Adaptive Process Parameter Adjustment Method for the Saw Blade Production Process

Abstract

The present invention discloses an adaptive process parameter adjustment method for the production process of saw blades, which relates to the technical field of processing control. The method includes: obtaining a plurality of process nodes of a target saw blade and the saw blade morphology information corresponding to the plurality of process nodes; performing thermal sensitivity and vibration sensitivity analysis to generate a plurality of thermal sensitivity parameters and a plurality of vibration sensitivity parameters corresponding to the plurality of process nodes; obtaining a plurality of node process parameters currently used by the plurality of process nodes; combining the plurality of node process parameters, the plurality of thermal sensitivity parameters and the plurality of vibration sensitivity parameters to perform node saw blade processing damage analysis to generate a plurality of damage indicators; based on the plurality of damage indicators, locating the nodes to be optimized and optimizing and adjusting the node process parameters of the nodes to be optimized. The technical problems of unstable processing quality and low production efficiency in the existing saw blade production process are solved, and the technical effects of improving the processing quality and production efficiency of saw blades are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of processing control, and particularly to an adaptive process parameter adjustment method for the production process of saw blades. Background Art

[0002] As a cutting tool widely used in the mechanical processing process, the production quality and performance of saw blades directly affect the processing efficiency and the quality of workpieces. During the production process of saw blades, problems such as thermal deformation and vibration interference will have a significant impact on the shape and service life of saw blades. However, in the current production process, the process parameters of saw blade production often adopt fixed settings, and it is difficult to dynamically adjust according to the requirements of different production stages and the state of saw blades, resulting in unstable processing quality and a high damage rate. In addition, due to the lack of accurate analysis of thermal sensitivity and vibration sensitivity during the processing process, the production process cannot effectively control thermal damage and vibration damage, further affecting the processing accuracy and reliability of saw blades. Therefore, how to optimize and adjust the process parameters in combination with the characteristics of saw blades at different stages during the production process to improve the processing quality and production efficiency of saw blades has become a technical problem to be solved urgently. Summary of the Invention

[0003] The present application provides an adaptive process parameter adjustment method for the production process of saw blades, which solves the technical problems of unstable processing quality and low production efficiency in the production process of saw blades in the prior art.

[0004] In view of the above problems, the present application provides an adaptive process parameter adjustment method for the production process of saw blades.

[0005] The present application provides an adaptive process parameter adjustment method for the production process of saw blades, and the method includes:

[0006] Obtain a plurality of process nodes of a target saw blade and the saw blade shape information corresponding to the plurality of process nodes; perform thermal sensitivity and vibration sensitivity analysis based on the saw blade shape information corresponding to the plurality of process nodes to generate a plurality of thermal sensitivity parameters and a plurality of vibration sensitivity parameters corresponding to the plurality of process nodes; obtain a plurality of node process parameters currently used by the plurality of process nodes; combine the plurality of node process parameters, the plurality of thermal sensitivity parameters and the plurality of vibration sensitivity parameters to perform node saw blade processing damage analysis to generate a plurality of damage indicators; based on the plurality of damage indicators, locate the nodes to be optimized and optimize and adjust the node process parameters of the nodes to be optimized.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] First, obtain multiple process nodes of the target saw blade and the saw blade morphology information corresponding to the multiple process nodes. Next, perform thermal sensitivity and vibration sensitivity analysis based on the saw blade morphology information corresponding to the multiple process nodes to generate multiple thermal sensitivity parameters and multiple vibration sensitivity parameters corresponding to the multiple process nodes. Further, obtain the multiple node process parameters currently used for the multiple process nodes. Then, combine the multiple node process parameters, the multiple thermal sensitivity parameters, and the multiple vibration sensitivity parameters to perform node saw blade processing damage analysis and generate multiple damage indicators. Finally, based on the multiple damage indicators, locate the nodes to be optimized and optimize and adjust the node process parameters of the nodes to be optimized. This solves the technical problems of unstable processing quality and low production efficiency in the prior art during the production process of saw blades, and achieves the technical effect of improving the processing quality and production efficiency of saw blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1 Schematic flowchart of the adaptive process parameter adjustment method for the saw blade production process provided by the embodiment of the present application.

