An efficient planar machining method for cylinder head machining

Abstract

This application relates to the technical field of grinding adjustment, and specifically relates to an efficient planar machining method for cylinder head machining, including: obtaining the cutting temperature and cutting force of the tool during each cylinder head planar machining process, the set cutting depth, cutting speed, and machining duration during each cylinder head planar machining process, screening the efficient machining duration, and based on the cutting speed range difference and the corresponding cutting depth range difference corresponding to all efficient machining durations, as well as the difference between the maximum value of the cutting depth corresponding to all efficient machining durations and the maximum value of the cutting depth sequence, combined with the data comparison situation of the cutting speed and the cutting depth, constructing a cutting speed up-regulation index to constrain the cutting speed, constructing a cutting mutation index and a tool wear confidence level to set the positions of food and natural enemies of the dragonfly optimization algorithm, and obtaining the optimal cutting speed. This application can improve the efficiency of cylinder head planar machining and ensure the accuracy of machining adjustment.

Description

Technical Field

[0001] This application relates to the technical field of grinding adjustment, and particularly to an efficient planar machining method for cylinder head machining. Background Art

[0002] As the power core of an automobile, the engine drives the normal driving of the vehicle. The cylinder head is one of the key components in the engine, mainly responsible for enclosing the cylinder and jointly forming the combustion chamber with the cylinder block. Due to the complex structure of the cylinder head and the fact that the machining process involves multiple links, its planar machining efficiency is particularly crucial in production. In the current machining process, the planar machining efficiency is mainly affected by factors such as the machining performance of the machining center (also known as a numerically controlled machine tool), tool design, and machining parameters. High-efficient planar machining efficiency can not only improve the production speed but also reduce the manufacturing cost. Therefore, improving the planar machining efficiency of the cylinder head is very important for the entire production process.

[0003] Currently, the machining efficiency is usually improved by adjusting the cutting speed. However, since excessive increase in the cutting speed will lead to increased vibration during the machining process, affecting the surface quality of the machined part and the tool life, the existing technology usually conducts feedback experiments through the exhaustive method. But when the existing technology conducts feedback experiments, it does not consider that the cutting depth needs to change according to the current size of the cylinder head, resulting in that at different cutting depths, the adjusted cutting speed not only cannot achieve the highest machining efficiency but may also cause tool wear and the adjustment effect is not good. Summary of the Invention

[0004] To solve the above technical problems, this application provides an efficient planar machining method for cylinder head machining to solve the existing problems.

[0005] An efficient planar machining method for cylinder head machining in this application adopts the following technical solution:

[0006] An embodiment of this application provides an efficient planar machining method for cylinder head machining, including the following steps:

[0007] Obtain the tool cutting temperature sequence and cutting force sequence during each cylinder head planar machining process. The cutting depth, cutting speed, and machining duration set during each cylinder head planar machining process respectively form a cutting depth sequence, a cutting speed sequence, and a machining duration sequence;

[0008] Cluster and screen the processing duration sequence to obtain efficient processing durations. Based on the range of cutting speeds and the range of cutting depths corresponding to all efficient processing durations, as well as the difference between the maximum value of the cutting depths corresponding to all efficient processing durations and the maximum value of the cutting depth sequence, and combining the degree of consistency in the changes of the cutting speed sequence and the cutting depth sequence, construct a cutting speed up - adjustment index. Then, combining the maximum and minimum values of the cutting speeds corresponding to all efficient processing durations, construct a cutting speed constraint condition;

[0009] According to the change of the cutting force in the cutting force sequence of each processing and the degree of mutation of each mutation point in the first - order difference sequence of the cutting force sequence, construct a cutting mutation index for each processing; Based on the change of the cutting temperature sequence corresponding to each processing and combining the cutting mutation index, construct the tool wear confidence level for each processing;

[0010] Perform threshold segmentation on the tool wear confidence levels of all processings, screen the optimal processing data and bad data to set the positions of food and natural enemies of the dragonfly optimization algorithm. Combining the cutting speed constraint condition, use the dragonfly optimization algorithm to obtain the optimal cutting speed and adjust the cutting speed for the machining of the cylinder head plane.

[0011] Preferably, the cutting temperature sequence and the cutting force sequence corresponding to each processing are respectively composed of the tool cutting temperatures and cutting forces at all acquisition times during each processing arranged in the order of acquisition times. The cutting depth sequence, the cutting speed sequence, and the processing duration sequence are respectively composed of the set cutting depths, cutting speeds, and processing durations during all processing times arranged in the order of processing times.

