High-performance multi-axis machining automation control platform for components

Abstract

The present invention discloses a high-performance multi-axis automated control platform for component machining, which relates to the field of machining automation technology. It is used to address the problem that the assessment of component machining difficulty often relies on manual experience or simple rules, which can lead to strong subjectivity and inaccurate assessments. Existing manufacturing processes often rely on a large amount of manual intervention and experience, and lack a fully automated, intelligent decision support system. The machining difficulty of each component varies depending on the strength and hardness of the raw materials, as well as the number of machining steps and tolerance requirements. Parts are classified according to their difficulty, and parts that are difficult to machine are processed step by step. The system can automatically select appropriate strategies and algorithms based on actual machining requirements, thereby improving machining efficiency and quality, reducing manual intervention, and increasing production efficiency. The system can also make adaptive adjustments based on real-time data during machining, improving the precision and flexibility of the machining process.

Description

Technical Field

[0001] The present invention relates to the technical field of machining automation, and more particularly to a high-performance component multi-axis machining automation control platform. Background Art

[0002] With the continuous development of modern manufacturing, the requirements for component machining accuracy, efficiency, and automation are becoming increasingly stringent. This is especially true in the fields of aerospace, automotive, precision instruments, and mold manufacturing. Component designs are becoming increasingly complex, dimensional requirements are becoming increasingly precise, and higher demands are being placed on the automation and intelligence of the machining process. To this end, multi-axis machining technology for high-performance components has emerged and is gradually becoming a vital component of advanced manufacturing. Traditional manual programming and operation methods are inefficient, require extensive human intervention, are prone to errors, and struggle to cope with complex machining requirements. Lacking intelligent scheduling and real-time feedback mechanisms, machine tool utilization can be low, leading to a waste of production resources.

[0003] The existing technology has the following deficiencies:

[0004] In existing technologies, the assessment of part processing difficulty often relies on manual experience or simple rules, which may lead to problems of strong subjectivity and inaccurate assessment. The selection of appropriate optimization algorithms often relies on experience or trial and error, and lacks intelligent adaptability for specific problems. In complex processing processes with multiple steps and multiple factors, existing technologies often have difficulty dealing with the complex interactions between steps and their impact on the final processing results, and are unable to model and optimize these complex processing processes to improve the controllability and optimization potential of the entire processing process. In general, existing manufacturing processes often rely on a lot of manual intervention and experience, and lack a fully automated and intelligent decision support system.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a high-performance component multi-axis machining automation control platform to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] High-performance component multi-axis machining automation control platform, including: information acquisition module, data processing module, step-by-step pre-processing module and algorithm selection module, and signal connections between modules;

[0009] Information acquisition module: obtains the strength and hardness of the part's raw materials, the number of processing steps for the part, and the required part tolerances;

[0010] Data processing module: Calculates the material characteristic coefficient of the part based on the strength and hardness of the part material, and calculates the processing difficulty coefficient based on the number of processing steps and the required part tolerance. The weighted coefficient is used to determine whether the part is easy to process.

[0011] Step-by-step preprocessing module: If the part is not easy to process, the steps are divided into steps, and the time series is used to calculate the correlation between the steps and the size of the recorded state space;

[0012] Algorithm selection module: Determines whether the policy gradient method or the Q-Learning exploratory learning algorithm is more suitable based on the correlation between steps and the size of the state space, selects whether the next step needs to be changed, and selects the optimal step to optimize the process.

[0013] In a preferred embodiment, the information acquisition module includes the following contents:

[0014] Record the strength and hardness of raw materials by referring to material manuals or standards;

[0015] Arrange reasonable steps according to the functions and requirements of the parts by process engineers, or simulate the processing of the parts through CAD and CAE software, and record the number of processing steps for the parts;

[0016] Part tolerances are based on the requirements of the part. If there are no clear tolerance requirements, refer to general standards, check relevant industry standards or technical requirements, and record the part tolerances.

