A temperature abnormality warning system during CNC drilling and milling

Abstract

The present invention discloses a temperature anomaly warning system in a CNC drilling and milling process, which relates to the technical field of adjustment systems for CNC machine tools, and includes: a temperature topology map construction module, a data acquisition and preprocessing module, a temperature difference and heat flow analysis module, a heat difference calculation and anomaly judgment module, and anomaly positioning and warning module. The present invention significantly improves the processing accuracy and the stability of equipment operation through a temperature anomaly warning system in a CNC drilling and milling process. By real-time monitoring of the temperature changes of various machine tool components, it can quickly capture and warn of potential temperature anomalies, effectively avoiding processing errors and workpiece quality degradation caused by temperature fluctuations; at the same time, accurate positioning and warning of temperature anomalies help to discover hidden dangers of mechanical failures in advance, reduce downtime due to failures, thereby extending the service life of the machine tool and improving overall production efficiency and equipment reliability.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a temperature anomaly warning system in a numerical control drilling and milling process. Background Art

[0002] In order to meet the growing precision requirements, people have put forward higher standards for the machining accuracy of mechanical parts. Traditional general machine tools or highly specialized automated machine tools often find it difficult to meet these stringent requirements. In this context, numerical control machine tools (CNC) that integrate multiple technological achievements such as electronic computers, automatic control, precision measurement and advanced mechanical structures came into being. CNC drilling and milling is an advanced manufacturing process that uses numerical control technology to control machine tools for drilling and milling operations. In this process, the computer controls the precise movement of the machine tool according to a pre-programmed program to achieve efficient drilling, milling and other complex machining operations.

[0003] In the process of CNC drilling and milling, it is extremely critical to monitor the temperature in real time and implement an abnormal warning system. Temperature changes are directly related to processing accuracy, product quality, maintenance of machine tools and cutting tools, production efficiency and overall economic benefits, and are also the core elements to ensure production safety. High temperature may cause deformation of the workpiece, thereby affecting the processing accuracy; excessive cutting temperature will accelerate tool wear and shorten its service life; in addition, if the temperature inside the machine tool is too high, it may damage the mechanical parts and affect its operating stability and accuracy. However, the traditional temperature abnormality warning system in the process of CNC drilling and milling is usually limited to simple temperature abnormality warning, lacking in-depth analysis and accurate identification of specific components that cause temperature abnormalities, which hinders the optimal configuration and refined management of the processing process, not only limiting the improvement of processing efficiency, but also may lead to unnecessary waste of resources and cost increase, ultimately affecting the quality and market competitiveness of the product.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a temperature abnormality warning system in the CNC drilling and milling process to overcome the above technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A temperature anomaly warning system in a CNC drilling and milling process, the system comprising: a temperature topology map building module, a data acquisition and preprocessing module, a temperature difference and heat flow analysis module, a heat difference calculation and anomaly judgment module, and anomaly positioning and warning module;

[0008] A temperature topology map construction module is used to construct a temperature topology map of a CNC drilling and milling machine tool using a depth-first search algorithm according to the connection relationship of various components in the CNC drilling and milling machine tool;

[0009] The data acquisition and preprocessing module is used to obtain real-time temperature data using temperature sensors distributed on various components of the CNC drilling and milling machine tools, and to preprocess the real-time temperature data;

[0010] The temperature difference and heat flow analysis module is used to calculate the temperature difference matrix and heat flow matrix between nodes in the temperature topology diagram based on the pre-processed real-time temperature data;

[0011] The heat difference calculation and abnormality judgment module is used to calculate the cumulative heat difference within a predetermined processing cycle based on the temperature difference matrix and the heat flow matrix, and obtain the temperature abnormality through the cumulative heat difference;

[0012] The abnormal location and early warning module is used to locate temperature abnormal components based on the temperature abnormality and use statistical analysis algorithms, trigger the early warning mechanism according to the temperature abnormal components, and formulate corresponding emergency strategies.

