Gantry numerical control milling machine machining method for self-adaptive intelligent tool path planning

Abstract

The invention provides a gantry numerical control milling machine machining method for self-adaptive intelligent tool path planning, and belongs to the technical field of numerical control milling machines, and the gantry numerical control milling machine machining method comprises the following steps: S1, obtaining three-dimensional model data of a workpiece, carrying out feature recognition and analysis on the three-dimensional model data, and determining to-be-machined features, including a plane, a curved surface, a hole and a groove, of the workpiece; s2, according to the to-be-machined features, in combination with a machining technology knowledge base, a proper tool type, tool size and initial cutting parameters are automatically matched; and S3, based on a self-adaptive algorithm, collecting state information of the gantry numerical control milling machine in the machining process in real time. According to the method, automatic feature recognition is realized through three-dimensional model analysis, manual intervention is reduced, and the processing preparation efficiency is improved; cutters and parameters are matched through a process knowledge base, the reasonability of an initial machining scheme can be ensured, and the trial and error cost is effectively reduced; and through real-time state monitoring and self-adaptive adjustment, the machining process is optimized, and the machining precision and the surface quality can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical control milling machine, in particular to a gantry numerical control milling machine machining method with adaptive intelligent tool path planning. BACKGROUND

[0002] The gantry numerical control milling machine is a high-precision machine tool, mainly used for machining of medium and large mechanical parts. It adopts a bridge-type gantry structure, has a high-rigidity frame and stable accuracy, and is suitable for machining of large-size parts. The traditional gantry numerical control milling machine machining relies on manual experience programming, and has the following problems:

[0003] Complex workpiece path planning is time-consuming and prone to errors, and is low in efficiency;

[0004] Fixed parameters cannot adapt to dynamic changes in the machining process (such as tool wear and uneven material), resulting in fluctuation of precision;

[0005] Manual monitoring is labor-intensive and difficult to respond to abnormal situations in real time, which can easily cause waste or equipment damage;

[0006] High skill requirements for operators, and talent shortage restricts the development of the industry.

[0007] Therefore, a gantry numerical control milling machine machining method with adaptive intelligent tool path planning is proposed. SUMMARY

[0008] The present application provides a gantry numerical control milling machine machining method with adaptive intelligent tool path planning to solve the problems raised in the background art.

[0009] The specific technical solution is as follows:

[0010] A gantry numerical control milling machine machining method with adaptive intelligent tool path planning, comprising the following steps: S1: obtaining three-dimensional model data of a workpiece, performing feature recognition and analysis on the three-dimensional model data, and determining the features to be machined of the workpiece, including planes, curved surfaces, holes, and grooves;

[0011] S2: automatically matching suitable tool types, tool sizes, and initial cutting parameters according to the features to be machined, in combination with a machining process knowledge base, the machining process knowledge base storing tool and cutting parameter data corresponding to different materials and different features;

[0012] S3: based on an adaptive algorithm, real-time acquisition of state information of the gantry numerical control milling machine in the machining process, the state information including cutting force, tool vibration, spindle power, and machining temperature, real-time adjustment of tool path and cutting parameters according to the state information to optimize the machining process.

[0013] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: the tool path and cutting parameters are adjusted in real time based on the adaptive algorithm, specifically: when the cutting force exceeds the preset threshold, the feed speed is reduced or the cutting depth is reduced; when the tool vibration amplitude exceeds the allowed range, the curvature of the tool path is adjusted or the cutting direction is changed; when the spindle power abnormally fluctuates, the cutting parameter combination is optimized. The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: when determining the tool path, a layered and zoned strategy is adopted, the machining area of the workpiece is divided into multiple sub-areas, the tool path is planned for different sub-areas, and a transition path is set at the junction of adjacent sub-areas to reduce the idle travel and sudden stop of the tool.

[0014] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: the machining process knowledge base is continuously updated and optimized through a machine learning algorithm, the machine learning algorithm is based on actual machining data, including machining quality feedback and tool life data, and adjusts and improves the tool and cutting parameter data in the knowledge base.