[0017] In a preferred embodiment, the data processing module includes the following:

[0018] The material characteristic coefficient is calculated based on the hardness and strength of the raw material of the part, and is calculated using the following formula: K m =α·H+β·σ, where: K m is the material characteristic coefficient, H is the hardness of the material, σ is the strength of the material, α and β are the weight coefficients of hardness and strength on processing difficulty;

[0019] Set a weighting factor to incorporate the number of processing steps and tolerance requirements into the criteria for judging whether a part is easy to process. Specifically, the comprehensive processing difficulty coefficient is calculated using the following formula: K process =γ·N+δ·T, where: K process is the machining difficulty coefficient, N is the number of machining steps, T is the tolerance requirement of the part, γ and δ are the weighting coefficients of the number of machining steps and tolerance requirement;

[0020] Combining the material property coefficient and the processing difficulty coefficient, the comprehensive processing difficulty coefficient is expressed by the following formula: K total=λ·K m +(1-λ)·K process , where: K total is the comprehensive processing difficulty coefficient, and λ is a weighting coefficient representing the relative importance between material properties and processing difficulty;

[0021] Set a threshold to judge the processing difficulty, set a threshold K threshold , judge according to the following rules: If K total ≤K threshold , the parts are easy to process and the processing difficulty is relatively low K total >K threshold , the parts are not easy to process and the processing difficulty is high.

[0022] In a preferred embodiment, the step-by-step pre-processing module includes the following:

[0023] For parts that are not easy to process, the processing is broken down into more detailed steps, recorded as S1, S2, S3...S i ..., where i is the number of the step, S i is the i-th step;

[0024] In the dependency matrix method of the processing process, a matrix is ​​constructed to represent the dependency relationship between steps, where each matrix element A ij Indicates step S i and step S j The dependency relationship between them, if A ij is a non-zero element, it means step S j For step S i There is a direct dependency relationship. By counting the number of non-zero elements in the matrix, the size of the direct dependency of each step is further derived and normalized.

[0025] For each step, the following information is recorded: operation type and operation parameters. The size of the state space is the number of combinations of all these operation parameters and state information. The operation type and parameters of each step can be selected independently, so the state space size of the step is the product of the number of operation types and the number of operation parameters, which is expressed as: state space = operation type × operation parameter.

[0026] In a preferred embodiment, the algorithm selection module includes the following contents:

[0027] After receiving the correlation between steps and the state space, the algorithm selection module defines the correlation between steps and the state space as input variables and divides them into different fuzzy sets;

[0028] The exploratory learning algorithm is defined as the output variable, which is divided into fuzzy sets;

[0029] Formulate fuzzy rules to describe the correlation between steps and the impact of state space definition on exploration learning algorithms;

[0030] Perform fuzzy reasoning based on fuzzy rules and determine the exploration learning algorithm;

[0031] Using the determined exploration learning algorithm, between two step nodes, before proceeding to the next step, the probability of continuing to use the next step and the probability of using another step are calculated. The probability of proceeding to the next step normally is marked as t. There is another 1-t probability of selecting another step at this node. The probabilities of the two steps are compared to decide whether to proceed to the next step normally or select another step. The decision is made based on the value of t. If t is higher, choose to continue using the next step, otherwise choose another step.

[0032] The technical effects and advantages of the high-performance multi-axis automated control platform for parts processing of the present invention are as follows:

[0033] The present invention takes into account the different strength and hardness of raw materials and the different numbers of processing steps and tolerance requirements, which lead to different processing difficulties for each part. According to the difficulty level of the parts, the parts that are not easy to process are processed step by step, and the reinforcement learning algorithm to be used is determined according to the size of the time series correlation and the size of the state space. The processing steps and the learning algorithm adopted can be intelligently adjusted. In this way, the system can automatically select appropriate strategies and algorithms according to actual processing needs, improve processing efficiency and quality, further enhance the automation and intelligence of the processing process, reduce human intervention, and improve production efficiency. The system can make adaptive adjustments based on real-time data during the processing to improve the accuracy and flexibility of the processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the structure of the high-performance component multi-axis machining automation control platform of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] Example 1

[0037] The invention discloses a high-performance component multi-axis machining automation control platform, comprising an information acquisition module, a data processing module, a step-by-step preprocessing module and an algorithm selection module, with signal connections between the modules.

