Intelligent high-precision height gauge and cnc lathe cooperative measurement and machining system
By establishing an intelligent system that enables real-time data interaction and dynamic parameter adjustment between a CNC lathe and a high-precision height gauge, the problem of independent measurement and machining in traditional systems has been solved. This system achieves high precision, real-time environmental factor offsetting and fault self-healing, thereby improving production efficiency and product quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHUHAI LONGFEI PRECISION MOULD CO LTD
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, the measurement and machining processes of CNC lathes and high-precision height gauges are independent, resulting in lag in parameter adjustment, inability to achieve real-time linkage, inability to effectively offset the influence of environmental factors, and lack of dynamic calibration mechanisms. This leads to poor machining accuracy and consistency, low production efficiency, insufficient remote control capabilities, low fault diagnosis accuracy, and inaccurate tool wear monitoring, all of which affect product quality and production efficiency.
It adopts industrial Ethernet and PROFINET dual communication protocols to achieve real-time data interaction, integrates a high-precision measurement module and a machining parameter adaptive adjustment module, combines multi-environmental factor calibration, filters noise through Kalman filtering algorithm, dynamically adjusts parameters, integrates fault diagnosis and early warning module, supports multi-station collaborative measurement, remote monitoring and control, and realizes intelligent parameter adjustment and fault self-healing.
It achieves high-precision, real-time measurement and processing linkage, dynamically offsets environmental interference, improves processing accuracy and consistency, increases production efficiency, reduces scrap rate and tool consumption, enhances remote monitoring and fault handling capabilities, and meets the needs of efficient and precision manufacturing.
Smart Images
Figure CN122151712A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of precision measurement and CNC machining collaboration technology, and in particular to a collaborative measurement and machining system for an intelligent high-precision height gauge and a CNC lathe. Background Technology
[0002] In high-end manufacturing industries such as aerospace, automotive, and precision electronics, the machining accuracy of workpiece height directly determines product performance and lifespan. Especially for complex structural components, micron-level dimensional deviations can lead to assembly failures or functional defects. CNC lathes, as core machining equipment, rely on preset parameters and tool conditions for machining accuracy. However, factors such as changes in environmental temperature and humidity, tool wear, and fluctuations in material properties during machining continuously generate dimensional deviations. High-precision height gauges are crucial for dimensional inspection. However, in traditional production models, measurement and machining processes are independent. Measurement data must be manually recorded, analyzed, and then manually input into the CNC lathe to adjust parameters, making real-time linkage impossible. This results in delayed deviation correction, making it difficult to meet the demands of high-precision, high-efficiency production.
[0003] Existing measurement and machining collaborative technologies have several significant shortcomings. At the collaborative control level, most systems use a single communication protocol, resulting in poor adaptability. Different brands and models of height gauges and CNC lathes are difficult to network and coordinate, and data exchange latency is high. Measurement and machining actions are prone to timing conflicts, affecting production continuity. Regarding measurement accuracy, compensation is only applied to temperature, without considering the combined effects of multiple environmental factors such as air pressure and humidity. Measurement data deviations are significant under complex working conditions, and the lack of dynamic calibration mechanisms leads to significant accuracy degradation over long-term operation. Parameter adjustment relies on fixed mapping models, failing to incorporate machining error prediction and tool wear conditions, resulting in insufficient targeted adjustments and poor product dimensional consistency. In multi-station production, the lack of a unified task allocation and data sharing mechanism prevents parallel measurement and machining, leading to low batch production efficiency.
[0004] Furthermore, the system suffers from weaknesses in remote control, fault handling, and multi-batch adaptability. Traditional systems are mostly local operations, unable to monitor production status and data remotely in real time, making remote collaboration and technical support difficult. Fault diagnosis relies solely on single-parameter monitoring, resulting in low accuracy, a lack of early warning and self-healing functions, and long equipment downtime. In multi-variety, small-batch production scenarios, batch switching requires manual resetting of measurement paths and processing parameters, which is cumbersome, error-prone, and inefficient. Meanwhile, tool wear monitoring largely relies on manual observation or fixed lifespan judgments, failing to deduce wear from measurement data and dynamically adjust parameters, leading to dimensional deviations and increased scrap rates in later products. These problems severely restrict production efficiency, product quality, and cost control in the precision manufacturing industry, necessitating a highly integrated, highly adaptive, and collaboratively efficient intelligent measurement and processing system. Summary of the Invention
[0005] The present invention proposes an intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system to solve the problems mentioned in the prior art.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system, comprising the following modules: The collaborative control module adopts both industrial Ethernet and PROFINET communication protocols to establish a real-time communication link between the height gauge and the CNC lathe, and integrates a motion control chip to coordinate the timing logic of the measurement actuator and the lathe machining mechanism; The high-precision measurement module integrates a laser displacement sensor and a ruby contact probe. It can automatically switch the measurement mode according to the surface roughness of the workpiece material. The built-in platinum resistance temperature sensor collects the ambient temperature in real time and uses a quadratic curve fitting algorithm to compensate for temperature error. The data processing module receives raw data from the high-precision measurement module, uses the Kalman filter algorithm to filter out environmental interference and equipment noise, calculates the deviation between the actual height of the workpiece and the theoretical design value, and analyzes the distribution law and trend of the deviation. The adaptive adjustment module for machining parameters dynamically adjusts the spindle speed, feed rate, depth of cut, and tool compensation value of the CNC lathe based on the deviation report output by the data processing module and in combination with the workpiece material characteristics and machining process requirements, and establishes a mapping relationship model between deviation and machining parameters. The real-time feedback module transmits the adjusted machining parameter commands to the CNC lathe control system in real time through the digital signal output interface, and synchronously receives the lathe's execution feedback signals. The safety protection module integrates an infrared ranging sensor and a mechanical limit switch to monitor the distance between the height gauge probe and the workpiece tool in real time. When the distance is less than the threshold, the measurement and processing actions are automatically paused. The built-in overload protection circuit prevents the equipment from being damaged due to abnormal current. The data storage and traceability module adopts a distributed database architecture to record information on the deviation value after processing the raw data of each measurement, and supports multi-dimensional retrieval and query.