[0015] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: when machining complex curved surfaces, an equal residual height-based tool path generation algorithm is adopted, the tool pitch and step length are automatically adjusted according to the curvature change of the curved surface to ensure that the machined curved surface has uniform surface quality.

[0016] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: it further includes virtual simulation before machining, a simulation model is used to simulate the machining process of the gantry numerical control milling machine, the planned tool path is verified and optimized, potential interference problems and machining defects are found in advance, and the tool path is adjusted.

[0017] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: when the tool is worn or damaged during the machining process, real-time detection is performed through a tool state monitoring system, the tool state monitoring system judges the wear or damage of the tool based on sensor data including vibration signals and current signals, once an abnormality is detected, the tool is automatically replaced, and the tool path and cutting parameters are re-optimized according to the characteristics of the new tool.

[0018] The gantry numerical control milling machine machining method of adaptive intelligent tool path planning, wherein: when machining multiple identical or similar workpieces, a workpiece machining template is established, the tool path, cutting parameters and adaptive adjustment strategy in the first machining process are stored as a template, the template can be directly called during subsequent machining, and fine tuning is performed according to the slight differences of actual workpieces to improve machining efficiency

[0019] The above-mentioned gantry CNC milling machine processing method with adaptive intelligent tool path planning, wherein: the gantry CNC milling machine is equipped with an intelligent control system, which integrates a processing technology knowledge base, an adaptive algorithm module, a tool status monitoring module and a virtual simulation module. The modules communicate and work together to achieve comprehensive intelligent control of the processing process.

[0020] The above-mentioned adaptive intelligent tool path planning gantry CNC milling machine processing method, wherein: during the processing, combined with the workpiece material removal rate model, the remaining processing time is predicted in real time, and the tool path and cutting parameters are dynamically adjusted according to the actual processing progress and remaining time to ensure that the processing task is completed on time and the processing quality is guaranteed.

[0021] The present invention has the following beneficial effects:

[0022] High precision: Multi-dimensional adaptive adjustment and real-time monitoring reduce processing errors and improve the forming accuracy of complex workpieces.

[0023] High efficiency: Intelligent path planning, template reuse and dynamic scheduling shorten processing time and improve machine tool utilization.

[0024] High reliability: Tool monitoring, virtual simulation, and automatic error correction mechanisms reduce failure rates and improve production stability.

[0025] Low threshold: high degree of automation, reducing dependence on manual experience and lowering operational difficulty.

[0026] Sustainability: Machine learning optimizes process parameters, reducing energy consumption and material waste, in line with the trend of green manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a gantry CNC milling machine processing method with adaptive intelligent tool path planning provided by an embodiment of the present invention;

[0028] Figure 2 A graph showing the change in machining accuracy over time for a gantry CNC milling machine machining method with adaptive intelligent tool path planning provided by an embodiment of the present invention;

[0029] Figure 3 A diagram showing the improvement in machining efficiency of a gantry CNC milling machine machining method using adaptive intelligent tool path planning provided by an embodiment of the present invention;

[0030] Figure 4 A graph showing the failure rate variation over time for the gantry CNC milling machine processing method with adaptive intelligent tool path planning provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions of the present application are further illustrated below in combination with the drawings and through specific embodiments.

[0032] The drawings are only used for exemplary illustration, and the representations are only schematic diagrams, not physical diagrams, and should not be understood as limiting the present patent; in order to better illustrate the embodiments of the present application, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0033] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and should not be understood as limiting the present patent, for those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0034] In the description of the present application, unless otherwise explicitly specified and limited, if the term "connection" and the like appear to indicate the connection relationship between components, the term should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication or interaction relationship between two components. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0035] Embodiments

[0036] As shown in the figure, wherein, Figures 1-4 The flow of the gantry numerical control milling machine processing method of adaptive intelligent tool path planning is shown; Figure 1 The change trend of machining precision with time is shown. Through adaptive adjustment and real-time monitoring, the machining precision is maintained at a relatively stable level, which reflects the effective control of the system on the machining precision; Figure 2 Through the change of processing time with the number of workpieces, the improvement of intelligent path planning, template reuse and dynamic scheduling on processing efficiency is shown. With the increase of the number of workpieces, the processing time gradually decreases, indicating that the system can significantly improve the processing efficiency; Figure 3 The change trend of failure rate with time is shown. Through tool monitoring, virtual simulation and automatic error correction mechanism, the failure rate gradually decreases, indicating that the system can significantly improve the production stability; Figure 4 ​

