A geometric adaptive machining error compensation and optimization method for complex surfaces

Abstract

The invention discloses a method for adaptive machining error compensation and optimization of complex curved surface geometry, which belongs to the technical field of numerical control machining and intelligent manufacturing, and comprises: dividing a composite curved surface into a plurality of regions in a manner of equal area, and collecting the precision data of each region after machining in real time during the curved surface machining; based on the precision data after the regional machining is completed; automatically adjusting machining parameters according to error results to achieve error compensation, and obtaining the compensation coefficient of the error compensation process to judge the error compensation capability; using an optimization algorithm to globally optimize the machining parameters according to the specific requirements of the machining task, so as to achieve a dual improvement in machining efficiency and machining quality; the invention realizes effective control of machining errors by monitoring the curved surface machining process in real time and making error judgments, and automatically adjusting the machining parameters according to the error results, and globally optimizing the machining parameters using an optimization algorithm, so as to improve the machining accuracy, efficiency and stability of complex curved surface parts.

Description

Technical Field

[0001] The present invention belongs to the technical field of CNC machining and intelligent manufacturing, and relates to complex surface geometry machining accuracy control and parameter optimization. Specifically, it is a complex surface geometry adaptive machining error compensation and optimization method, which is used to improve the machining quality and efficiency of complex surface parts, and comprehensively evaluate the error compensation capability. Background Art

[0002] Complex curved surface parts occupy an important position in high-end manufacturing fields such as aerospace, automobile manufacturing, and mold processing due to their unique geometric shapes and wide application requirements. The machining accuracy and surface quality of such parts directly affect the performance and service life of the product, so extremely high requirements are placed on machining accuracy and efficiency. However, during the machining process, due to the influence of many factors such as the diversity of material properties, the limitation of machine tool accuracy, tool wear, and changes in the machining environment, machining errors often occur, resulting in the geometric accuracy and surface quality of the parts being difficult to meet the design requirements, which not only affects the quality and performance of the product, but also increases the production cost and cycle.

[0003] Existing surface machining error compensation technologies mainly focus on static compensation and simple dynamic compensation. Static compensation technologies, such as tool radius compensation and tool wear compensation, can handle certain fixed system errors, but cannot cope with dynamic changes in the machining process, such as tool wear and thermal deformation. Existing dynamic compensation technologies, such as compensation based on real-time error feedback, can usually only adjust a single parameter in real time, such as adjusting the feed speed, and lack comprehensive consideration of multi-parameter coupling effects, such as adjusting the feed speed and spindle speed at the same time, making it difficult to achieve the optimal compensation effect.

[0004] Taking the NURBS surface-based rapid detection and compensation method for machining errors disclosed in Chinese invention patent CN103777570A as an example, this method evaluates machining errors based on NURBS surfaces by measuring limited feature points on the surface and compensates for errors by adjusting control points. Although the detection efficiency has been improved to a certain extent, this method only focuses on the error compensation of local feature points on the surface, without fully considering the error distribution in other areas, which may lead to incomplete compensation of local errors and inability to guarantee the machining accuracy of the overall surface. Secondly, the compensation process only relies on iterative adjustment of control points, without considering the impact of dynamic changes during the machining process, and lacks the ability to handle dynamic errors and real-time compensation, resulting in relatively low efficiency of the compensation process, making it difficult to meet high-precision production requirements.

[0005] More importantly, the existing surface geometry, including the above-mentioned invention patents, cannot comprehensively analyze and evaluate the processing error compensation capability during the processing error compensation process, and can only determine whether the currently processed surface meets the process standards. Because if the surface processing error compensation capability cannot be comprehensively and effectively evaluated, it will be difficult to optimize the surface processing error compensation capability, thereby affecting the subsequent surface processing quality. At the same time, the existing error compensation method cannot realize real-time monitoring and adjustment of processing errors, and can only perform error correction after the processing is completed. This delayed error compensation method increases the processing cycle and may lead to the production of a large number of unqualified parts, thereby increasing production costs. In addition, in complex surface processing, the selection of processing parameters (such as cutting speed, feed rate, spindle speed, etc.) has an important impact on processing quality and efficiency. However, the optimization of processing parameters in the existing technology mainly relies on experience or simple trial and error methods, and fails to make full use of advanced optimization algorithms for global optimization.

[0006] In summary, the existing technology still has many defects and shortcomings in the error compensation of complex surface machining. These problems restrict the further improvement of the machining quality and efficiency of complex surface parts. Therefore, it is urgent to propose effective solutions to the above-mentioned technical defects. Summary of the invention

[0007] 1. Purpose of the invention

[0008] In view of the above-mentioned defects and shortcomings of the prior art, the purpose of the present invention is to provide a method for adaptive machining error compensation and optimization of complex surface geometry, which solves the problems that the prior art cannot comprehensively and effectively evaluate the error compensation capability of surface machining, and is difficult to optimize the error compensation capability of surface machining, by performing comprehensive error monitoring and real-time compensation on complex surfaces, combined with adaptive error compensation strategies and multi-parameter coupling optimization. Specifically, the present invention adopts a method combining partition monitoring with error evaluation, divides the complex surface into multiple areas of equal area and collects the accuracy data of the areas after machining, dynamically adjusts the machining parameters based on the error results to automatically perform error compensation, and uses an optimization algorithm to globally optimize the machining parameters, which significantly improves the machining efficiency while meeting the machining accuracy. In addition, the present invention ensures the stability and efficiency of the error compensation process by monitoring the machining performance in real time and generating early warning signals or optimization suggestions.

