High-precision multi-axis machining composite numerical control machine tool control system

Abstract

The invention relates to the technical field of numerical control machine tool control, in particular to a high-precision multi-axis machining composite numerical control machine tool control system. The prediction module determines a radius time sequence curve and a length time sequence curve according to the cutter radius and the cutter length respectively, and constructs a cutter wear prediction model in combination with the vibration amplitude and a preset motion path of the cutter; the analysis module determines the cutter wear degree according to the cutter wear prediction model, and determines a key influence path section and a non-key influence path section according to the vibration amplitude; and the optimization module is used for determining a correction direction and a path correction amount according to the key influence path section, the non-key influence path section and the wear degree of the tool so as to determine a new tool movement path, and determining an adjustment mode of the cutting speed / feeding speed according to the wear degree of the tool. The machining precision is improved.

Description

Technical Field

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

[0002] The multi-axis machining composite CNC machine tool control system is an advanced control system for CNC machine tools. It can realize the precise control and coordinated movement of multiple coordinate axes, enabling the machine tool to perform multiple different types of machining operations at the same time and complete the machining of parts with complex shapes. It has the characteristics of high precision, high efficiency and high flexibility, and is widely used in aerospace, automobile manufacturing, mold processing and other fields.

[0003] Chinese patent publication number: CN114265363B, discloses a CNC machine tool processing path intelligent optimization method and system, including: inputting initialization data; performing preliminary training actions according to the initialization data, and providing guidance for the selection of preliminary training actions through a social learning particle swarm method, so as to select the next processing primitive; setting a loop training to select the next processing primitive, and constraining the behavior of selecting the next processing primitive and setting a corresponding score value according to the constraint, and obtaining the score value by combining the constraint; judging whether all training tasks are completed, if completed, converting the data of the processing state at this time into initialization data, and performing iterative loop training; if not completed, updating the processing state; continuing the iterative loop training, judging whether the set maximum number of iterative training times is reached, if reached, outputting the group of processing paths with the highest score value, and if not reached, updating the processing state.

[0004] It can be seen that the above technical solution takes the processing task and tool status as input, realizes real-time online intelligent guidance of the processing path, and sets tool life constraints at the same time, so that the tool can select the next working element according to the optimal path, but does not incorporate tool wear into the dynamic adjustment mechanism of the path. Summary of the invention

[0005] In order to overcome the problem in the prior art that the CNC machine tool control system does not incorporate tool wear into the dynamic optimization mechanism of the path when optimizing the tool path, the present invention provides a high-precision multi-axis machining composite CNC machine tool control system, comprising: 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, and 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; an analysis module, which is connected to the acquisition module and the prediction module respectively, and is used to determine the degree of tool wear according to the tool wear prediction model, and to determine the key influencing path segment and the non-key influencing path segment in the preset motion path according to the vibration amplitude; an optimization module connected to the analysis module, for determining a correction direction and a path correction amount of a tool according to the critical influencing path segment, the non-critical influencing path segment and the degree of tool wear, determining a new tool movement path according to the path correction amount, the correction direction and a preset movement path, and determining an adjustment method for a cutting speed / feed speed according to the degree of tool wear; The tool wear degree includes radial wear degree and axial wear degree.

[0006] Furthermore, the prediction module divides the preset motion path into several discrete path segments according to the geometric characteristics of the workpiece, and 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 the several discrete path segments, the tool radius timing curve, the tool length timing curve and the vibration amplitude.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, the analysis module 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 the non-critical influence path segment based on the influence degree.

[0010] Furthermore, 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.

[0011] Furthermore, the optimization module determines the path correction amount under the non-critical influencing path segment according to the radial wear degree and the axial wear degree corresponding to the non-critical influencing path segment, and determines whether to correct the tool path of the non-critical influencing path segment according to the comparison result of the path correction amount and the correction amount threshold.

[0012] 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 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.

[0013] Furthermore, 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.

[0014] Furthermore, 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.