[0011] Figure 2 Schematic flowchart of the node saw blade processing damage analysis in the adaptive process parameter adjustment method for the saw blade production process provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] By providing the adaptive process parameter adjustment method for the saw blade production process, the present application solves the technical problems of unstable processing quality and low production efficiency in the prior art during the production process of saw blades.

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0014] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0015] Examples are as follows Figure 1 As shown, the embodiments of the present application provide an adaptive process parameter adjustment method for the saw blade production process. Among them, the method includes:

[0016] Obtain multiple process nodes of the target saw blade and the saw blade morphology information corresponding to the multiple process nodes.

[0017] According to the production process flow of the target saw blade, determine multiple key process nodes involved. These process nodes include but are not limited to heat treatment, cutting, grinding, stress relief treatment, etc. Each process node represents an important processing link in the saw blade production process.

[0018] At each process node, collect the saw blade morphology information. The saw blade morphology information includes but is not limited to the geometric dimensions of the saw blade (such as thickness, diameter), surface flatness, edge morphology characteristics, and morphological changes in key parts. During the specific collection process, various technical means can be used. For example, the saw blade can be optically scanned by a high-precision laser scanner to obtain its morphological characteristics, the surface flatness and roughness can be detected by a surface morphology analyzer, and the key dimensions such as the thickness and diameter of the saw blade can be accurately measured by a special measuring instrument. By integrating each process node with its corresponding saw blade morphology information, a complete data set containing multiple process nodes and their morphology information is formed, laying a foundation for subsequent thermal sensitivity and vibration sensitivity analysis, as well as saw blade processing damage analysis, thereby realizing intelligent and refined control of the production process.

[0019] Based on the saw blade morphology information corresponding to the multiple process nodes, perform thermal sensitivity and vibration sensitivity analysis to generate multiple thermal sensitivity parameters and multiple vibration sensitivity parameters corresponding to the multiple process nodes.

[0020] By analyzing the saw blade morphology information, identify the morphological characteristics of each process node, such as the uniformity of the saw blade thickness, the integrity of the edge morphology, and the change in surface flatness. These characteristics are the key factors affecting the thermal sensitivity and vibration sensitivity of the saw blade. Subsequently, according to the thermodynamic characteristics and mechanical dynamic characteristics of the saw blade, combined with the possible temperature rise situation and vibration frequency change during the processing, establish a corresponding analysis model.

[0021] In the thermal sensitivity analysis, based on the morphological information and thermodynamic properties, the thermal deformation trend of the saw blade under the condition of machining temperature rise is simulated and calculated. The specific analysis content includes the deformation amount, stress distribution of the saw blade at high temperature, and the influence of high temperature conditions on the edge integrity. Finally, the thermal sensitivity parameters corresponding to each process node are generated to quantify the sensitivity of the saw blade to thermal effects during the machining process of each node.

[0022] In the vibration sensitivity analysis, by combining the morphological characteristics and vibration response characteristics of the saw blade, the vibration state of the saw blade under different machining conditions is simulated, and the risks of deformation, crack propagation or other morphological defects that may occur under vibration conditions are analyzed. The specific analysis content includes the vibration frequency, amplitude, and the influence of vibration on the edge and surface morphology of the saw blade. Finally, the vibration sensitivity parameters corresponding to each process node are generated to quantify the machining stability of the saw blade under vibration conditions.

[0023] By performing the above thermal sensitivity and vibration sensitivity analyses on multiple process nodes respectively, the thermal sensitivity parameters and vibration sensitivity parameters corresponding to each process node can be obtained. These parameters provide a quantitative basis for the subsequent saw blade machining damage analysis and also provide scientific guidance for optimizing process parameters to reduce machining damage.

[0024] Furthermore, based on the saw blade morphological information corresponding to the multiple process nodes, thermal sensitivity and vibration sensitivity analyses are performed to generate multiple thermal sensitivity parameters and multiple vibration sensitivity parameters corresponding to the multiple process nodes, including:

[0025] Based on the saw blade morphological information corresponding to the multiple process nodes, the influence analysis of saw blade deformation at different temperatures is performed to generate the multiple thermal sensitivity parameters; based on the saw blade morphological information corresponding to the multiple process nodes, the influence analysis of saw blade deformation under different vibration intensities is performed to generate the vibration sensitivity parameters.