[0009] (II) Technical solution

[0010] In order to achieve the purpose of the invention and solve the technical problems, the present invention adopts the following technical solutions:

[0011] A complex surface geometry adaptive machining error compensation and optimization method is used to realize error monitoring, adaptive compensation and machining parameter optimization in the complex surface machining process, and improve the machining accuracy, efficiency and quality of complex surface parts. The method comprises at least the following steps when implemented:

[0012] SS1. Area division and precision data collection

[0013] Divide the complex curved surface to be processed into multiple processing areas in an equal area manner, and collect the actual processing accuracy data of each area after processing in real time through high-precision sensors and machine vision technology during the surface processing process, wherein the actual processing accuracy data at least includes surface geometric accuracy data, surface processing quality data, processing stability data and tool status data;

[0014] SS2. Error analysis and regional marking

[0015] According to the design requirements and material properties of the complex surface to be processed, standard accuracy data is preset and used as a benchmark for error judgment. Based on the actual processing accuracy data collected after the area processing is completed, the actual processing accuracy data after the area processing is completed is compared with the preset standard accuracy data and an error judgment result is generated. If the actual processing accuracy data is greater than or equal to the preset standard accuracy data, it means that the processing of the current area meets the preset process standard, and the next area is processed. If the actual processing accuracy data is less than the preset standard accuracy data, it means that the initial processing of the current area does not meet the preset process standard, and the current area needs to be error compensated, and the current area is marked as an error area.

[0016] SS3. Adaptive Error Compensation

[0017] According to the error judgment result generated in step SS2, the machining parameters are adjusted dynamically in real time based on the PID control algorithm for the error area to achieve adaptive compensation of the machining error and ensure that the error area reaches the preset process standard. If the compensation effect reaches the preset process standard, the error compensation of the current area is terminated. If the compensation effect does not reach the preset process standard, the iterative compensation adjustment is continued until the preset process standard is reached. The machining parameters at least include cutting speed, feed rate, spindle speed and tool path. The control law of the PID control algorithm is expressed as:

[0018]

[0019] in, u ( t ) is the time t Processing parameter adjustment instructions, e ( t ) is the time tThe real-time deviation value is used to indicate the difference between the actual processing accuracy data and the preset standard accuracy data. K p , K i and K d They are the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm, corresponding to the adjustment weights of the current impact of the deviation, the past cumulative impact and the future change trend impact;

[0020] SS4. Compensation coefficient calculation

[0021] During the error compensation process, the preceding processing completion time and the compensation processing completion time of each error area are obtained and marked as the preceding time of the error area respectively. TQ i and error zone compensation time TB i The compensation coefficient of the surface machining process is calculated by the following algorithm formula XB :

[0022]

[0023] in, i is the error region number and i =1,2,3,..., j , j represents the number of error regions, TBK i Compensate time for error zone TB i The beginning moment, TQJ i The leading time of the error area TQ i The end moment, D ( TB i ) is the variance of the compensation time of all error regions, is the mean value of the compensated response, To compensate for the energy efficiency value, is the compensation applicable coefficient;

[0024] SS5. Evaluation of compensation effect

[0025] The compensation coefficient of the surface machining process calculated in step SS4 is XB Compare the value with the preset compensation coefficient threshold. If the compensation coefficient of the surface machining process is XB If the compensation coefficient exceeds the preset compensation coefficient threshold, a compensation warning signal is generated. XB If the preset compensation coefficient threshold is not exceeded, a compensation qualified signal is generated;

[0026] SS6. Global optimization of machining parameters

[0027] Based on the compensation effect evaluation results in step SS5, the optimization algorithm is used to globally optimize the processing parameters according to the specific requirements of the processing task. By analyzing the historical processing data, real-time error data and compensation coefficients XB , establish a multi-objective optimization model for machining efficiency and machining quality, generate the optimal machining parameter combination, and dynamically adjust the cutting speed, feed rate, spindle speed and tool path machining parameters to achieve a dual improvement in machining efficiency and machining quality;

[0028] SS7. Processing quality assessment and abnormal signal generation (optional step)

[0029] A monitoring period of duration L1 is set, and multi-dimensional data including at least the number of times compensation warning signals are generated, quality inspection data of processed parts, and processing efficiency are collected during the monitoring period. The compensation warning risk value, surface processing defect value, surface work efficiency value, and surface processing analysis value are obtained through numerical calculation. The processing performance of complex surface geometry processing during the monitoring period is analyzed, and a surface processing qualified signal or a surface processing abnormal signal is generated through analysis to achieve comprehensive monitoring and evaluation of the processing process.