[0015] Compared with the prior art, the beneficial effect of the present invention is that the present invention collects the tool radius, tool length and vibration amplitude during the processing, constructs a tool wear prediction model, effectively determines the degree of tool wear, and uses the vibration amplitude to distinguish between critical and non-critical influencing path segments. The optimization module determines the correction direction and path correction amount according to the critical influencing path segments, non-critical influencing path segments 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 processing path, improves processing accuracy, reduces the defective rate, and extends the tool life.

[0016] Furthermore, the present invention discretizes the preset motion path to perform refined analysis on different processing areas, and at the same time establishes the mathematical relationship between factors such as tool radius timing curve, tool length timing curve, vibration amplitude, discrete path segments and tool wear, to form a tool wear prediction model, and predict the degree of tool wear 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 processing path, improving processing accuracy, reducing the defective rate, and extending the tool life.

[0017] Furthermore, 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.

[0018] Furthermore, the analysis module of the present invention determines the degree of 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 influencing path segment and the non-critical influencing path segment according to the degree of influence, so as to subsequently perform path correction on the critical influencing path segment and the non-critical influencing path segment, thereby optimizing the tool motion path, reducing the negative impact of tool wear on the processing path, and improving the processing accuracy.

[0019] Furthermore, 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 influencing path segment and the non-critical influencing path segment, so as to correct the tool path of the critical influencing path segment and the non-critical influencing path segment, thereby ensuring the optimization of the tool motion path, reducing the negative impact of tool wear on the processing path, and improving the processing accuracy.

[0020] Furthermore, the present invention determines the contact position of the tool with the workpiece on the critical path segment through the contact geometric relationship between the tool and the workpiece on the critical influencing path to determine the tool wear position, providing a reliable basis for the subsequent accurate determination of the speed reduction amount of the cutting speed / feed speed, avoiding processing quality problems caused by tool wear, and effectively improving the processing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the structure of a high-precision multi-axis machining composite CNC machine tool control system according to an embodiment of the present invention; Figure 2 This is a control step diagram of a high-precision multi-axis machining composite CNC machine tool control system according to an embodiment of the present invention; Figure 3 A diagram showing the steps of constructing a tool wear prediction model according to an embodiment of the present invention; Figure 4 A decision diagram for determining an adjustment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0024] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" 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, or it can be the internal communication of 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 circumstances.

[0025] See also Figure 1 , Figure 2 As shown, Figure 1 This is a schematic diagram of the structure of a high-precision multi-axis machining composite CNC machine tool control system according to an embodiment of the present invention. Figure 2 The control step diagram of the high-precision multi-axis machining composite CNC machine tool control system according to the embodiment of the present invention; specifically, the embodiment of the present invention provides a high-precision multi-axis machining composite CNC machine tool control system, including: 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, and 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; an analysis module, which is connected to the acquisition module and the prediction module respectively, and is used to determine the degree of tool wear according to the tool wear prediction model, and to determine the key influencing path segment and the non-key influencing path segment in the preset motion path according to the vibration amplitude; an optimization module connected to the analysis module, for determining a correction direction and a path correction amount of a tool according to the critical influencing path segment, the non-critical influencing path segment and the degree of tool wear, determining a new tool movement path according to the path correction amount, the correction direction and a preset movement path, and determining an adjustment method for a cutting speed / feed speed according to the degree of tool wear; The tool wear degree includes radial wear degree and axial wear degree.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] The present invention collects the tool radius, tool length and vibration amplitude during the processing, constructs a tool wear prediction model, effectively determines the degree of tool wear, and uses the vibration amplitude to distinguish between key and non-key influencing path segments. The optimization module determines the correction direction and path correction amount according to the key influencing path segments, non-key influencing path segments 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 processing path, improves processing accuracy, reduces the defective rate, and extends the tool life.

[0031] See also Figure 3As shown, it is a step diagram of constructing a tool wear prediction model according to an embodiment of the present invention; specifically, the prediction module divides the preset motion path into a number of discrete path segments according to the geometric characteristics of the workpiece, and 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 the number of discrete path segments, the tool radius timing curve, the tool length timing curve and the vibration amplitude.