[0012] Preferably, the screening of the efficient processing duration includes: using a clustering algorithm to cluster the processing duration sequence to obtain multiple clustering clusters, and denoting the clustering cluster with the smallest average value of the processing durations in the clustering cluster as the efficient clustering cluster, and the processing duration in the efficient clustering cluster as the efficient processing duration.

[0013] Preferably, the expression of the cutting speed up - adjustment index is:

[0014] ; where A is the cutting speed up - adjustment index; is the maximum value in the cutting depth sequence; QX is the maximum value of the cutting depth data corresponding to all efficient processing durations; B is the speed adjustment coefficient, which is specifically determined by the degree of consistency in the changes of the cutting speed and the cutting depth corresponding to the efficient processing duration; C is the unit speed change index of the cutting speed, which is specifically the average value of the ratios of the elements at the same positions in the cutting speed sequence and the cutting depth sequence.

[0015] Preferably, the speed adjustment coefficient is further the ratio of the range between the cutting speeds corresponding to all the high-efficiency machining durations to the range between the cutting depths corresponding to all the high-efficiency machining durations.

[0016] Preferably, the cutting speed constraint condition is specifically: ; where are respectively the minimum value and the maximum value in the cutting speed data corresponding to all the high-efficiency machining durations; SD is the cutting speed during planar machining; A is the cutting speed up-regulation index.

[0017] Preferably, the expression of the cutting mutation index for each machining is: ; where is the cutting mutation index for the u-th machining; is the normal coefficient of the cutting force change for the u-th machining, specifically the ratio of the absolute value of the sum of all negative numbers in the first-order difference sequence of the cutting force sequence for the u-th machining to the sum of all positive numbers; is the mutation fluctuation coefficient of the cutting force for the u-th machining, specifically: taking the average value of the absolute values of the differences between each mutation point in the first-order difference sequence of the cutting force sequence for the u-th machining and its adjacent elements, denoting it as the mutation mean of each mutation point, and taking the product of the average value of the mutation means of all the mutation points and the variance as the mutation fluctuation coefficient of the cutting force for the u-th machining; is the variance of the cutting force sequence; is the number of mutation points in the first-order difference sequence of the cutting force sequence for the u-th machining.

[0018] Preferably, the expression of the tool wear confidence level for each machining is:

[0019] ; where is the tool wear confidence level for the u-th machining; is the cutting mutation index for the u-th machining; is the temperature rise difference coefficient for the u-th machining, specifically: dividing the cutting temperature sequence for the u-th machining to obtain each temperature subsequence, linearly fitting each temperature subsequence, calculating the average value of the difference between the maximum fitting line slope and the slopes of all other fitting lines, and then taking the product with the maximum fitting line slope as the temperature rise difference coefficient for the u-th machining.

[0020] Preferably, the screening of the optimal machining data and the defective data includes:

[0021] Threshold segmentation is performed on all tool wear confidence levels to obtain the wear threshold. The processing data corresponding to all tool wear confidence levels greater than or equal to the wear threshold is recorded as bad data, and the remaining processing data is recorded as good data. The processing data that belongs to both good data and the high-efficiency clustering cluster is recorded as the optimal processing data, where the processing data includes the cutting force sequence, cutting temperature sequence, cutting speed data, cutting depth data, and processing duration in each processing process.

[0022] Preferably, the food and natural enemy positions of the dragonfly optimization algorithm are the optimal processing data and bad data respectively; where the objective function expression of the dragonfly optimization algorithm is: ; in the formula, Y is the objective function value; is the function to take the minimum value; SC is the processing duration; is the tool wear confidence level of the u-th processing.

[0023] This application has at least the following beneficial effects:

[0024] By constructing the cutting speed increase index, this application can fully consider the cutting depth difference between high-efficiency and low-efficiency machining, and dynamically adjust the maximum upper limit of the cutting speed based on this. At the same time, considering that too high a cutting speed can improve machining efficiency but may cause tool damage, a cutting mutation index is constructed to analyze the cutting force change characteristics at the current cutting speed and preliminarily evaluate the tool wear state. Then, combined with the temperature characteristics, the tool wear confidence level is further constructed to realize a comprehensive evaluation of the tool state.