[0030] SS8. Tool status detection and life assessment (optional step)

[0031] When machining is completed or the surface geometry machining is stopped, the surface cracks, wear and high-risk areas of the tool are detected and analyzed to evaluate its life status. Based on this, it is determined whether to generate a tool life end signal. If the tool inspection result shows that it does not meet the machining requirements, a tool life end signal is generated and a reminder is given to replace the tool to prevent the machining quality from being affected by tool failure.

[0032] (III) Technical Effect

[0033] Compared with the prior art, the complex surface geometry adaptive machining error compensation and optimization method of the present invention has the following beneficial and significant technical effects:

[0034] (1) In the present invention, the surface machining process is monitored in real time and error judgment is performed. According to the error judgment result, the machining parameters are adjusted in real time and dynamically in combination with the PID control algorithm to achieve adaptive compensation of the machining error. The machining parameters are globally optimized using the optimization algorithm. The error compensation process is monitored and tracked and the compensation efficiency is accurately judged in combination with the calculation of the compensation coefficient. When the compensation warning signal is generated, the cause is investigated and analyzed and corresponding improvement measures are taken, thereby ensuring the error compensation efficiency and improving the surface machining quality.

[0035] (2) The present invention introduces the concept of compensation coefficient and comprehensively evaluates the error compensation capability through three indicators: compensation response mean, compensation energy efficiency value and compensation applicability coefficient. The compensation response mean reflects the response speed of error compensation, the compensation energy efficiency value reflects the stability and efficiency of error compensation, and the compensation applicability coefficient reflects the adaptability of the error compensation method to different processing areas. The combination of these three indicators can evaluate the effect of error compensation more comprehensively and objectively. By comparing the compensation coefficient with a preset threshold, a compensation qualification signal or a compensation warning signal can also be generated to promptly remind the operator to intervene and adjust, further improving the reliability and stability of the processing process;

[0036] (3) In the present invention, the processing performance of complex surface geometry processing during the monitoring period is analyzed, and when a surface processing abnormal signal is generated, the operator is reminded to adjust the subsequent processing management plan and promptly strengthen the subsequent processing supervision, thereby ensuring the surface processing effect and processing efficiency of the parts and reducing the processing cost. In addition, by detecting and analyzing the tool to evaluate its life status, the corresponding tool is scrapped in time to avoid processing accidents, ensure the surface processing quality and processing stability, significantly reduce the management difficulty of the operator, and further improve the level of intelligence and automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0038] Figure 1 It is a flow chart of the method for adaptive machining error compensation and optimization of complex curved surface geometry in the present invention;

[0039] Figure 2 Schematic diagram of the implementation process of step SS7 in the present invention;

[0040] Figure 3 It is a schematic diagram of the implementation flow of step SS8 in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments, and the described embodiments are exemplary and are intended to be used to explain the present invention, and cannot be understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] Embodiment 1:

[0043] like Figure 1As shown, the complex surface geometry adaptive machining error compensation and optimization method proposed in the embodiment of the present invention mainly includes the following steps when implemented:

[0044] SS1. Area division and precision data collection

[0045] The complex surface to be processed is divided into multiple areas of equal area. During the surface processing, high-precision sensors and machine vision technology are used (real-time monitoring is performed through laser rangefinders, displacement sensors, force sensors, etc.) to collect the actual processing accuracy data of each area after processing. The actual processing accuracy data at least includes surface geometry accuracy data, surface processing quality data, processing stability data, tool status data, etc., to provide data support for error analysis and prediction.

[0046] Preferably, the surface geometric accuracy data includes at least the size deviation, shape error and position error of the processing area, the surface processing quality data includes at least the roughness value (Ra) and waviness value (Rw) of the regional surface, the processing stability data includes at least the cutting force, feed force and vibration signal generated during the processing, and the tool status data includes at least the degree of tool wear, cutting trajectory deviation and tool surface temperature change.

[0047] SS2. Error analysis and regional marking

[0048] Standard accuracy data is preset according to the design requirements and material properties of the complex surface to be processed and used as the basis for error judgment. Based on the actual processing accuracy data after the area processing is completed, the actual processing accuracy data after the area processing is completed is compared with the preset standard accuracy data to generate an error result: if the actual processing accuracy data is greater than or equal to the preset standard accuracy data, it means that the initial processing of the area meets the process standards, and the next area is processed; if the actual processing accuracy data is less than the preset standard accuracy data, it means that the initial processing of the area does not meet the process standards, and the area needs to be error compensated and marked as an error area.

[0049] SS3. Adaptive Error Compensation

[0050] According to the error judgment result generated in step SS2, the processing parameters (including cutting speed, feed rate, spindle speed, etc.) and tool path are adjusted dynamically in real time using intelligent algorithms (such as PID control algorithms) to achieve real-time adaptive compensation of errors; and the compensation strategy can be dynamically adjusted according to the processing stage, material properties and tool wear to ensure that errors in the processing process are effectively controlled, to ensure the surface processing effect and to improve the level of automation and intelligence. Preferably, the processing parameters are adjusted dynamically in real time based on the PID control algorithm for the error area to achieve adaptive compensation of the processing error and ensure that the error area meets the preset process standard. If the compensation effect meets the preset process standard, the error compensation of the current area is terminated. If the compensation effect does not meet the preset process standard, the compensation adjustment is continued iteratively until the preset process standard is reached. The processing parameters include at least cutting speed, feed rate, spindle speed and tool path. The control law of the PID control algorithm is expressed as:

[0051]

[0052] in, u ( t ) is the time t Processing parameter adjustment instructions, e ( t ) is the time t The real-time deviation value is used to indicate the difference between the actual processing accuracy data and the preset standard accuracy data. K p , K i and K d They are the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm, corresponding to the adjustment weights of the current impact of the deviation, the past cumulative impact and the future change trend impact.