[0032] It is understandable that different workpieces have different geometric features, 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 the preset motion path is divided according to the geometric features of the workpiece, the wear of the tool on the corresponding path can be more accurately analyzed for each discrete path segment, thereby improving the accuracy of tool wear prediction. At the same time, the machine tool can accurately control the motion trajectory of the tool based on the information of each discrete path segment, thereby improving the accuracy and reliability of processing.

[0033] It is understandable that with long-term processing, the time series of tool radius and tool length can be obtained. The tool radius time series curve and the tool length time series curve reflect the evolution of tool wear in the radial and axial directions over time. The vibration amplitude reflects the influence of dynamic factors on tool wear during processing. The discrete path segments, tool radius time series curve, tool length time series curve and vibration amplitude are comprehensively considered to construct a tool wear prediction model, which can more comprehensively describe the complex process of tool wear.

[0034] In a specific embodiment, the geometric features of the workpiece are analyzed to determine the various geometric features contained in the workpiece, such as straight lines, curves (including arcs, spline curves, etc.), planes, curved surfaces, corners, edges, etc. The boundaries between these different geometric features are used as division points, and the preset motion paths are disconnected at these boundaries to form discrete path segments. Specifically, the division can be performed at the connection between the straight line and the arc in the workpiece, or at the intersection of the plane and the curved surface, and the coordinates of the boundary points are calculated according to the parameters of the geometric features, and the calculated division points are connected in sequence to form a number of discrete path segments. In specific applications, the division of discrete path segments can be achieved with the help of computer-aided design (CAD) and computer-aided manufacturing (CAM) software. These software can automatically identify the geometric features of the workpiece and divide the path according to preset rules and parameters.

[0035] By discretizing the preset motion path, the present invention can perform refined analysis on different processing areas, and at the same time establish the mathematical relationship between factors such as tool radius timing curve, tool length timing curve, vibration amplitude, discrete path segments and tool wear, to form a tool wear prediction model, and predict the degree of tool wear in real time so that adjustment measures can be taken in time, such as optimizing the tool motion path, effectively reducing the negative impact of tool wear on the processing path, improving processing accuracy, reducing the defective rate, and extending the service life of the tool.

[0036] 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 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.

[0037] Specifically, 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.

[0038] It is understandable that the time series curves of tool radius and length can reflect the wear status of the tool at different times. By analyzing these curves, the change trend of tool radius and length under each discrete path segment can be clarified. Combining the change trend of radius and length with vibration amplitude, a tool wear prediction model that comprehensively considers multiple factors can be constructed.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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; 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.

[0043] 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.

[0044] In a specific embodiment, a multiple linear regression model is used to describe the relationship between the vibration amplitude A, path length L and tool wear degree c corresponding to each discrete path segment, 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, the values ​​of a, b and c are fitted using methods such as the least squares method, 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. The degree of influence is determined based on a and b. If the difference between a and b is too large, a and b are standardized and the average is taken to obtain the degree of influence.

[0045] In another specific embodiment, a nonlinear model, such as a neural network model, may also be used. The vibration amplitude and path length are used as neurons in the input layer, and the tool wear degree is used as neurons in the output layer. The complex nonlinear relationship between them is learned by training the neural network to obtain the degree of influence. In implementation, the specific method for calculating the degree of influence can be determined based on actual conditions. No specific limitation is made here and no further elaboration is given.

[0046] In a specific embodiment, a and b are standardized and the average is taken to obtain the degree of influence. The value range of the preset influence threshold is set to 0.15-0.25. Preferably, the value range of the preset influence threshold is 0.2. In implementation, the value range and preferred value of the preset influence threshold can be determined according to actual conditions. No specific limitation is made here and no further description is given.

[0047] The analysis module of the present invention 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 influencing path segment and the non-critical influencing path segment according to the influence degree, so as to subsequently perform path correction on the critical influencing path segment and the non-critical influencing path segment, thereby optimizing the tool motion path, reducing the negative impact of tool wear on the processing path, and improving the processing accuracy.

[0048] Specifically, 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.

