Numerical control machining method for wood-substituted transition mold based on equal-residue dynamic machining

Abstract

The invention discloses a numerical control machining method for a wood substitute transition mold based on equal-residue dynamic machining. The numerical control machining method comprises the following steps that S1, a machining path is optimized; s11, designing a knowledge enhanced path planning model; s12, generating an equal-residual-height cutter contact track; s2, cutting parameter collaborative optimization; s21, designing a multi-target parameter optimization model; s22, designing a dynamic parameter adjustment strategy; s3, numerical control machine tool parameter joint optimization design; s31, servo shaft parameter matching is carried out; and S32, cooling system cooperative control is carried out. According to the method, multi-dimensional collaborative optimization is adopted, and path planning, parameter optimization, machine tool debugging and cooling control are fused to form a closed-loop optimization system; according to the method, intelligent decision making is adopted, a knowledge graph and reinforcement learning are combined, and self-adaptive adjustment of a processing strategy is achieved; and high-precision guarantee is achieved, and the machining precision is improved to + / -0.02 mm through servo parameter matching and cooling control.

Description

Technical Field

[0001] The present invention relates to the technical field of CNC machining technology for wood substitute transition molds, and in particular to a CNC machining method for wood substitute transition molds based on equal residual dynamic machining, which is suitable for high-precision and high-efficiency machining of wood substitute material molds / models. Background Art

[0002] Wood substitute materials (such as polyurethane and epoxy resin-based composites) are widely used in transition mold manufacturing due to their low cost, easy processing, and good dimensional stability. Existing technologies for five-axis CNC machining path optimization rely primarily on NURBS curve fitting or empirical parameter adjustment, which is difficult to adapt to the complex processing environment of wood substitute materials. For example, traditional methods do not fully utilize the thermal expansion coefficient of wood substitute materials. The distribution characteristics of cutting forces lead to fluctuations in machining accuracy. In addition, insufficient matching of servo axis parameters (such as position loop gain differences) will aggravate quadrant jumps and affect surface machining quality.

[0003] The following technical problems exist in the existing five-axis CNC machining of wood transition molds: 1) Low processing efficiency: The hardness of wood substitute materials is relatively high (Shore hardness can reach 70-90). The traditional layered cutting strategy does not fully consider the cutting resistance characteristics of the material, resulting in long processing time.

[0004] 2) Poor surface quality: The toughness of the wood substitute is relatively strong, and conventional tool path planning is prone to defects such as edge chipping and tool marks, especially in complex curved areas.

[0005] 3) Rapid tool wear: The high temperature (up to 200-300°C) generated during the cutting process of wood substitute materials accelerates tool wear and increases the frequency of tool changes.

[0006] 4) Difficulty in controlling deformation: Wood substitutes are prone to deformation due to stress release during processing, especially in thin-walled structure areas. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a CNC machining method for a wood transition die based on equal residual dynamic machining, which significantly improves machining efficiency, precision and surface quality through multi-dimensional collaborative optimization.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: A CNC machining method for a wood transition die based on equal residual dynamic machining is characterized in that it comprises the following steps: S1: machining path optimization; S11: Design a knowledge-enhanced path planning model; -Build a knowledge graph that includes machine tool status parameters, wood substitute material characteristics, and tool information; -Use reinforcement learning to train the machining path optimization model, extract state space features through the graph neural network layer, and combine it with the path optimization layer to generate adaptive tool paths; -Introducing dynamic weight sets to weight state space features and achieve refined path planning in complex surface areas; S12: Generation of tool contact trajectory with equal residual height; - Based on the adaptive projection offset algorithm, the cutting distance is dynamically adjusted according to the normal curvature radius and allowable residual height of the wood substitute material; S2: collaborative optimization of cutting parameters; S21: Design a multi-objective parameter optimization model; -Establish an optimization function with machining efficiency M, tool life T, and surface roughness Ra as targets and combined with the weight coefficient W; -Use particle swarm optimization to solve the optimal parameter combination and optimize spindle speed, feed rate and cutting depth; S22: Design dynamic parameter adjustment strategy; - Combined with real-time monitoring of cutting force and temperature data, dynamic adjustment of feed rate and cutting depth; S3: Joint optimization design of CNC machine tool parameters; S31: Servo axis parameter matching; -Based on the circularity test, the position loop gain and speed loop bandwidth parameters are optimized through four steps of iteration; - S31-1: Identify motor and load inertia; - S31-2: Optimize linear gain; - S31-3: Adjust position loop gain based on circularity mismatch; - S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jump; S32: Cooling system coordinated control; -Adopting a composite cooling method combining air cooling and water cooling, the surface temperature of the wood substitute is monitored in real time through a temperature sensor, and it is controlled within the range of 30-50℃ to reduce thermal deformation.

