A high-precision multi-axis machining composite CNC machine tool control system

Abstract

The present invention relates to the field of numerical control machine tool control technology, and particularly relates to a high-precision multi-axis machining composite numerical control machine tool control system, including: a collection module; a prediction module respectively determines a radius time series curve and a length time series curve according to the tool radius and the tool length, and constructs a tool wear prediction model in combination with the vibration amplitude and the preset movement path of the tool; an analysis module determines the tool wear degree according to the tool wear prediction model, and determines the critical influence path segment and the non-critical influence path segment according to the vibration amplitude; an optimization module determines the correction direction and the path correction amount according to the critical influence path segment, the non-critical influence path segment and the tool wear degree to determine the new tool movement path, and determines the adjustment method for the cutting speed / feed speed according to the tool wear degree. The present invention incorporates tool wear into the dynamic adjustment mechanism of the path, improving the machining accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machine tool control, and particularly to a high-precision multi-axis machining composite numerical control machine tool control system. Background Art

[0002] The multi-axis machining composite numerical control machine tool control system is an advanced control system for numerical control machine tools, which can achieve precise control and coordinated movement of multiple coordinate axes, enabling the machine tool to simultaneously perform various different types of machining operations, complete the machining of complex-shaped parts, and has the characteristics of high precision, high efficiency, and high flexibility. It is widely used in fields such as aerospace, automobile manufacturing, and mold processing.

[0003] Chinese Patent Publication No.: CN114265363B, discloses a method and system for intelligent optimization of the machining path of a numerical control machine tool, including: inputting initialization data; performing initial training actions according to the initialization data, and at the same time providing guidance for the selection of the initial training actions through the social learning particle swarm method, so as to select the next machining primitive; setting cyclic training to select the next machining primitive, and at the same time constraining the behavior of selecting the next machining primitive and setting corresponding score values according to the constraints, and obtaining score values in combination with the constraints; judging whether all training tasks are completed. If completed, convert the data of the current machining state into initialization data and perform iterative cyclic training; if not completed, update the machining state; continue iterative cyclic training, judge whether the set maximum iterative training times are reached. If reached, output the set of machining paths with the highest score value. If not reached, update the machining state.

[0004] It can be seen that the above technical solution takes the machining task and the tool state as inputs, realizes real-time online intelligent guidance for the machining path, and at the same time sets tool life constraints, enabling the tool to select the next working primitive according to the optimal path, but does not incorporate tool wear into the dynamic adjustment mechanism of the path. Summary of the Invention

[0005] To overcome the problem that the tool wear is not incorporated into the dynamic optimization mechanism of the path when the numerical control machine tool control system optimizes the tool path in the prior art, the present invention provides a high-precision multi-axis machining composite numerical control machine tool control system, including:

[0006] An acquisition module, which is used to collect the tool radius, tool length, and vibration amplitude in real time during the machining process;

[0007] A prediction module, which is connected to the acquisition module, and is used to respectively determine a radius time series curve and a length time series curve according to the tool radius and the tool length, and construct a tool wear prediction model based on the radius time series curve, the length time series curve, the vibration amplitude, and the preset movement path of the tool;

[0008] An analysis module, which is respectively connected to the acquisition module and the prediction module, is used to determine the tool wear degree according to the tool wear prediction model, and determine the key influence path segments and non-key influence path segments in the preset motion path according to the vibration amplitude;

[0009] An optimization module, which is connected to the analysis module, is used to determine the correction direction of the tool and the path correction amount according to the key influence path segments, the non-key influence path segments and the tool wear degree, determine the new tool motion path according to the path correction amount, the correction direction and the preset motion path, and determine the adjustment method for the cutting speed / feed speed according to the tool wear degree;

[0010] Wherein, the tool wear degree includes the radial wear degree and the axial wear degree.

[0011] Further, the prediction module divides the preset motion path into several discrete path segments according to the geometric characteristics of the workpiece, respectively constructs a tool radius time series curve and a tool length time series curve according to the tool radius and the tool length, and constructs a tool wear prediction model according to the several discrete path segments, the tool radius time series curve, the tool length time series curve and the vibration amplitude.