[0026] For the thermal sensitivity analysis, by simulating the deformation response of the saw blade under different temperature conditions, the influence of thermal effects on saw blade machining is quantitatively evaluated. Specifically, according to the saw blade morphological information collected at multiple process nodes, such as geometric dimensions, thickness distribution, surface flatness, etc., combined with the thermal expansion coefficient, thermal conductivity and temperature field distribution characteristics of the saw blade material, a thermodynamic model is established. In the thermodynamic model, the influence of different machining temperatures on the deformation of the saw blade is gradually simulated, and the deformation trend, stress concentration area and morphological changes of the saw blade at high temperature are analyzed. Based on the above analysis results, multiple thermal sensitivity parameters are generated, specifically including the thermal deformation amount of the saw blade at different temperatures, the characteristics of thermal stress distribution, and the degree of influence of thermal effects on the edge morphology, etc. These thermal sensitivity parameters can comprehensively reflect the thermal stability of the saw blade during the machining process and provide a scientific basis for optimizing process parameters.

[0027] In vibration sensitivity analysis, based on the saw blade morphology information corresponding to multiple process nodes, the deformation response of the saw blade under different vibration intensity conditions is simulated, and the influence of vibration effects on saw blade machining is quantitatively analyzed. Specifically, by combining the geometric characteristics and dynamic characteristics of the saw blade, a vibration analysis model is established to simulate the state of the saw blade under different vibration frequencies and amplitudes. In the vibration analysis model, the influence of different vibration frequencies and amplitudes on the deformation of the saw blade is gradually simulated, and the morphological changes of the saw blade, possible structural defects (such as edge cracks, surface ripples), and the influence of vibration on machining accuracy are analyzed. Based on the above analysis results, multiple vibration sensitivity parameters are generated, specifically including the vibration deformation amount under different vibration intensities, the vibration stress concentration situation, and the degree of influence of vibration on the morphological stability of the saw blade. These vibration sensitivity parameters can reflect the adaptability of the saw blade to the dynamic vibration environment during the machining process, providing an optimization direction for vibration control during the machining process.

[0028] Obtain the multiple node process parameters currently used by the multiple process nodes.

[0029] Determine the operating characteristics and related machining requirements of each process node during the production of the target saw blade; these process nodes generally cover production steps such as heat treatment, cutting, grinding, stress relief, etc., and each node corresponds to a specific set of machining parameters; through real-time monitoring and data collection of the production equipment, extract the process parameter information actually used by each current process node, including but not limited to heat treatment process parameters (such as heating temperature, cooling rate, holding time, etc.), cutting process parameters (such as cutting speed, feed rate, cutting depth, etc.), grinding process parameters (such as grinding speed, grinding force, abrasive particle size, etc.), stress relief parameters (such as temperature during heat stress relief, cooling rate, etc.).

[0030] Combine the multiple node process parameters, the multiple thermal sensitivity parameters, and the multiple vibration sensitivity parameters for node saw blade machining damage analysis to generate multiple damage indicators.

[0031] By correlating the processing parameters (such as cutting speed, grinding force, etc.) of each process node with the thermal sensitivity parameters (such as thermal deformation amount, thermal stress concentration area) and vibration sensitivity parameters (such as vibration amplitude, vibration stress distribution), the damage risk of the saw blade during the processing can be comprehensively evaluated. Combining the process parameters and thermal sensitivity parameters, the temperature distribution and thermal deformation during the processing are simulated to evaluate the influence of high-temperature processing on the edge, surface morphology, and internal stress distribution of the saw blade, and to determine the thermal damage characteristics. Based on the vibration sensitivity parameters and processing parameters, the influence of the vibration frequency and amplitude during the processing on the structural stability of the saw blade is analyzed to evaluate whether there are problems such as crack propagation, edge chipping, or surface unevenness caused by vibration. According to the results of the processing damage analysis, damage indicators corresponding to multiple process nodes are generated to quantify the processing damage degree of the saw blade at different nodes. Among them, the damage indicators include but are not limited to thermal damage indicators (such as thermal deformation amount, thermal stress concentration coefficient, etc.), vibration damage indicators (such as morphological deviation amount caused by vibration, vibration stress intensity, etc.). By combining the process parameters, thermal sensitivity parameters, and vibration sensitivity parameters of multiple nodes for the processing damage analysis of the node saw blade, multiple damage indicators can be generated to evaluate the damage degree and potential risks of the saw blade under different process nodes. This provides important guidance and support for the production and processing of saw blades.

