Complex curved surface precision machining method based on tool path optimization

Abstract

The invention relates to the technical field of precision machining, and particularly discloses a complex curved surface precision machining method based on tool path optimization, which comprises the following steps: S1, acquiring a geometric model of a curved surface to be machined and tool kinetic parameters; s2, the optimal machining step length, the cutter roll angle and the feeding speed are calculated in real time according to the geometric features of the curved surface and the dynamic parameters of the cutter; s3, generating a self-adaptive tool path based on the dynamic optimization parameters in the step S2; s4, synchronously monitoring the cutting load in the machining process, and dynamically correcting cutter path parameters according to load feedback; and S5, outputting the optimized tool path instruction to the numerical control machine tool to execute machining. By calculating the Gaussian curvature and the average curvature of the curved surface in real time, dynamically adjusting the machining step length and the feeding speed, and automatically reducing the step length and reducing the feeding speed in a curvature sudden change area, the residual height of a steep area is controlled within a precise standard range, and the surface roughness is reduced by more than 30% compared with that of a traditional method; and the consistency of machining precision of each area of the complex curved surface is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of precision machining, and in particular to a method for precision machining of complex curved surfaces based on tool path optimization. Background Art

[0002] In the field of precision machining of complex surfaces, traditional tool path planning technology has significant limitations: First, the use of fixed parameters such as machining step size and feed rate makes it difficult to adapt to the dynamic changes in surface curvature, resulting in excessive residual height in steep areas and low machining efficiency in flat areas, creating a contradiction between precision and efficiency. Second, there is a lack of a real-time monitoring mechanism for cutting load. When local cutting force is overloaded, it can easily cause tool chipping or machine vibration, seriously affecting the stability of the machining process. Third, there is no tool wear prediction model based on surface characteristics and machining parameters, which cannot provide early warning of tool wear, resulting in fluctuations in machining quality and increased production costs. Fourth, existing methods cannot balance the multi-objective optimization of machining time, surface quality, and tool life, and rely on manual and repeated parameter debugging, which prolongs the production cycle. In addition, the real-time calculation amount for complex surfaces is huge. Traditional serial computing architectures have computational delays when processing areas with high curvature changes, which cannot meet the real-time requirements of precision machining. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method for precision machining of complex curved surfaces based on tool path optimization, which solves the problems in the above-mentioned background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for precision machining of complex curved surfaces based on tool path optimization, comprising the following steps:

[0005] S1. Obtaining the geometric model of the surface to be processed and the tool dynamic parameters;

[0006] S2. Calculate the optimal machining step length, tool roll angle, and feed rate in real time based on surface geometry and tool dynamics parameters;

[0007] S3. Generate an adaptive tool path based on the dynamic optimization parameters of step S2;

[0008] S4. Simultaneously monitor the cutting load during the machining process and dynamically modify the tool path parameters based on the load feedback;

[0009] S5. Output the optimized tool path instructions to the CNC machine tool for processing.

[0010] Preferably, the surface geometric features in step S2 include Gaussian curvature K and mean curvature H, which are calculated as follows:

[0011]

[0012] Among them, kmax and kmin are the extreme values ​​of the principal curvature at a certain point on the surface; this formula quantifies the bending characteristics of the surface at a certain point through the product and mean of the extreme values ​​of the principal curvature; the Gaussian curvature K reflects the overall curvature of the surface, and the mean curvature H reflects the local concavity and convexity of the surface. The combination of the two provides the surface geometry basis for subsequent parameter calculations.

[0013] Preferably, the step S2 calculates the optimal processing step length L opt , the calculation formula is:

[0014]

[0015] where R tool is the tool radius, k1 and k2 are tool material coefficients, and α is the curvature weight factor, 0.5≤α≤1.5.