[0012] Further, the prediction module determines the radius change trend and the length change trend of the tool under each discrete path segment according to the tool radius time series curve and the tool length time series curve, and constructs a tool wear prediction model according to the radius change trend, the length change trend and the corresponding vibration amplitude.

[0013] Further, the analysis module determines the axial wear degree and the radial wear degree on each of the discrete path segments according to the tool wear prediction model.

[0014] Further, the analysis module determines the influence degree of each discrete path segment on the tool wear according to the vibration amplitude and the path length corresponding to each discrete path segment, and determines the key influence path segments and the non-key influence path segments based on the influence degree.

[0015] Further, the optimization module determines the initial correction amount under the key influence path segment according to the radial wear degree and the axial wear degree corresponding to the key influence path segment, and determines the path correction amount corresponding to the key influence path segment according to the influence degree and the initial correction amount.

[0016] Further, the optimization module determines the path correction amount under the non-key influence path segment according to the radial wear degree and the axial wear degree corresponding to the non-key influence path segment, and determines whether to correct the tool path of the non-key influence path segment according to the comparison result between the path correction amount and the correction amount threshold.

[0017] Further, the optimization module determines the total wear degree based on the radial wear degree and the axial wear degree, and determines the adjustment method for the cutting speed / feed speed according to the comparison result between the total wear degree and the preset wear degree, including:

[0018] If the total wear degree is greater than the preset wear degree, the cutting speed / feed speed is reduced.

[0019] Further, the optimization module determines the speed reduction amount of the cutting speed / feed speed according to the total wear degree, the preset wear degree, and the tool wear position.

[0020] Further, the optimization module determines the contact part of the tool and the workpiece on the critical path segment according to the contact geometry between the tool and the workpiece on the critical influence path segment to determine the tool wear position.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention collects the tool radius, tool length, and vibration amplitude during the machining process, constructs a tool wear prediction model, effectively determines the tool wear degree, and uses the vibration amplitude to distinguish the critical and non-critical influence path segments. The optimization module determines the correction direction and path correction amount according to the critical influence path segment, non-critical influence path segment, and tool wear degree, generates a new tool movement path, and adjusts the cutting speed / feed speed. Incorporating tool wear into the dynamic adjustment mechanism of the path effectively reduces the negative impact of tool wear on the machining path, improves the machining accuracy, reduces the defective rate, and prolongs the tool service life.

[0022] Further, by discretizing the preset movement path, the present invention can perform refined analysis for different machining areas, and at the same time establish the mathematical relationship between factors such as the tool radius time series curve, tool length time series curve, vibration amplitude, and discrete path segments and tool wear, form a tool wear prediction model, and real-time predict the tool wear degree, so as to take adjustment measures in time, such as optimizing the tool movement path, effectively reducing the negative impact of tool wear on the machining path, improving the machining accuracy, reducing the defective rate, and prolonging the tool service life.

[0023] Further, the prediction module of the present invention determines the radius change trend and length change trend of the tool under each discrete path segment according to the tool radius time series curve and tool length time series curve, and constructs a tool wear prediction model in combination with the corresponding vibration amplitude under each discrete path segment to determine the radial wear degree and axial wear degree of the tool, so as to take adjustment measures in time, such as optimizing the tool movement path, effectively reducing the negative impact of tool wear on the machining path, and improving the machining accuracy.

[0024] Furthermore, the analysis module of the present invention determines the influence degree of each discrete path segment on tool wear according to the vibration amplitude and path length corresponding to each discrete path segment, and determines the critical influence path segment and non-critical influence path segment according to the influence degree, so as to perform path correction on the critical influence path segment and non-critical influence path segment subsequently, thereby optimizing the tool motion path, reducing the negative impact of tool wear on the machining path, and improving the machining accuracy.

[0025] Furthermore, the optimization module of the present invention determines the path correction amount corresponding to the corresponding path segment according to the radial wear degree and axial wear degree corresponding to the critical influence path segment and non-critical influence path segment, so as to correct the tool paths of the critical influence path segment and non-critical influence path segment, ensure the optimization of the tool motion path, reduce the negative impact of tool wear on the machining path, and improve the machining accuracy.