[0025] The beneficial effects are as follows: This application considers the problem that the existing cutting speed adjustment method does not consider the cutting depth, resulting in poor machining efficiency and possible tool wear; by constructing the speed increase index and tool wear confidence level, this application can obtain the maximum cutting speed without causing tool wear, thus significantly improving the plane machining efficiency of the cylinder head. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a flowchart of the steps of an efficient plane machining method for cylinder head machining provided by this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on a high-efficiency planar machining method for cylinder head machining according to the present application, including its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, terms such as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the said element. In addition, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0030] The following will specifically describe the specific solution of a high-efficiency planar machining method for cylinder head machining provided by the present application in conjunction with the accompanying drawings.

[0031] A high-efficiency planar machining method for cylinder head machining provided by an embodiment of the present application. Specifically, please refer to Figure 1 , including the following steps:

[0032] Step 1: Obtain the tool cutting temperature sequence and cutting force sequence during each cylinder head planar machining process. The cutting depth, cutting speed and machining duration set during each cylinder head planar machining process respectively form a cutting depth sequence, a cutting speed sequence and a machining duration sequence.

[0033] In this embodiment, a four-axis machining center is used to perform planar machining on the bottom surface of the cylinder head after rough machining. The cylinder head is clamped in a four-point symmetric and uniform distribution manner, the tool rake angle is set to 5°, and the machine tool speed is set to 2000 r / min.

[0034] The cutting temperature of the tool during each machining of the bottom surface of the cylinder head is obtained in real time through the central control system of the machining center, and at the same time, the cutting force data of the tool during the bottom surface machining process is collected by a cutting force measuring instrument. All data is collected synchronously and in real time, the data collection frequency is 100HZ, and the data collection duration is from the start to the end of the machining of the bottom surface of the cylinder head, obtaining the cutting temperature and cutting force of the tool at each collection moment, and respectively constructing a cutting temperature sequence and a cutting force sequence for each machining of the bottom surface of the cylinder head according to the order of the collection moments.

[0035] At the same time, the set cutting depth, cutting speed, and machining duration during each plane machining of the bottom surface of the cylinder head are obtained. The cutting speed data, cutting depth data, cutting force sequence, cutting temperature sequence, and machining duration during each machining of the bottom surface of the cylinder head are all recorded as one set of plane machining data.

[0036] To achieve the analysis of the plane machining of the cylinder head, M sets of plane machining data of the bottom surface of the cylinder head are obtained from the historical database of the four-axis machining center. In this embodiment, M is taken as 200, and a cutting speed sequence, a cutting depth sequence, and a machining duration sequence are constructed in the chronological order of plane machining. To eliminate the influence of the dimension between data, all data is processed by maximum-minimum normalization.

[0037] Step 2: Cluster and screen the efficient machining durations for the machining duration sequence. According to the cutting speed range and the corresponding cutting depth range corresponding to all efficient machining durations, and the difference between the maximum value of the cutting depth corresponding to all efficient machining durations and the maximum value of the cutting depth sequence, and combined with the consistency degree of the changes in the cutting speed sequence and the cutting depth sequence, a cutting speed up-regulation index is constructed, and then combined with the maximum and minimum values of the cutting speed corresponding to all efficient machining durations, a cutting speed constraint condition is constructed.

[0038] Since after the initial rough machining, only burrs, protrusions and other excess materials are removed from each surface of the cylinder head, but at this time the surface of the cylinder head is not smooth enough, and the size of the cylinder head does not meet the production requirements, so further cutting operations are required to improve the flatness of the plane of the cylinder head and at the same time adjust the size of the cylinder head.

[0039] However, due to certain dimensional differences among the cylinder heads after rough machining, there are also certain differences in the cutting depth of the bottom surface of the cylinder heads in the historical database. At the same time, the changes in cutting speed and cutting depth will also cause the cutting force during plane machining to change. Excessive cutting force will not only lead to poor stability between the machining center and the cylinder head, but also cause severe wear of the cutting tool, resulting in deformation of the cylinder head or a decrease in surface quality, and thus a change in the flatness after machining. Therefore, first, the data with the shortest machining time can be selected through the clustering algorithm to analyze the degree of change in cutting speed and cutting depth under the highest machining efficiency; then, further evaluate the impact of cutting speed on tool wear.