[0038] Information acquisition module: obtains the strength and hardness of the part's raw materials, the number of processing steps for the part, and the required part tolerances;

[0039] Data processing module: Calculates the material characteristic coefficient of the part based on the strength and hardness of the part material, and calculates the processing difficulty coefficient based on the number of processing steps and the required part tolerance. The weighted coefficient is used to determine whether the part is easy to process.

[0040] Step-by-step preprocessing module: If the part is not easy to process, the steps are divided into steps, and the time series is used to calculate the correlation between the steps and the size of the recorded state space;

[0041] Algorithm selection module: Determines whether the policy gradient method or the Q-Learning exploratory learning algorithm is more suitable based on the correlation between steps and the size of the state space, selects whether the next step needs to be changed, and selects the optimal step to optimize the process.

[0042] The information acquisition module is used to obtain the strength and hardness of the part's raw materials, the number of processing steps, and the required part tolerances. The specific process is as follows:

[0043] By referring to material manuals or standards, such as the "Steel Handbook", "Mechanical Engineering Handbook" or national standards, you can understand the strength and hardness of raw materials. Common engineering materials have detailed physical and mechanical properties data, and the strength and hardness of raw materials are recorded.

[0044] Strength: Usually includes yield strength, tensile strength, fatigue strength, etc., which refers to the ability of the material surface to resist being pressed or scratched by external objects. Simply put, the higher the hardness, the less likely the material is to be scratched or deformed. Hardness is usually an indicator used to describe the surface properties of a material and is mainly related to the microstructure of the material.

[0045] Hardness: Hardness affects the wear resistance and machinability of parts. It refers to a material's ability to resist damage or deformation under external forces. It represents the maximum load a material can withstand without breaking or fracturing, and reflects the material's deformation or fracture limit under external forces. A stronger material generally indicates a higher resistance to external forces, pressure, or other physical loads.

[0046] The processing steps of parts are generally clearly stated in the process route or process card. Process engineers will make reasonable step arrangements based on the functions and requirements of the parts, or simulate the processing of the parts through CAD and CAE software. The software can provide recommended processing steps based on the design and processing technology of the parts, and record the number of processing steps for the obtained parts.

[0047] Part tolerances are based on the requirements of the part. If there are no clear tolerance requirements or if general standards need to be referenced, consult relevant industry standards or technical requirements. Refer to industry standards, such as: ISO 2768: General tolerance standard, applicable to most non-precision parts, GB / T 1804-2000: China's general tolerance standard, and record the part tolerances.

[0048] The data processing module calculates the material characteristic coefficient of the part based on the strength and hardness of the part material, and calculates the processing difficulty coefficient based on the number of processing steps and the required part tolerance, and then uses weighted methods to determine whether the part is easy to process. The specific process is as follows:

[0049] The material characteristic coefficient is usually calculated based on the hardness and strength of the raw material of the part, which will directly affect the difficulty of processing the part. The material characteristic coefficient can be calculated using the following formula: K m =α·H+β·σ, where: K m is the material characteristic coefficient, H is the hardness of the material, σ is the strength of the material, α and β are the weight coefficients of hardness and strength on processing difficulty. These weight coefficients can be determined through experience or experiments. Usually, hardness has a greater impact on processing difficulty, so hardness may be given a greater weight.

[0050] Set a weighting factor to incorporate the number of processing steps and tolerance requirements into the criteria for judging whether a part is easy to process. Specifically, the comprehensive processing difficulty coefficient is calculated using the following formula: K process =γ·N+δ·T, where: K process is the processing difficulty coefficient, N is the number of processing steps, T is the tolerance requirement of the part, γ and δ are the weighted coefficients of the number of processing steps and tolerance requirements, which are usually set through experience or process data.

[0051] Combining the material property coefficient and the processing difficulty coefficient, the comprehensive processing difficulty coefficient is expressed by the following formula: K total =λ·K m +(1-λ)·K process , where: K total is the comprehensive processing difficulty coefficient, and λ is a weighting coefficient representing the relative importance between material properties and processing difficulty.

[0052] Set the threshold to judge the processing difficulty, once the comprehensive processing difficulty coefficient K is obtainedtotal , you can set a threshold to determine whether a part is "easy to process" or "not easy to process". The specific judgment can be made by comparing it with empirical data or industry standards and setting a threshold K threshold , judge according to the following rules: If K total ≤K threshold , the parts are easy to process and the processing difficulty is relatively low K total >K threshold , the parts are not easy to process and the processing difficulty is high.