[0037] The gantry numerical control milling machine machining method provided by the embodiment of the adaptive intelligent tool path planning comprises the following steps:

[0038] S1: Obtain three-dimensional model data of a workpiece, perform feature recognition and analysis on the three-dimensional model data, determine the features to be machined of the workpiece, including planes, curved surfaces, holes and grooves;

[0039] S2: According to the features to be machined, in combination with a machining process knowledge base, automatically match suitable tool types, tool sizes and initial cutting parameters, the machining process knowledge base stores tool and cutting parameter data corresponding to different materials and different features;

[0040] S3: Based on an adaptive algorithm, real-time collection of state information of the gantry numerical control milling machine in the machining process, the state information includes cutting force, tool vibration, spindle power and machining temperature, real-time adjustment of the tool path and the cutting parameters according to the state information to optimize the machining process.

[0041] Through three-dimensional model analysis to realize automatic feature recognition, reduce manual intervention and improve machining preparation efficiency; through matching of tools and parameters by the process knowledge base, the rationality of the initial machining scheme can be ensured, and the trial and error cost can be effectively reduced; through real-time state monitoring and adaptive adjustment, the machining process is optimized, and the machining precision and surface quality can be improved.

[0042] Specifically, in the embodiment, the adaptive algorithm is used to real-time adjust the tool path and the cutting parameters, specifically: when the cutting force exceeds a preset threshold, the feed speed is reduced or the cutting depth is reduced; when the tool vibration amplitude exceeds the allowable range, the curvature of the tool path is adjusted or the cutting direction is changed; when the spindle power abnormally fluctuates, the cutting parameter combination is optimized.

[0043] Through cutting force threshold control, tool overload can be avoided, tool life can be prolonged, and machining errors caused by uneven stress can be reduced; through vibration control, surface roughness can be improved, and machining defects caused by resonance or abnormal vibration can be prevented; through spindle power optimization, energy consumption can be reduced, machine overload can be avoided, and machining stability can be improved.

[0044] The adaptive algorithm includes a multi-parameter collaborative optimization equation for calculating a real-time adjustment factor, and the equation expression is as follows:

[0045]

[0046] Wherein:

[0047] k(t) is a machining parameter adjustment factor at time t, used to correct the feed speed or the cutting depth, and the value range is close to 1;

[0048] α is a preset adjustment coefficient, and the value range is (0, 1).

[0049] w i (t) is the dynamic weight of the i-th state parameter at time t, which satisfies

[0050] x i (t) is the real-time collected state parameter (including cutting force, tool vibration, spindle power);

[0051] x i,th is the safety threshold of the corresponding parameter, which triggers the adjustment of the machining parameter when it is exceeded;

[0052] norm(x) is the normalization function, defined as:

[0053] At the same time, the dynamic weight update equation is

[0054] where β is the learning rate, which controls the step size of weight update to avoid oscillation in the optimization process;

[0055] e(t) is the machining error feedback value, which comes from the workpiece surface roughness detection or size deviation measurement.

[0056] Suppose the state parameters collected at a certain time are:

[0057] Cutting force F(t) = 1.2F_th;

[0058] Tool vibration A(t) = 0.9A_th;

[0059] Spindle power P(t) = 1.1P_th;

[0060] The preset parameter α = 0.6, the initial weights w_1 = 0.4, w_2 = 0.3, w_3 = 0.3, and the learning rate β = 0.1.

[0061] 1. Calculate the normalized deviation:

[0062]

[0063] 2. Calculate the adjustment factor:

[0064] k(t) = 1-0.6·(0.4·0.2+0.3·0+0.3·0.1) = 1-0.6·0.11 = 0.934

[0065] That is, the feed speed adjustment is 93.4% of the original value;

[0066] 3. Update the weights (assuming machining error e(t) = 0.05):

[0067] w1(t+1) = 0.4 + 0.1 · 0.05 · 0.2 = 0.401,

[0068] w3(t+1) = 0.3 + 0.1 · 0.05 · 0.1 = 0.3005

[0069] The total weight is kept at 1 by normalization.