[0040] Take the machining time sequence as the input of the DPC density clustering algorithm, and use the cross-validation method to obtain the truncation distance of the DPC clustering algorithm. The output of the DPC clustering algorithm is multiple clusters, and the cluster with the smallest mean value in the clusters is denoted as the high-efficiency cluster.

[0041] Furthermore, in this embodiment, the machining time in the high-efficiency cluster is denoted as the high-efficiency machining time, and the range of the cutting depths corresponding to all the high-efficiency machining times is denoted as the maximum depth difference; the range of the cutting speeds corresponding to all the high-efficiency machining times is denoted as the maximum speed difference. The ratio of the maximum speed difference to the maximum depth difference is denoted as the speed adjustment coefficient. The larger the speed adjustment coefficient, the greater the change in cutting speed required to achieve the ideal machining efficiency as the cutting depth changes.

[0042] Furthermore, quantify the influence degree between cutting speed and cutting depth. Specifically, calculate the average value of the ratios of the elements at the same positions in the cutting speed sequence and the cutting depth sequence, and denote it as the unit speed change index of the cutting speed. The unit speed change index reflects the proportional relationship between cutting speed and cutting depth.

[0043] Furthermore, combine the speed adjustment coefficient, the unit speed change index of the cutting speed, and the difference between the maximum value in the cutting depth sequence and the maximum value in the cutting depth data corresponding to all the high-efficiency machining times to determine the cutting speed up-regulation index. The specific expression in this embodiment is: . In the formula, A is the cutting speed up-regulation index; is the maximum value in the cutting depth sequence; QX is the maximum value in the cutting depth data corresponding to all the high-efficiency machining times; B is the speed adjustment coefficient; C is the unit speed change index of the cutting speed.

[0044] In the above formula, It reflects the degree of difference between the maximum cutting depth in the historical processed data and the maximum cutting depth when the machining efficiency is relatively high. The larger the difference value, the larger the range within which the cutting speed can continue to be adjusted. The speed adjustment coefficient B reflects the degree to which the cutting speed needs to be adjusted when the cutting depth changes. The unit speed change index C reflects the degree of change in the cutting speed when the cutting depth undergoes a unit change. The cutting speed increase index A reflects the range within which the cutting speed can still increase for the machining data with relatively low machining efficiency compared to the machining data with high machining efficiency.

[0045] Based on the cutting speed corresponding to the high-efficiency machining duration and in combination with the cutting speed increase index, a cutting speed constraint condition is constructed. The specific cutting speed constraint expression is: . In the formula, are respectively the minimum value and the maximum value in the cutting speed data corresponding to all high-efficiency machining durations; SD is the cutting speed during planar machining; A is the cutting speed increase index.

[0046] Step 3: According to the change situation of the cutting force in each machining cutting force sequence and the mutation degree of each mutation point in the first-order difference sequence of the cutting force sequence, a cutting mutation index for each machining is constructed; based on the change situation of the cutting temperature sequence corresponding to each machining and in combination with the cutting mutation index, a tool wear confidence level for each machining is constructed.

[0047] Furthermore, the faster the cutting speed, the higher the cutting efficiency at this time and the shorter the machining duration. However, if only the cutting speed is increased, it will also cause the situation of instability of the cylinder head during planar machining and may also lead to severe wear of the tool, thus affecting the surface quality and machining efficiency of the cylinder head.

[0048] If the value of the cutting speed is relatively reasonable, during planar machining, the change of the cutting force will always be relatively stable. As the cutting time increases, the temperature of the cutting tool of the cylinder head rises, making the machined part of the cylinder head soften, and its cutting force will slowly decrease. If the cutting speed is set unreasonably and the cutting speed is too fast, resulting in significant wear of the tool during the cutting process, it will cause an instantaneous increase in the cutting force and cause severe fluctuations in the cutting force.

[0049] In this embodiment, taking the cutting force sequence corresponding to the u-th machining as an example for analysis, analyzing the data change situation of the cutting force sequence and the mutation situation of the first-order difference sequence of the cutting force sequence, a cutting mutation index is constructed. The specific process is as follows:

[0050] In this embodiment, for the convenience of expression and understanding, the first-order difference sequence of the cutting force sequence at the u-th machining is denoted as the cutting force change sequence, and the absolute value h1 of the sum of all negative numbers and the sum h2 of all positive numbers in the cutting force change sequence are calculated respectively, and the ratio of h1 to h2 is denoted as the change normal coefficient of the cutting force at the u-th machining. The smaller the change normal coefficient is, the smaller the sum of negative elements is compared with the sum of positive elements in the cutting force change sequence, and the less it conforms to the characteristic that the overall cutting force sequence decreases slowly.