[0053] SS4. Compensation coefficient calculation

[0054] During the error compensation process, the preceding processing completion time of each error area is obtained, and then the compensation processing completion time of the error area is obtained, and the preceding processing completion time of the error area and the compensation processing completion time of the error area are marked as the preceding time of the error area, respectively. TQ i and error zone compensation time TB i The compensation response mean, compensation energy efficiency value and compensation applicable coefficient are calculated by error area pre-order time and error area compensation time respectively. The compensation response mean, compensation energy efficiency value and compensation applicable coefficient are processed to obtain the compensation coefficient of the surface machining process. XB :

[0055]

[0056] in, i is the error region number and i =1,2,3,..., j , j represents the number of error regions, TBK i Compensate time for error zone TB i The beginning moment, TQJ i The leading time of the error area TQ i The end moment, D ( TB i ) is the variance of the compensation time of all error regions, is the mean value of the compensated response, To compensate for the energy efficiency value, is the compensation applicable coefficient.

[0057] SS5. Evaluation of compensation effect

[0058] The compensation coefficient of the surface machining process calculated in step SS4 is XB Compare the value with the preset compensation coefficient threshold. If the compensation coefficient of the surface machining process is XB If the compensation coefficient exceeds the preset compensation coefficient threshold, a compensation warning signal is generated. XB If the preset compensation coefficient threshold is not exceeded, a compensation qualified signal is generated.

[0059] SS6. Global optimization of machining parameters

[0060] Based on the compensation effect evaluation results in step SS5, the optimization algorithm is used to globally optimize the processing parameters according to the specific requirements of the processing task. By analyzing the historical processing data, real-time error data and compensation coefficients XB , establish a multi-objective optimization model of machining efficiency and machining quality, generate the optimal machining parameter combination, and dynamically adjust the cutting speed, feed rate, spindle speed and tool path machining parameters to achieve a dual improvement in machining efficiency and machining quality.

[0061] SS7. Processing quality assessment and abnormal signal generation (optional step)

[0062] A monitoring period of duration L1 is set, and multi-dimensional data including at least the number of times compensation warning signals are generated, quality inspection data of processed parts, and processing efficiency are collected during the monitoring period. The compensation warning risk value, surface processing defect value, surface work efficiency value, and surface processing analysis value are obtained through numerical calculation. The processing performance of complex surface geometry processing during the monitoring period is analyzed, and a surface processing qualified signal or a surface processing abnormal signal is generated through analysis to achieve comprehensive monitoring and evaluation of the processing process.

[0063] SS8. Tool status detection and life assessment (optional step)

[0064] When machining is completed or the surface geometry machining is stopped, the surface cracks, wear and high-risk areas of the tool are detected and analyzed to evaluate its life status. Based on this, it is determined whether to generate a tool life end signal. If the tool inspection result shows that it does not meet the machining requirements, a tool life end signal is generated and a reminder is given to replace the tool to prevent the machining quality from being affected by tool failure.

[0065] Through the above steps, the present invention can realize real-time monitoring, dynamic compensation and global optimization of errors in the process of complex surface geometry processing, significantly improve processing accuracy, efficiency and quality, and realize intelligent monitoring and management of the processing process.

[0066] Embodiment 2:

[0067] On the basis of the above-mentioned embodiment 1, this embodiment 2 further provides an implementation method for further optimizing and supplementing step SS3 in the above-mentioned method. Specifically, the specific process of automatically adjusting the processing parameters using an intelligent algorithm (such as a PID control algorithm, etc.) is as follows:

[0068] Using the preset control algorithm, the cutting parameters are dynamically adjusted according to the specific value of the dimensional accuracy deviation. The preset control algorithm is specifically a PID control algorithm. The PID control algorithm will calculate the adjustment instructions of the processing parameters according to the dimensional accuracy deviation value. Specifically, the processing parameters are dynamically adjusted by calculating the deviation value, the accumulation of the deviation value and the change rate of the deviation value. The PID control algorithm is:

[0069]

[0070] in, u ( t ) is the time t Processing parameter adjustment instructions, e ( t ) is the time t The real-time deviation value is used to indicate the difference between the actual processing accuracy data and the preset standard accuracy data. K p , K i andK d They are the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm, corresponding to the adjustment weights of the current impact of the deviation, the past cumulative impact and the future change trend impact.

[0071] Then, according to the specific value of the size deviation, the adjustment instruction generation unit first calculates the deviation value e ( t ), that is, the difference between the actual processing size and the preset processing size, and then use the PID algorithm to calculate the adjustment instructions for the cutting speed, feed rate and spindle speed parameters to reduce or eliminate the dimensional deviation. Finally, use the preset PID control algorithm to dynamically adjust the processing parameters.