[0026] Furthermore, the present invention determines the contact part of the tool with the workpiece on the critical path segment to determine the tool wear position by the contact geometric relationship between the tool and the workpiece on the critical influence path, provides a reliable basis for accurately determining the reduction amount of the cutting speed / feed speed subsequently, avoids machining quality problems caused by tool wear, and can effectively improve the machining quality. Description of the Drawings

[0027] Figure 1 It is a schematic structural diagram of the high-precision multi-axis machining composite CNC machine tool control system according to the embodiment of the present invention;

[0028] Figure 2 It is a control step diagram of the high-precision multi-axis machining composite CNC machine tool control system according to the embodiment of the present invention;

[0029] Figure 3 It is a step diagram of constructing a tool wear prediction model according to the embodiment of the present invention;

[0030] Figure 4 It is a decision diagram for determining the adjustment method according to the embodiment of the present invention. Detailed Embodiments

[0031] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0032] The preferred embodiments of the present invention will be described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0033] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0034] Please refer to Figure 1 、 Figure 2 as shown in Figure 1 which is a schematic structural diagram of the high-precision multi-axis machining composite numerical control machine tool control system according to an embodiment of the present invention, Figure 2 and which is a control step diagram of the high-precision multi-axis machining composite numerical control machine tool control system according to an embodiment of the present invention; specifically, the embodiment of the present invention provides a high-precision multi-axis machining composite numerical control machine tool control system, including:

[0035] An acquisition module, which is used to collect the tool radius, tool length, and vibration amplitude in real time during the machining process;

[0036] A prediction module, which is connected to the acquisition module and is used to respectively determine a radius time series curve and a length time series curve according to the tool radius and the tool length, and construct a tool wear prediction model based on the radius time series curve, the length time series curve, the vibration amplitude, and the preset movement path of the tool;

[0037] An analysis module, which is respectively connected to the acquisition module and the prediction module, and is used to determine the tool wear degree according to the tool wear prediction model, and determine the key influence path segments and non-key influence path segments in the preset movement path according to the vibration amplitude;

[0038] An optimization module, which is connected to the analysis module and is used to determine the correction direction and path correction amount of the tool according to the key influence path segments, the non-key influence path segments, and the tool wear degree, determine the new tool movement path according to the path correction amount, the correction direction, and the preset movement path, and determine the adjustment method for the cutting speed / feed speed according to the tool wear degree;

[0039] wherein, the tool wear degree includes the radial wear degree and the axial wear degree.

[0040] It is understandable that in the actual processing process, tool wear will cause changes in the shape of the tool's cutting edge, a reduction in the tool radius, etc. These changes will affect the distribution of cutting force, processing accuracy and surface quality. Moreover, the tool is a dynamic process. When the tool is worn to a certain extent and the cutting ability decreases, the originally planned processing path is no longer optimal. The tool path needs to be adjusted, such as changing the cutting depth, feed speed or tool trajectory, to ensure the processing quality.

[0041] It is understandable that vibration will cause periodic impact and alternating stress between the tool and the workpiece, accelerating the wear of the tool material. At the same time, vibration will also cause the tool to produce small displacements and swings during the processing, causing the actual processing path to deviate from the preset motion path, thereby affecting the dimensional accuracy and surface roughness of the workpiece and reducing the processing quality. Therefore, when constructing the tool wear prediction model, the vibration amplitude is taken into account.

[0042] In a specific embodiment, the tool radius refers to the radius size of the tool cutting part, the tool length refers to the length of the tool from the tool handle to the cutting edge, and the preset motion path refers to the tool motion trajectory pre-planned according to the workpiece shape, size and processing requirements before processing, which specifies the tool's route during the entire processing process. The vibration amplitude is a physical quantity that describes the severity of the tool vibration.