[0051] Furthermore, since the cutting force will increase instantaneously if tool wear occurs, and the cutting force data fluctuates greatly after the instantaneous increase. Therefore, the cutting force change sequence is used as the input of the PELT mutation point algorithm to obtain all the mutation points in the cutting force change sequence. The PELT mutation point detection is a well-known technology and will not be elaborated here.

[0052] Taking each mutation point as the center, calculate the average value of the absolute values of the differences between each mutation point and its previous element and the next element, which is denoted as the mutation mean value of each mutation point. Further, the product of the average value of the mutation mean values of all mutation points and the variance is denoted as the mutation fluctuation coefficient of the cutting force at the u-th machining. The larger the mutation fluctuation coefficient is, the greater the degree of instantaneous mutation at each mutation point is, and the greater the difference in the degree of instantaneous mutation between each mutation point is.

[0053] Furthermore, according to the change normal coefficient, the mutation fluctuation coefficient, and the fluctuation degree of the cutting force sequence, calculate the cutting mutation index for each machining. In this embodiment, the specific expression is: . In the formula, is the cutting mutation index for the u-th machining; is the change normal coefficient of the cutting force at the u-th machining; is the mutation fluctuation coefficient of the cutting force at the u-th machining; is the variance of the cutting force sequence; is the number of mutation points in the first-order difference sequence of the cutting force sequence at the u-th machining;

[0054] Among them, the smaller the change normal coefficient is, the less the change trend of the cutting force conforms to the characteristic of slow overall decrease in the cutting force sequence at the u-th machining, that is, the greater the possibility of abnormal increase in the cutting force; the larger the mutation fluctuation coefficient is, the greater the difference in the degree of instantaneous mutation at each mutation point and the overall mutation difference, and the greater the possibility of tool wear; the larger the variance of the cutting force sequence is, the greater the fluctuation degree of the cutting force sequence is; at the same time, the more mutation points are detected in the cutting force change sequence, the larger the cutting mutation index in the machining process is, and the higher the possibility of tool wear in the corresponding plane machining process is.

[0055] Furthermore, before wear occurs, the cutting temperature gradually and slowly increases with the duration of the tool during the cutting process. However, if wear occurs, more heat is generated, causing the tool temperature to suddenly rise, and the rate of temperature increase is very fast, much greater than the normal heating rate. At the same time, the worn tool is unstable during the cutting process, which also causes large fluctuations in temperature.

[0056] Therefore, in this embodiment, the cutting temperature sequence of the u-th machining is used as the input of the BG sequence segmentation algorithm, and the output of the BG sequence segmentation algorithm is multiple temperature subsequences. All temperature subsequences are respectively used as the input of the least squares method for linear fitting, and the output of the least squares method is the corresponding fitting line. Further, calculate the average value of the difference between the slope of the maximum fitting line and the slopes of all other fitting lines, and then multiply it by the slope of the maximum fitting line as the temperature rise difference coefficient of the u-th machining. The BG sequence segmentation algorithm and the least squares method are well-known technologies and will not be elaborated here.

[0057] It can be understood that the larger the temperature rise difference coefficient, the faster the temperature rise rate of the temperature subsequence corresponding to the slope of the maximum fitting line compared to the temperature rise rates of other temperature subsequences.

[0058] Let ; In the formula, is the tool wear confidence of the u-th machining; is the cutting mutation index of the u-th machining; is the temperature rise difference coefficient of the u-th machining.

[0059] If the cutting mutation index is larger, it indicates that the possibility of abnormal mutation of the cutting force during the u-th plane machining is greater; the larger the temperature rise difference coefficient, it indicates that the possibility of abnormal increase in temperature data during the u-th plane machining is greater, and the corresponding tool wear confidence is greater, then the possibility of tool wear during the u-th plane machining is greater.

[0060] Step 4: Perform threshold segmentation on the tool wear confidence of all machinings, screen the optimal machining data and bad data, set the food and predator positions of the dragonfly optimization algorithm, and combine the cutting speed constraint conditions to use the dragonfly optimization algorithm to obtain the optimal cutting speed and adjust the cutting speed of the cylinder head plane machining.