[0007] Furthermore, it also includes a measurement accuracy calibration module, which is implemented using a formula. Calculate the dynamic calibration coefficients, where Calibration coefficients for measurement accuracy. The weighting is based on the effect of temperature. The weighting is influenced by air pressure. Humidity affects the weight and + + =1, This is the difference between the actual ambient temperature and the standard temperature. For the maximum permissible temperature deviation, This represents the difference between the actual ambient air pressure and the standard air pressure. For the maximum permissible air pressure deviation, This represents the difference between the actual ambient humidity and the standard humidity. This represents the maximum permissible humidity deviation.
[0008] Furthermore, it also includes a processing error prediction module, which uses a formula... Predicting height errors during the processing, among which To predict machining height error, The influence coefficient of spindle speed. The feed rate influence coefficient. This is the cutting depth influence coefficient. The factor representing the influence of processing time. This refers to the spindle speed of a CNC lathe. For feed rate, For cutting depth, This refers to the processing time per cycle.
[0009] Furthermore, it also includes a multi-station collaborative measurement module, which supports the networking and linkage of multiple high-precision height gauges with CNC lathes. Through the collaborative control module, measurement tasks and processing sequences are uniformly allocated, a data sharing channel between stations is established, and a station priority setting function is integrated, which can adjust the task execution order of different stations according to the production plan.
[0010] Furthermore, it also includes a remote monitoring and control module, which builds a cloud data transmission channel based on IoT technology, integrates edge computing nodes to preprocess measurement data processing parameters and equipment operating status, filters redundant information and uploads it to the cloud platform in real time. It has functions such as remote parameter adjustment task issuance, fault alarm permission hierarchical management, adopts end-to-end encrypted transmission protocol and device identity authentication mechanism, integrates AI data analysis engine to explore parameter optimization space in the production process, and supports remote assistance function.
[0011] Furthermore, it also includes a fault diagnosis and early warning module, which collects the operating data of each module in real time, establishes a database of equipment fault characteristics and a fault tree model, identifies sensor fault types by integrating multi-dimensional data through machine learning algorithms, issues audible and visual early warning signals in advance and pushes step-by-step fault troubleshooting guidelines, automatically starts self-healing programs to adjust operating parameters to restore normal operation for minor faults, triggers equipment shutdown protection for serious faults and simultaneously notifies maintenance personnel, records the entire life cycle information of faults, and forms equipment maintenance files.
[0012] Furthermore, it also includes a temperature and humidity adaptive compensation module, which integrates a high-precision temperature and humidity sensor and a local environment monitoring probe to collect real-time data on the overall temperature and humidity of the processing environment and the local temperature and humidity of the workpiece processing area. It establishes a database of thermophysical parameters covering various materials, dynamically calls parameters corresponding to the workpiece material, establishes a compensation model based on the rate of change of temperature and humidity, dynamically adjusts the measurement reference value and the compensation amount of processing parameters, sets differentiated compensation strategies for different processing stages, and introduces a compensation effect verification mechanism.
[0013] Furthermore, it also includes a tool wear correlation adjustment module, which continuously monitors the dimensional changes of the workpiece after machining using a high-precision height gauge, and combines the cutting information to infer the tool wear amount, establishes a nonlinear mapping relationship model between tool wear amount and machining parameter adjustment, uses machine learning algorithms to predict tool wear trend, automatically adjusts parameters when the wear amount approaches a set threshold, issues a tool replacement prompt when the wear amount reaches the threshold, and records the cumulative tool machining time and the number of machined workpieces. The system automatically identifies the spare tool model and switches to it, and synchronously adjusts the machining parameters to adapt to the new tool.
[0014] Furthermore, it also includes a product batch adaptation module, which supports storing complete parameter sets for different product batches, establishing a quick index for batch parameter retrieval, and allowing quick retrieval of the corresponding parameter template by scanning a code or entering the batch number when switching product batches. It automatically adapts to the measurement mode without requiring manual reset, allows for local fine-tuning and optimization of template parameters, integrates batch quality analysis functions, automatically compares the dimensional deviation data of different workpieces within the same batch with the dimensional deviation distribution between different batches, identifies the causes of batch quality fluctuations, generates batch quality analysis reports and process optimization suggestions, is compatible with direct parsing of CAD design files and process files, and automatically extracts key dimensional parameters to generate adapted measurement and processing schemes.
[0015] Furthermore, it also includes a measurement path optimization module, which uses the A* algorithm to combine the workpiece's 3D model with the actual processing scenario to plan the collision-free optimal measurement path. It monitors sudden interference generated during processing through visual sensors and adjusts the measurement path in real time. It supports multi-probe collaborative path planning and dynamically adjusts the path node density according to measurement accuracy requirements. Users can choose between efficiency-first, accuracy-first, or balanced modes based on production needs. The path planning results can be previewed through a visual interface, and manual drag-and-drop adjustment of path nodes is supported. It also has a path simulation function to simulate the movement trajectory of the measurement probes to verify the rationality of the path and generate a path planning report.
[0016] Compared with existing technologies, the beneficial effects of this invention are: The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system of the present invention comprehensively solves the pain points of existing technologies, and achieves a leapfrog improvement in collaborative control accuracy, measurement reliability, machining adaptability, production efficiency and operation and maintenance convenience, providing an integrated intelligent solution for precision manufacturing.