[0043] In a specific embodiment, the acquisition module includes the following types of components: a visual sensor for collecting tool length and tool radius, the visual sensor is installed above the processing area, preferably, the visual sensor is an industrial camera, by shooting the tool image, using the image processing algorithm to identify and measure the tool radius and tool length. A vibration sensor for collecting vibration amplitude, preferably, the vibration sensor is an acceleration sensor, the acceleration sensor is arranged on the tool handle or on the machine tool spindle, and the acceleration sensor is arranged on the tool handle to most directly collect the vibration signal of the tool during the cutting process, and can reflect the vibration state of the tool in real time. The vibration sensor is arranged on the machine tool spindle to indirectly obtain the vibration information of the tool. In implementation, the type of sensor included in the acquisition module can be determined according to actual conditions, and is not specifically limited here and will not be repeated.

[0044] During the acquisition and processing of the present invention, the tool radius, tool length, and vibration amplitude are collected to construct a tool wear prediction model, effectively determine the degree of tool wear, and use the vibration amplitude to distinguish critical and non-critical influence path segments. The optimization module determines the correction direction and path correction amount according to the critical influence path segment, non-critical influence path segment, and tool wear degree, generates a new tool movement path, and adjusts the cutting speed / feed speed. Incorporating tool wear into the dynamic adjustment mechanism of the path can effectively reduce the negative impact of tool wear on the machining path, improve machining accuracy, reduce the defective rate, and extend the tool life.

[0045] Please refer to Figure 3 as shown, which is a step diagram for constructing a tool wear prediction model according to an embodiment of the present invention; specifically, the prediction module divides the preset movement path into several discrete path segments according to the geometric characteristics of the workpiece, and respectively constructs a tool radius time series curve and a tool length time series curve according to the tool radius and the tool length, and constructs a tool wear prediction model according to several discrete path segments, the tool radius time series curve, the tool length time series curve, and the vibration amplitude.

[0046] It can be understood that different workpieces have different geometric feature parts, and the corresponding tool cutting conditions are different. For example, at the corner of the workpiece, the cutting direction of the tool will change. After dividing the preset movement path according to the geometric characteristics of the workpiece, the wear condition of the tool on the corresponding path can be analyzed more accurately for each discrete path segment, thereby improving the accuracy of tool wear prediction. At the same time, the machine tool can accurately control the movement trajectory of the tool according to the information of each discrete path segment, improving the accuracy and reliability of machining.

[0047] It can be understood that with long-term machining, a time series of the tool radius and the tool length can be obtained. The tool radius time series curve and the tool length time series curve reflect the evolution law of tool wear in the radial and axial directions over time, and the vibration amplitude reflects the influence of dynamic factors during the machining process on tool wear. Considering these factors such as discrete path segments, tool radius time series curve, tool length time series curve, and vibration amplitude comprehensively to construct a tool wear prediction model can more comprehensively describe the complex process of tool wear.

[0048] In a specific embodiment, the geometric features of the workpiece are analyzed to determine various geometric features included in the workpiece, such as straight lines, curves (including arcs, spline curves, etc.), planes, curved surfaces, corners, edges, etc. Using the boundaries between these different geometric features as dividing points, the preset motion path is disconnected at these boundaries to form discrete path segments. Specifically, division can be carried out at the connection between a straight line and an arc in the workpiece, or at the intersection line between a plane and a curved surface. The coordinates of the boundary points are calculated based on the parameters of the geometric features, and the calculated dividing points are connected in sequence to form several discrete path segments. In specific applications, computer-aided design (CAD) and computer-aided manufacturing (CAM) software can be used to achieve the division of discrete path segments. These software can automatically identify the geometric features of the workpiece and perform path division according to preset rules and parameters.

[0049] By discretizing the preset motion path, the present invention can perform refined analysis on different machining areas. At the same time, a mathematical relationship between factors such as the tool radius time series curve, the tool length time series curve, the vibration amplitude, and the discrete path segments and tool wear is established to form a tool wear prediction model, and the wear degree of the tool is predicted in real time, so as to take adjustment measures in time, such as optimizing the tool motion path, effectively reducing the negative impact of tool wear on the machining path, improving the machining accuracy, reducing the defective rate, and extending the service life of the tool.

[0050] Specifically, the prediction module determines the radius change trend and length change trend of the tool under each discrete path segment according to the tool radius time series curve and the tool length time series curve, and constructs a tool wear prediction model according to the radius change trend, length change trend, and the corresponding vibration amplitude.