[0032] Furthermore, as Figure 2 shown, by combining the multiple node process parameters, the multiple thermal sensitivity parameters, and the multiple vibration sensitivity parameters for the processing damage analysis of the node saw blade, multiple damage indicators are generated, including:

[0033] Temperature prediction and vibration prediction during processing based on the multiple node process parameters to generate multiple saw blade surface temperature data and multiple saw blade vibration data; thermal damage risk analysis based on the multiple saw blade surface temperature data and the multiple thermal sensitivity parameters to generate multiple thermal damage risk indicators; vibration damage risk analysis based on the multiple saw blade vibration data and the multiple vibration sensitivity parameters to generate multiple vibration damage risk indicators; and based on the multiple thermal damage risk indicators and the multiple vibration damage risk indicators, a combined analysis of thermal damage and vibration damage is performed on the multiple process nodes to generate the multiple damage indicators.

[0034] Based on the processing parameters of multiple process nodes (such as processing speed, cutting depth, heat treatment temperature, etc.), using machine learning or numerical analysis methods (such as finite element analysis), temperature prediction models and vibration prediction models during the saw blade processing are constructed. These models take the process parameters as inputs, predict the surface temperature and vibration state of the saw blade under different process nodes, and generate multiple surface temperature data and vibration data during the processing of the saw blade at each process node. Subsequently, based on the saw blade surface temperature data and thermal sensitivity parameters (such as thermal expansion coefficient, critical value of thermal deformation, etc.), the risk of thermal damage caused by temperature rise is analyzed, including calculating multiple thermal damage risk indicators such as thermal deformation amount, thermal stress distribution, and edge breakage probability. At the same time, combining the saw blade vibration data and vibration sensitivity parameters (such as vibration response characteristics, vibration damage threshold, etc.), the damage risk caused by vibration to the shape and structural stability of the saw blade is analyzed, and multiple vibration damage risk indicators including vibration deformation amplitude, vibration stress intensity, and vibration processing deviation are generated. After obtaining multiple thermal damage risk indicators and multiple vibration damage risk indicators, a fusion analysis of thermal damage and vibration damage at each process node is carried out to evaluate the saw blade processing damage under the combined action of multiple factors, and multiple damage indicators are generated. The damage indicators reflect the quantitative evaluation of the damage degree under different process conditions.

[0035] Furthermore, based on the multiple thermal damage risk indicators and the multiple vibration damage risk indicators, a fusion analysis of thermal damage and vibration damage is carried out on the multiple process nodes to generate the multiple damage indicators, including:

[0036] Connect to the saw blade production monitoring system, collect the historical saw blade temperature record dataset, historical saw blade vibration record dataset, and corresponding historical saw blade production quality record set for each process node; analyze the historical saw blade temperature record dataset and the historical saw blade vibration record dataset to establish a historical thermal damage risk indicator set and a historical vibration damage risk indicator set; train the damage fusion weights for each process node based on the historical saw blade production quality record set, the historical thermal damage risk indicator set, and the historical vibration damage risk indicator set; generate corresponding damage fusion layers with the indicator fusion weights of each process node; perform a fusion of thermal damage and vibration damage on the multiple process nodes with the damage fusion layers of each process node to generate the multiple damage indicators.

[0037] Specifically, connect to the saw blade production monitoring system to collect historical data of each process node, including the historical saw blade temperature record dataset, the historical saw blade vibration record dataset, and the corresponding historical saw blade production quality record set. These historical data record the temperature changes, vibration characteristics of the saw blade in different process nodes, and the relationship between them and production quality; conduct in-depth analysis on the historical saw blade temperature record dataset and the historical saw blade vibration record dataset, extract the feature information related to thermal damage and the feature information related to vibration damage during the processing respectively, and establish the historical thermal damage risk index set and the historical vibration damage risk index set; based on the historical saw blade production quality record set and the extracted thermal damage and vibration damage risk index sets, train the damage fusion weights of each process node, and generate the index fusion weights of each process node by analyzing the relative influence degree of thermal damage and vibration damage on production quality; establish the corresponding damage fusion layer with the index fusion weights of each process node, and conduct weighted integration of the risk indexes of thermal damage and vibration damage in the fusion layer to reflect the comprehensive influence of multiple damage factors on processing quality; based on the damage fusion layer, conduct the fusion analysis of thermal damage and vibration damage on each process node to generate multiple damage indexes.