[0053] For ease of understanding, the following example is given: Assume the following data: material hardness H = 150HB, material strength σ = 600MPa, number of processing steps N = 10; tolerance requirement T = 0.05mm, set weighting coefficients α = 0.6, β = 0.4, γ = 0.5, δ = 0.5, λ = 0.7.

[0054] Calculate material characteristic coefficient: K m =α·H+β·σ=330, calculate the processing difficulty coefficient: K process =γ·N+δ·T=5.025, calculate the comprehensive processing difficulty coefficient: K total =λ·K m +(1-λ)·K process =232.5075, assuming the threshold is set to K threshold =200, to judge whether the part is easy to process: K total >K threshold Therefore, it is judged that the part is not easy to process.

[0055] Step-by-step preprocessing module: If the part is not easy to process, the steps are divided into steps, and the time series is used to calculate the correlation between the steps and the size of the recorded state space. The specific process is as follows:

[0056] For parts that are not easy to process, the processing process should be broken down into more detailed steps. Each processing step may include some sub-steps or operations, such as roughing, finishing, heat treatment, inspection and finishing, and surface treatment. Each large processing step can be broken down into small and operable steps, which helps to more clearly understand the complexity of the processing process and the relationship between each step, recorded as S1, S2, S3...S i ..., where i is the number of the step, S i is the i-th step.

[0057] In the dependency matrix method of the processing process, we construct a matrix to represent the dependency relationship between steps, where each matrix element A ij Indicates step S i and step Sj The dependency relationship between them, if A ij is a non-zero element, it means step S j For step S i There is a direct dependency relationship. The matrix can be used to analyze and calculate the dependencies between steps. By counting the number of non-zero elements in the matrix, the direct dependency size of each step (that is, the degree of their influence on other steps) can be further derived and normalized so that the final dependency size is unified to [0,1] for subsequent analysis.

[0058] For example: Let's take a three-order matrix consisting of three steps for a simple explanation. The matrix A is: Among them A 12 =1, is a non-zero element, which means that step 2 is dependent on step 1, A 23 =0, which is a zero element, proves that there is no dependency between step 3 and step 2, and there is only one non-zero element in the matrix.

[0059] It should be noted that if you want to quantify the strength of the dependency, you can use not only 0 and 1 in the matrix, but also other values ​​(such as 0.1, 0.5, 0.8, etc.) to represent different degrees of dependency. The calculation is to count the number of non-zero elements. In addition to direct dependencies, you can also calculate compound dependencies (indirect dependencies) through path analysis. This is usually achieved through matrix multiplication, for example, by calculating the power of the matrix (A 2 ,A 3 ,…) can be used to derive the impact of indirect dependencies, which can be set by technical personnel and will not be described in detail here.

[0060] The size of the state space usually depends on the optional operations of each step and the possible states of each step. For each step, the following information is recorded: operation type (such as cutting, welding, etc.) and operation parameters (such as cutting depth, angle, etc.). The size of the state space is the number of combinations of all these operation parameters and state information. The operation type and parameters of each step can be selected independently, so the state space size of the step is the product of the number of operation types and the number of operation parameters, which can be expressed as: state space = operation type × operation parameter.

[0061] For example, consider a manufacturing process where steps allow you to select different operation types and parameters for each operation. There are three operation types: cutting, welding, and grinding. Each operation has five parameter options. For example: Cutting: Cutting depth (1, 2, 3, 4, 5) and angle (45°, 90°, 135°, 180°). Welding: Welding temperature (low, medium, high) and duration (short, medium, long). Grinding: Grinding speed (slow, medium, fast) and force (light, medium, heavy). For each operation, the number of combinations of operation type and parameters can be calculated: Cutting: There are five depth options and four angle options, so the number of cutting combinations is 5 x 4 = 20. Welding: There are three temperature options and three duration options, so the number of welding combinations is 3 x 3 = 9. Grinding: There are three speed options and three force options, so the number of grinding combinations is 3 x 3 = 9.