[0070] Technical effects of this equation:

[0071] Multi-parameter collaborative optimization: By weighted summation of cutting force, vibration, power and other parameters, avoid optimization deviation caused by single parameter adjustment, improve machining stability.

[0072] Adaptive learning ability: Dynamic weight update mechanism enables the system to automatically optimize the influence weight of each parameter according to historical machining data, adapt to different materials and working conditions.

[0073] Precision and efficiency balance: Adjustment factor k(t) real-time correction of machining parameters, reduce tool wear and machine tool loss under the premise of ensuring precision, such as automatically reducing the feed speed when cutting force overload, to avoid tool collapse or workpiece scrap.

[0074] Process iterative evolution: The weight update equation continuously optimizes the control strategy based on machining error feedback, enabling the system to accumulate experience in batch production and reduce manual parameter adjustment workload.

[0075] Working principle of the equation:

[0076] 1. Data acquisition and preprocessing: Real-time acquisition of cutting force, vibration, spindle power and other data by sensors, comparison with preset threshold and calculation of deviation by norm function;

[0077] 2. Adjustment factor calculation: The multi-parameter collaborative optimization equation calculates k(t) according to the deviation and dynamic weight, and is used to correct the feed speed or cutting depth.

[0078] 3. Machining parameter adjustment: The numerical control system dynamically updates the tool path parameters according to k(t), such as v new = v old · k(t);

[0079] 4. Error feedback and learning: After machining, the error e(t) is obtained through quality detection, and the weight update equation is used to optimize the parameter weight for the next machining;

[0080] 5. Iterative optimization: The above process is continuously iterated in each machining, forming a closed-loop control of "monitoring-adjustment-learning", gradually improving the adaptability of the machining scheme.

[0081] Currently, traditional adaptive machining usually adopts single parameter threshold control (such as adjusting only cutting force), without considering the coupling effect of multiple physical quantities; the equation realizes the collaborative optimization of multiple parameters through weighted summation and dynamic weight updating, has better practicability, and the equation parameters can be calibrated through actual machining data, are suitable for different materials and workpiece characteristics, and have stronger engineering adaptability and popularization value compared with the prior art; in addition, the equation introduces the weight iteration mechanism in machine learning into numerical control machining parameter adjustment, so that the system has autonomous optimization capability, solves the pain point of traditional methods that rely on manual experience for parameter adjustment, and significantly improves the intelligent level of machining.

[0082] Specifically, in the embodiment: when determining the tool path, a layered partition strategy is adopted, the machining area of the workpiece is divided into multiple sub-areas, the tool path is planned for different sub-areas respectively, and a transition path is arranged at the junction of the tool paths of adjacent sub-areas to reduce the idle travel and sudden stop and rapid movement of the tool.

[0083] The layered partition strategy shortens the idle travel of the tool, reduces the non-cutting time, and can improve the machining efficiency; the transition path design avoids sudden stop and rapid movement of the tool, can reduce the machine tool wear and tear, and improves the machining smoothness.

[0084] Specifically, in the embodiment: the machining process knowledge base is continuously updated and optimized through a machine learning algorithm, the machine learning algorithm is based on actual machining data including machining quality feedback and tool life data, and adjusts and perfects the tool and cutting parameter data in the knowledge base.

[0085] The knowledge base is continuously optimized through machine learning, so that the machining scheme is continuously improved with experience accumulation, and can adapt to new materials and complex features; the data-driven parameter adjustment improves the machining consistency, and can reduce the quality fluctuation caused by human experience difference.

[0086] Specifically, in the embodiment: when machining a complex curved surface, an equal residual height-based tool path generation algorithm is adopted, the tool travel and step are automatically adjusted according to the curvature change of the curved surface, so as to ensure that the machined curved surface has uniform surface quality.

[0087] The equal residual height algorithm is used to ensure the uniformity of curved surface machining, reduces the subsequent polishing process, and can improve the machining precision of complex curved surfaces; by automatically adjusting the travel and step to adapt to the curvature of the curved surface, overcutting or undercutting can be avoided, and the curved surface forming quality is improved.