[0009] Furthermore, in S11: machine tool status parameters include spindle speed, feed speed, cutting depth, wood substitute material properties include hardness, thermal expansion coefficient, tool information includes diameter, coating type / composition knowledge map.

[0010] Furthermore, in S12: line spacing ; Where R is the normal curvature radius, r is the tool radius, and h is the residual height.

[0011] Furthermore, in S21: in is the weight coefficient.

[0012] Furthermore, in S22: when the cutting force exceeds the threshold, the control system automatically reduces the feed speed by 15% and increases the cutting depth by 0.1 mm to balance the processing efficiency and tool life.

[0013] Furthermore, in S31: S31-1: Identify the motor and load inertia, with an error of <5%; S31-2: Optimize the linear gain, with a position loop gain difference of <10%; S31-3: Adjust the position loop gain based on the roundness mismatch, with a target of <0.02mm; S31-4: Turn on the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps, with a jump value of <0.01mm.

[0014] Furthermore, in S32: a composite cooling method combining air cooling at -20°C and water cooling at 5-10°C is adopted.

[0015] The present invention provides a CNC machining method for a wood transition module based on equal residual dynamic machining, which adopts multi-dimensional collaborative optimization: integrating path planning, parameter optimization, machine tool debugging and cooling control to form a closed-loop optimization system; it adopts intelligent decision-making: combining knowledge graphs with reinforcement learning to achieve adaptive adjustment of machining strategies; it has high-precision guarantee: through servo parameter matching and cooling control, the machining accuracy is improved to ±0.02mm; it is high-efficiency and low-cost: the machining efficiency is improved by more than 30%, and the tool cost is reduced by 30%-40%.

[0016] The present invention provides a CNC machining method for a wood transition die based on equal residual dynamic machining, which significantly improves machining efficiency, precision and surface quality through multi-dimensional collaborative optimization. DETAILED DESCRIPTION

[0017] To make the technical solution and advantages of the present invention more clear, the technical solution of the present invention will be further clearly and completely described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only partial embodiments of the present invention and are only used to explain the present invention, not to limit the present invention. It should be noted that, for ease of description, only the parts / details related to the present invention are shown in the specific embodiments. Other relevant parts can refer to the general design. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other to obtain new embodiments.

[0018] All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention. Furthermore, unless otherwise defined, technical or scientific terms used in the description of the present invention shall have the same meanings as those generally understood by persons of ordinary skill in the art to which the present invention belongs.

[0019] A numerical control machining optimization method for a wood-substitute transition mold, characterized in that it comprises the following steps: S1: machining path optimization; S11: Design a knowledge-enhanced path planning model; -Build a knowledge graph that includes machine tool status parameters (including spindle speed, feed rate, and cutting depth), wood substitute material properties (including hardness and thermal expansion coefficient), and tool information (including diameter and coating type / composition); -Use reinforcement learning to train the machining path optimization model, extract state space features through the graph neural network layer, and combine it with the path optimization layer to generate adaptive tool paths; -Introducing dynamic weight sets to weight state space features and achieve refined path planning in complex surface areas; S12: Generation of tool contact trajectory with equal residual height; - Based on the adaptive projection offset algorithm, the cutting distance is dynamically adjusted according to the normal curvature radius and the allowable residual height (usually 0.05-0.1mm) of the wood substitute material; Line Spacing ; Where R is the normal curvature radius, r is the tool radius, and h is the residual height.