[0017] The accuracy and adaptability of collaborative control are significantly enhanced. A dual-communication protocol architecture and dynamic switching function enable seamless networking of different equipment models. Millisecond-level data interaction and action synchronization mechanisms completely resolve timing conflicts between measurement and processing, ensuring production continuity. Measurement accuracy and stability are greatly improved. A combination of dynamic calibration for multiple environmental factors and adaptive temperature and humidity compensation, along with multi-dimensional data correction and compensation model optimization, effectively counteracts external interferences such as temperature, air pressure, and humidity. Combined with optical focusing and error filtering algorithms, high measurement accuracy is maintained even during long-term operation, providing reliable data support for parameter adjustments.
[0018] The intelligent and targeted optimization of machining parameter adjustments integrates machining error prediction and tool wear correlation analysis to predict dimensional deviations in advance and dynamically adjust parameters, reducing the number of corrections. Simultaneously, it optimizes cutting parameters based on tool wear trends and indicates replacement timing, significantly improving product dimensional consistency and reducing scrap rates and tool consumption costs. Multi-station and remote control capabilities are greatly enhanced. A unified task allocation and data sharing mechanism enables parallel measurement and machining, improving batch production efficiency. A remote module combining cloud-based linkage and edge computing supports real-time monitoring of multiple devices, parameter adjustment, and remote collaboration. An AI data analysis engine provides production optimization suggestions, and technical support responses are faster.
[0019] The system significantly improves fault handling and batch adaptation efficiency. The fault diagnosis module, with its multi-dimensional data fusion, accurately identifies fault types and locations. Early warning, self-healing, and tiered processing functions greatly reduce downtime and maintenance costs. The product batch adaptation module enables rapid parameter template retrieval and customized fine-tuning, is compatible with external file parsing, and significantly reduces switching operation steps and time, adapting to the production needs of small batches and multiple product varieties. The measurement path optimization module balances measurement efficiency and accuracy through collision-free path planning and dynamic node density adjustment, avoiding probe damage. Overall, the system achieves full-process automation and intelligence in measurement, analysis, adjustment, and feedback, significantly improving production efficiency and product quality stability, reducing manual intervention and maintenance costs, and driving the precision manufacturing industry towards intelligent and efficient transformation. Attached Figure Description
[0020] Figure 1 This is a schematic block diagram of the intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system proposed in this invention; Figure 2 Line graphs showing the dimensional accuracy stability under different processing methods; Figure 3 A bar chart showing the data before and after calibration for measurement accuracy; Figure 4 A bar chart showing the time taken for multiple batch switching processes; Figure 5 This is a scatter plot showing the relationship between tool wear and dimensional deviation. Detailed Implementation
[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.
[0024] Reference Figures 1 to 5 A smart, high-precision height gauge and CNC lathe collaborative measurement and machining system includes the following modules: The collaborative control module adopts industrial Ethernet and PROFINET dual communication protocols to establish a real-time communication link between the height gauge and the CNC lathe, realizing millisecond-level data interaction and action synchronization. It integrates a motion control chip to coordinate the timing logic of the measurement actuator and the lathe machining mechanism, avoids conflicts between measurement and machining actions, and supports dynamic switching of communication protocols to adapt to different models of equipment. The high-precision measurement module integrates a laser displacement sensor and a ruby contact probe. It can automatically switch measurement modes according to the surface roughness of the workpiece material. The built-in platinum resistance temperature sensor collects the ambient temperature in real time and uses a quadratic curve fitting algorithm for temperature error compensation. The measurement range covers micrometers to millimeters. The resolution of the measurement of small dimensions is improved through an optical focusing system. The data processing module receives raw data from the high-precision measurement module, uses the Kalman filter algorithm to filter out environmental interference and equipment noise, calculates the deviation between the actual height of the workpiece and the theoretical design value, analyzes the distribution law and trend of the deviation, and generates a detailed data report including the deviation value, deviation type and influence range. The adaptive adjustment module for machining parameters dynamically adjusts the spindle speed, feed rate, depth of cut, and tool compensation value of the CNC lathe based on the deviation report output by the data processing module and in combination with the workpiece material characteristics and machining process requirements. It establishes a mapping relationship model between deviation and machining parameters to ensure the accuracy and adaptability of parameter adjustment. The real-time feedback module transmits the adjusted machining parameter instructions to the CNC lathe control system in real time through the digital signal output interface, and synchronously receives the execution feedback signal from the lathe to verify whether the parameters are executed accurately. If an execution deviation occurs, the collaborative control module is immediately triggered to make secondary adjustments. The safety protection module integrates an infrared ranging sensor and a mechanical limit switch to monitor the distance between the height gauge probe and the workpiece tool in real time. It sets a safety distance threshold and automatically pauses measurement and processing when the distance is less than the threshold. It also has a built-in overload protection circuit to prevent equipment damage due to abnormal current and supports one-button triggering of emergency stop signal. The data storage and traceability module adopts a distributed database architecture to record information such as the raw data of each measurement, the deviation value after processing, the processing parameters, product number, production time, and operator. It supports multi-dimensional retrieval and query by product batch, production period, workpiece model, etc. The data retention period can be set independently to meet the needs of quality traceability and production management.
[0025] This invention also includes a measurement accuracy calibration module, which is configured using a formula. Calculate the dynamic calibration coefficients, where Calibration coefficients for measurement accuracy. The weighting is based on the effect of temperature. The weighting is influenced by air pressure. Humidity affects the weight and + + =1, This is the difference between the actual ambient temperature and the standard temperature. For the maximum permissible temperature deviation, This represents the difference between the actual ambient air pressure and the standard air pressure. For the maximum permissible air pressure deviation, This represents the difference between the actual ambient humidity and the standard humidity. To maximize the allowable humidity deviation, the measurement data is corrected based on the calibration coefficient, dynamically offsetting the impact of environmental factors on measurement accuracy and improving measurement stability in complex environments.