[0061] Furthermore, obtain the tool wear confidence of each machining according to the above process of this embodiment, and use all the tool wear confidences as the input of the Otsu threshold method. The output of the Otsu threshold method is the wear threshold. Mark the machining data corresponding to the tool wear confidences greater than or equal to the wear threshold as bad data, and mark the remaining machining data as good data. Further, mark the machining data that belongs to both good data and the efficient clustering cluster as the optimal machining data.

[0062] In order to find the optimal cutting speed, the present application uses the dragonfly optimization algorithm for optimal solution. Since the initial distribution positions of the dragonflies in the dragonfly algorithm directly affect the convergence speed and calculation accuracy of the algorithm, and a good initial distribution can accelerate the finding of the global optimal solution. Therefore, in this embodiment, the optimal machining data is used as the food position (local optimal solution) in the dragonfly optimization algorithm, and the bad data is used as the natural enemy position (local worst solution). The number of dragonflies is the number of machining times M obtained from the historical database, which is taken as 200 in this embodiment. After placing the dragonflies at the food position, the remaining dragonflies are randomly distributed, but not at the natural enemy position.

[0063] Construct the objective function: . In the formula, Y is the objective function value; is the function to take the minimum value; SC is the machining duration; is the tool wear confidence level for the u-th machining. Set the relevant parameters of the dragonfly optimization algorithm as follows: separation weight is 0.1, alignment weight is 0.1, aggregation weight is 0.7, food factor is 1, natural enemy weight is 1, inertia weight is initially 0.9 and linearly decreases to 0.4 with the number of iterations, and the number of iterations is 50. Take the number of dragonflies, initial positions, various parameter weights, objective function, and set cutting depth as the input of the dragonfly algorithm. The output of the dragonfly algorithm is the cutting speed that minimizes the objective function value. Thus, on the premise of not causing tool wear, the machining duration during the machining of the cylinder head plane is the shortest through the output optimal cutting speed.

[0064] Thus, according to the above process of this embodiment, the machining process of the cylinder head plane is adjusted, which can improve the machining efficiency of the cylinder head plane.

[0065] It can be understood that the reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Thus, if "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. appear in different places in this specification, they do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprise", "include", "have" and their variants all mean "include but not limited to", unless otherwise specifically emphasized in other ways.

[0066] It should be noted that the sequence of the above embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the above specific embodiments of this specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or consecutive order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. At the same time, the magnitudes of the sequence numbers of the steps in the embodiments do not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0067] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An efficient planar machining method for cylinder head machining, characterized in that, It includes the following steps: Obtain the tool cutting temperature sequence and cutting force sequence during each cylinder head plane machining process. The cutting depth, cutting speed, and machining duration set for each cylinder head plane machining process respectively form a cutting depth sequence, a cutting speed sequence, and a machining duration sequence; Cluster and screen the efficient machining durations for the machining duration sequence. According to the cutting speed range difference and the corresponding cutting depth range difference corresponding to all efficient machining durations, as well as the difference between the maximum value of the cutting depth corresponding to all efficient machining durations and the maximum value of the cutting depth sequence, and combined with the degree of consistency in the changes of the cutting speed sequence and the cutting depth sequence, construct a cutting speed up - regulation index. Then, combined with the maximum and minimum values of the cutting speed corresponding to all efficient machining durations, construct a cutting speed constraint condition; Construct a cutting mutation index for each machining based on the change of the cutting force in the cutting force sequence of each machining and the mutation degree of each mutation point in the first - order difference sequence of the cutting force sequence. Based on the change of the cutting temperature sequence corresponding to each machining and combined with the cutting mutation index, construct the tool wear confidence level for each machining; Perform threshold segmentation on the tool wear confidence levels of all machinings to screen out the optimal machining data and bad data. Set the positions of the food and natural enemies of the dragonfly optimization algorithm, and combined with the cutting speed constraint condition, use the dragonfly optimization algorithm to obtain the optimal cutting speed and adjust the cutting speed of the cylinder head plane machining.

2. The high-efficiency planar machining method for cylinder head machining according to claim 1, characterized in that, The cutting temperature sequence and cutting force sequence corresponding to each machining are respectively composed of the tool cutting temperatures and cutting forces at all acquisition times during each machining process arranged in the order of acquisition times. The cutting depth sequence, cutting speed sequence, and machining duration sequence are respectively composed of the cutting depths, cutting speeds, and machining durations set during all machining processes arranged in the order of machining times.