[0020] There are usually many ways to express the function of the line spacing L. As long as it is a limited function of R, r, and h, no specific limitation is required.

[0021] S2: collaborative optimization of cutting parameters; S21: Design a multi-objective parameter optimization model; -Establish an optimization function with machining efficiency M, tool life T, and surface roughness Ra as targets: in is the weight coefficient; Optimization function There are usually many ways to express the expression function, as long as it is a limited function about M, T, and Ra, and no specific limitation is required.

[0022] -Particle swarm optimization (PSO) was used to solve the optimal parameter combination, focusing on optimizing the spindle speed (10,000-20,000 rpm), feed rate (8,000-10,000 mm / min), and cutting depth (0.5-1.5 mm).

[0023] S22: Design dynamic parameter adjustment strategy; - Combined with real-time monitoring of cutting force (collected by force sensors) and temperature data (through infrared temperature measurement), the feed rate (±10%) and cutting depth (±0.2mm) are dynamically adjusted.

[0024] In one embodiment, when the cutting force exceeds a threshold value (eg, 500 N), the control system automatically reduces the feed rate by 15% and increases the cutting depth by 0.1 mm to balance machining efficiency and tool life.

[0025] S3: Joint optimization design of CNC machine tool parameters; S31: Servo axis parameter matching; -Based on the roundness test (radius 100mm, speed 2000mm / min), the position loop gain, speed loop bandwidth and other parameters were optimized through four steps of iteration; - S31-1: Identify motor and load inertia (error <5%); - S31-2: Optimized linear gain (position loop gain difference <10%); - S31-3: Adjust position loop gains based on roundness mismatch (target <0.02mm); - S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jump (jump value <0.01mm).

[0026] S32: Cooling system coordinated control; - A composite cooling method combining air cooling (-20°C) and water cooling (5-10°C) is used. The surface temperature of the substitute wood is monitored in real time through a temperature sensor and controlled within the range of 30-50°C to reduce thermal deformation.

[0027] In one embodiment, a process for processing a wood transition mold for a wind turbine blade is taken as an example: (1) Model construction: Use 3D software (Catia) to import the wood model (size 1500 × 800 × 300 mm) and divide the mesh (cell size 2 mm); (2) Path planning: -Generates initial paths based on knowledge graphs and uses reinforcement learning models to optimize paths in complex curved areas (such as the blade root transition zone), reducing air cutting travel by 20%; -Using the equal residual height algorithm, the line spacing is adjusted from 2mm to 1.5mm in areas with large curvature changes (normal curvature radius <50mm); (3) Parameter settings: -Tool selection: 12mm diameter carbide coated milling cutter (4 flutes, 30° helix angle); -Cutting parameters: spindle speed 18000 rpm, feed rate 9000 mm / min, cutting depth 1.2 mm; -Cooling parameters: air cooling flow rate 50L / min, water cooling flow rate 30L / min; (4) CNC machine tool debugging: -Performed servo axis parameter optimization, reducing roundness mismatch from 0.035mm to 0.018mm; -The disturbance prediction observer is turned on, and the quadrant jump value is reduced from 0.022mm to 0.009mm; (5) Processing verification: -Processing time: shortened from 8 hours using traditional methods to 5.5 hours, an efficiency improvement of 31.25%; -Surface quality: Surface roughness Ra is reduced from 3.2μm to 1.6μm, achieving a mirror effect; -Tool life: increased from 5 pieces / edge to 8 pieces / edge, with a cost reduction of 37.5%.