[0072] The working principle of the present invention is as follows: when in use, the surface machining process is monitored in real time and error judgment is performed, and the machining parameters are automatically adjusted according to the error results, thereby achieving effective control of the machining error. The machining parameters are globally optimized using an optimization algorithm, thereby improving the machining efficiency and surface quality. The error compensation process is monitored and tracked, and the compensation efficiency performance is accurately judged. When a compensation warning signal is generated, a cause investigation and analysis is performed and corresponding improvement measures are taken, thereby ensuring the error compensation efficiency to improve the surface machining quality.

[0073] Embodiment 3:

[0074] On the basis of the above-mentioned Example 1, this Example 3 further provides an implementation method for further optimizing and supplementing step SS4 in the above-mentioned method.

[0075] First, specifically, the compensation response mean The calculation process is as follows: First, obtain the preceding time of each error area TQ i And the corresponding error area compensation time TB i , then the error area compensation time TB i The starting time and the preceding time of the error zone TQ i The difference calculation is performed at the end time to obtain the compensation response value of each error area; secondly, the compensation response values ​​of all error areas are averaged to obtain the compensation response mean. The compensation response mean reflects the length of time after the initial processing of the surface is completed and the compensation processing is started after the error judgment. The larger the compensation response mean, the weaker the adaptive processing error compensation ability is, and the smaller the compensation response mean, the stronger the adaptive processing error compensation ability is.

[0076] Second, specifically, compensation energy efficiency value The calculation process is: Get the compensation time for each error area TB i , all error areas are compensated for time TB i Variance processing is performed to obtain the compensation stability value; then the compensation time of all error areas is averaged to obtain the mean value of surface compensation energy efficiency, and the compensation stability value is multiplied with the mean value of surface compensation energy efficiency to obtain the compensation energy efficiency value. The compensation energy efficiency value reflects the consistency of the compensation work time when different error areas are performing error compensation work. If the compensation energy efficiency value is smaller, it means that when performing processing compensation work in different areas, the compensation time is more consistent. Therefore, the smaller the compensation energy efficiency value, the higher and more stable the work efficiency of the processing error compensation is.

[0077] Third, specifically, the compensation applicable coefficient The calculation process is as follows: First, obtain the preceding time of each error area TQ i And the corresponding error area compensation time TB i , the start time of the error area compensation time TBK i End time of the previous time of the error area TQJ i The difference calculation is performed to obtain the compensation response value of each error area; then the compensation response value of the error area is compared with the previous time of the curve TQ i Perform ratio calculation to obtain the compensation applicable value of each error area; finally, average the compensation applicable values ​​of all error areas to obtain the compensation applicable coefficient. The compensation applicable coefficient reflects the relationship between the response time and the processing time ratio of all error areas, because the leading time of the error area can indirectly reflect the processing difficulty of the area, that is, the larger the leading time of the error area, the greater the processing difficulty, and the smaller the response time, the stronger the adaptive processing error compensation ability. Therefore, the smaller the value of the compensation applicable coefficient, the stronger the processing error compensation ability.

[0078] In step SS5 of the present invention, the compensation coefficient of the curved surface machining process calculated in step SS4 is XB Compare the value with the preset compensation coefficient threshold. If the compensation coefficient of the surface machining process is XB If the preset compensation coefficient threshold is exceeded, it indicates that the error compensation efficiency of the complex surface is poor overall, and a compensation warning signal is generated. XBIf the preset compensation coefficient threshold is not exceeded, it indicates that the error compensation efficiency of the complex surface is generally good, and a compensation qualified signal is generated. In the actual processing and production process, after the evaluation of the compensation effect is completed, the generated compensation warning signal or compensation qualified signal is sent to the machine tool processing management end. When the machine tool processing management end receives the compensation warning signal, it issues a corresponding warning to remind the operator to investigate and analyze the cause and take corresponding improvement measures, thereby ensuring the error compensation efficiency and improving the surface processing quality.

[0079] Embodiment 4:

[0080] On the basis of the above-mentioned embodiment 1, this embodiment 4 further provides an implementation method for further optimizing and supplementing step SS7 in the above-mentioned method. Specifically, when performing processing quality assessment and abnormal signal generation, step SS7 in the embodiment of the present invention specifically includes:

[0081] A monitoring period of duration L1 (preferably L1 may be twenty days) is set, and multi-dimensional data including at least the number of times compensation warning signals are generated, quality inspection data of processed parts, and processing efficiency are collected during the monitoring period. The compensation warning risk value, surface processing defect value, surface work efficiency value, and surface processing analysis value are obtained through numerical calculation. The processing performance of complex surface geometry processing during the monitoring period is analyzed, and a surface processing qualified signal or a surface processing abnormality signal is generated through analysis; subsequently, the surface processing qualified signal or the surface processing abnormality signal is sent to the machine tool processing management end, and the machine tool processing management end issues a corresponding warning when receiving the surface processing abnormality signal, so as to remind the operator to adjust the subsequent processing management plan, and promptly strengthen the subsequent processing supervision, so as to ensure the part surface processing effect and processing efficiency and reduce the processing cost.