[0026] This invention also includes a processing error prediction module, which uses a formula... Predicting height errors during the processing, among which To predict machining height error, The influence coefficient of spindle speed. The feed rate influence coefficient. This is the cutting depth influence coefficient. The factor representing the influence of processing time. This refers to the spindle speed of a CNC lathe. For feed rate, For cutting depth, To determine the processing time per cycle, processing parameters are adjusted in advance based on predicted errors, reducing the number of subsequent deviation corrections and improving processing efficiency and dimensional consistency.
[0027] This invention also includes a multi-station collaborative measurement module, which supports the networking and linkage of multiple high-precision height gauges with CNC lathes. Through the collaborative control module, measurement tasks and processing sequences are uniformly allocated, and a data sharing channel between stations is established to realize synchronous measurement of the same workpiece at multiple positions or parallel measurement and processing of multiple workpieces. The integrated station priority setting function can adjust the task execution order of different stations according to the production plan, adapting to batch production and customized processing scenarios.
[0028] This invention also includes a remote monitoring and control module, which builds a cloud data transmission channel based on IoT technology. It integrates edge computing nodes to preprocess measurement data, processing parameters, and equipment operating status, filters redundant information, and uploads it to the cloud platform in real time. It supports remote viewing of production progress data reports and equipment health status through multiple devices such as mobile terminals and computers. It has functions such as remote parameter adjustment task issuance, fault alarm permission hierarchical management, and end-to-end encrypted transmission protocol and device identity authentication mechanism to ensure data security. It integrates an AI data analysis engine to explore parameter optimization space in the production process, automatically generates production improvement suggestions, and supports remote assistance functions. Technicians can guide on-site operations through real-time screens to improve problem-solving efficiency.
[0029] This invention also includes a fault diagnosis and early warning module, which collects real-time operating data from each module, covering parameters such as current, voltage, communication delay, measurement deviation, vibration frequency, and temperature changes. It establishes a fault feature database and fault tree model, and uses machine learning algorithms to integrate multi-dimensional data to identify fault types such as sensor faults, communication interruptions, mechanical jamming, and circuit abnormalities. It accurately locates the module and specific location where the fault occurs, issues audible and visual warning signals in advance, and pushes step-by-step fault troubleshooting guidelines. It supports fault level classification, automatically initiates self-healing programs to adjust operating parameters and restore normal operation for minor faults, and triggers equipment shutdown protection for serious faults while simultaneously notifying maintenance personnel. It records the entire lifecycle information of the fault, including the time of occurrence, cause, handling process, and results, forming an equipment maintenance file. Based on historical fault data, it predicts maintenance cycles, achieves preventive maintenance, and reduces unplanned downtime.
[0030] This invention also includes a temperature and humidity adaptive compensation module, which integrates a high-precision temperature and humidity sensor and a local environment monitoring probe. It collects real-time data on the overall temperature and humidity of the processing environment and the local temperature and humidity of the workpiece processing area, establishing a database of thermophysical parameters covering various materials such as metals, plastics, ceramics, and composite materials. It dynamically calls parameters such as the coefficient of thermal expansion and contraction and thermal conductivity of the corresponding workpiece material, and establishes a compensation model based on the rate of temperature and humidity change. It dynamically adjusts the measurement reference value and the compensation amount of the processing parameters, setting differentiated compensation strategies for different processing stages. In the early stages of processing, it focuses on rapidly compensating for initial temperature and humidity deviations; in the middle stages, it tracks temperature and humidity changes in real-time and dynamically fine-tunes the parameters; and in the later stages, it stabilizes the compensation parameters to ensure dimensional accuracy. A compensation effect verification mechanism is introduced, using measurement data to reverse-verify the accuracy of the compensation, continuously optimizing the compensation model parameters, and improving the dimensional stability of the product under long-term operation.
[0031] This invention also includes a tool wear correlation adjustment module. It continuously monitors the dimensional changes of the workpiece after machining using a high-precision height gauge. Combined with information such as cutting parameters, workpiece material hardness, and machining time, it infers the tool wear amount, establishing a nonlinear mapping relationship model between tool wear and machining parameter adjustments. Machine learning algorithms predict tool wear trends, anticipating the time point when the tool reaches its wear limit. When the wear amount approaches a set threshold, it automatically adjusts parameters such as feed rate, depth of cut, and cutting speed to extend the effective tool life. When the wear amount reaches the threshold, it issues a tool replacement prompt and records the cumulative machining time and number of workpieces processed. It supports multi-tool rotation and adaptation, automatically identifying and switching to spare tool models, and synchronously adjusting the machining parameters of the new tool. It integrates tool life data analysis, comparing the wear patterns and lifespans of tools from different brands to provide data support for tool selection, reducing product dimensional deviations caused by tool wear, and lowering scrap rates and production costs.
[0032] This invention also includes a product batch adaptation module, which supports storing complete parameter sets such as theoretical dimensional parameters, processing technology templates, measurement path planning schemes, and quality inspection standards for different product batches. It establishes a quick-access index for batch parameters, allowing for rapid retrieval of corresponding parameter templates by scanning a code or entering the batch number when switching product batches. It automatically adapts to measurement modes, measurement paths, processing parameters, and safety thresholds without requiring manual resetting. It allows for local fine-tuning and optimization of template parameters to adapt to the production needs of small-batch, multi-variety production. It integrates batch quality analysis functions, automatically comparing dimensional deviation data of different workpieces within the same batch with the dimensional deviation distribution between different batches, identifying the causes of batch quality fluctuations, generating batch quality analysis reports and process optimization suggestions, supporting external parameter import functions, and directly parsing CAD design files and process files. It automatically extracts key dimensional parameters to generate adapted measurement and processing schemes, supports customized parameter saving, and stores parameter schemes for specially customized products separately for convenient subsequent repeated production calls, improving production switching efficiency and batch quality consistency.