[0013] Furthermore, the temperature topology map construction module includes: a component connection relationship extraction submodule, a node and edge mapping submodule, a graph structure initialization submodule and a temperature topology map construction submodule;

[0014] A component connection relationship extraction submodule is used to extract the connection relationship between various components of the CNC drilling and milling machine tool from the CNC drilling and milling machine tool database;

[0015] The node and edge mapping submodule is used to analyze the connection relationship between the various components of the CNC drilling and milling machine tool, and map each component of the CNC drilling and milling machine tool to a node in the graph, and map the heat transfer path between the components to an edge in the graph;

[0016] The graph structure initialization submodule is used to initialize the graph structure based on the mapped nodes and edges;

[0017] The temperature topology graph construction submodule is used to traverse each node and edge in the initialization graph structure using a depth-first search algorithm to construct a temperature topology graph.

[0018] Further, the temperature difference and heat flow analysis module includes: a temperature difference matrix calculation submodule and a heat flow matrix construction submodule;

[0019] The temperature difference matrix calculation submodule is used to calculate the temperature difference between each node in the temperature topology diagram based on the preprocessed real-time temperature data to form a temperature difference matrix;

[0020] The heat flow matrix construction submodule is used to evaluate the heat flow amount and construct the heat flow matrix based on the thermodynamic characteristics, component materials, connection methods and temperature difference matrix of the CNC drilling and milling machine tools using the heat flow formula.

[0021] Furthermore, the heat flow formula is:

[0022]

[0023] In the formula, q Indicates the amount of heat flow;

[0024] d ( x,y,z ) represents the thermal conductivity at high temperature d The heat transfer coefficient in the x, y and z directions;

[0025] n Indicates the normal direction;

[0026] S represents the heat transfer area;

[0027] t Indicates time.

[0028] Further, the heat difference calculation and abnormality judgment module includes: a cycle setting and initialization submodule, a heat difference calculation submodule, a cumulative heat difference acquisition submodule and a temperature abnormality judgment submodule;

[0029] The cycle setting and initialization submodule is used to set the processing cycle and initialize the cumulative heat difference variable based on each node in the temperature topology graph;

[0030] The heat difference calculation submodule is used to traverse the temperature difference matrix and the heat flow matrix according to the time step based on the predetermined processing cycle, and calculate the heat difference of each node through the heat flow and the temperature difference;

[0031] The cumulative heat difference acquisition submodule is used to accumulate the heat difference of each node into the corresponding cumulative heat difference variable;

[0032] The temperature anomaly judgment submodule is used to calculate the similarity between the preset threshold and the cumulative heat difference based on the temperature similarity calculation method to obtain the temperature anomaly degree.

[0033] Furthermore, based on the temperature similarity calculation method, the similarity between the preset threshold and the cumulative heat difference is calculated to obtain the temperature anomaly degree including:

[0034] Calculate the similarity of the temperature distribution pattern between the preset threshold and the accumulated heat difference;

[0035] Calculate the similarity of temperature change trend between the preset threshold and the accumulated heat difference;

[0036] The temperature similarity is calculated based on the calculated temperature distribution pattern similarity and temperature change trend similarity, and the temperature anomaly is obtained through the temperature similarity.

[0037] Furthermore, calculating the similarity of the temperature change trend between the preset threshold and the cumulative heat difference includes:

[0038] The finite element analysis algorithm is used to calculate the change trend of the preset threshold and the accumulated heat difference to obtain the temperature gradient direction;

[0039] The frequency of occurrence of each temperature gradient direction is counted, the angle of the temperature gradient direction is used as the horizontal axis of the histogram, the statistical number of the temperature gradient direction is used as the vertical axis of the histogram, and the main direction of the temperature change trend is used as the peak value of the histogram;

[0040] The temperature change trend similarity is calculated using the change trend similarity calculation formula.

[0041] Furthermore, the calculation formula for the change trend similarity is:

[0042]

[0043] In the formula, D Indicates the similarity of temperature change trend between the preset threshold and the accumulated heat difference;

[0044] α 1 indicates the main direction of the preset threshold temperature change trend;

[0045] β 1 indicates the preset threshold temperature gradient direction;

[0046] α 2 indicates the main direction of the trend of cumulative heat difference;

[0047] β 2 represents the direction of the temperature gradient of the cumulative heat difference.