[0082] More specifically, Figure 2 As shown, the analysis process of complex surface geometry processing performance is as follows:

[0083] The number of times the compensation warning signal is generated during the monitoring period is collected and the ratio is calculated with the total surface processing time during the monitoring period to obtain the compensation warning risk value, and the quality inspection information of the parts processed during the monitoring period (i.e., the determination information on whether the surface processing quality of all parts meets the requirements) is obtained. Based on all the quality inspection information, the number ratio of defective surface processing products (i.e., the surface processing quality of the corresponding parts does not meet the requirements) is obtained and marked as the surface processing defective product value;

[0084] The number of parts processed during the monitoring period is collected and the ratio is calculated with the total surface processing time to obtain the surface efficiency value, using the formula: Surface processing defect value WP and surface ergonomics WLPerform numerical calculations to obtain surface machining analysis values WX ,in sw 1. sw 2 is the preset proportional coefficient, sw 2> sw 1>0, and the surface processing analysis value WX The larger the value is, the worse the surface processing effect and efficiency of the part are.

[0085] Compensate for the warning risk value and the surface processing analysis value WX The preset compensation warning risk threshold and the preset surface processing analysis threshold are compared respectively. If the compensation warning risk value or the surface processing analysis value WX If the corresponding preset threshold is exceeded, it indicates that the surface processing performance of the part during the monitoring period is poor, and a surface processing abnormality signal is generated.

[0086] Furthermore, if the compensation warning risk value and surface processing analysis value WX If the number of tools scrapped during the monitoring period does not exceed the corresponding preset threshold, all tools scrapped during the monitoring period are obtained, and the processing time of the corresponding tool before scrapping is collected and marked as the pre-damage working time value, and the pre-damage working time value is compared with the preset pre-damage working time threshold. If the pre-damage working time value does not exceed the preset pre-damage working time threshold, it indicates that the service life of the corresponding tool is short, and the corresponding tool is marked as an abnormal damage object, and the number of corresponding abnormal damage objects during the monitoring period is obtained and marked as the abnormal damage detection value; and the pre-damage working time values ​​of all tools scrapped during the monitoring period are averaged to obtain the tool damage inspection value, through the formula:

[0087]

[0088] Compensation for early warning risk value QL , surface machining analysis value WX , abnormal damage detection value QS and tool damage inspection value QW Numerical calculations are performed to obtain the surface machining table evaluation QX ,in hy 1. hy 2. hy 3. hy 4 is a preset proportional coefficient with a value greater than zero, and the surface processing table evaluation value QX The larger the value, the worse the overall performance of surface machining during the monitoring period; QX Compared with the preset surface processing table evaluation threshold, if the surface processing table evaluation value QX If the preset surface processing evaluation threshold is exceeded, it indicates that the surface processing operation performance during the monitoring period is generally poor, and a surface processing abnormality signal is generated; if the surface processing evaluation value is QXIf the preset surface processing evaluation threshold is not exceeded, it indicates that the surface processing operation performance during the monitoring period is generally good, and a surface processing qualified signal is generated.

[0089] Embodiment 5:

[0090] On the basis of the above-mentioned embodiment 1, this embodiment 5 further provides an implementation method for further optimization and supplementation of step SS8 in the above-mentioned method. When performing tool status detection and life assessment in step SS8, when the machining is completed or the surface geometry machining is stopped, the surface cracks, wear and high-risk areas of the tool are detected and analyzed to assess its life status, and it is judged whether to generate a tool life end signal based on this, and the tool life end signal is sent to the machining management end. When the machining management end receives the tool life end signal, it issues a corresponding warning and scraps the corresponding tool in time to avoid machining accidents, which is conducive to ensuring the surface machining quality and machining stability, reducing management difficulty, and improving the level of intelligence and automation.

[0091] In a further preferred embodiment, Figure 3 As shown in the figure, the process of generating the tool end-of-life signal is as follows:

[0092] During the machining process, the surface image of the tool is collected, and its surface cracks and chipped corners are identified based on the surface image. The identified surface cracks and chipped corners are marked as target objects. If the corresponding target object has dimension data that does not meet the requirements (that is, the corresponding surface cracks or chipped corners are serious), the corresponding target object is marked as a high-damage object; if there is a high-damage object on the tool, it indicates that the corresponding tool is no longer suitable for continued machining, and a tool end-of-life signal is generated; if there is no high-damage object on the tool, the wear amount of several positions on the tool is collected, the wear amount of all positions is averaged to obtain the wear performance value, and the wear amount with the largest value on the tool is marked as the wear amplitude table value;

[0093] and dividing the tool into several squares. If the surface cracks or corner chips involve the corresponding squares, the corresponding squares are marked as red squares, and the number of red squares on the tool is obtained and the ratio of the number of red squares to the total number of squares is calculated to obtain the red square detection value; and marking the gathering area of ​​the red squares as the red square danger zone, marking the number of red squares in the corresponding red square danger zone as the red square gathering value, and marking the red square gathering value with the largest value as the red square gathering amplitude value;

[0094] By formula Wear performance value PN , Wear range table value YF , Red grid detection value HM and red grid amplitude HF Numerical calculation to obtain tool life evaluation value LW ,in rq 1. rq 2.rq 3. rq 4 is a preset proportional coefficient with a value greater than zero, and the tool life evaluation value LW The larger the value, the worse the current condition of the tool is, and the less suitable it is for continued processing;

[0095] Tool life rating LW Compare the tool life value with the preset tool life threshold. LW If the tool life threshold is exceeded, it indicates that the tool is in poor condition and is not suitable for continued processing, and a tool life end signal is generated.