[0038] Furthermore, based on the multiple saw blade surface temperature data and the multiple thermal sensitivity parameters, conduct thermal damage risk analysis to generate multiple thermal damage risk indexes, including:

[0039] With the multiple thermal sensitivity parameters as constraints, collect the historical saw blade surface temperature records and historical saw blade damage parameters corresponding to the multiple process nodes; conduct analysis based on the historical saw blade surface temperature records and historical saw blade damage parameters to identify the relationship between the saw blade surface temperature and the saw blade deformation parameters, and generate multiple node damage identification layers; input the multiple saw blade surface temperature data into the multiple node damage identification layers for damage risk identification to generate the multiple thermal damage risk indexes.

[0040] Specifically, taking multiple thermal sensitivity parameters as constraints, historical saw blade surface temperature records and historical saw blade damage parameters corresponding to multiple process nodes are collected. These historical data include the temperature change curve on the saw blade surface during the machining process and the corresponding damage parameters such as deformation, cracks, and stress concentration. Through these data, the internal correlation between temperature and saw blade damage can be reflected. Based on the historical saw blade surface temperature records and historical saw blade damage parameters, the key features of the saw blade surface temperature (such as temperature peak value, heating rate, temperature distribution range, etc.) and the corresponding damage features (such as deformation amount, crack generation probability, surface damage condition, etc.) are extracted. Machine learning algorithms (such as regression analysis, neural network, etc.) or statistical methods (such as correlation analysis, regression analysis, etc.) are used to identify the relationship between the saw blade surface temperature and the saw blade deformation parameters (such as deformation amount, crack length, etc.), thereby constructing a node damage identification layer that can reflect the relationship between temperature and damage risk. Each node damage identification layer corresponds to a process node and can identify the damage risk under this node based on the saw blade surface temperature data. The saw blade surface temperature data of the current process node is input into the generated node damage identification layer for automatic identification and evaluation of the damage risk. The node damage identification layer analyzes the input temperature data, combines the relationship between temperature and damage parameters obtained from training in the historical data, identifies the potential thermal damage risk of the saw blade under the current temperature condition, and predicts the possible deformation amount, stress distribution, and probability of edge damage. Based on the identification results, multiple thermal damage risk indicators are generated, and these indicators include but are not limited to the thermal deformation amount of the saw blade, the degree of thermal stress concentration, and the surface damage risk level, etc.

[0041] The process of generating multiple vibration damage risk indicators based on multiple saw blade vibration data and multiple vibration sensitivity parameters is similar to the above process of generating multiple thermal damage risk indicators. Specifically, based on historical vibration data and damage parameters, the key features of vibration (such as peak amplitude, frequency distribution, acceleration change, etc.) and the corresponding damage features (such as crack length, structural deformation amount, etc.) are extracted, and through techniques such as regression analysis, support vector machine (SVM), or neural network, the non-linear relationship between vibration features and saw blade damage is identified, and then a vibration damage identification layer is generated for each process node to analyze the potential damage risk of the saw blade under the current vibration condition. The saw blade vibration data of the current process node is input into the vibration damage identification layer for analysis to generate vibration damage risk indicators, including quantitative indicators such as the deformation amplitude caused by vibration, stress distribution characteristics, crack propagation probability, and surface damage risk.

[0042] Based on the multiple damage indicators, the nodes to be optimized are located, and the node process parameters of the nodes to be optimized are optimized and adjusted.

[0043] Combine the thermal damage risk index and the vibration damage risk index to comprehensively evaluate the damage indices of multiple process nodes. According to the set damage threshold and risk level criteria, determine which process nodes have damage indices exceeding the preset allowable range (such as thermal deformation amount, vibration stress concentration coefficient, or crack propagation probability, etc.), and mark these process nodes as nodes to be optimized. For each node to be optimized, combine the current process parameters of the node (such as cutting speed, machining temperature, feed rate, vibration amplitude, etc.), and analyze the correlation between the process parameters and the damage index; by tracing back historical data, identify which process parameters have the greatest impact on the thermal damage or vibration damage of the current node (for example, too high machining temperature may lead to increased thermal deformation, or too fast feed speed may cause high-frequency vibration), so as to determine the key process parameters that need to be adjusted. Based on the damage characteristics of the node to be optimized, construct a process parameter optimization model; the optimization model can adopt multi-objective optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.), with the goal of reducing the damage risk and balancing the relationship between various process parameters. For example, take the risk indices of thermal damage and vibration damage as the objective function, use the current process parameters and sensitivity parameters of the node as constraint conditions, and calculate the optimal combination of process parameters through model calculation. Apply the process parameter results output by the optimization model to the node to be optimized, and adjust its process parameters in real time. For example, reduce the machining temperature to reduce the risk of thermal deformation, or optimize the cutting speed and feed rate to reduce the vibration amplitude. By adjusting the key process parameters, minimize the machining damage of the node and improve the production quality of the saw blade.