[0048] Furthermore, the abnormality location and early warning module includes: a historical data collection and marking submodule, a data visualization and analysis submodule, an abnormal component location submodule, and a detection and early warning submodule;

[0049] The historical data collection and marking submodule is used to collect the historical temperature data of CNC drilling and milling machine tools, and mark the normal temperature data and abnormal temperature data in the historical temperature data;

[0050] The data visualization and analysis submodule is used to draw a temperature data point graph by taking the temperature in the historical temperature data as the horizontal coordinate and the temperature anomaly as the vertical coordinate, and to represent the normal temperature data and the temperature anomaly data as coordinate points in the temperature data point graph;

[0051] The abnormal component location submodule is used to judge abnormalities of real-time data based on the temperature data point graph and locate abnormal temperature components using the linear discriminant analysis algorithm;

[0052] The detection and warning submodule is used to trigger the warning mechanism according to the located temperature abnormal components and formulate corresponding emergency strategies based on the warning mechanism.

[0053] Furthermore, based on the temperature data point graph, the linear discriminant analysis algorithm is used to judge the abnormality of the real-time data, and the components with abnormal temperature are located, including:

[0054] According to each category in the temperature data point map, the discrete matrix of each category is calculated, and the discrete matrix of each category is accumulated to obtain the total discrete matrix within the category;

[0055] Combine the total discrete matrix within the class with the linear discriminant analysis algorithm to select the best separation projection direction;

[0056] Calculate the eigenvector through the optimal projection direction, and establish the discriminant function based on the eigenvector;

[0057] Calculate the discrimination threshold based on historical data and the discrimination function;

[0058] The temperature anomaly of real-time data is judged by using the discriminant function and the discriminant threshold, the temperature anomaly data points are obtained, and the CNC drilling and milling machine tool parts are located according to the temperature anomaly data points.

[0059] The beneficial effects of the present invention are:

[0060] 1. The present invention significantly improves the processing accuracy and the stability of equipment operation through the temperature anomaly warning system in the CNC drilling and milling process. By real-time monitoring the temperature changes of various parts of the machine tool, it can quickly capture and warn of potential temperature anomalies, effectively avoiding processing errors and workpiece quality degradation caused by temperature fluctuations; at the same time, the precise positioning and warning of temperature anomalies help to discover hidden dangers of mechanical failures in advance, reduce downtime due to failures, thereby extending the service life of the machine tool and improving overall production efficiency and equipment reliability.

[0061] 2. The present invention optimizes the production process of CNC drilling and milling through intelligent data analysis and early warning mechanisms. Based on the temperature difference and heat flow analysis of real-time temperature data, it can accurately calculate the cumulative heat difference, evaluate the temperature anomaly, and formulate emergency strategies based on this. It not only reduces production interruptions and material waste caused by faults, but also helps enterprises to reasonably arrange production plans, optimize resource allocation, and improve production efficiency.

[0062] 3. The present invention can timely detect and warn of high temperature or abnormal temperature changes by real-time monitoring of the temperature changes of various machine tool components, effectively prevent the occurrence of safety accidents such as fire and equipment damage, and ensure the safety of equipment and personnel; in addition, by integrating multiple functional modules such as data acquisition, preprocessing, analysis, judgment and early warning, it realizes intelligent monitoring and management of the processing process, improves the company's informatization level and intelligent manufacturing capabilities, not only improves production efficiency and product quality, but also lays a solid foundation for the company's future sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 The invention is a principle block diagram of a temperature anomaly warning system in a CNC drilling and milling process according to an embodiment of the present invention.

[0065] In the figure:

[0066] 1. Temperature topology map construction module; 2. Data acquisition and preprocessing module; 3. Temperature difference and heat flow analysis module; 4. Heat difference calculation and abnormality judgment module; 5. Abnormal location and early warning module. DETAILED DESCRIPTION

[0067] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0068] According to an embodiment of the present invention, a temperature anomaly warning system during a CNC drilling and milling process is provided.

[0069] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1As shown, according to the temperature anomaly warning system in the CNC drilling and milling process of the embodiment of the present invention, the system includes: a temperature topology map construction module 1, a data acquisition and preprocessing module 2, a temperature difference and heat flow analysis module 3, a heat difference calculation and anomaly judgment module 4 and anomaly positioning and warning module 5;

[0070] The temperature topology map construction module 1 is used to construct the temperature topology map of the CNC drilling and milling machine tool by using a depth-first search algorithm according to the connection relationship between the components in the CNC drilling and milling machine tool.