[0062] Therefore, the state space size for each step is the number of operation types multiplied by the number of combinations of corresponding operation parameters. If there are three operation types (cutting, welding, grinding), their state space sizes are: 3 × (20 + 9 + 9) = 114. Therefore, the state space size of this step is 114.

[0063] The algorithm selection module determines whether the policy gradient method or the Q-Learning exploratory learning algorithm is more suitable based on the correlation between the steps and the size of the state space. It then selects whether the next step needs to be changed and selects the optimal step to optimize the process. The specific process is as follows:

[0064] Q-Learning is a value-based reinforcement learning algorithm that selects the optimal policy by estimating the value function Q(s,a) of each state-action pair. The core idea of ​​Q-Learning is to update the Q value through the Bellman equation to gradually approach the optimal policy. Q-Learning is a value-based offline algorithm. When interacting with the environment, it does not require explicit policy learning. Instead, it indirectly improves the policy by updating the Q value. By updating the Q value of each state-action pair, the optimal action is gradually found.

[0065] The policy gradient method maximizes the cumulative reward by optimizing the policy itself. Unlike Q-Learning, the policy gradient method directly parameterizes the policy and selects the optimal action through gradient updates.

[0066] After receiving the correlation between steps and the state space, the algorithm selection module defines the correlation between steps and the state space as input variables and divides them into different fuzzy sets.

[0067] For example, "Low", "Medium", "High" for the dependencies between steps, and "Small", "Medium", "Large" for the state space.

[0068] Define the exploratory learning algorithm as an output variable and divide it into fuzzy sets, for example, "Q-Learning", "Gradient" for the exploratory learning algorithm.

[0069] Formulate a set of fuzzy rules to describe the impact of different input variables on the output variable. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0070] Label the association between steps as G, the state space as Z, and the exploration learning algorithm as Algorithm, then we can define:

[0071] Rule 1:IF(G is Low)AND(Z is Small)THEN(Algorithm is Q-Learning)

[0072] Rule 2:IF(G is High)AND(Z is Large)THEN(Algorithm is Gradient) ...

[0074] Perform fuzzy reasoning based on fuzzy rules and determine the solution for the exploration learning algorithm.

[0075] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes three fuzzy sets as an example, the correlation between steps, state space and exploration learning algorithm can actually be divided into more than three sets to facilitate better precise adjustment according to different situations.

[0076] Furthermore, for the judgment of the correlation between steps and the high, medium and low state space, thresholds can be set according to actual conditions. For example, when the correlation between steps exceeds 0.7, it is marked as "High", and when the state space is higher than 100, it is marked as "Large", and so on. I will not go into details here.

[0077] The established exploratory learning algorithm can be used to calculate the probability of continuing with the next step or taking another step before proceeding to the next step between two step nodes. The probability of proceeding normally to the next step is marked as t. At this node, there is another probability of choosing another step 1-t. Comparing the probabilities of the two steps determines whether to proceed normally to the next step or choose another step.

[0078] Consider step nodes S1 and S2. Which step do you need to choose at that node? Based on the current node state, calculate the probability t of selecting step S1 and the probability 1-t of selecting step S2. Make a decision based on the value of t: if t is high, select step S1; otherwise, select step S2. Select the optimal action. When subtle changes occur during operation, the part processing performance is evaluated at the time of the exploration probability. If the part processing performance is good, make adjustments based on the subtle difference.

[0079] It should be noted that the above modules, applied to the high-performance multi-axis automated control platform for parts machining, can classify parts according to machining difficulty, calculate the correlation between steps and the size of the state space for parts that are difficult to machine, and automatically select the most appropriate learning algorithm. Through intelligent algorithm selection, the platform can continuously optimize the machining process based on real-time data and machining feedback, ultimately improving the efficiency and quality of the entire parts machining process, reducing unnecessary time waste during machining, lowering error rates, and thus reducing production costs. In addition, based on the optimized machining paths and strategies, faster production cycles can be achieved and production efficiency can be improved. This enables the platform to make optimal decisions when faced with parts of different types and complexities, thereby improving the intelligence level of the entire production system.