[0044] Furthermore, based on the multiple damage indices, locate the nodes to be optimized, and optimize and adjust the node process parameters of the nodes to be optimized, including:

[0045] Conduct a full-process fusion impact analysis on the multiple damage indices to locate the nodes to be optimized; with the goal of minimizing the damage index of the node to be optimized, optimize the node process parameters of the node to be optimized to generate process parameter optimization results; adjust the process parameters of the node to be optimized with the process parameter optimization results.

[0046] Specifically, a full - process fusion impact analysis is conducted on multiple damage indicators. The damage indicators of each node are correlated according to the time sequence of the technological process to evaluate their impact on downstream nodes or the overall processing quality, thereby accurately locating the node to be optimized with the most severe damage or the greatest impact on production. Taking the minimum damage indicator of the node to be optimized as the goal, a multi - objective optimization model is constructed. By inputting the current process parameters of the node (such as processing temperature, cutting speed, feed rate, vibration amplitude, etc.) and relevant constraint conditions, combined with the damage laws in historical data, an optimal combination of process parameters that can minimize the damage indicator is calculated and generated, such as the adjusted processing temperature range, optimal cutting speed, or dynamic feed control value, etc. Based on the optimization results, the process parameters of the node to be optimized are actually adjusted.

[0047] Furthermore, conducting a full - process fusion impact analysis on the multiple damage indicators to locate the node to be optimized includes:

[0048] For the adjacent first process node and second process node among the multiple process nodes, where the first process node is before the second process node; conduct an analysis of the damage superposition effect on the first process node and the second process node to generate a first damage superposition effect coefficient, where the first damage superposition effect coefficient represents the degree of influence of the damage indicator of the first process node on the damage indicator of the second process node; use the first damage superposition effect coefficient to optimize the multiple damage indicators and then locate the process nodes whose optimized damage indicators are greater than the preset damage indicator to generate the node to be optimized.

[0049] Specifically, select the adjacent first process node and second process node in the technological process, where the first process node is before the second process node, representing the front - back relationship of the time sequence in the technological process; based on the damage indicator data of the two process nodes, conduct an analysis of the superposition effect of the damage indicator of the first process node on the damage indicator of the second process node, calculate and generate a first damage superposition effect coefficient to quantify the cumulative or amplifying effect of the damage of the first process node on the second process node, such as how the thermal damage or vibration damage generated by the first node affects the processing quality and damage risk of the second node; use the first damage superposition effect coefficient as the weight to optimize and adjust the damage indicators of all process nodes, recalculate the damage indicator of each process node, and compare the result with the preset damage indicator threshold to locate the process nodes with a comprehensive damage indicator greater than the preset threshold and mark them as the nodes to be optimized.

[0050] Calculate the first damage superposition effect coefficient. Optionally, obtain the damage index data of the first process node and the second process node, including thermal damage risk indicators (such as thermal deformation amount, thermal stress concentration value, etc.) and vibration damage risk indicators (such as vibration amplitude, vibration stress distribution, etc.); take the damage index of the first process node as the input variable and the damage index of the second process node as the output variable to establish a correlation model between the two; use linear regression analysis, weighted superposition model or machine learning methods (such as support vector machines, neural networks, etc.) to identify how the damage of the first process node affects the damage of the second process node through the transfer of the processing process; through model training and historical data fitting, extract the contribution weights of each damage index of the first process node to various damage indexes of the second process node. For example, the thermal deformation amount of the first process node may have an amplifying effect on the vibration amplitude of the second process node, while the vibration stress of the first process node may exacerbate the thermal stress concentration of the second process node; quantify the above analysis results as the first damage superposition effect coefficient, which is used as a parameter to describe the influence degree of the damage of the first process node on the second process node.