[0071] Specifically, the temperature topology map construction module 1 includes: a component connection relationship extraction submodule, a node and edge mapping submodule, a graph structure initialization submodule and a temperature topology map construction submodule;

[0072] A component connection relationship extraction submodule is used to extract the connection relationship between various components of the CNC drilling and milling machine tool from the CNC drilling and milling machine tool database;

[0073] The node and edge mapping submodule is used to analyze the connection relationship between the various components of the CNC drilling and milling machine tool, and map each component of the CNC drilling and milling machine tool to a node in the graph, and map the heat transfer path between the components to an edge in the graph;

[0074] The graph structure initialization submodule is used to initialize the graph structure based on the mapped nodes and edges;

[0075] The temperature topology graph construction submodule is used to traverse each node and edge in the initialization graph structure using a depth-first search algorithm to construct a temperature topology graph.

[0076] The data acquisition and preprocessing module 2 is used to obtain real-time temperature data using temperature sensors distributed on various components of the CNC drilling and milling machine tool, and to preprocess the real-time temperature data.

[0077] It should be noted that the locations of temperature sensors are determined based on the thermodynamic characteristics of the machine tool and the thermal sensitivity of key components. Usually, these locations include the spindle, feed shaft, motor, bearings, and cutting area. The temperature sensors are connected to the system via cables or wirelessly. The collected real-time temperature data is preprocessed, including data cleaning, data smoothing, data conversion, and data compression.

[0078] The temperature difference and heat flow analysis module 3 is used to calculate the temperature difference matrix and the heat flow matrix between each node in the temperature topology diagram based on the pre-processed real-time temperature data.

[0079] Specifically, the temperature difference and heat flow analysis module 3 includes: a temperature difference matrix calculation submodule and a heat flow matrix construction submodule;

[0080] The temperature difference matrix calculation submodule is used to calculate the temperature difference between each node in the temperature topology diagram based on the preprocessed real-time temperature data to form a temperature difference matrix;

[0081] The heat flow matrix construction submodule is used to evaluate the heat flow amount and construct the heat flow matrix based on the thermodynamic characteristics, component materials, connection methods and temperature difference matrix of the CNC drilling and milling machine tools using the heat flow formula.

[0082] It should be noted that the following are the following: thermodynamic property analysis: determine the main heat sources of the machine tool, such as the motor, spindle and cutting process, and analyze the thermal power output of each heat source; component material and thermal properties: list the materials of all key components of the machine tool, and find out the thermal conductivity, specific heat capacity and other thermophysical properties of these materials; connection method analysis: identify the connection methods between components (such as bolt connections, welding and contact surfaces, etc.), and evaluate the impact of these connections on heat transfer; temperature difference matrix construction: measure or estimate the temperature difference between the various components of the machine tool to form a temperature difference matrix. In addition, the dimension of the temperature difference matrix should match the number of components, and each element in the matrix represents the temperature difference between two components.

[0083] Create a square matrix corresponding to the number of components to store the heat flow between the components; traverse each temperature difference in the temperature difference matrix, and select a suitable heat flow formula to calculate the heat flow according to the connection method and material properties between the components; fill the calculated heat flow into the corresponding position of the heat flow matrix to construct the heat flow matrix.

[0084] Specifically, the heat flow formula is:

[0085]

[0086] In the formula, q Indicates the amount of heat flow;

[0087] d ( x,y,z ) represents the thermal conductivity at high temperature d The heat transfer coefficient in the x, y and z directions;

[0088] n Indicates the normal direction;

[0089] S represents the heat transfer area;

[0090] t Indicates time.

[0091] The heat difference calculation and abnormality judgment module 4 is used to calculate the cumulative heat difference within a predetermined processing cycle based on the temperature difference matrix and the heat flow matrix, and obtain the temperature abnormality degree through the cumulative heat difference.

[0092] Specifically, the heat difference calculation and abnormality judgment module 4 includes: a cycle setting and initialization submodule, a heat difference calculation submodule, a cumulative heat difference acquisition submodule and a temperature abnormality judgment submodule;

[0093] The cycle setting and initialization submodule is used to set the processing cycle and initialize the cumulative heat difference variable based on each node in the temperature topology graph.