[0051] Specifically, the analysis module determines the axial wear degree and radial wear degree on each of the discrete path segments according to the tool wear prediction model.

[0052] It can be understood that the time series curves of the tool radius and length can reflect the wear state of the tool at different times. By analyzing these curves, the change trends of the tool radius and length under each discrete path segment can be clarified. Combining the change trends of the radius and length with the vibration amplitude can construct a tool wear prediction model that comprehensively considers multiple factors.

[0053] In a specific embodiment, during the machining process, sensors are used in real time to collect tool radius, tool length and vibration amplitude data, laser displacement sensors are used to measure tool radius and length, and acceleration sensors are used to measure vibration amplitude. The tool radius and tool length corresponding to each discrete path segment are determined to form a corresponding tool radius time series curve and tool length time series curve and a corresponding vibration amplitude sequence. Polynomial fitting, moving average and other methods are used to determine the radius change trend and length change trend of the tool under each discrete path segment. The radius change trend and length change trend are associated with the corresponding vibration amplitude to prepare for the construction of a tool wear prediction model. Specifically, the radius change trend, length change trend and vibration amplitude of each discrete path segment are used as input features, and the axial wear degree and radial wear degree are used as output labels to construct a training data set and a test data set, and a tool wear prediction model is constructed based on the training data set, and verification and evaluation are performed based on the test set. In this way, the prediction accuracy of the tool wear prediction model can be guaranteed, and the accurate axial wear degree and radial wear degree on each discrete path segment can be output.

[0054] The prediction module of the present invention determines the radius change trend and length change trend of the tool under each discrete path segment according to the tool radius timing curve and the tool length timing curve, and constructs a tool wear prediction model in combination with the corresponding vibration amplitude under each discrete path segment to determine the radial wear degree and axial wear degree of the tool, so as to take adjustment measures in time, such as optimizing the tool motion path, effectively reducing the negative impact of tool wear on the processing path, and improving the processing accuracy.

[0055] Specifically, the analysis module determines the influence of each discrete path segment on tool wear according to the vibration amplitude and path length corresponding to each discrete path segment, and determines the critical influence path segment and the non-critical influence path segment based on the influence degree.

[0056] Specifically, if the influence degree is greater than or equal to a preset influence threshold, the discrete path segment is determined to be a critical influence path segment;

[0057] If the impact degree is less than a preset impact threshold, the discrete path segment is determined to be a non-critical impact path segment.

[0058] It is understandable that the greater the cutting force and the more intense the vibration of the discrete path segment, the greater the impact on tool wear. Based on the preset impact threshold, the key impact path segment and the non-key impact path segment are distinguished. If the vibration on a discrete path segment has a greater impact on tool wear, it means that it is a key impact path segment and the tool is more susceptible to wear. The path length and vibration amplitude of the key impact path segment are positively correlated with the impact degree. The longer the path length and the higher the vibration amplitude, the greater the impact degree.

[0059] In a specific embodiment, a multiple linear regression model is used to describe the relationship between the vibration amplitude A, the path length L corresponding to each discrete path segment, and the tool wear degree c, that is, W = aA + bL + c, where a and b are coefficients to be determined, and c is a constant term. Through a large amount of experimental data, methods such as the least squares method are used to fit the values of a, b, and c, so as to establish a specific relationship model. In the multiple linear regression model, a represents the contribution of the vibration amplitude to tool wear, and b represents the contribution of the path length to tool wear. Based on a and b, the influence degree is determined. If the difference between a and b is too large, then a and b are standardized, and the mean value is taken to obtain the influence degree.

[0060] In another specific embodiment, a non-linear model, such as a neural network model, can also be used. The vibration amplitude and the path length are used as neurons in the input layer, and the tool wear degree is used as a neuron in the output layer. By training the neural network to learn the complex non-linear relationship between them, and then calculating the influence degree. In the implementation, the specific method for calculating the influence degree can be determined according to the actual situation, which is not specifically limited here and will not be elaborated further.

[0061] In a specific embodiment, a and b are standardized, and the mean value is taken to obtain the influence degree. It is set that the value range of the preset influence threshold is 0.15 - 0.25. Preferably, the value range of the preset influence threshold is 0.2. In the implementation, the value range and the preferred value of the preset influence threshold can be determined according to the actual situation, which is not specifically limited here and will not be elaborated further.