[0051] Furthermore, with the goal of minimizing the damage index of the node to be optimized, optimize the node process parameters of the node to be optimized to generate a process parameter optimization result, including:

[0052] Establish a node parameter control memory bank for the node to be optimized; based on the node parameter control memory bank, with the goal of minimizing the damage index of the node to be optimized, optimize the node process parameters of the node to be optimized to generate the process parameter optimization result.

[0053] By collecting historical process parameter data related to the node to be optimized, the corresponding damage indexes and relevant process environment information, form a node parameter control memory bank. This memory bank contains the corresponding relationship between node process parameters (such as processing temperature, cutting speed, feed rate, etc.), external environment parameters (such as equipment performance, raw material characteristics, etc.) and node damage indexes (such as thermal damage risk value, vibration damage risk value, etc.), and through data cleaning and normalization processing, ensure the structuring and usability of the memory bank.

[0054] With the goal of minimizing the damage index of the node to be optimized, an optimization model is constructed, and an appropriate algorithm (such as genetic algorithm, particle swarm optimization algorithm or gradient descent method) is selected to optimize and solve the process parameters of the node. During the optimization process, the memory bank serves as the basis for parameter reference. By calling the historical data and parameter combinations in the memory bank, the process parameter range that is effective in reducing the damage index is initially screened out. On this basis, combined with the current damage characteristics and process requirements of the node to be optimized, the parameters are further adjusted, such as reducing the processing temperature to reduce thermal damage, or optimizing the feed rate to reduce the vibration impact. The optimization model takes the minimization of the damage index as the objective function, and at the same time incorporates constraint conditions such as production efficiency and equipment performance into the optimization scope to ensure that the optimization results have both theoretical optimality and practical operability. Through the solution process of the optimization model, the optimal process parameter combination of the node to be optimized is output, such as the exact values of the adjusted processing temperature, cutting speed or feed rate. These optimization results will be directly used to adjust the process parameters of the node to be optimized and recorded in the node parameter control memory bank as the basic data for subsequent optimization and iteration.

[0055] In summary, the embodiments of the present application have at least the following technical effects:

[0056] First, a plurality of process nodes of the target saw blade and the saw blade morphology information corresponding to the plurality of process nodes are obtained. Then, based on the saw blade morphology information corresponding to the plurality of process nodes, thermal sensitivity and vibration sensitivity analyses are performed to generate a plurality of thermal sensitivity parameters and a plurality of vibration sensitivity parameters corresponding to the plurality of process nodes. Further, a plurality of node process parameters currently used by the plurality of process nodes are obtained. Then, a node saw blade processing damage analysis is performed by combining the plurality of node process parameters, the plurality of thermal sensitivity parameters and the plurality of vibration sensitivity parameters to generate a plurality of damage indices. Finally, based on the plurality of damage indices, the node to be optimized is located, and the node process parameters of the node to be optimized are optimized and adjusted. The technical problems of unstable processing quality and low production efficiency in the existing saw blade production process are solved, and the technical effects of improving the saw blade processing quality and production efficiency are achieved.

[0057] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above description of specific embodiments of this specification is made. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0059] This specification and the accompanying drawings are merely illustrative of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An adaptive process parameter adjustment method for the saw blade production process, characterized in that The method includes: Obtaining multiple process nodes of a target saw blade and saw blade morphology information corresponding to the multiple process nodes; Performing thermal sensitivity and vibration sensitivity analysis based on the saw blade morphology information corresponding to the multiple process nodes, and generating multiple thermal sensitivity parameters and multiple vibration sensitivity parameters corresponding to the multiple process nodes; Obtaining multiple node process parameters currently used by the multiple process nodes; Combining the multiple node process parameters, the multiple thermal sensitivity parameters, and the multiple vibration sensitivity parameters to perform node saw blade processing damage analysis, and generating multiple damage indicators; Based on the multiple damage indicators, locating nodes to be optimized and optimizing and adjusting the node process parameters of the nodes to be optimized; Combining the multiple node process parameters, the multiple thermal sensitivity parameters, and the multiple vibration sensitivity parameters to perform node saw blade processing damage analysis, and generating multiple damage indicators, including: Performing temperature prediction and vibration prediction during processing based on the multiple node process parameters, and generating multiple saw blade surface temperature data and multiple saw blade vibration data; Performing thermal damage risk analysis based on the multiple saw blade surface temperature data and the multiple thermal sensitivity parameters, and generating multiple thermal damage risk indicators; Performing vibration damage risk analysis based on the multiple saw blade vibration data and the multiple vibration sensitivity parameters, and generating multiple vibration damage risk indicators; Based on the multiple thermal damage risk indicators and the multiple vibration damage risk indicators, performing fusion analysis of thermal damage and vibration damage on the multiple process nodes, and generating the multiple damage indicators, including: Connecting to a saw blade production monitoring system, and collecting a historical saw blade temperature record data set, a historical saw blade vibration record data set, and a corresponding historical saw blade production quality record set for each process node; Analyzing the historical saw blade temperature record data set and the historical saw blade vibration record data set, and establishing a historical thermal damage risk indicator set and a historical vibration damage risk indicator set; Training the damage fusion weights of each process node based on the historical saw blade production quality record set, the historical thermal damage risk indicator set, and the historical vibration damage risk indicator set, and generating the damage fusion weight of each process node by analyzing the relative influence degree of thermal damage and vibration damage on production quality; Generating corresponding damage fusion layers with the damage fusion weights of each process node; Performing fusion of thermal damage and vibration damage on the multiple process nodes with the damage fusion layers of each process node, and generating the multiple damage indicators.

