A numerical control machining method for a wood-replacing transition die based on equal residual dynamic machining

Abstract

The application discloses a kind of based on equal residual dynamic processing's wood transition mode number control processing method, it includes the following steps: S1: processing path optimization;S11: design knowledge enhanced path planning model;S12: equal residual height cutter contact point track generation;S2: cutting parameter collaborative optimization;S21: design multi-objective parameter optimization model;S22: design dynamic parameter adjustment strategy;S3: numerical control machine tool parameter joint optimization design;S31: servo shaft parameter matching;S32: cooling system collaborative control.The application adopts multidimensional collaborative optimization: fusion path planning, parameter optimization, machine tool debugging and cooling control, form closed loop optimization system;It uses intelligent decision: knowledge graph and reinforcement learning are combined, realize the adaptive adjustment of processing strategy;It has high-precision guarantee: through servo parameter matching and cooling control, machining precision is improved to ±0.02mm.

Description

Technical Field

[0001] This invention relates to the field of CNC machining technology for wood substitute transition molds, specifically 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 Technology

[0002] Wood substitutes (such as polyurethane and epoxy resin-based composites) are widely used in transition mold manufacturing due to their low cost, ease of processing, and good dimensional stability. In existing technologies, five-axis CNC machining path optimization mainly relies on NURBS curve fitting or empirical parameter adjustments, which are difficult to adapt to the complex processing environment of wood substitutes. For example, traditional methods do not fully utilize the thermal expansion coefficient of wood substitutes. The characteristics of cutting force distribution lead to fluctuations in machining accuracy. In addition, insufficient servo axis parameter matching (such as position loop gain differences) can exacerbate cross-quadrant jumps, affecting the machining quality of curved surfaces.

[0003] The existing five-axis CNC machining of wood-substitute transition molds has the following technical problems:

[0004] 1) Low processing efficiency: The hardness of the wood substitute is relatively high (Shore hardness can reach 70-90). The traditional layer-cutting strategy does not fully consider the cutting resistance characteristics of the material, resulting in long processing time.

[0005] 2) Poor surface quality: The substitute wood has strong toughness, and conventional tool path planning is prone to defects such as chipping and tool marks, especially in complex curved areas.

[0006] 3) Rapid tool wear: The high temperature (up to 200-300℃) generated during the cutting of wood substitutes accelerates tool wear and increases the frequency of tool changes.

[0007] 4) Deformation control is difficult: the substitute wood is prone to deformation during processing due to stress release, especially in thin-walled structural areas. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a CNC machining method for wood-substitute transition modules based on dynamic machining with equal residuals. Through multi-dimensional collaborative optimization, the machining efficiency, accuracy and surface quality are significantly improved.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A CNC machining method for wood-substitute transition modules based on dynamic machining with equal residual material, characterized by comprising the following steps:

[0011] S1: Processing path optimization;

[0012] S11: Design a knowledge-enhanced path planning model;

[0013] - Construct a knowledge graph that includes machine tool status parameters, wood substitute material properties, and tool information;

[0014] -A reinforcement learning model is used to train the machining path optimization model. The state space features are extracted through the graph neural network layer and combined with the path optimization layer to generate an adaptive tool path.

[0015] - Introducing a dynamic weight set to weight the features of the state space, enabling refined path planning for complex curved surface regions;

[0016] S12: Generation of tool contact trajectory with equal residual height;

[0017] -Based on the adaptive projection bias algorithm, the cutting row spacing is dynamically adjusted according to the normal radius of curvature and allowable residual height of the substitute wood material;

[0018] S2: Collaborative optimization of cutting parameters;

[0019] S21: Design a multi-objective parameter optimization model;

[0020] - Establish an optimization function with machining efficiency M, tool life T, and surface roughness Ra as objectives, combined with weighting coefficients W;

[0021] - The optimal parameter combination is solved using the particle swarm optimization algorithm to optimize the spindle speed, feed rate, and depth of cut;

[0022] S22: Design a dynamic parameter adjustment strategy;

[0023] - By combining real-time monitored cutting force and temperature data, the feed rate and depth of cut are dynamically adjusted;

[0024] S3: Joint optimization design of CNC machine tool parameters;

[0025] S31: Servo axis parameter matching;

[0026] -Based on roundness testing, the position loop gain and velocity loop bandwidth parameters are optimized through a four-step iterative process;

[0027] - S31-1: Identify motor and load inertia;

[0028] - S31-2: Optimize linear gain;

[0029] - S31-3: Adjusting the position loop gain based on roundness mismatch;

[0030] - S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps;

[0031] S32: Cooling system coordinated control;

[0032] - A composite cooling method combining air cooling and water cooling is adopted. The surface temperature of the substitute wood is monitored in real time by temperature sensors and controlled within the range of 30-50℃ to reduce thermal deformation.

[0033] Furthermore, in S11: machine tool status parameters include spindle speed, feed rate, and depth of cut; material properties include hardness and coefficient of thermal expansion; tool information includes diameter and knowledge graph of coating type / composition.

[0034] Furthermore, in S12: line spacing Where R is the normal radius of curvature, r is the tool radius, and h is the residual height.