[0062] The analysis module of the present invention determines the influence degree of each discrete path segment on tool wear according to the vibration amplitude and the path length corresponding to each discrete path segment, and determines the key influence path segment and the non-key influence path segment according to the influence degree, so as to perform path correction on the key influence path segment and the non-key influence path segment subsequently, thereby optimizing the tool movement path, reducing the negative impact of tool wear on the machining path, and improving the machining accuracy.

[0063] Specifically, the optimization module determines the initial correction amount under the key influence path segment according to the radial wear degree and the axial wear degree corresponding to the key influence path segment, and determines the path correction amount corresponding to the key influence path segment according to the influence degree and the initial correction amount.

[0064] It can be understood that the key influence path segment has a greater impact on tool wear and the final machining result, so more refined processing is required. First, the initial correction amount is determined, and then the path correction amount is determined by combining the influence degree and the initial correction amount to reduce tool wear and ensure machining accuracy and quality. For example, in the machining of precision parts, some path segments directly determine the key dimensional accuracy of the parts, and the corresponding path segments are the key influence path segments.

[0065] Specifically, the optimization module determines the path correction amount under the non-critical impact path segment according to the radial wear degree and axial wear degree corresponding to the non-critical impact path segment, and determines whether to correct the tool path of the non-critical impact path segment according to the comparison result between the path correction amount and the correction amount threshold.

[0066] It can be understood that the non-critical impact path segment has a relatively small impact on tool wear and machining results. The path correction amount can be directly determined according to its radial and axial wear degrees and compared with the correction amount threshold to decide whether to correct, reducing unnecessary calculations and adjustments while ensuring machining quality.

[0067] It can be understood that the path correction amount is to compensate for the impact of tool wear on machining accuracy, so that the movement of the tool on the critical impact path segment and the non-critical impact path segment can be closer to the ideal machining path to ensure machining quality.

[0068] It can be understood that the correction amount threshold is a standard value determined according to factors such as machining accuracy requirements. If the path correction amount is less than the correction amount threshold, it means that the wear of the tool on the non-critical impact path segment has a small impact on machining accuracy and is within an acceptable range, and there is no need to correct the tool path of the non-critical impact path segment; on the contrary, if the path correction amount is greater than or equal to the correction amount threshold, it means that the tool wear has an impact on the machining accuracy of the non-critical impact path segment, and the tool path of the non-critical impact path segment needs to be corrected to compensate for the wear by adjusting the movement path of the tool.

[0069] In a specific embodiment, the initial correction amount under the critical impact path segment is the average value of the sum of the radial wear degree and axial wear degree corresponding to the critical impact path segment, and the path correction amount under the critical impact path segment = the corresponding initial correction amount × (1 + influence degree). The path correction amount under the non-critical impact path segment is the average value of the sum of the radial wear degree and axial wear degree corresponding to the non-critical impact path segment. The value range of the correction amount threshold is 0.01 mm to 0.03 mm. Preferably, the value of the correction amount threshold is 0.02 mm. In practice, the value range and preferred value of the correction amount threshold can be determined according to the actual situation, which is not specifically limited here and will not be elaborated further.

[0070] The optimization module of the present invention determines the path correction amount under the corresponding path segment according to the radial wear degree and axial wear degree corresponding to the critical impact path segment and the non-critical impact path segment, so as to correct the tool paths of the critical impact path segment and the non-critical impact path segment, ensure the optimization of the tool movement path, reduce the negative impact of tool wear on the machining path, and improve machining accuracy.

[0071] Please refer to Figure 4As shown, it is a decision diagram for determining the adjustment method in an embodiment of the present invention. Specifically, the optimization module determines the total wear degree based on the radial wear degree and the axial wear degree, and determines the adjustment method for the cutting speed / feed speed according to the comparison result between the total wear degree and the preset wear degree, including:

[0072] If the total wear degree is greater than the preset wear degree, the cutting speed / feed speed is reduced.