2. The adaptive process parameter adjustment method for the saw blade production process according to claim 1, wherein, Performing thermal sensitivity and vibration sensitivity analysis based on the saw blade morphology information corresponding to the multiple process nodes, and generating multiple thermal sensitivity parameters and multiple vibration sensitivity parameters corresponding to the multiple process nodes, including: Performing analysis of the influence of saw blade deformation at different temperatures based on the saw blade morphology information corresponding to the multiple process nodes, and generating the multiple thermal sensitivity parameters; Performing analysis of the influence of saw blade deformation at different vibration intensities based on the saw blade morphology information corresponding to the multiple process nodes, and generating the vibration sensitivity parameters.

3. The adaptive process parameter adjustment method for the saw blade production process according to claim 1, wherein Based on the multiple saw blade surface temperature data and the multiple thermal sensitivity parameters, perform thermal damage risk analysis to generate multiple thermal damage risk indicators, including: Taking the multiple thermal sensitivity parameters as constraints, collect the historical saw blade surface temperature records and historical saw blade damage parameters corresponding to the multiple process nodes; Based on the historical saw blade surface temperature records and historical saw blade damage parameters, conduct analysis to identify the relationship between the saw blade surface temperature and the saw blade deformation parameters, and generate multiple node damage identification layers; Input the multiple saw blade surface temperature data into the multiple node damage identification layers for damage risk identification to generate the multiple thermal damage risk indicators.

4. The adaptive process parameter adjustment method for the saw blade production process according to claim 1, characterized in that Based on the multiple damage indicators, locate the nodes to be optimized and optimize and adjust the node process parameters of the nodes to be optimized, including: Conduct a full-process fusion impact analysis on the multiple damage indicators to locate the nodes to be optimized; Taking the minimum damage indicator of the node to be optimized as the goal, optimize the node process parameters of the node to be optimized to generate a process parameter optimization result; Adjust the process parameters of the node to be optimized with the process parameter optimization result.

5. The adaptive process parameter adjustment method for the saw blade production process according to claim 4, characterized in that Conduct a full-process fusion impact analysis on the multiple damage indicators to locate the nodes to be optimized, including: Select adjacent first process node and second process node from the multiple process nodes, where the first process node is located before the second process node; Conduct a damage superposition effect analysis on the first process node and the second process node to generate a first damage superposition effect coefficient, where the first damage superposition effect coefficient represents the influence degree of the damage indicator of the first process node on the damage indicator of the second process node; Using the first damage superposition effect coefficient to optimize the multiple damage indicators and then locate the process nodes whose optimized damage indicators are greater than the preset damage indicator to generate the nodes to be optimized.

6. The adaptive process parameter adjustment method for the saw blade production process according to claim 4, characterized in that, Taking the minimum damage indicator of the node to be optimized as the goal, optimize the node process parameters of the node to be optimized to generate a process parameter optimization result, including: Establish a node parameter control memory bank for the node to be optimized; Based on the node parameter control memory bank, taking the minimization of the damage indicator of the node to be optimized as the goal, optimize the node process parameters of the node to be optimized to generate the process parameter optimization result.

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