[0096] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A complex surface geometry adaptive machining error compensation and optimization method, used for error monitoring, adaptive compensation and machining parameter optimization in the process of tool machining complex surfaces, characterized in that: The method comprises at least the following steps when implemented: SS1. Divide the complex curved surface to be processed into multiple processing areas in an equal area manner, and collect the actual processing accuracy data of each area after processing in real time through high-precision sensors and machine vision technology during the surface processing process. The actual processing accuracy data at least includes surface geometric accuracy data, surface processing quality data, processing stability data and tool status data; SS2. Preset standard accuracy data according to the design requirements and material properties of the complex surface to be processed and use it as the basis for error judgment. Based on the actual processing accuracy data collected after the area is processed, compare the actual processing accuracy data after the area is processed with the preset standard accuracy data and generate an error judgment result. If the actual processing accuracy data is greater than or equal to the preset standard accuracy data, process the next area. If the actual processing accuracy data is less than the preset standard accuracy data, error compensation processing is required for the current area, and the current area is marked as an error area. SS3. According to the error judgment result, based on the PID control algorithm for the error area, the processing parameters are adjusted dynamically in real time by calculating the deviation value, the accumulation of the deviation value and the change rate of the deviation value to achieve adaptive compensation of the processing error. If the compensation effect reaches the preset process standard, the error compensation of the current area is terminated. If the compensation effect does not reach the preset process standard, the iterative compensation adjustment is continued until the preset process standard is reached. The processing parameters at least include cutting speed, feed rate, spindle speed and tool path. The control law of the PID control algorithm is expressed as: in, u ( t ) is the time t Processing parameter adjustment instructions, e ( t ) is the time t The real-time deviation value is used to indicate the difference between the actual processing accuracy data and the preset standard accuracy data. K p , K i and K d They are the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm, corresponding to the adjustment weights of the current impact of the deviation, the past cumulative impact and the future change trend impact; SS4. During the error compensation process, obtain the previous processing completion time and compensation processing completion time of each error area, and mark them as the previous time of the error area respectively. TQ i and error zone compensation time TB i The compensation coefficient of the surface machining process is calculated by the following algorithm formula XB : in, i is the error region number and i =1,2,3,..., j , j represents the number of error regions, TBK i Compensate time for error zone TB i The beginning moment, TKMv i The leading time of the error area TQ i The end moment, D ( TB i ) is the variance of the compensation time of all error regions, is the mean value of the compensated response, To compensate for the energy efficiency value, is the compensation applicable coefficient; SS5. Set the compensation factor XB Compare the value with the preset compensation coefficient threshold. If the compensation coefficient of the surface machining process is XB If the compensation coefficient exceeds the preset threshold, a compensation warning signal is generated. XB If the preset compensation coefficient threshold is not exceeded, a compensation qualified signal is generated; SS6. Based on the evaluation results of compensation effect, the optimization algorithm is used to globally optimize the processing parameters by analyzing the historical processing data, real-time error data and compensation coefficients. XB , establish a multi-objective optimization model of processing efficiency and processing quality, and generate the optimal combination of processing parameters.

2. The complex surface geometry adaptive machining error compensation and optimization method according to claim 1, characterized in that: In step SS1, the surface geometric accuracy data includes at least the size deviation, shape error and position error of the processing area, the surface processing quality data includes at least the roughness value and waviness value of the regional surface, the processing stability data includes at least the cutting force, feed force and the vibration signal generated during the processing, and the tool status data includes at least the degree of tool wear, cutting trajectory deviation and tool surface temperature change.

3. The complex surface geometry adaptive machining error compensation and optimization method according to claim 1, characterized in that: In step SS4, the calculation process of the compensation response mean is as follows: first, the leading time of each error region is obtained TQ i And the corresponding error area compensation time TB i , the start time of the error area compensation time TBK i End time of the previous time of the error area TKMv i The difference calculation is performed to obtain the compensation response value of each error area; then the compensation response values ​​of all error areas are averaged to obtain the compensation response mean to measure the response speed of the error compensation.

4. The complex surface geometry adaptive machining error compensation and optimization method according to claim 3 is characterized in that: In step SS4, the calculation process of the compensation energy efficiency value is as follows: Obtain the compensation time of each error area TB i , all error areas are compensated for time TB i Perform variance processing to obtain the compensated stable value; compensate all error areas for time TB i The mean value is processed to obtain the mean value of the surface compensation energy efficiency; the calculated compensation stability value and the mean value of the surface compensation energy efficiency are multiplied to obtain the compensation energy efficiency value, which is used to evaluate the stability of the compensation process.