[0094] It should be noted that the accumulated heat difference variable is initialized to 0.

[0095] The heat difference calculation submodule is used to traverse the temperature difference matrix and the heat flow matrix according to the time step based on the predetermined processing cycle, and calculate the heat difference of each node through the heat flow and temperature difference.

[0096] It should be noted that the time step includes every second, every minute, etc.

[0097] The formula for calculating the heat difference is:

[0098]

[0099] In the formula, △ H Indicates the heat difference; T Indicates temperature difference; V Indicates the heat flow rate; N represents the number of time steps; i Indicates the number of nodes.

[0100] The cumulative heat difference acquisition submodule is used to accumulate the heat difference of each node into the corresponding cumulative heat difference variable.

[0101] The temperature anomaly judgment submodule is used to calculate the similarity between the preset threshold and the cumulative heat difference based on the temperature similarity calculation method to obtain the temperature anomaly degree.

[0102] Specifically, based on the temperature similarity calculation method, the similarity between the preset threshold and the cumulative heat difference is calculated to obtain the temperature anomaly degree including:

[0103] Calculate the temperature distribution pattern similarity between the preset threshold and the accumulated heat difference.

[0104] It should be noted that the calculation formula for distribution pattern similarity is:

[0105]

[0106] In the formula, F Indicates the similarity of the temperature distribution pattern between the preset threshold and the accumulated heat difference; k 1 represents the ratio of the preset threshold temperature distribution pattern to the temperature;k 2 represents the ratio of the cumulative heat difference temperature distribution pattern to the temperature.

[0107] Calculate the similarity of temperature change trend between the preset threshold and the accumulated heat difference.

[0108] Specifically, calculating the temperature change trend similarity between the preset threshold and the cumulative heat difference includes:

[0109] The finite element analysis algorithm is used to calculate the change trend of the preset threshold and the accumulated heat difference to obtain the temperature gradient direction.

[0110] The frequency of occurrence of each temperature gradient direction is counted, the angle of the temperature gradient direction is used as the horizontal axis of the histogram, the statistical number of the temperature gradient direction is used as the vertical axis of the histogram, and the main direction of the temperature change trend is used as the peak value of the histogram.

[0111] The temperature change trend similarity is calculated using the change trend similarity calculation formula.

[0112] Specifically, the calculation formula for the change trend similarity is:

[0113]

[0114] In the formula, D Indicates the similarity of temperature change trend between the preset threshold and the accumulated heat difference;

[0115] α 1 indicates the main direction of the preset threshold temperature change trend;

[0116] β 1 indicates the preset threshold temperature gradient direction;

[0117] α 2 indicates the main direction of the trend of cumulative heat difference;

[0118] β 2 represents the direction of the temperature gradient of the cumulative heat difference.

[0119] The temperature similarity is calculated based on the calculated temperature distribution pattern similarity and temperature change trend similarity, and the temperature anomaly is obtained through the temperature similarity.

[0120] It should be noted that the temperature distribution pattern similarity and temperature change trend similarity are calculated using the temperature similarity calculation formula as follows:

[0121]

[0122] In the formula, M Indicates temperature similarity; F Indicates the similarity of the temperature distribution pattern between the preset threshold and the accumulated heat difference;D Indicates the similarity of temperature change trend between the preset threshold and the accumulated heat difference.

[0123] According to historical temperature data or expert knowledge, a benchmark temperature similarity is set, which represents the temperature similarity level under normal working conditions. The currently calculated temperature similarity is compared with the benchmark similarity, and the temperature anomaly is calculated in some way (such as difference, ratio or exponential function, etc.).

[0124] The abnormal location and early warning module 5 is used to locate the temperature abnormal components based on the temperature abnormality and use the statistical analysis algorithm, and trigger the early warning mechanism according to the temperature abnormal components to formulate corresponding emergency strategies.

[0125] Specifically, the abnormal location and early warning module includes: historical data collection and marking submodule, data visualization and analysis submodule, abnormal component location submodule and detection and early warning submodule;

[0126] The historical data collection and marking submodule is used to collect the historical temperature data of CNC drilling and milling machine tools, and mark the normal temperature data and abnormal temperature data in the historical temperature data.