[0028] The present invention provides a CNC machining method for a wood transition module based on equal residual dynamic machining, which adopts multi-dimensional collaborative optimization: integrating path planning, parameter optimization, machine tool debugging and cooling control to form a closed-loop optimization system; it adopts intelligent decision-making: combining knowledge graphs with reinforcement learning to achieve adaptive adjustment of machining strategies; it has high-precision guarantee: through servo parameter matching and cooling control, the machining accuracy is improved to ±0.02mm; it is high-efficiency and low-cost: the machining efficiency is improved by more than 30%, and the tool cost is reduced by 30%-40%.

[0029] The present invention provides a CNC machining method for a wood transition die based on equal residual dynamic machining, which significantly improves machining efficiency, precision and surface quality through multi-dimensional collaborative optimization.

[0030] The above-mentioned embodiments are illustrative of the present invention, not limiting thereof. It is understood that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A CNC machining method for wood transition module based on equal residual dynamic machining, characterized in that: It includes the following steps: S1: machining path optimization; S11: Design a knowledge-enhanced path planning model; -Build a knowledge graph that includes machine tool status parameters, wood substitute material characteristics, and tool information; -Use reinforcement learning to train the machining path optimization model, extract state space features through the graph neural network layer, and combine it with the path optimization layer to generate adaptive tool paths; -Introducing dynamic weight sets to weight state space features and achieve refined path planning in complex surface areas; S12: Generation of tool contact trajectory with equal residual height; - Based on the adaptive projection offset algorithm, the cutting distance is dynamically adjusted according to the normal curvature radius and allowable residual height of the wood substitute material; S2: collaborative optimization of cutting parameters; S21: Design a multi-objective parameter optimization model; -Establish an optimization function with machining efficiency M, tool life T, and surface roughness Ra as targets and combined with the weight coefficient W; -Use particle swarm optimization to solve the optimal parameter combination and optimize spindle speed, feed rate and cutting depth; S22: Design dynamic parameter adjustment strategy; - Combined with real-time monitoring of cutting force and temperature data, dynamic adjustment of feed rate and cutting depth; S3: Joint optimization design of CNC machine tool parameters; S31: Servo axis parameter matching; -Based on the circularity test, the position loop gain and speed loop bandwidth parameters are optimized through four steps of iteration; - S31-1: Identify motor and load inertia; - S31-2: Optimize linear gain; - S31-3: Adjust position loop gain based on circularity mismatch; - S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jump; S32: Cooling system coordinated control; -Adopting a composite cooling method combining air cooling and water cooling, the surface temperature of the wood substitute is monitored in real time through a temperature sensor, and it is controlled within the range of 30-50℃ to reduce thermal deformation.

2. A CNC machining method for a wood transition module based on equal residual dynamic machining as claimed in claim 1, characterized in that: in, In S11: machine tool status parameters include spindle speed, feed rate, cutting depth, wood substitute material properties include hardness, thermal expansion coefficient, tool information includes diameter, coating type / composition knowledge map.

3. A CNC machining method for a wood transition module based on equal residual dynamic machining as claimed in claim 2, characterized in that: in, S12: Line spacing ; Where R is the normal curvature radius, r is the tool radius, and h is the residual height.

4. A CNC machining method for a wood transition module based on equal residual dynamic machining as claimed in claim 3, characterized in that: in, In S21: in is the weight coefficient.

5. The method for CNC machining of a wood transition module based on equal residual dynamic machining according to claim 1, characterized in that: in, In S22: When the cutting force exceeds the threshold, the control system automatically reduces the feed rate by 15% and increases the cutting depth by 0.1 mm to balance the machining efficiency and tool life.

6. A CNC machining method for a wood transition module based on equal residual dynamic machining as claimed in claim 5, characterized in that: in, In S31: S31-1: Identify the motor and load inertia, with an error of <5%; S31-2: Optimize the linear gain, with a position loop gain difference of <10%; S31-3: Adjust the position loop gain based on the roundness mismatch, with a target of <0.02mm; S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps, with a jump value of <0.01mm.

7. A CNC machining method for a wood transition module based on equal residual dynamic machining as claimed in claim 6, characterized in that: in, In S32: a composite cooling method combining air cooling at -20℃ and water cooling at 5-10℃ is adopted.