[0088] Specifically, in the embodiment: it also includes virtual simulation before machining, a simulation model is used to simulate the machining process of the gantry numerical control milling machine, the planned tool path is verified and optimized, potential interference problems and machining defects are found in advance, and the tool path is adjusted.

[0089] Through virtual simulation to find interference problems in advance, avoid collision accidents in actual processing, can reduce the risk of equipment damage; through path optimization to reduce the number of trial cutting, shorten the production cycle, can reduce material waste.

[0090] Specifically, in this embodiment: when the tool wears or breaks during processing, real-time detection is performed through the tool state monitoring system, the tool state monitoring system judges the wear or breakage of the tool based on sensor data including vibration signals and current signals, and once an abnormality is detected, the tool is automatically replaced, and the tool path and cutting parameters are re-optimized according to the characteristics of the new tool.

[0091] Through real-time monitoring of tool state to find wear / breakage in time, avoid workpiece scrap, can improve the reliability of processing; use automatic tool changing and parameter re-optimization to reduce downtime, improve machine tool utilization.

[0092] Specifically, in this embodiment: when processing multiple identical or similar workpieces, a workpiece processing template is established, the tool path, cutting parameters and self-adaptive adjustment strategy in the first processing process are stored as a template, and the template can be directly called during subsequent processing, and fine tuning is performed according to the small differences of actual workpieces, to improve processing efficiency.

[0093] Using template processing to reduce the amount of repeated programming work, shorten the preparation time of multi-piece processing, can improve batch production efficiency; through the fine tuning mechanism to ensure the consistency of similar workpiece processing, can reduce the operation threshold.

[0094] Specifically, in this embodiment: the gantry numerical control milling machine is equipped with an intelligent control system, the intelligent control system integrates a processing technology knowledge base, a self-adaptive algorithm module, a tool state monitoring module and a virtual simulation module, and the modules communicate and work together to realize comprehensive intelligent control of the processing process.

[0095] Through the intelligent control system integrating multiple modules to work together, realize the automatic control of the whole process of processing, can reduce the manual intervention; through the data intercommunication between the modules to optimize the decision logic, can improve the system response speed and overall performance.

[0096] Specifically, in this embodiment: during processing, the remaining processing time is predicted in real time in combination with the removal rate model of the workpiece material, and the tool path and cutting parameters are dynamically adjusted according to the actual processing progress and remaining time to ensure that the processing task is completed on time and the processing quality is guaranteed

[0097] Through processing time prediction and dynamic adjustment to ensure timely completion of tasks, optimize production plans, suitable for orders sensitive to delivery dates; use the quality and efficiency balancing mechanism to shorten the processing cycle under the premise of ensuring accuracy.

[0098] In summary, the adaptive intelligent tool path planning gantry CNC milling machine processing method provided by the embodiment has the following advantages:

[0099] High precision: Multi-dimensional adaptive adjustment and real-time monitoring reduce processing errors and improve the forming precision of complex workpieces.

[0100] High efficiency: Intelligent path planning, template reuse and dynamic scheduling shorten processing time and improve machine tool utilization.

[0101] High reliability: Tool monitoring, virtual simulation and automatic error correction mechanism reduce failure rate and improve production stability.

[0102] Low threshold: High degree of automation, reducing dependence on manual experience and reducing operation difficulty.

[0103] Sustainability: Machine learning optimizes process parameters, reduces energy consumption and material waste, and meets the green manufacturing trend.

[0104] Working principle

[0105] 1. Data acquisition and analysis: Obtain workpiece features through three-dimensional modeling, and match the initial processing scheme combined with the knowledge base.

[0106] 2. Real-time monitoring and feedback: Sensors collect cutting force, vibration and other data, and compare with preset thresholds to trigger adjustments.

[0107] 3. Adaptive optimization: Based on feedback data, dynamically adjust tool path and parameters to ensure optimal processing state.

[0108] 4. Continuous learning: Actual processing data feeds back to the knowledge base, and iteratively optimizes the process scheme through machine learning.