[0033] This invention also includes a measurement path optimization module, which uses the A* algorithm combined with the workpiece's 3D model and the actual processing scenario to plan a collision-free optimal measurement path. It integrates real-time dynamic obstacle avoidance, using a visual sensor to monitor sudden interferences such as chips, burrs, and tool vibrations generated during processing, and adjusts the measurement path in real time to avoid interference areas. It supports multi-probe collaborative path planning; when the system is configured with multiple measurement probes, it optimizes the measurement area and path of each probe to avoid interference between probes, achieving parallel measurement and improving efficiency. It dynamically adjusts the path node density according to measurement accuracy requirements, increasing the number of path nodes in high-precision measurement areas to ensure measurement accuracy, and reducing the number of nodes in regular measurement areas to improve measurement speed. It supports a balance between path efficiency and accuracy adjustment; users can choose between efficiency-first, accuracy-first, or balanced modes according to production needs. The path planning results can be previewed through a visual interface, and manual drag-and-drop adjustment of path nodes is supported. It also has a path simulation function to simulate the movement trajectory of the measurement probes to verify the rationality of the path, generating a path planning report that records information such as path length, measurement time node distribution, etc., providing a reference for subsequent path optimization and ensuring a highly efficient and accurate measurement process.
[0034] The following two examples further illustrate the specific implementation of this system: Example 1: Application of precision shaft machining in aerospace This embodiment is applied to the machining of precision shaft parts in the aerospace field. The part is made of titanium alloy and the height dimensional tolerance requirement is at the micrometer level. Collaborative measurement and machining are required to ensure dimensional accuracy and stability, and to fully implement the functions and technical solutions of all modules of the system.
[0035] The collaborative control module uses industrial Ethernet and PROFINET dual communication protocols to build a communication link, with data interaction latency controlled within 5 milliseconds. It integrates a motion control chip to coordinate the action sequence of the measurement actuator and the lathe machining mechanism, and avoids conflicts through preset action priority logic. When the height measuring probe approaches the machining area, the lathe spindle automatically reduces its speed. After the measurement is completed, it quickly resumes the machining state. It also supports automatic switching of the appropriate communication protocol according to the equipment model.
[0036] The high-precision measurement module integrates a laser displacement sensor and a ruby contact probe. It automatically switches to laser measurement mode when the detected surface roughness value is no greater than 0.8 micrometers, and switches to contact measurement mode when the surface roughness value is greater than 0.8 micrometers. A built-in platinum resistance temperature sensor collects ambient temperature data in real time, and a quadratic curve fitting algorithm is used for temperature error compensation. An optical focusing system improves the resolution of minute dimension measurements to 0.1 micrometers, covering a measurement range from 5 micrometers to 50 millimeters.
[0037] After receiving the raw measurement data, the data processing module uses the Kalman filter algorithm to filter out noise generated by environmental vibration and electromagnetic interference, calculates the deviation between the actual height of the part and the theoretical design value, analyzes the trend of the deviation with processing time, generates a detailed data report including the deviation value, deviation type and influence range, and synchronously transmits it to the processing parameter adaptive adjustment module and the data storage module.
[0038] The adaptive adjustment module for machining parameters dynamically adjusts the lathe spindle speed, feed rate, depth of cut, and tool compensation value based on deviation reports and the material properties of titanium alloys, such as hardness and thermal conductivity, as well as milling process requirements. It establishes a mapping relationship model between deviation and machining parameters. For example, when the deviation value increases, the feed rate is appropriately reduced and the depth of cut is finely adjusted.
[0039] The real-time feedback module transmits the adjusted parameter commands to the lathe control system through the digital signal output interface and synchronously receives the lathe execution feedback signal. If the parameter execution deviation is detected to exceed 0.2 micrometers, the collaborative control module is immediately triggered to readjust the parameters.
[0040] The safety protection module integrates an infrared ranging sensor and a mechanical limit switch. The safety distance threshold is set to 10 mm. When the distance between the measuring probe and the workpiece or tool is less than this value, the system automatically pauses the measurement and processing. The built-in overload protection circuit monitors the operating current of the equipment. When the current exceeds 1.2 times the rated value, it automatically cuts off the power. It also supports one-button shutdown via the emergency stop button on the control panel.
[0041] The data storage and traceability module adopts a distributed database architecture to record the original data of each measurement, the processed deviation value, the adjusted processing parameters, product number, production time, operator and other information. It supports multi-dimensional retrieval and query by product batch, production time period and workpiece model. The data retention period is set to 3 years.
[0042] The measurement accuracy calibration module uses the formula Calculate calibration coefficients and set... =0.4、 =0.3、 =0.3, standard temperature 20℃, standard air pressure 101.3kPa, standard humidity 50%RH, actual ambient temperature 25℃, air pressure 100.8kPa, humidity 65%RH, maximum allowable temperature deviation ±8℃, air pressure deviation ±5kPa, humidity deviation ±30%RH, calculated as follows The measurement data is corrected based on this coefficient.
[0043] The machining error prediction module uses formulas Prediction error, setting =0.002、 =0.003、 =0.005、 =0.001, spindle speed =3000 r / min, feed rate =0.1mm / r, depth of cut =0.2mm, processing time =15min, calculated as follows The processing parameters are adjusted in advance based on the prediction error.
[0044] The multi-station collaborative measurement module supports networking and linkage of 4 height gauges and 2 lathes. It allocates measurement tasks through the collaborative control module, realizes synchronous measurement of both ends and the middle area of the same shaft part, and integrates the station priority setting function to adjust the station execution order according to the production plan.