5. The complex surface geometry adaptive machining error compensation and optimization method according to claim 4, characterized in that: In step SS4, the calculation process of the compensation applicable coefficient is: First, obtain the preceding time of each error area TQ i And the corresponding error area compensation time TB i , the start time of the error area compensation time TBK i End time of the previous time of the error area TKMv i The difference calculation is performed to obtain the compensation response value of each error area; then the calculated compensation response value of the error area is compared with the previous time of the curve TQ i A ratio operation is performed to obtain the compensation applicable value for each error area; then the compensation applicable values ​​of all error areas are averaged to obtain the compensation applicable coefficient, which is used to reflect the matching degree between the compensation and the regional processing time.

6. The complex surface geometry adaptive machining error compensation and optimization method according to claim 1, characterized in that: It also includes step SS7 of processing quality assessment and abnormal signal generation: A monitoring period is set, and multi-dimensional data including at least the number of times compensation warning signals are generated, quality inspection data of processed parts, and processing efficiency are collected during the monitoring period. The compensation warning risk value, surface processing defect value, surface work efficiency value, and surface processing analysis value are obtained through numerical calculation. The processing performance of complex surface geometry processing during the monitoring period is analyzed, and a surface processing qualified signal or a surface processing abnormal signal is generated through analysis to achieve comprehensive monitoring and evaluation of the processing process.

7. The complex surface geometry adaptive machining error compensation and optimization method according to claim 6, characterized in that: In step SS7, the generation process of the surface machining abnormal signal is as follows: The number of times the compensation warning signals are generated during the monitoring period is collected and the ratio is calculated with the total surface processing time during the monitoring period to obtain the compensation warning risk value; based on all quality inspection data of the parts processed during the monitoring period, the number of defective surface processing products is obtained and marked as the surface processing defective value; the number of parts processed during the monitoring period is collected and the ratio is calculated with the total surface processing time to obtain the surface efficiency value; the surface processing analysis value is obtained by numerically calculating the surface processing defective value and the surface efficiency value; if the compensation warning risk value or the surface processing analysis value exceeds the corresponding preset threshold, a surface processing abnormal signal is generated.

8. The complex surface geometry adaptive machining error compensation and optimization method according to claim 6, characterized in that: In step SS7, the process of generating the surface processing qualified signal is as follows: If both the compensation warning risk value and the surface processing analysis value do not exceed the corresponding preset thresholds, all the scrapped tools during the monitoring period are obtained, and the processing time of the corresponding tools before scrapping is collected and marked as the pre-damage working time value. If the pre-damage working time value does not exceed the preset pre-damage working time threshold, the corresponding tool is marked as an abnormal damage object, and the number of corresponding abnormal damage objects during the monitoring period is obtained and marked as the abnormal damage detection value; the pre-damage working time values ​​of all scrapped tools during the monitoring period are averaged to obtain the tool damage inspection value, and the surface processing table evaluation value is obtained by numerically calculating the compensation warning risk value, the surface processing analysis value, the abnormal damage detection value and the tool damage inspection value. If the surface processing table evaluation value exceeds the preset surface processing table evaluation threshold, a surface processing abnormal signal is generated. If the surface processing table evaluation value does not exceed the preset surface processing table evaluation threshold, a surface processing qualified signal is generated.

9. The complex surface geometry adaptive machining error compensation and optimization method according to claim 1, characterized in that: It also includes step SS8 for tool status detection and life assessment: When machining is completed or the surface geometry machining is stopped, the surface cracks, wear and high-risk areas of the tool are detected and analyzed to evaluate its life status. Based on this, it is determined whether to generate a tool life end signal. If the tool inspection result shows that it does not meet the machining requirements, a tool life end signal is generated and a reminder is given to replace the tool to prevent the machining quality from being affected by tool failure.

10. The complex surface geometry adaptive machining error compensation and optimization method according to claim 9, characterized in that: In step SS8, the process of generating the tool end-of-life signal is as follows: During the machining process, the surface image of the tool is collected and its surface cracks and chipped corners are identified based on the surface image. The identified surface cracks and chipped corners are marked as target objects. If the corresponding target object has dimension data that does not meet the requirements, the corresponding target object is marked as a high-damage object. If there is a high-damage object on the tool, a tool life end signal is generated. If there is no high-damage object on the tool, the wear amount of several positions on the tool is collected, the wear amount of all positions is averaged to obtain the wear performance value, and the wear amount with the largest value on the tool is marked as the wear amplitude table value; The tool surface is divided into several squares. If the surface cracks or corner chips involve the corresponding squares, the corresponding squares are marked as red squares. The number of red squares on the tool is obtained and the ratio of the number of red squares to the total number of squares is calculated to obtain the red square detection value; the clustering area of ​​the red squares is marked as the red square danger zone, the number of red squares in the corresponding red square danger zone is marked as the red square clustering value, and the red square clustering value with the largest value is marked as the red square clustering amplitude value; The tool life evaluation value is obtained by numerically calculating the wear performance value, wear amplitude table value, red grid detection value and red grid amplitude value. If it exceeds the preset tool life evaluation threshold, a tool life end signal is generated.

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