[0016] Preferably, the optimization formula for the tool rake angle θ in step S2 is:

[0017]

[0018] in is the mean curvature gradient, is the maximum radial cutting force allowed by the tool, β and γ are stiffness compensation coefficients; this formula reflects the surface change trend through the average curvature gradient. The greater the change trend, The larger the value, the tool inclination angle θ is adjusted accordingly to adapt to the changes in the curved surface; at the same time, combined with the maximum radial cutting force allowed by the tool, excessive cutting force is used to avoid damaging the tool, and the β and γ coefficients are compensated and adjusted according to the tool stiffness characteristics.

[0019] Preferably, the feed speed V in step S2 is f The optimization formula is:

[0020]

[0021] Among them, MRR target is the target material removal rate, a p 、a e are the axial / radial cutting depths, λ is the curvature change sensitivity coefficient, and ΔK is the Gaussian curvature difference between adjacent tool positions;

[0022] In the formula, first pass Calculate the theoretical feed rate that meets the target material removal rate and compare it with the maximum feed rate V of the machine tool. max Take the minimum value to ensure safe processing; then adjust the feed speed according to the Gaussian curvature difference ΔK of adjacent tool positions. The greater the curvature change, the greater the e -λ·|ΔK| The smaller the value, the lower the feed speed is, ensuring the processing quality.

[0023] Preferably, the dynamic correction of the cutting load in step S4 adopts closed-loop control:

[0024] When the real-time cutting force Factual>1.2Fref, the following adjustments are triggered:

[0025]

[0026] Among them F ref The closed-loop control compares the real-time cutting force with the theoretical reference cutting force. When the real-time cutting force exceeds 1.2 times the theoretical value, the feed rate Vf and the machining step length Lopt are proportionally reduced to reduce the cutting load and ensure the stability of the machining process.

[0027] Preferably, in the S3 stage, a constant residual height strategy is adopted for the steep area, and the residual height h satisfies:

[0028]

[0029] Where η is the surface complexity tolerance coefficient. This formula is based on the machining step size Lopt, the tool radius Rtool, and the average curvature H. It controls the residual height in steep areas to ensure the quality of the machined surface. By setting the surface complexity tolerance coefficient η, the upper limit of the residual height can be adjusted according to different machining accuracy requirements.

[0030] Preferably, the tool wear prediction model is introduced in the S2 stage:

[0031] The real-time estimation formula of tool wear W is:

[0032]

[0033] where k w is the wear coefficient, μ is the curvature influence coefficient; the tool wear amount is predicted in real time by integrating the feed speed Vf and Gaussian curvature K of each point on the tool path; when W>Wthreshold, the feed speed is automatically reduced and the tool compensation command is triggered.

[0034] Preferably, a multi-objective optimization function is established:

[0035]

[0036] Where T mach is the total processing time, is the average tool wear, and ω1, ω2, and ω3 are configurable weight coefficients. This function comprehensively considers the three objectives of total machining time, average tool wear, and maximum residual height. By adjusting the weight coefficients, a trade-off between machining efficiency, tool life, and machining quality can be optimized according to different machining requirements.

[0037] Preferably, a parallel computing architecture is used in the S2 stage to dynamically partition the surface:

[0038] When the curvature change rate , the area is marked as a high optimization priority area, and the calculation frequency is increased to 3 times the base frequency.

[0039] The present invention provides a method for precision machining of complex curved surfaces based on tool path optimization, which has the following beneficial effects:

[0040] 1. Adaptive precision control and surface quality improvement: By calculating the Gaussian curvature and average curvature of the surface in real time, the processing step size and feed speed are dynamically adjusted. The step size and feed speed are automatically reduced in areas with sudden changes in curvature, so that the residual height in steep areas is controlled within the precision standard range. The surface roughness is reduced by more than 30% compared with traditional methods, ensuring the consistency of processing accuracy in all areas of complex surfaces.

[0041] 2. Machining process stability and extended tool life: Through the cutting load closed-loop monitoring system, when the real-time cutting force exceeds the threshold, the machining parameters are automatically adjusted to control the cutting force fluctuation within a safe range, effectively reducing abnormal tool wear; combined with the tool wear prediction model, the tool wear status is evaluated in real time and the compensation mechanism is triggered, extending the tool life by 20%-40%.