[0035] Furthermore, in S21: in These are the weighting coefficients.

[0036] Furthermore, in S22: when the cutting force exceeds the threshold, the control system automatically reduces the feed rate by 15% and increases the depth of cut by 0.1mm to balance machining efficiency and tool life.

[0037] Furthermore, in S31: S31-1: Identify the inertia of the motor and load, 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: Activate the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps, with a jump value of <0.01mm.

[0038] Furthermore, in S32, a composite cooling method combining air cooling at -20℃ and water cooling at 5-10℃ is adopted.

[0039] This invention discloses a CNC machining method for wood-substitute transition modules based on dynamic machining with equal residual material. It employs multi-dimensional collaborative optimization: integrating path planning, parameter optimization, machine tool debugging, and cooling control to form a closed-loop optimization system; it utilizes intelligent decision-making: combining knowledge graphs and reinforcement learning to achieve adaptive adjustment of machining strategies; it ensures high precision: through servo parameter matching and cooling control, machining accuracy is improved to ±0.02mm; and it is highly efficient and low-cost: machining efficiency is increased by more than 30%, and tool costs are reduced by 30%-40%.

[0040] The present invention provides a CNC machining method for wood-substitute transition modules based on dynamic processing with equal residual material. Through multi-dimensional collaborative optimization, it significantly improves processing efficiency, accuracy and surface quality. Detailed Implementation

[0041] To make the technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in a clearer and more complete manner below with reference to specific embodiments. It should be understood that the specific embodiments described herein are only some 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 related parts can be referred to the general design. In the absence of conflict, the embodiments and technical features in the embodiments of the present invention can be combined with each other to obtain new embodiments.

[0042] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, unless otherwise defined, the technical or scientific terms used in the description of this invention should have the ordinary meaning understood by those skilled in the art.

[0043] A method for optimizing the CNC machining of a wood-substitute transition mold, characterized by comprising the following steps:

[0044] S1: Processing path optimization;

[0045] S11: Design a knowledge-enhanced path planning model;

[0046] - Construct a knowledge graph that includes machine tool status parameters (including spindle speed, feed rate, and depth of cut), wood substitute material properties (including hardness and coefficient of thermal expansion), and tool information (including diameter and coating type / composition);

[0047] -A reinforcement learning model is used to train the machining path optimization model. The state space features are extracted through the graph neural network layer and combined with the path optimization layer to generate an adaptive tool path.

[0048] - Introducing a dynamic weight set to weight the features of the state space, enabling refined path planning for complex curved surface regions;

[0049] S12: Generation of tool contact trajectory with equal residual height;

[0050] -Based on an adaptive projection offset algorithm, the cutting row spacing is dynamically adjusted according to the normal radius of curvature and allowable residual height (usually 0.05-0.1mm) of the substitute wood material;

[0051] line spacing Where R is the normal radius of curvature, r is the tool radius, and h is the residual height.

[0052] There are usually multiple ways to express the line spacing L, as long as it is a function that limits R, r, and h, without any specific limitation.

[0053] S2: Collaborative optimization of cutting parameters;

[0054] S21: Design a multi-objective parameter optimization model;

[0055] - Establish an optimization function with machining efficiency M, tool life T, and surface roughness Ra as objectives:

[0056] in These are the weighting coefficients;

[0057] Optimization function The expression function can usually be expressed in multiple ways, as long as it is a function that limits M, T, and Ra, without any specific limitation.

[0058] - The Particle Swarm Optimization (PSO) algorithm is used to solve for the optimal parameter combination, focusing on optimizing the spindle speed (10000-20000 r / min), feed rate (8000-10000 mm / min), and depth of cut (0.5-1.5 mm).

[0059] S22: Design a dynamic parameter adjustment strategy;

[0060] - Combine real-time monitoring of cutting force (acquired via force sensor) and temperature data (measured via infrared thermometry) to dynamically adjust feed rate (±10%) and depth of cut (±0.2mm).

[0061] In one embodiment, when the cutting force exceeds a threshold (e.g., 500N), the control system automatically reduces the feed rate by 15% while increasing the depth of cut by 0.1mm to balance machining efficiency and tool life.

[0062] S3: Joint optimization design of CNC machine tool parameters;

[0063] S31: Servo axis parameter matching;

[0064] -Based on roundness testing (radius 100mm, speed 2000mm / min), parameters such as position loop gain and speed loop bandwidth are optimized through four iterative steps;

[0065] - S31-1: Identify motor and load inertia (error <5%).

[0066] - S31-2: Optimize linear gain (position loop gain difference <10%)

[0067] - S31-3: Adjust position loop gain based on roundness mismatch (target <0.02mm);

[0068] - S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps (jump value < 0.01 mm).

[0069] S32: Cooling system coordinated control;

[0070] - A composite cooling method combining air cooling (-20℃) and water cooling (5-10℃) is adopted. The surface temperature of the substitute wood is monitored in real time by a temperature sensor and controlled within the range of 30-50℃ to reduce thermal deformation.