[0127] The data visualization and analysis submodule is used to draw a temperature data point graph with the temperature in the historical temperature data as the horizontal coordinate and the temperature anomaly as the vertical coordinate, and to represent the normal temperature data and the abnormal temperature data as coordinate points in the temperature data point graph.

[0128] The abnormal component positioning submodule is used to judge abnormalities of real-time data based on the temperature data point graph and locate components with abnormal temperatures using a linear discriminant analysis algorithm.

[0129] Specifically, based on the temperature data point graph, the linear discriminant analysis algorithm is used to judge the abnormality of the real-time data, and the components with abnormal temperature are located, including:

[0130] According to each category in the temperature data point map, the discrete matrix of each category is calculated, and the discrete matrices of each category are accumulated to obtain the total discrete matrix within the category.

[0131] It should be noted that each category in the temperature data point diagram includes a normal temperature data category and an abnormal temperature data category.

[0132] The formula for the discrete matrix is:

[0133]

[0134] In the formula, T represents a discrete matrix; h Represents temperature data; H represents a historical temperature dataset; grepresents the mean vector; r Indicates the degree of temperature abnormality.

[0135] The total discrete matrix within the class is combined with the linear discriminant analysis algorithm to select the projection direction with the best separation.

[0136] The eigenvector is calculated through the optimal projection direction, and the discriminant function is established based on the eigenvector.

[0137] Based on historical data and the discriminant function, the discrimination threshold is calculated.

[0138] It should be noted that the formula of the discriminant function is:

[0139]

[0140] In the formula, f represents the discriminant function; c represents the discrimination threshold; r Indicates the degree of temperature anomaly; h Represents temperature data.

[0141] The formula for the discrimination threshold is:

[0142]

[0143] In the formula, c represents the discrimination threshold; n Indicates the number of CNC drilling and milling machine parts; c 1 represents the feature vector; r Indicates the degree of temperature anomaly; c represents the discrimination threshold; g represents the mean vector.

[0144] The temperature anomaly of real-time data is judged by using the discriminant function and the discriminant threshold, the temperature anomaly data points are obtained, and the CNC drilling and milling machine tool parts are located according to the temperature anomaly data points.

[0145] It should be noted that when f Less than c 1 When the CNC drilling and milling machine tool parts are regarded as normal temperature data, that is, normal parts, when f Greater than c 1 When the temperature of the CNC drilling and milling machine tool parts is abnormal, that is, the abnormal parts are regarded as abnormal parts, so as to locate the abnormal CNC drilling and milling machine tool parts.

[0146] The detection and warning submodule is used to trigger the warning mechanism according to the located temperature abnormal components and formulate corresponding emergency strategies based on the warning mechanism.

[0147] It should be noted that the emergency strategies include:

[0148] Determine the composition and responsibilities of the emergency response team, including technical experts, safety personnel, production managers, etc., and ensure that each member is clear about his or her role and action plan.

[0149] Analyze the impact of abnormal temperature components on production processes, product quality, safety and the environment; assess the scope and severity of losses that may be caused by the abnormalities.

[0150] Develop response measures: Isolation and identification, physically isolate abnormal parts and set up obvious warning signs; backup plan, activate backup equipment or production lines to ensure that production is not affected or the impact is minimized; fault diagnosis and repair, organize technical personnel to troubleshoot, develop and implement repair plans; notification and communication, promptly inform relevant departments and personnel of the situation and keep information flowing.

[0151] After the fault is repaired, a system recovery plan is formulated, including equipment testing, production restart and other steps to ensure that the recovery process is safe and orderly and to avoid secondary failures.