[0042] 3. Multi-objective collaborative optimization and improved processing efficiency: Through multi-objective optimization functions, collaborative control of processing time, tool wear, and surface quality is achieved. Optimization weights are flexibly configured according to the needs of different fields such as aerospace and automotive molds, resulting in a 15%-25% improvement in processing efficiency. A parallel computing architecture is used to increase the calculation frequency in areas with high curvature changes, shortening the overall path planning time by more than 40% to meet real-time processing needs.

[0043] 4. Improved engineering application flexibility and automation: Key parameters can be flexibly adjusted according to the characteristics of the tool material and the processed material, adapting to the precision machining of various materials such as titanium alloy and aluminum alloy; through the equal residual height strategy and dynamic partition calculation, full-process automated parameter optimization from rough machining to finishing is achieved, significantly reducing manual intervention and improving the versatility and intelligence of the machining process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic flow chart of a method for precision machining of complex curved surfaces based on tool path optimization described in the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, the present invention provides a technical solution: a method for precision machining of complex curved surfaces based on tool path optimization, comprising the following steps:

[0047] S1. Obtaining the geometric model of the surface to be processed and the tool dynamic parameters;

[0048] S2. Calculate the optimal machining step length, tool roll angle, and feed rate in real time based on surface geometry and tool dynamics parameters;

[0049] S3. Generate an adaptive tool path based on the dynamic optimization parameters of step S2;

[0050] S4. Simultaneously monitor the cutting load during the machining process and dynamically modify the tool path parameters based on the load feedback;

[0051] S5. Output the optimized tool path instructions to the CNC machine tool for processing.

[0052] More specifically, the surface geometric features in step S2 include Gaussian curvature K and mean curvature H, which are calculated as follows:

[0053]

[0054] Where kmax and kmin are the extreme values ​​of the principal curvature at a point on the surface. This formula quantifies the curvature characteristics of the surface at a point by multiplying the product of the extreme values ​​of the principal curvature and the mean. The Gaussian curvature K reflects the overall curvature of the surface, and the mean curvature H reflects the local concavity and convexity of the surface. The combination of the two provides the surface geometry foundation for subsequent parameter calculations.

[0055] The step S2 of calculating the surface geometric features specifically includes the following steps:

[0056] S2.1 discretizes the obtained geometric model of the surface to be processed and divides the surface into multiple small grid units;

[0057] S2.2 For each grid cell, calculate its principal curvature direction and principal curvature extreme values ​​kmax and kmin based on the principles of differential geometry;

[0058] S2.3 Calculate the Gaussian curvature K and mean curvature H corresponding to each grid cell according to the above formula.

[0059] More specifically, the step S2 calculates the optimal processing step length L opt , the calculation formula is:

[0060]

[0061] where R tool is the tool radius, k1 and k2 are tool material coefficients, α is the curvature weight factor, 0.5≤α≤1.5;

[0062] The specific steps include:

[0063] S2.4 reads the Gaussian curvature K and mean curvature H of each grid cell calculated in step S2.3;

[0064] S2.5 Determine tool material coefficients k1 and k2 based on the actual tool material, and set the curvature weight factor α;

[0065] S2.6 Substitute the above parameters into the formula and calculate the optimal processing step length Lopt corresponding to each grid unit.

[0066] More specifically, the optimization formula for the tool rake angle θ in step S2 is:

[0067]

[0068] in is the mean curvature gradient, is the maximum radial cutting force allowed by the tool, and β and γ are stiffness compensation coefficients. This formula reflects the surface change trend through the average curvature gradient. The greater the change trend, The larger the value, the tool inclination angle θ is adjusted accordingly to adapt to the changes in the curved surface; at the same time, combined with the maximum radial cutting force allowed by the tool, excessive cutting force is used to avoid damaging the tool, and the β and γ coefficients are compensated and adjusted according to the tool stiffness characteristics.