[0073] It can be understood that the total wear degree is the wear degree of the tool on the entire preset movement path. If the total wear degree is greater than the preset wear degree, it means that the tool wear exceeds the expectation and the speed needs to be reduced. When the tool wear positions are different, it will affect the cutting process. For example, when the tool edge part is worn, the cutting force will increase, the temperature will rise, which will lead to a decline in machining accuracy and a deterioration of surface quality. At this time, it is necessary to significantly reduce the cutting speed / feed speed to reduce the cutting force and heat and ensure the machining quality. For other non-critical wear positions on the tool surface, the impact on the cutting process is relatively small, and the speed reduction amount is relatively low.

[0074] In a specific embodiment, the total wear degree = axial weight × average value of the corresponding axial wear degree on several discrete path segments + radial weight × average value of the corresponding radial wear degree on several discrete path segments. Among them, the sum of the axial weight and the radial weight is 1, and the values of the axial weight and the radial weight are determined according to the tool type and processing technology used. For example, for a milling cutter, if it is mainly used for face milling and the cutting force is mainly in the axial direction, the axial weight value is 0.7 and the radial weight is 0.3. For a turning tool, when performing external turning, the radial wear will directly affect the dimensional accuracy of the workpiece, and the axial wear has a relatively small impact on the machining accuracy. The radial weight is 0.8 and the axial weight is 0.2. If performing face turning, the axial wear has a greater impact on the flatness of the end face, and the axial weight is adjusted to 0.6 and the radial weight is adjusted to 0.4. Data training can be carried out according to the specific tool type and processing technology to construct a weight distribution model to determine the specific axial weight and radial weight.

[0075] In a specific embodiment, the value range of the preset wear degree is 0 to 0.1. Preferably, the value range of the preset wear degree is 0.5. In practice, the value range and the preferred value of the preset wear degree can be determined according to the actual situation, and no specific limitation is made here and will not be elaborated further.

[0076] Specifically, the optimization module determines the contact part of the tool and the workpiece on the key path segment according to the contact geometry between the tool and the workpiece on the key influence path segment to determine the tool wear position.

[0077] It is understandable that tool wear is essentially the result of the action of various factors such as friction and cutting force in the cutting process. The contact geometry determines the distribution of these factors on the tool. For example, in milling machining, geometric parameters such as the contact angle and contact area between the cutting edge of the tool and the workpiece surface directly affect the magnitude of the cutting force and friction force borne by each point of the cutting edge. When the contact angle is unreasonable, one side of the cutting edge will bear a greater force, resulting in increased wear at that location. Therefore, starting from the contact geometry can explain the generation of the tool wear location at the root cause.

[0078] In a specific embodiment, an analytic geometry model can be established according to the contact geometry between the tool and the workpiece. Taking milling machining as an example, given the contact geometry parameters such as the contact angle and contact area between the cutting edge of the tool and the workpiece surface, the coordinates of the contact points between the cutting edge of the tool and the workpiece surface can be determined through geometric calculations to determine the tool wear location.

[0079] In another specific embodiment, a model of the tool-workpiece system can be established using finite element analysis software. The tool and the workpiece are meshed, and boundary conditions such as material properties, contact conditions, and cutting loads are defined. By simulating the cutting process, the stress, strain, and temperature distributions inside the tool are calculated. Based on these distribution results, the tool wear location can be determined.

[0080] Specifically, the optimization module determines the reduction amount of the cutting speed / feed speed according to the total wear degree, the preset wear degree, and the tool wear location.

[0081] In a specific embodiment, the calculation formula for the reduction amount of the cutting speed is as follows:

[0082] where is the reduction amount of the cutting speed, and is the weight corresponding to the tool wear location.

[0083] The calculation formula for the reduction amount of the feed speed is as follows:

[0084] ,

[0085] where is the reduction amount of the cutting speed, is the weight corresponding to the tool wear location.

[0086] The calculation formula for the reduction amount of the feed speed is as follows:

[0087] ,

[0088] In machining, the influence degrees of different tool wear positions on the workpiece dimension accuracy are different. The corresponding weights of the tool wear positions need to be determined according to the specific machining process and tool type. Quantify the corresponding weights of the tool wear positions to 0-1. The position that is more prone to wear corresponds to a larger weight. In implementation, the value range of the corresponding weights of the tool wear positions and the preferred values can be determined according to the actual situation. Determine the initial cutting speed and the initial feed rate according to the specific machining parameters. No specific limitations are made here and it will not be elaborated further.