[0109] Method of use

[0110] 1. Import the three-dimensional model of the workpiece, and the system automatically identifies the processing features.

[0111] 2. The system recommends tools and initial parameters based on the knowledge base, and the user can fine-tune and confirm.

[0112] 3. Run virtual simulation to verify the reasonableness of the path and correct potential problems.

[0113] 4. Start actual processing, and the system monitors and automatically adjusts parameters in real time.

[0114] 5. After processing is completed, the system records data for knowledge base updates and establishes processing templates for similar workpieces.

[0115] The above merely preferred embodiments of the present application and are not intended to limit the embodiments and protection scope of the present application. Those skilled in the art should be able to understand that any equivalent substitutions and obvious changes made according to the present application description and drawings should be included in the protection scope of the present application.

Claims

1. A gantry CNC milling machine processing method with adaptive intelligent tool path planning, characterized in that: The steps include: S1: Acquire three-dimensional model data of a workpiece, perform feature recognition and analysis on the three-dimensional model data, and determine the features to be processed of the workpiece, including planes, curved surfaces, holes, and slots; S2: Automatically matching the appropriate tool type, tool size, and initial cutting parameters based on the features to be processed and in combination with a processing technology knowledge base, wherein the processing technology knowledge base stores tool and cutting parameter data corresponding to different materials and features; S3: Based on an adaptive algorithm, the state information of the gantry CNC milling machine during the machining process is collected in real time. The state information includes cutting force, tool vibration, spindle power, and machining temperature. The tool path and cutting parameters are adjusted in real time according to the state information to optimize the machining process.

2. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: The real-time adjustment of the tool path and cutting parameters based on the adaptive algorithm is as follows: when the cutting force exceeds a preset threshold, the feed rate is reduced or the cutting depth is reduced; when the tool vibration amplitude exceeds the allowable range, the curvature of the tool path is adjusted or the cutting direction is changed; when the spindle power fluctuates abnormally, the cutting parameter combination is optimized.

3. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: When determining the tool path, a hierarchical partitioning strategy is adopted to divide the workpiece processing area into multiple sub-areas. Tool paths are planned for different sub-areas separately, and transition paths are set at the connection of tool paths in adjacent sub-areas to reduce the tool's idle travel and sudden stops and movements.

4. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: The machining process knowledge base is continuously updated and optimized through a machine learning algorithm, which adjusts and improves the tool and cutting parameter data in the knowledge base based on actual machining data, including machining quality feedback and tool life data.

5. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: When machining complex surfaces, a tool path generation algorithm based on constant residual height is used to automatically adjust the tool spacing and step length according to the curvature change of the surface to ensure that the machined surface has uniform surface quality.

6. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: It also includes virtual simulation before processing, using simulation models to simulate the processing of gantry CNC milling machines, verifying and optimizing planned tool paths, discovering potential interference problems and processing defects in advance, and adjusting tool paths.

7. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: When a tool is worn or damaged during machining, it is detected in real time by the tool condition monitoring system. The tool condition monitoring system determines the wear or damage of the tool based on sensor data, including vibration signals and current signals. Once an abnormality is detected, the tool is automatically replaced and the tool path and cutting parameters are re-optimized based on the characteristics of the new tool.

8. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1 is characterized in that: When processing multiple identical or similar workpieces, a workpiece processing template is established, and the tool path, cutting parameters, and adaptive adjustment strategy of the first processing process are stored as a template. The template can be directly called during subsequent processing and fine-tuned according to the slight differences in the actual workpiece to improve processing efficiency.

9. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to claim 1, characterized in that: The gantry CNC milling machine is equipped with an intelligent control system, which integrates a processing technology knowledge base, an adaptive algorithm module, a tool status monitoring module and a virtual simulation module. The modules communicate and work together to achieve comprehensive intelligent control of the processing process.

10. The gantry CNC milling machine processing method with adaptive intelligent tool path planning according to any one of claims 1 to 9, characterized in that: During the machining process, the remaining machining time is predicted in real time in combination with the workpiece material removal rate model. The tool path and cutting parameters are dynamically adjusted according to the actual machining progress and remaining time to ensure that the machining task is completed on time and the machining quality is guaranteed.

Images