[0069] The step S2 of calculating the tool rake angle θ specifically includes the following steps:

[0070] S2.7 Based on the average curvature H obtained in step S2.3, calculate the average curvature gradient between each grid cell

[0071] S2.8 Determine the maximum allowable radial cutting force of the tool and stiffness compensation coefficients β and γ;

[0072] S2.9 Substitute the above parameters into the formula to calculate the tool rake angle θ corresponding to each grid cell.

[0073] More specifically, the feed speed V in step S2 f The optimization formula is:

[0074]

[0075] MRR target is the target material removal rate, a p 、a e are the axial / radial cutting depths, λ is the curvature change sensitivity coefficient, and ΔK is the Gaussian curvature difference between adjacent tool positions.

[0076] In the formula, first pass Calculate the theoretical feed rate that meets the target material removal rate and compare it with the maximum feed rate V of the machine tool. max Take the minimum value to ensure safe processing; then adjust the feed speed according to the Gaussian curvature difference ΔK of adjacent tool positions. The greater the curvature change, the greater the e -λ·|ΔK| The smaller the value, the lower the feed speed to ensure the processing quality;

[0077] The specific steps include:

[0078] S2.10 sets the target material removal rate MRRtarget, axial / radial cutting depth a p , ae, and curvature change sensitivity coefficient λ;

[0079] S2.11 reads the Gaussian curvature K of each grid cell calculated in step S2.3, and calculates the Gaussian curvature difference ΔK between adjacent tool positions;

[0080] S2.12 Calculate separately and

[0081] S2.13 Substitute the above parameters into the formula to calculate the feed rate V corresponding to each grid unit f .

[0082] More specifically, the dynamic correction of the cutting load in step S4 adopts closed-loop control:

[0083] When the real-time cutting force Factual>1.2Fref, the following adjustments are triggered:

[0084]

[0085] Among them F ref The closed-loop control compares the real-time cutting force with the theoretical reference cutting force. When the real-time cutting force exceeds 1.2 times the theoretical value, the feed rate Vf and the machining step length Lopt are proportionally reduced to reduce the cutting load and ensure a stable machining process.

[0086] The step S4 performs dynamic correction of cutting load, specifically comprising the following steps:

[0087] S4.1 During the machining process, the cutting force data is collected in real time through sensors to obtain the real-time cutting force Factual;

[0088] S4.2 compares the real-time cutting force Factual with the theoretical reference cutting force Fref;

[0089] S4.3 If Factual>1.2F ref , calculate the adjusted feed speed Vf' and processing step length Lopt' according to the above formula respectively;

[0090] S4.4 feeds back the adjusted parameters to the tool path generation module to dynamically correct the tool path parameters.

[0091] More specifically, in the S3 stage, the equal residual height strategy is adopted for the steep area, and the residual height h satisfies:

[0092]

[0093] Where η is the surface complexity tolerance factor (0.01 to 0.05 mm). This formula, based on the machining step length Lopt, the tool radius Rtool, and the average curvature H, controls the stub height in steep areas to ensure surface quality. By setting the surface complexity tolerance factor η, the upper limit of the stub height can be adjusted to meet different machining accuracy requirements.

[0094] The step S3 adopts the equal residual height strategy, which specifically includes the following steps:

[0095] S3.1 determines whether each area is a steep area based on the average curvature H of each grid cell and the optimal processing step length Lopt calculated in step S2;

[0096] S3.2 For steep areas, set the surface complexity tolerance coefficient η and calculate the residual height h according to the formula;

[0097] S3.3 If the residual height h does not meet Then adjust the machining step length Lopt or the tool tilt angle θ and recalculate the residual height until the conditions are met;

[0098] S3.4 generates an adaptive tool path based on the adjusted parameters.

[0099] More specifically, the tool wear prediction model is introduced in the S2 stage:

[0100] The real-time estimation formula of tool wear W is:

[0101]

[0102] where k wis the wear coefficient, and μ is the curvature influence coefficient. By integrating the feed rate Vf and Gaussian curvature K at each point on the tool path, the tool wear amount is predicted in real time. When W>Wthreshold, the feed rate is automatically reduced and the tool compensation command is triggered.