[0089] The present invention determines the contact part between the tool and the workpiece on the key path segment by the contact geometric relationship between the tool and the workpiece on the key influence path to determine the tool wear position, providing a reliable basis for accurately determining the speed reduction amount of the cutting speed / feed rate subsequently, avoiding machining quality problems caused by tool wear, and effectively improving the machining quality.

[0090] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A high-precision multi-axis machining composite CNC machine tool control system, characterized in that: include: A collection module, which is used to collect tool radius, tool length and vibration amplitude in real time during the machining process; a prediction module connected to the acquisition module, for determining a radius time series curve and a length time series curve according to the tool radius and the tool length, respectively, building a tool wear prediction model based on the radius time series curve, the length time series curve, the vibration amplitude and a preset motion path of the tool, and dividing the preset motion path into a plurality of discrete path segments according to geometric features of a workpiece; an analysis module, which is connected to the acquisition module and the prediction module respectively, and is used to determine the axial wear degree and radial wear degree of the tool on each discrete path segment according to the tool wear prediction model, and determine the influence degree of each discrete path segment on the tool wear according to the vibration amplitude and path length corresponding to each discrete path segment, and determine the key influence path segment and the non-key influence path segment based on the influence degree; An optimization module is connected to the analysis module, and is used to determine the correction direction and path correction amount of the tool according to the critical influencing path segment, the non-critical influencing path segment, the radial wear degree, and the axial wear degree, determine the new tool movement path according to the path correction amount, correction direction, and a preset motion path, and determine the adjustment method for the cutting speed / feed speed according to the axial wear degree and the radial wear degree.

2. The high-precision multi-axis machining composite CNC machine tool control system according to claim 1 is characterized in that: The prediction module constructs a tool radius timing curve and a tool length timing curve according to the tool radius and the tool length respectively, and constructs a tool wear prediction model according to a number of discrete path segments, the tool radius timing curve, the tool length timing curve and the vibration amplitude.

3. The high-precision multi-axis machining composite CNC machine tool control system according to claim 2 is characterized in that: The prediction module determines the radius change trend and length change trend of the tool under each discrete path segment according to the tool radius timing curve and the tool length timing curve, and constructs a tool wear prediction model according to the radius change trend, length change trend and corresponding vibration amplitude.

4. The high-precision multi-axis machining composite CNC machine tool control system according to claim 1, characterized in that: The optimization module determines an initial correction amount under the key influencing path segment according to the radial wear degree and the axial wear degree corresponding to the key influencing path segment, and determines a path correction amount corresponding to the key influencing path segment according to the influence degree and the initial correction amount.

5. The high-precision multi-axis machining composite CNC machine tool control system according to claim 4, characterized in that: The optimization module determines the path correction amount under the non-critical impact path segment according to the radial wear degree and the axial wear degree corresponding to the non-critical impact path segment, and determines whether to correct the tool path of the non-critical impact path segment according to the comparison result of the path correction amount and the correction amount threshold.

6. The high-precision multi-axis machining composite CNC machine tool control system according to claim 1, characterized in that: The optimization module determines the total wear degree based on the radial wear degree and the axial wear degree, and determines the adjustment method of the cutting speed / feed speed according to the comparison result of the total wear degree and the preset wear degree, including: If the total wear degree is greater than a preset wear degree, the cutting speed / feed speed is reduced.

7. The high-precision multi-axis machining composite CNC machine tool control system according to claim 6, characterized in that: The optimization module determines a speed reduction amount of the cutting speed / feed speed according to the total wear degree, the preset wear degree, and the tool wear position.

8. The high-precision multi-axis machining composite CNC machine tool control system according to claim 7, characterized in that: The optimization module determines the contact position of the tool with the workpiece on the critical path segment according to the contact geometric relationship between the tool and the workpiece on the critical influencing path segment to determine the tool wear position.

Images