[0049] It is understandable that the key influencing path segment has a greater impact on tool wear and the final processing results, so it needs more sophisticated processing. First, determine the initial correction amount, and then determine the path correction amount based on the degree of influence and the initial correction amount to reduce tool wear and ensure processing accuracy and quality. For example, in the processing of precision parts, some path segments directly determine the key dimensional accuracy of the parts, and the corresponding path segments are the key influencing path segments.

[0050] Specifically, the optimization module determines the path correction amount under the non-critical influencing path segment according to the radial wear degree and axial wear degree corresponding to the non-critical influencing path segment, and determines whether to correct the tool path of the non-critical influencing path segment according to the comparison result of the path correction amount and the correction amount threshold.

[0051] It is understandable that the non-critical path segments have relatively little impact on tool wear and machining results. The path correction amount can be directly determined based on the radial and axial wear levels, and compared with the correction amount threshold to decide whether to make corrections, thereby ensuring machining quality while reducing unnecessary calculations and adjustments.

[0052] It can be understood that the path correction amount is to compensate for the influence of tool wear on machining accuracy, so that the movement of the tool on the critical and non-critical path segments can be closer to the ideal machining path to ensure machining quality.

[0053] It can be understood that the correction value threshold is a standard value determined based on factors such as the machining accuracy requirements. If the path correction value is less than the correction value threshold, it means that the wear of the tool on the non-critical influencing path segment has a small impact on the machining accuracy and is within an acceptable range, and there is no need to correct the tool path of the non-critical influencing path segment. On the contrary, if the path correction value is greater than or equal to the correction value threshold, it means that the tool wear has an impact on the machining accuracy of the non-critical influencing path segment, and it is necessary to correct the tool path of the non-critical influencing path segment and compensate for the wear by adjusting the tool's motion path.

[0054] In a specific embodiment, the initial correction amount under the critical influencing path segment is the sum average of the radial wear degree and the axial wear degree corresponding to the critical influencing path segment, and the path correction amount under the critical influencing path segment = the corresponding initial correction amount × (1 + degree of influence). The path correction amount under the non-critical influencing path segment is the sum average of the radial wear degree and the axial wear degree corresponding to the non-critical influencing path segment. The value range of the correction amount threshold is 0.01mm~0.03mm. Preferably, the value of the correction amount threshold is 0.02mm. In implementation, the value range and preferred value of the correction amount threshold can be determined according to actual conditions, and are not specifically limited here and will not be repeated.

[0055] 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 influencing path segment and the non-critical influencing path segment, so as to correct the tool path of the critical influencing path segment and the non-critical influencing path segment, ensure the optimization of the tool motion path, reduce the negative impact of tool wear on the processing path, and improve the processing accuracy.

[0056] See also Figure 4 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: If the total wear degree is greater than a preset wear degree, the cutting speed / feed speed is reduced.

[0057] It is understandable that the total wear degree is the wear degree of the tool on the entire preset motion path. If the total wear degree is greater than the preset wear degree, it means that the tool wear exceeds expectations and the speed needs to be reduced. Different wear positions of the tool will affect the cutting process. For example, wear of the tool blade will increase the cutting force and temperature, which will lead to a decrease in machining accuracy and poor surface quality. At this time, it is necessary to significantly reduce the cutting speed / feed speed to reduce cutting force and heat to ensure machining quality. Other non-critical wear positions on the tool surface have relatively little impact on the cutting process, and the speed reduction is low.

[0058] In a specific embodiment, the total wear degree = axial weight × the average of the corresponding axial wear degree on several discrete path segments + radial weight × the average 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 type of tool used and the processing technology. For example, for a milling cutter, if it is mainly used for plane milling, the cutting force is mainly in the axial direction, the axial weight is 0.7, and the radial weight is 0.3. For a turning tool, when performing external cylindrical turning, radial wear will directly affect the dimensional accuracy of the workpiece, and the influence of axial wear on the processing accuracy is relatively small. The radial weight is 0.8 and the axial weight is 0.2. If end face turning is performed, the axial wear has a greater influence 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.