[0103] The step S2 performs tool wear prediction, specifically comprising the following steps:

[0104] S2.14 determines the wear coefficient kw and the curvature influence coefficient μ, as well as the tool wear threshold Wthreshold;

[0105] S2.15 The feed rate V of each grid unit calculated in step S2 f and Gaussian curvature K, dividing the tool path into multiple tiny line segments;

[0106] S2.16 For each tiny line segment, calculate the tool wear increment corresponding to the line segment according to the formula;

[0107] S2.17 accumulates the wear increments of each line segment to obtain the tool wear amount W on the current tool path;

[0108] S2.18 If W>W threshold , the feed speed is reduced and a tool compensation instruction is generated and sent to the CNC machine control system.

[0109] More specifically, a multi-objective optimization function is established:

[0110]

[0111] Where T mach is the total processing time, is the average tool wear, and ω1, ω2, and ω3 are configurable weight coefficients. This function comprehensively considers the three objectives of total machining time, average tool wear, and maximum residual height. By adjusting the weight coefficients, a trade-off between machining efficiency, tool life, and machining quality can be optimized according to different machining requirements.

[0112] The establishment of a multi-objective optimization function for parameter optimization specifically includes the following steps:

[0113] S9.1 sets the configurable weight coefficients ω1, ω2, ω3;

[0114] S9.2 Calculate the total processing time T based on the processing step length, tool tilt angle, feed speed and other parameters calculated in steps S2-S3 mach , average tool wear and maximum residual height max(h);

[0115] S9.3 Substitute the above parameters into the multi-objective optimization function and calculate the function value;

[0116] S9.4 adjusts parameters such as machining step length, tool tilt angle, feed rate, etc., and repeats steps S9.2-S9.3 until the function value reaches the minimum to determine the optimal machining parameters.

[0117] More specifically, in the S2 stage, a parallel computing architecture is used to dynamically partition the surface:

[0118] When the curvature change rate When , the area is marked as a high optimization priority area, and the calculation frequency is increased to 3 times the basic frequency;

[0119] The step S2 adopts a parallel computing architecture to perform dynamic surface partitioning, specifically including the following steps:

[0120] S2.19 Calculate the curvature change rate between adjacent grid cells based on the Gaussian curvature K of each grid cell calculated in step S2.3

[0121] S2.20 sets the curvature change rate threshold δ;

[0122] S2.21 will change the curvature rate Compared with the threshold δ, if The corresponding area is marked as a high optimization priority area;

[0123] S2.22 allocates different computing resources and frequencies to different areas. For high-optimization priority areas, the computing frequency is increased to three times the base frequency, and the optimal processing step length, tool roll angle, feed speed and other parameters of each area are calculated in parallel.

[0124] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for precision machining of complex curved surfaces based on tool path optimization, characterized in that: The following steps are involved: S1. Obtaining the geometric model of the surface to be processed and the tool dynamic parameters; S2. Calculate the optimal machining step length, tool roll angle, and feed rate in real time based on surface geometry and tool dynamics parameters; S3. Generate an adaptive tool path based on the dynamic optimization parameters of step S2; S4. Simultaneously monitor the cutting load during the machining process and dynamically modify the tool path parameters based on the load feedback; S5. Output the optimized tool path instructions to the CNC machine tool for processing.

2. A method for precision machining of complex curved surfaces based on tool path optimization according to claim 1, characterized in that: The surface geometric features in step S2 include Gaussian curvature K and mean curvature H, which are calculated as follows: Among them, kmax and kmin are the extreme values ​​of the principal curvature at a certain point on the surface; this formula quantifies the bending characteristics of the surface at a certain point through the product and mean of the extreme values ​​of the principal curvature; the Gaussian curvature K reflects the overall curvature of the surface, and the mean curvature H reflects the local concavity and convexity of the surface. The combination of the two provides the surface geometry basis for subsequent parameter calculations.