[0080] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0081] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0082] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0084] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0085] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. High-performance multi-axis machining automation control platform for parts, characterized by: include: Information acquisition module, data processing module, step-by-step preprocessing module and algorithm selection module, and signal connections between modules; Information acquisition module: obtains the strength and hardness of the part's raw materials, the number of processing steps for the part, and the required part tolerances; Data processing module: Calculates the part's raw material characteristic coefficient based on the strength and hardness of the part's raw material, and calculates the processing difficulty coefficient based on the number of processing steps and the required part tolerance. The raw material characteristic coefficient and the processing difficulty coefficient are weighted to obtain a comprehensive processing difficulty coefficient to determine whether the part is easy to process. The material characteristic coefficient is calculated based on the hardness and strength of the raw material of the part, using the following formula: ,in: is the material characteristic coefficient, H is the hardness of the material, σ is the strength of the material, α and β are the weight coefficients of hardness and strength to the material characteristic coefficient respectively; Set a weighting factor to factor the number of machining steps and tolerance requirements into the criteria for determining whether a part is easy to machine. The machining difficulty coefficient is calculated using the following formula: N+δ T, where: is the processing difficulty coefficient, N is the number of processing steps, T is the tolerance requirement of the part, and δ are weighting factors for the number of machining steps and tolerance requirements; Combining the material property coefficient and the processing difficulty coefficient, the comprehensive processing difficulty coefficient is expressed by the following formula: ,in: is the comprehensive processing difficulty coefficient, and λ is a weighting coefficient representing the relative importance between material properties and processing difficulty; Set a threshold to judge the processing difficulty. Set a threshold , judge according to the following rules: If ≤ , the parts are easy to process and the processing difficulty is relatively low; > , then the parts are not easy to process and the processing difficulty is high; Step-by-step preprocessing module: If the part is not easy to process, the processing steps of the part are divided into steps, and the time series is used to calculate the correlation between the steps and the size of the recorded state space; Algorithm selection module: Based on the correlation between steps and the size of the state space, it determines whether the policy gradient method or the Q-Learning exploration learning algorithm is more suitable to choose whether the next step needs to be changed and select the optimal step to optimize the process.

2. The high-performance multi-axis component machining automation control platform according to claim 1 is characterized by: Record the strength and hardness of raw materials by referring to material manuals or standards; Arrange reasonable steps according to the functions and requirements of the parts by process engineers, or simulate the processing of the parts through CAD and CAE software, and record the number of processing steps for the parts; Part tolerances are based on the requirements of the part. If there are no clear tolerance requirements, refer to general standards, check relevant industry standards or technical requirements, and record the part tolerances.

3. The high-performance multi-axis component processing automation control platform according to claim 1 is characterized in that ； For parts that are not easy to process, the processing process is broken down into more detailed steps, recorded as , where the subscript i is the sequence number of each step, is the i-th step; In the dependency matrix method of the processing process, a matrix is ​​constructed to represent the dependency relationship between steps, where each matrix element Indicates step and steps If the dependency relationship between is a non-zero element, it means the step For steps There is a direct dependency relationship. By counting the number of non-zero elements in the matrix, the size of the direct dependency of each step is further derived and normalized. For each step, the following information is recorded: operation type and operation parameters. The size of the state space is the number of combinations of all these operation parameters and state information. The operation type and parameters of each step can be selected independently, so the state space size of the step is the product of the number of operation types and the number of operation parameters, which is expressed as: 。 4. The high-performance multi-axis component machining automation control platform according to claim 3 is characterized by: After receiving the correlation between steps and the state space, the algorithm selection module defines the correlation between steps and the state space as input variables and divides them into different fuzzy sets; The exploratory learning algorithm is defined as the output variable, which is divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the size of the correlation between steps and the size of the state space on the exploration learning algorithm; Perform fuzzy reasoning based on fuzzy rules and determine the exploration learning algorithm; Using the determined exploration learning algorithm, between two step nodes, before proceeding to the next step, the probability of continuing to use the next step and the probability of using another step are calculated. The probability of proceeding to the next step normally is marked as t. There is another 1-t probability of selecting another step at this node. The probabilities of the two steps are compared to decide whether to proceed to the next step normally or select another step. The decision is made based on the value of t. If t is higher, choose to continue using the next step, otherwise choose another step.

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