[0045] The remote monitoring and control module builds a cloud channel based on IoT technology. After integrating pre-processed data from edge computing nodes, it uploads the data to the cloud platform. It supports remote viewing of production progress, data reports, and equipment status via computers and tablets. It has remote parameter adjustment and task distribution functions. It uses end-to-end encrypted transmission and identity authentication to ensure security, and the AI data analysis engine generates parameter optimization suggestions.
[0046] The fault diagnosis and early warning module collects data such as current, voltage, communication delay, and measurement deviation. It uses machine learning algorithms to identify problems such as sensor faults and communication interruptions, and issues audible and visual warnings in advance, along with troubleshooting guidelines. Minor faults are automatically self-healed, while serious faults trigger shutdown protection.
[0047] The temperature and humidity adaptive compensation module integrates high-precision sensors to collect overall and local temperature and humidity data, calls the coefficient of thermal expansion and contraction of titanium alloy, dynamically adjusts the measurement reference value and processing parameter compensation amount, and adopts differentiated compensation strategies for each stage of processing.
[0048] The tool wear correlation adjustment module infers the tool wear amount from the measurement data, establishes a nonlinear mapping model, adjusts the cutting parameters when the wear amount is close to the threshold, and issues a replacement prompt when the threshold is reached, supporting multi-tool rotation adaptation.
[0049] The product batch adaptation module stores the parameter set of the shaft part, which can be quickly called by batch number, supports template fine-tuning, and is compatible with CAD file parsing and parameter extraction.
[0050] The measurement path optimization module uses the A* algorithm combined with a 3D model to plan the path, avoiding protruding areas of the workpiece, increasing the number of nodes in high-precision areas, and reducing the number of nodes in regular areas. The path can be previewed and manually corrected.
[0051] Table 1 Comparison of Machining Effects for Precision Shaft Parts in Aerospace Table 1 clearly demonstrates the application value of the system of this invention. Traditional machining methods involve independent measurement and machining, resulting in dimensional accuracy highly susceptible to environmental factors and tool wear, poor stability, high scrap rates, low production efficiency, time-consuming on-site troubleshooting for equipment malfunctions, and cumbersome parameter resetting for multiple batch switching. This invention, through collaborative control, multi-dimensional compensation, and intelligent adjustment, significantly improves dimensional accuracy stability, substantially increases production efficiency, greatly reduces scrap rates, shortens processing time with fault diagnosis and early warning functions, simplifies switching processes with multi-batch adaptation modules, fully meets the machining requirements of aerospace precision parts, and ensures product quality and production continuity.
[0052] Example 2: Mass Production Application of Automobile Engine Pistons This embodiment is applied to the mass production of automotive engine pistons. The pistons are made of aluminum alloy and require multi-batch, high-precision processing, balancing production efficiency and dimensional consistency, and fully implementing all module functions and technical solutions of the system.
[0053] The collaborative control module uses industrial Ethernet and PROFINET dual communication protocols to build a communication link, with data interaction latency controlled within 8 milliseconds. It integrates a motion control chip to coordinate the timing of measurement and processing actions, avoids conflicts through action timing planning, reduces the lathe's feed speed when the measuring probe enters the processing area, and quickly recovers after the measurement is completed. It supports automatic switching of communication protocols according to the lathe and height gauge models.
[0054] The high-precision measurement module integrates a laser displacement sensor and a ruby contact probe. When the piston surface roughness value is no greater than 1.0 micrometer, it switches to laser measurement mode; when the surface roughness value is greater than 1.0 micrometer, it switches to contact measurement mode. The built-in platinum resistance temperature sensor collects the ambient temperature in real time and uses a quadratic curve fitting algorithm to compensate for temperature errors. The optical focusing system improves the measurement resolution to 0.2 micrometers, and the measurement range covers 10 micrometers to 80 millimeters.
[0055] After receiving the raw data, the data processing module uses the Kalman filter algorithm to filter noise, calculates the deviation between the actual piston height and the theoretical value, analyzes the distribution pattern of the deviation, generates a detailed data report, and transmits it synchronously to the relevant modules.
[0056] The adaptive adjustment module for machining parameters dynamically adjusts the spindle speed, feed rate, depth of cut, and tool compensation value based on deviation reports, combined with the characteristics of aluminum alloy materials and turning process requirements. It establishes a mapping relationship model between deviations and machining parameters to ensure accurate and appropriate parameter adjustments.
[0057] The real-time feedback module transmits the adjusted parameter instructions through a digital signal interface, receives the lathe execution feedback signal, and triggers a secondary adjustment when it detects that the parameter execution deviation exceeds 0.3 micrometers.
[0058] The safety protection module integrates an infrared ranging sensor and a mechanical limit switch. The safety distance threshold is set to 15 mm. When the distance is less than this value, the operation will automatically stop. The built-in overload protection circuit monitors the operating current. When the current exceeds 1.3 times the rated value, the power will be automatically cut off. It also supports one-button emergency stop.
[0059] The data storage and traceability module adopts a distributed database architecture to record information such as measurement data, deviation values, processing parameters, product numbers, and production time. It supports multi-dimensional retrieval and query, and the data retention period is set to 2 years.
[0060] The measurement accuracy calibration module uses the formula Calculate calibration coefficients and set... =0.4、 =0.3、 =0.3, standard temperature 20℃, standard air pressure 101.3kPa, standard humidity 50%RH, actual ambient temperature 28℃, air pressure 102.1kPa, humidity 40%RH, maximum allowable temperature deviation ±10℃, air pressure deviation ±6kPa, humidity deviation ±30%RH, calculated as follows The measurement data is corrected based on this coefficient.