[0152] In summary, with the help of the above technical solution of the present invention, the temperature anomaly warning system in the CNC drilling and milling process significantly improves the processing accuracy and the stability of equipment operation. By real-time monitoring of the temperature changes of various parts of the machine tool, it can quickly capture and warn of potential temperature anomalies, effectively avoiding processing errors and workpiece quality degradation caused by temperature fluctuations; at the same time, the precise positioning and early warning of temperature anomalies help to discover hidden mechanical failures in advance, reduce downtime, thereby extending the service life of the machine tool, and improving overall production efficiency and equipment reliability. Through intelligent data analysis and early warning mechanisms, the production process of CNC drilling and milling is optimized. Based on the temperature difference and heat flow analysis of real-time temperature data, the accumulated heat difference can be accurately calculated, the temperature anomaly can be evaluated, and emergency strategies can be formulated based on this, which not only reduces production interruptions and material waste caused by failures, but also helps enterprises to reasonably arrange production plans, optimize resource allocation, and improve production efficiency. By real-time monitoring of the temperature changes of various machine tool components, high temperature or abnormal temperature changes can be discovered and warned in a timely manner, effectively preventing the occurrence of safety accidents such as fire and equipment damage, and ensuring the safety of equipment and personnel; in addition, by integrating multiple functional modules such as data collection, preprocessing, analysis, judgment and warning, intelligent monitoring and management of the processing process is realized, which improves the company's informatization level and intelligent manufacturing capabilities, not only improving production efficiency and product quality, but also laying a solid foundation for the company's future sustainable development.

[0153] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A temperature abnormality warning system during CNC drilling and milling, characterized in that: The system includes: a temperature topology map construction module, a data acquisition and preprocessing module, a temperature difference and heat flow analysis module, a heat difference calculation and abnormality judgment module, and an abnormality positioning and early warning module; The temperature topology map construction module is used to construct a temperature topology map of the CNC drilling and milling machine tool by using a depth-first search algorithm according to the connection relationship of various components in the CNC drilling and milling machine tool; The data acquisition and preprocessing module is used to obtain real-time temperature data using temperature sensors distributed on various components of the CNC drilling and milling machine tool, and to preprocess the real-time temperature data; The temperature difference and heat flow analysis module is used to calculate the temperature difference matrix and heat flow matrix between nodes in the temperature topology diagram based on the pre-processed real-time temperature data; The heat difference calculation and abnormality judgment module is used to calculate the cumulative heat difference within a predetermined processing cycle based on the temperature difference matrix and the heat flow matrix, and obtain the temperature abnormality through the cumulative heat difference; The abnormal location and early warning module is used to locate temperature abnormal components based on the temperature abnormality using a statistical analysis algorithm, and trigger an early warning mechanism according to the temperature abnormal components to formulate corresponding emergency strategies; The heat difference calculation and abnormality judgment module includes: a cycle setting and initialization submodule, a heat difference calculation submodule, a cumulative heat difference acquisition submodule and a temperature abnormality judgment submodule; The cycle setting and initialization submodule is used to set the processing cycle and initialize the cumulative heat difference variable based on each node in the temperature topology graph; The heat difference calculation submodule is used to traverse the temperature difference matrix and the heat flow matrix according to the time step based on the predetermined processing cycle, and calculate the heat difference of each node through the heat flow and the temperature difference; The cumulative heat difference acquisition submodule is used to accumulate the heat difference of each node into the corresponding cumulative heat difference variable; The temperature anomaly judgment submodule is used to calculate the similarity between the preset threshold and the cumulative heat difference based on the temperature similarity calculation method to obtain the temperature anomaly degree; The temperature anomaly degree is obtained by calculating the similarity between the preset threshold and the cumulative heat difference based on the temperature similarity calculation method, and includes: Calculate the similarity of the temperature distribution pattern between the preset threshold and the accumulated heat difference; Calculate the similarity of temperature change trend between the preset threshold and the accumulated heat difference; The temperature similarity is calculated based on the calculated temperature distribution pattern similarity and temperature change trend similarity, and the temperature anomaly is obtained through the temperature similarity; The calculation of the temperature change trend similarity between the preset threshold and the accumulated heat difference includes: The finite element analysis algorithm is used to calculate the change trend of the preset threshold and the accumulated heat difference to obtain the temperature gradient direction; The frequency of occurrence of each temperature gradient direction is counted, the angle of the temperature gradient direction is used as the horizontal axis of the histogram, the statistical number of the temperature gradient direction is used as the vertical axis of the histogram, and the main direction of the temperature change trend is used as the peak value of the histogram; The temperature change trend similarity is calculated using the change trend similarity calculation formula.