[0059] In a specific embodiment, the preset wear degree has a value range of 0 to 0.1. Preferably, the preset wear degree has a value range of 0.5. In implementation, the preset wear degree has a value range and a preferred value that can be determined based on actual conditions. No specific limitation is made here and no further elaboration is given.

[0060] Specifically, 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.

[0061] It is understandable that tool wear is essentially the result of the friction, cutting force and other factors that affect the contact area between the tool and the workpiece during the cutting process. The contact geometry determines the distribution of these factors on the tool. For example, in milling, the contact angle, contact area and other geometric parameters between the cutting edge of the tool and the workpiece surface directly affect the cutting force and friction force borne by each point on the cutting edge. When the contact angle is unreasonable, one side of the cutting edge will bear greater force, resulting in increased wear on that part. Therefore, starting from the contact geometry can explain the source of tool wear.

[0062] In a specific embodiment, an analytical geometry model can be established based on the contact geometry relationship between the tool and the workpiece. Taking milling as an example, the contact geometry relationship parameters such as the contact angle and contact area between the tool edge and the workpiece surface are known. Through geometric calculation, the coordinates of the contact point between the tool edge and the workpiece surface can be determined to determine the tool wear position.

[0063] In another specific embodiment, a finite element analysis software can be used to establish a model of the tool-workpiece system. 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 distribution inside the tool are calculated. Based on these distribution results, the tool wear position can be determined.

[0064] Specifically, 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.

[0065] In a specific embodiment, the calculation formula of the speed reduction amount of the cutting speed is as follows: Among them, is the speed reduction of cutting speed, and is the corresponding weight of tool wear position.

[0066] The calculation formula for the speed reduction of the feed speed is as follows: , in, is the reduction in cutting speed, is the weight corresponding to the tool wear position.

[0067] The calculation formula for the speed reduction of the feed speed is as follows: , In mechanical processing, different tool wear positions have different degrees of influence on the dimensional accuracy of the workpiece. The corresponding weight of the tool wear position should be determined according to the specific processing technology and tool type. The corresponding weight of the tool wear position is quantized to 0-1. The easier the wear position is, the greater the corresponding weight is. In implementation, the value range and preferred value of the corresponding weight of the tool wear position can be determined according to actual conditions. The initial cutting speed and initial feed speed are determined according to specific processing parameters. No specific limitation is made here and no further description is given.

[0068] The present invention determines the contact position of the tool with the workpiece on the key path segment through the contact geometric relationship between the tool and the workpiece on the key influencing path to determine the tool wear position, providing a reliable basis for the subsequent accurate determination of the speed reduction amount of the cutting speed / feed speed, avoiding processing quality problems caused by tool wear, and can effectively improve the processing quality.

[0069] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying 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, and 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; an analysis module, which is connected to the acquisition module and the prediction module respectively, and is used to determine the degree of tool wear according to the tool wear prediction model, and to determine the key influencing path segment and the non-key influencing path segment in the preset motion path according to the vibration amplitude; an optimization module connected to the analysis module, for determining a correction direction and a path correction amount of a tool according to the critical influencing path segment, the non-critical influencing path segment and the degree of tool wear, determining a new tool movement path according to the path correction amount, the correction direction and a preset movement path, and determining an adjustment method for a cutting speed / feed speed according to the degree of tool wear; The tool wear degree includes radial wear degree and axial 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 divides the preset motion path into a number of discrete path segments according to the geometric features of the workpiece, and 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 the 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 2 or 3, characterized in that: 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.

5. The high-precision multi-axis machining composite CNC machine tool control system according to claim 4, characterized in that: The analysis module 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 the non-critical influence path segment based on the influence degree.

6. The high-precision multi-axis machining composite CNC machine tool control system according to claim 5, 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.

7. The high-precision multi-axis machining composite CNC machine tool control system according to claim 6, 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.

8. 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.

9. The high-precision multi-axis machining composite CNC machine tool control system according to claim 8, 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.

10. The high-precision multi-axis machining composite CNC machine tool control system according to claim 5, 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.

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