[0061] The machining error prediction module uses formulas Prediction error, setting =0.001、 =0.002、 =0.004、 =0.0008, spindle speed =2500 r / min, feed rate =0.15mm / r, depth of cut =0.3mm, processing time =10min, calculated The processing parameters are adjusted in advance based on the predicted error.
[0062] The multi-station collaborative measurement module supports networking and linkage of 6 height gauges and 3 lathes. It allocates measurement and processing tasks through the collaborative control module, realizes parallel measurement and processing of multiple pistons, and integrates the station priority setting function to adjust the execution order according to the production plan.
[0063] The remote monitoring and control module is built on a cloud channel based on IoT technology. Data is preprocessed by edge computing nodes and then uploaded. It supports remote viewing of production progress and equipment status via mobile phone and computer, and has remote parameter adjustment and task distribution functions. Encrypted transmission and identity authentication ensure data security, and the AI engine generates production optimization suggestions.
[0064] The fault diagnosis and early warning module collects operational data from each module, identifies fault types through machine learning algorithms, issues early warnings and pushes troubleshooting guidelines, automatically heals minor faults, and triggers shutdown protection and notifies maintenance personnel for serious faults.
[0065] The temperature and humidity adaptive compensation module collects overall and local temperature and humidity data, calls up the thermophysical parameters of aluminum alloy, and dynamically adjusts the measurement reference value and processing parameter compensation amount. Differentiated compensation strategies are adopted for each stage of processing.
[0066] The tool wear correlation adjustment module uses measurement data to infer the wear amount and establishes a mapping model. When the wear amount approaches the threshold, the parameters are adjusted, and when the threshold is reached, a replacement prompt is given. It supports multi-tool rotation adaptation.
[0067] The product batch adaptation module stores parameter sets for pistons from different batches, allows for quick template retrieval via barcode scanning, supports fine-tuning and optimization, and is compatible with CAD file parsing for parameter extraction.
[0068] The measurement path optimization module uses the A* algorithm to plan the path, avoid the burr area of the workpiece, dynamically adjust the density of path nodes, and the path can be previewed and manually corrected.
[0069] Table 2 Comparison of Mass Production Effects of Automobile Engine Pistons Table 2 clearly demonstrates the application advantages of the system of this invention. Traditional machining methods suffer from poor piston size consistency, low single-batch production efficiency, high tool consumption costs, and time-consuming manual parameter settings for multi-batch switching, all while lacking remote control capabilities, making it difficult to meet the demands of large-scale production. This invention, through multi-station collaboration, intelligent parameter adjustment, and tool wear correlation optimization, significantly improves dimensional consistency and production efficiency, reduces tool consumption costs, shortens switching time with a multi-batch adaptation module, and supports real-time monitoring and parameter adjustment with a remote control module. It fully adapts to the high-efficiency and precise requirements of automotive parts mass production, helping enterprises improve their production management level and market competitiveness.
[0070] Reference Figure 2 This figure visually illustrates the differences in dimensional accuracy stability under different machining methods. Traditional machining methods lack multi-dimensional environmental compensation and real-time parameter adjustment mechanisms, making them significantly affected by factors such as temperature and tool wear. Dimensional deviations fluctuate greatly across different machining periods, with values consistently above 7.8 μm, reaching a maximum of 10.1 μm, failing to meet the tolerance requirements for precision parts machining. The system of this invention, through modules such as dynamic calibration of measurement accuracy, adaptive temperature and humidity compensation, and tool wear-related adjustment, effectively counteracts various interference factors. Dimensional deviations across different machining periods remain stable within the 1.2-1.5 μm range, with minimal fluctuations. This fully demonstrates the system's core advantages in dimensional accuracy control, providing stable dimensional assurance for precision machining in aerospace, automotive manufacturing, and other fields, significantly improving product quality consistency.
[0071] Reference Figure 3 This figure clearly reflects the actual application effect of the measurement accuracy calibration module. In the uncalibrated state, the deviation values differ significantly under different measurement environments, especially reaching 7.0 μm under complex conditions, and exceeding 5 μm under high temperature and low pressure, and low temperature and high pressure environments. This is mainly due to the failure to comprehensively consider the combined effects of environmental factors such as temperature, air pressure, and humidity. By introducing a dynamic calibration coefficient calculation formula, integrating the weights of multiple environmental parameters to calculate the calibration value and correct the measurement data, the deviation under various environments is controlled within 1.3 μm, even under complex conditions. This significantly reduces the interference of environmental factors on measurement accuracy, verifies the adaptability and effectiveness of the calibration module in multiple scenarios, and provides a precise measurement data foundation for subsequent processing parameter adjustments.
[0072] Reference Figure 4This diagram visually demonstrates the efficiency-enhancing effect of the product batch adaptation module. Traditional processing methods require manual setup of measurement paths, processing parameters, and safety thresholds for each batch, a cumbersome and error-prone process. Each batch switchover takes over 25 minutes, sometimes as long as 30 minutes, severely hindering the efficiency of small-batch, multi-variety production. This invention's system, through preset parameter templates, quick index calls, and compatibility with external file parsing, achieves automatic parameter adaptation during batch switching. Only minor manual adjustments are needed to complete the switchover, with each batch switchover taking only 4.5-5.2 minutes – a significant reduction in time. This effectively improves production switchover efficiency, adapts to the diverse and flexible production needs of modern manufacturing, reduces non-processing time, and increases equipment utilization.
[0073] Reference Figure 5 This diagram clearly demonstrates the core value of the tool wear correlation adjustment module. Traditional machining methods lack a correlation mechanism between tool wear and parameter adjustment. Dimensional deviations increase linearly with tool wear, reaching as high as 14.0 μm when wear reaches 0.25 mm, far exceeding tolerance requirements and necessitating frequent tool replacements, leading to increased costs. This invention's system uses measurement data to infer tool wear, establishes a nonlinear mapping model, and dynamically adjusts machining parameters. Even when tool wear increases to 0.25 mm, dimensional deviations are controlled within 1.5 μm, effectively extending tool life, reducing tool replacement frequency, lowering production costs, avoiding production interruptions due to frequent tool changes, and ensuring the continuity and dimensional stability of mass production.
