting rotation speed, cutting force and temperature data, calculating initial processing parameters according to the rotation speed, cutting force and temperature data, performing parameter adjustment based on the initial processing parameters, dynamically optimizing the spindle power output, and obtaining an adjusted power output; Based on the adjusted power output, monitoring parameter changes during continuous processing, performing real-time feedback, and obtaining feedback adjustment results; analyzing the feedback adjustment results, identifying deviations from predetermined processing standards, and obtaining deviation results; Receive the deviation result, continuously compare with the set parameters, detect the deviation, calculate the correction value, obtain the correction instruction and apply it; Conduct stability tests to determine the performance of the machine tool spindle under each working condition, adjust the machine tool settings based on the performance, and obtain the optimization solution.

2. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps for obtaining the initial processing parameters are: Collect the rotation speed, cutting force and temperature data and summarize them to get the data set; Based on the data set, calculating a work efficiency index; Based on the work efficiency index, combined with material removal rate and tool wear, initial machining parameters are obtained.

3. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps for obtaining the adjusted power output are: Based on the initial machining parameters, adjusting the spindle speed and feed rate to match different cutting force and temperature conditions; Continuously track the adjustment effect of spindle speed and feed rate, analyze the stability and efficiency of power output, check whether the power output is consistent with processing requirements, and obtain real-time monitoring results; According to the real-time monitoring result, the spindle power output is adjusted, the adjustment strategy is optimized, and the adjusted power output is obtained.

4. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps of obtaining the feedback adjustment result are: Based on the adjusted power output, continuously collecting spindle power output data; Calculating a dynamic response index based on the power output data; According to the dynamic response index, the performance stability and processing efficiency of the spindle are analyzed, and feedback adjustment results are generated.

5. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps for obtaining the deviation result are: Based on the feedback adjustment result, calculate the deviation index ; According to the deviation index, it is determined whether a deviation occurs, and a deviation result is generated.

6. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps of obtaining the correction instruction are as follows: Based on the deviation result, continuously comparing the set processing parameters, and calculating the correction value of each parameter; A correction instruction is generated based on the average correction value, and the setting of the machine tool is adjusted using the correction instruction.

7. The adaptive axis power intelligent optimization method based on gantry machining according to claim 1 is characterized in that: The steps for obtaining the optimization scheme are: Run the machine tool spindle under different working conditions and record the spindle speed stability, vibration data and temperature records; Analyze speed stability, vibration data and temperature records, analyze the performance of the machine tool spindle under each working condition, and obtain analysis results; Based on the analysis results, the speed setting is changed or the feed rate is adjusted to obtain an optimization solution.

8. An adaptive axis power intelligent optimization system according to any one of claims 1 to 7, characterized in that: include: The initial parameter calculation module collects the speed, cutting force and temperature data, combines the speed, cutting force and temperature data, performs the power output calculation of the initial state of the spindle, and obtains the initial power parameters; The dynamic optimization and adjustment module performs real-time adjustment of the spindle power based on the initial power parameters, compares the set parameter standards, detects deviations, calculates and applies correction values, and obtains the power output adjustment results; The stability analysis module receives the power output adjustment results, monitors the continuous parameter changes during the processing, performs stability tests, adjusts the machine tool settings based on the test feedback, optimizes the overall processing performance, and obtains the optimization solution results.

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