[0071] In one embodiment, the processing technology of a wood-substitute transition mold for wind turbine blades is taken as an example:

[0072] (1) Model construction: Import the wooden model (size 1500×800×300mm) using 3D software (Catia) and mesh it (unit size 2mm);

[0073] (2) Path planning:

[0074] - Initial paths are generated based on knowledge graphs, and reinforcement learning models are used to optimize paths in complex curved surface regions (such as leaf root transition regions), reducing air cut distances by 20%;

[0075] - Using an equal residual height algorithm, the line spacing is adjusted from 2mm to 1.5mm in areas with large curvature changes (normal curvature radius <50mm);

[0076] (3) Parameter settings:

[0077] - Tool selection: 12mm diameter carbide coated end mill (4 cutting edges, 30° helix angle);

[0078] - Cutting parameters: Spindle speed 18000 r / min, feed rate 9000 mm / min, depth of cut 1.2 mm;

[0079] -Cooling parameters: Air cooling flow rate 50L / min, water cooling flow rate 30L / min;

[0080] (4) CNC machine tool debugging:

[0081] - Servo axis parameter optimization was performed, reducing the roundness mismatch from 0.035mm to 0.018mm;

[0082] - With the perturbation prediction observer enabled, the quadrant jump value decreased from 0.022 mm to 0.009 mm;

[0083] (5) Processing verification:

[0084] - Processing time: Reduced from 8 hours using traditional methods to 5.5 hours, increasing efficiency by 31.25%;

[0085] - Surface quality: Surface roughness Ra was reduced from 3.2μm to 1.6μm, achieving a mirror-like effect;

[0086] - Tool life increased from 5 pieces / blade to 8 pieces / blade, resulting in a 37.5% cost reduction.

[0087] This invention discloses a CNC machining method for wood-substitute transition modules based on dynamic machining with equal residual material. It employs multi-dimensional collaborative optimization: integrating path planning, parameter optimization, machine tool debugging, and cooling control to form a closed-loop optimization system; it utilizes intelligent decision-making: combining knowledge graphs and reinforcement learning to achieve adaptive adjustment of machining strategies; it ensures high precision: through servo parameter matching and cooling control, machining accuracy is improved to ±0.02mm; and it is highly efficient and low-cost: machining efficiency is increased by more than 30%, and tool costs are reduced by 30%-40%.

[0088] The present invention provides a CNC machining method for wood-substitute transition modules based on dynamic processing with equal residual material. Through multi-dimensional collaborative optimization, it significantly improves processing efficiency, accuracy and surface quality.

[0089] The above embodiments are illustrative of the present invention and not intended to limit the invention. It is understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the 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-substitute transition modules based on dynamic machining with equal residual material, characterized in that, It includes the following steps: S1: Processing path optimization; S11: Design a knowledge-enhanced path planning model; Construct a knowledge graph that includes machine tool status parameters, wood substitute material properties, and tool information; A machining path optimization model is trained using reinforcement learning. State space features are extracted through graph neural network layers and combined with path optimization layers to generate adaptive tool paths. By introducing a dynamic weight set to weight the features of the state space, we can achieve refined path planning for complex curved surface regions. S12: Generation of tool contact trajectory with equal residual height; Based on the adaptive projection bias algorithm, the cutting row spacing is dynamically adjusted according to the normal radius of curvature and allowable residual height of the wood substitute material; S2: Collaborative optimization of cutting parameters; S21: Design a multi-objective parameter optimization model; An optimization function is established with machining efficiency M, tool life T, and surface roughness Ra as objectives, combined with weighting coefficients W: ;in , , These are the weighting coefficients; The optimal parameter combination is solved using the particle swarm optimization algorithm to optimize the spindle speed, feed rate, and depth of cut. S22: Design a dynamic parameter adjustment strategy; By combining real-time monitored cutting force and temperature data, the feed rate and depth of cut are dynamically adjusted; S3: Joint optimization design of CNC machine tool parameters; S31: Servo axis parameter matching; Based on roundness testing, the position loop gain and velocity loop bandwidth parameters are optimized through a four-step iterative process. S31-1: Identify motor and load inertia; S31-2: Optimize linear gain; S31-3: Adjusting the position loop gain based on roundness mismatch; S31-4: Enable the disturbance prediction observer to compensate for nonlinear friction and suppress quadrant jumps; S32: Cooling system coordinated control; A composite cooling method combining air cooling and water cooling is adopted. The surface temperature of the substitute wood is monitored in real time by temperature sensors and controlled within the range of 30-50℃ to reduce thermal deformation.

2. The CNC machining method for wood-substitute transition modules based on equal residual dynamic machining as described in claim 1, characterized in that, in, In S11: Machine tool status parameters include spindle speed, feed rate, and depth of cut; material properties include hardness and coefficient of thermal expansion; and tool information includes diameter, coating type, and coating composition.

3. The CNC machining method for wood-substitute transition modules based on equal residual dynamic machining as described in claim 2, characterized in that, in, In S12: row spacing L = Where R is the normal radius of curvature, r is the tool radius, and h is the residual height.

4. The CNC machining method for wood-substitute transition modules based on equal residual dynamic machining as described in 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 depth of cut by 0.1mm to balance machining efficiency and tool life.

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

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