3. The method for complex surface precision machining based on tool path optimization according to claim 2, characterized in that: The step S2 calculates the optimal processing step length L opt , the calculation formula is: where R tool is the tool radius, k1 and k2 are tool material coefficients, and α is the curvature weight factor, 0.5≤α≤1.

5.

4. The method for complex curved surface precision machining based on tool path optimization according to claim 3, characterized in that: The optimization formula for the tool rake angle θ in step S2 is: in is the mean curvature gradient, is the maximum radial cutting force allowed by the tool, β and γ are stiffness compensation coefficients; this formula reflects the surface change trend through the average curvature gradient. The greater the change trend, The larger the value, the tool inclination angle θ is adjusted accordingly to adapt to the changes in the curved surface; at the same time, combined with the maximum radial cutting force allowed by the tool, excessive cutting force is used to avoid damaging the tool, and the β and γ coefficients are compensated and adjusted according to the tool stiffness characteristics.

5. The method for complex surface precision machining based on tool path optimization according to claim 4, characterized in that: The feed speed V in step S2 f The optimization formula is: Among them, MRR target is the target material removal rate, a p 、a e are the axial / radial cutting depths, λ is the curvature change sensitivity coefficient, and ΔK is the Gaussian curvature difference between adjacent tool positions; In the formula, first pass Calculate the theoretical feed rate that meets the target material removal rate and compare it with the maximum feed rate V of the machine tool. max Take the minimum value to ensure safe processing; then adjust the feed speed according to the Gaussian curvature difference ΔK of adjacent tool positions. The greater the curvature change, the greater the e -λ·|ΔK| The smaller the value, the lower the feed speed is, ensuring the processing quality.

6. The method for complex curved surface precision machining based on tool path optimization according to claim 5, characterized in that: The dynamic correction of cutting load in step S4 adopts closed-loop control: When the real-time cutting force Factual>1.2Fref, the following adjustments are triggered: Among them F ref The closed-loop control compares the real-time cutting force with the theoretical reference cutting force. When the real-time cutting force exceeds 1.2 times the theoretical value, the feed rate Vf and the machining step length Lopt are proportionally reduced to reduce the cutting load and ensure the stability of the machining process.

7. The method for complex surface precision machining based on tool path optimization according to claim 6, characterized in that: In the S3 stage, the equal residual height strategy is adopted for the steep area, and the residual height h satisfies: Where η is the surface complexity tolerance coefficient. This formula is based on the machining step size Lopt, the tool radius Rtool, and the average curvature H. It controls the residual height in steep areas to ensure the quality of the machined surface. By setting the surface complexity tolerance coefficient η, the upper limit of the residual height can be adjusted according to different machining accuracy requirements.

8. The method for complex surface precision machining based on tool path optimization according to claim 1, characterized in that: Introducing tool wear prediction model in S2 stage: The real-time estimation formula of tool wear W is: where k w is the wear coefficient, μ is the curvature influence coefficient; the tool wear amount is predicted in real time by integrating the feed speed Vf and Gaussian curvature K of each point on the tool path; when W>Wthreshold, the feed speed is automatically reduced and the tool compensation command is triggered.

9. The method for complex curved surface precision machining based on tool path optimization according to claim 1, characterized in that: Establish a multi-objective optimization function: Where T mach is the total processing time, is the average tool wear, and ω1, ω2, and ω3 are configurable weight coefficients. This function comprehensively considers the three objectives of total machining time, average tool wear, and maximum residual height. By adjusting the weight coefficients, a trade-off between machining efficiency, tool life, and machining quality can be optimized according to different machining requirements.

10. The method for complex curved surface precision machining based on tool path optimization according to claim 9, characterized in that: In the S2 stage, a parallel computing architecture is used to dynamically partition the surface: When the curvature change rate , the area is marked as a high optimization priority area, and the calculation frequency is increased to 3 times the base frequency.

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