3. The high-efficiency planar machining method for cylinder head machining according to claim 1, characterized in that, The screening of the efficient machining durations includes: using a clustering algorithm to cluster the machining duration sequence to obtain multiple clustering clusters, and recording the clustering cluster with the smallest average machining duration of the clustering clusters as the efficient clustering cluster, and the machining duration in the efficient clustering cluster as the efficient machining duration.

4. The high-efficiency planar machining method for cylinder head machining according to claim 1, characterized in that, The expression of the cutting speed up - regulation index is: ; where A is the cutting speed increase index; is the maximum value in the cutting depth sequence; QX is the maximum value in the cutting depth data corresponding to all high-efficiency machining durations; B is the speed adjustment coefficient, which is specifically determined by the degree of consistency between the cutting speed and the change in cutting depth corresponding to the high-efficiency machining duration; C is the unit speed change index of the cutting speed, which is specifically the average value of the ratios of all elements at the same positions in the cutting speed sequence and the cutting depth sequence.

5. The high-efficiency planar machining method for cylinder head machining according to claim 4, characterized in that The speed adjustment coefficient is further the ratio of the range difference between the cutting speeds corresponding to all efficient machining durations to the range difference between the cutting depths corresponding to all efficient machining durations.

6. The high-efficiency planar machining method for cylinder head machining according to claim 1, wherein The specific cutting speed constraint conditions are as follows: ; In the formula, are respectively the minimum and maximum values among the cutting speed data corresponding to all high-efficiency machining durations; SD is the cutting speed during planar machining; A is the cutting speed up-regulation index.

7. The high-efficiency planar machining method for cylinder head machining according to claim 1, characterized in that, The expression of the cutting mutation index for each machining is as follows: ; In the formula, is the cutting mutation index for the \(u\)th machining; is the change normal coefficient of the cutting force for the \(u\)th machining, specifically the ratio of the absolute value of the sum of all negative numbers in the first-order difference sequence of the cutting force sequence for the \(u\)th machining to the sum of all positive numbers; is the mutation fluctuation coefficient of the cutting force for the \(u\)th machining, specifically: the average value of the absolute values of the differences between each mutation point in the first-order difference sequence of the cutting force sequence for the \(u\)th machining and its adjacent elements is denoted as the mutation mean of each mutation point, and the product of the average value of the mutation means of all the mutation points and the variance is used as the mutation fluctuation coefficient of the cutting force for the \(u\)th machining; is the variance of the cutting force sequence; is the number of mutation points in the first-order difference sequence of the cutting force sequence for the \(u\)th machining.

8. The high-efficiency planar machining method for cylinder head machining according to claim 1, characterized in that, The expression of the tool wear confidence level for each machining is: ; where, is the tool wear confidence level for the u-th machining; is the cutting mutation index for the u-th machining; is the temperature rise difference coefficient for the u-th machining. Specifically, the cutting temperature sequence for the u-th machining is segmented to obtain each temperature subsequence, each temperature subsequence is linearly fitted, the difference between the maximum fitting line slope and the slopes of all other fitting lines is calculated and averaged, and then the product of the result and the maximum fitting line slope is used as the temperature rise difference coefficient for the u-th machining.

9. The high-efficiency planar machining method for cylinder head machining according to claim 3, characterized in that The screening of the optimal machining data and bad data includes: Perform threshold segmentation on all tool wear confidence levels to obtain a wear threshold. Record the machining data corresponding to all tool wear confidence levels greater than or equal to the wear threshold as bad data, and record the remaining machining data as good data. Record the machining data that belongs to both good data and the efficient clustering cluster as the optimal machining data, where the machining data includes the cutting force sequence, cutting temperature sequence, cutting speed data, cutting depth data, and machining duration during each machining process.

10. An efficient planar machining method for cylinder head machining as described in claim 1, characterized in that, The positions of the food and natural enemies in the dragonfly optimization algorithm are the optimal processing data and bad data respectively; among them, the objective function expression of the dragonfly optimization algorithm is: ; in the formula, Y is the objective function value; is the minimum value function; SC is the processing duration; is the tool wear confidence level for the u-th processing.

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