2. The temperature abnormality warning system in the CNC drilling and milling process according to claim 1 is characterized in that: The temperature topology map construction module includes: a component connection relationship extraction submodule, a node and edge mapping submodule, a graph structure initialization submodule and a temperature topology map construction submodule; The component connection relationship extraction submodule is used to extract the connection relationship between various components of the CNC drilling and milling machine tool from the CNC drilling and milling machine tool database; The node and edge mapping submodule is used to analyze the connection relationship between the various components of the CNC drilling and milling machine tool, and map each component of the CNC drilling and milling machine tool to a node in the graph, and map the heat transfer path between the various components to an edge in the graph; The graph structure initialization submodule is used to initialize the graph structure based on the mapped nodes and edges; The temperature topology map construction submodule is used to traverse each node and edge in the initialization graph structure using a depth-first search algorithm to construct a temperature topology map.

3. The temperature abnormality warning system in the CNC drilling and milling process according to claim 1 is characterized in that: The temperature difference and heat flow analysis module includes: a temperature difference matrix calculation submodule and a heat flow matrix construction submodule; The temperature difference matrix calculation submodule is used to calculate the temperature difference between each node in the temperature topology diagram based on the preprocessed real-time temperature data to form a temperature difference matrix; The heat flow matrix construction submodule is used to evaluate the heat flow amount and construct the heat flow matrix using the heat flow formula according to the thermodynamic characteristics of the CNC drilling and milling machine, the component materials, the connection method and the temperature difference matrix.

4. The temperature abnormality warning system in the CNC drilling and milling process according to claim 3 is characterized in that: The heat flow formula is: In the formula, q Indicates the amount of heat flow; δ ( x,y,z ) represents the thermal conductivity at high temperature δ exist x, y, z Heat transfer coefficient in three directions; n Indicates the normal direction; S represents the heat transfer area; t Indicates time.

5. The temperature abnormality warning system in the CNC drilling and milling process according to claim 1 is characterized in that: The change trend similarity calculation formula is: In the formula, D Indicates the similarity of temperature change trend between the preset threshold and the accumulated heat difference; α 1 indicates the main direction of the preset threshold temperature change trend; β 1 indicates the preset threshold temperature gradient direction; α 2 indicates the main direction of the trend of cumulative heat difference; β 2 represents the direction of the temperature gradient of the cumulative heat difference.

6. The temperature abnormality warning system in the CNC drilling and milling process according to claim 1 is characterized in that: The abnormal location and early warning module includes: a historical data collection and marking submodule, a data visualization and analysis submodule, an abnormal component location submodule and a detection and early warning submodule; The historical data collection and marking submodule is used to collect historical temperature data of the CNC drilling and milling machine tool, and mark normal temperature data and abnormal temperature data in the historical temperature data; The data visualization and analysis submodule is used to draw a temperature data point graph using the temperature in the historical temperature data as the horizontal coordinate and the temperature anomaly as the vertical coordinate, and to represent the normal temperature data and the temperature anomaly data as coordinate points in the temperature data point graph; The abnormal component positioning submodule is used to judge abnormalities of real-time data based on the temperature data point graph using a linear discriminant analysis algorithm to locate components with abnormal temperatures; The detection and warning submodule is used to trigger the warning mechanism according to the located temperature abnormal components, and formulate corresponding emergency strategies based on the warning mechanism.

7. The temperature abnormality warning system in the CNC drilling and milling process according to claim 6 is characterized in that: Based on the temperature data point graph, the linear discriminant analysis algorithm is used to judge the abnormality of the real-time data, and the location of the temperature abnormal component includes: According to each category in the temperature data point map, the discrete matrix of each category is calculated, and the discrete matrix of each category is accumulated to obtain the total discrete matrix within the category; Combine the total discrete matrix within the class with the linear discriminant analysis algorithm to select the best separation projection direction; Calculate the eigenvector through the optimal projection direction, and establish the discriminant function based on the eigenvector; Calculate the discrimination threshold based on historical data and the discrimination function; The temperature anomaly of real-time data is judged by using the discriminant function and the discriminant threshold, the temperature anomaly data points are obtained, and the CNC drilling and milling machine tool components are located according to the temperature anomaly data points.

Images