[0074] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A collaborative measurement and machining system for an intelligent high-precision height gauge and a CNC lathe, characterized in that, Includes the following modules: The collaborative control module adopts both industrial Ethernet and PROFINET communication protocols to establish a real-time communication link between the height gauge and the CNC lathe, and integrates a motion control chip to coordinate the timing logic of the measurement actuator and the lathe machining mechanism; The high-precision measurement module integrates a laser displacement sensor and a ruby contact probe. It can automatically switch the measurement mode according to the surface roughness of the workpiece material. The built-in platinum resistance temperature sensor collects the ambient temperature in real time and uses a quadratic curve fitting algorithm to compensate for temperature error. The data processing module receives raw data from the high-precision measurement module, uses the Kalman filter algorithm to filter out environmental interference and equipment noise, calculates the deviation between the actual height of the workpiece and the theoretical design value, and analyzes the distribution law and trend of the deviation. The adaptive adjustment module for machining parameters dynamically adjusts the spindle speed, feed rate, depth of cut, and tool compensation value of the CNC lathe based on the deviation report output by the data processing module and in combination with the workpiece material characteristics and machining process requirements, and establishes a mapping relationship model between deviation and machining parameters. The real-time feedback module transmits the adjusted machining parameter commands to the CNC lathe control system in real time through the digital signal output interface, and synchronously receives the lathe's execution feedback signals. The safety protection module integrates an infrared ranging sensor and a mechanical limit switch to monitor the distance between the height gauge probe and the workpiece tool in real time. When the distance is less than the threshold, the measurement and processing actions are automatically paused. The built-in overload protection circuit prevents the equipment from being damaged due to abnormal current. The data storage and traceability module adopts a distributed database architecture to record information on the deviation value after processing the raw data of each measurement, and supports multi-dimensional retrieval and query.
2. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a measurement accuracy calibration module, which is implemented using the formula. Calculate the dynamic calibration coefficients, where Calibration coefficients for measurement accuracy. The weighting is based on the effect of temperature. The weighting is influenced by air pressure. Humidity affects the weight and + + =1, This is the difference between the actual ambient temperature and the standard temperature. For the maximum permissible temperature deviation, This represents the difference between the actual ambient air pressure and the standard air pressure. For the maximum permissible air pressure deviation, This represents the difference between the actual ambient humidity and the standard humidity. This represents the maximum permissible humidity deviation.
3. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a processing error prediction module, which uses formulas Predicting height errors during the processing, among which To predict machining height error, The influence coefficient of spindle speed. The feed rate influence coefficient. This is the cutting depth influence coefficient. The factor representing the influence of processing time. This refers to the spindle speed of a CNC lathe. For feed rate, For cutting depth, This refers to the processing time per cycle.
4. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a multi-station collaborative measurement module, which supports networking and linkage of multiple high-precision height gauges with CNC lathes. Through the collaborative control module, measurement tasks and processing sequences are uniformly allocated, a data sharing channel between stations is established, and a station priority setting function is integrated, which can adjust the task execution order of different stations according to the production plan.
5. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a remote monitoring and control module, which builds a cloud data transmission channel based on IoT technology, integrates edge computing nodes to preprocess measurement data processing parameters and equipment operating status, filters redundant information and uploads it to the cloud platform in real time. It has functions such as remote parameter adjustment task issuance, fault alarm permission hierarchical management, end-to-end encrypted transmission protocol and device identity authentication mechanism, integrates AI data analysis engine to explore parameter optimization space in the production process, and supports remote assistance function.
6. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a fault diagnosis and early warning module, which collects the operating data of each module in real time, establishes a database of equipment fault characteristics and a fault tree model, identifies sensor fault types by integrating multi-dimensional data through machine learning algorithms, issues audible and visual early warning signals in advance and pushes step-by-step fault troubleshooting guidelines, automatically starts self-healing programs to adjust operating parameters and restore normal operation for minor faults, triggers equipment shutdown protection for serious faults and simultaneously notifies maintenance personnel, records fault lifecycle information, and forms equipment maintenance files.
7. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a temperature and humidity adaptive compensation module, which integrates a high-precision temperature and humidity sensor and a local environment monitoring probe to collect real-time data on the overall temperature and humidity of the processing environment and the local temperature and humidity of the workpiece processing area. It establishes a database of thermophysical parameters covering various materials, dynamically calls parameters corresponding to the workpiece material, establishes a compensation model based on the rate of change of temperature and humidity, dynamically adjusts the measurement reference value and the compensation amount of processing parameters, sets differentiated compensation strategies for different processing stages, and introduces a compensation effect verification mechanism.
8. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a tool wear correlation adjustment module, which continuously monitors the dimensional changes of the workpiece after machining using a high-precision height gauge, and combines the cutting information to infer the tool wear amount, establishes a nonlinear mapping relationship model between tool wear amount and machining parameter adjustment, uses machine learning algorithms to predict tool wear trend, automatically adjusts parameters when the wear amount approaches a set threshold, issues a tool replacement prompt when the wear amount reaches the threshold, and records the cumulative tool machining time and the number of machined workpieces. The system automatically identifies the spare tool model and switches to it, and synchronously adjusts the machining parameters to adapt to the new tool.
9. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a product batch adaptation module, which supports storing complete parameter sets for different product batches, establishing a quick index for batch parameter retrieval, and allowing quick retrieval of the corresponding parameter template by scanning a code or entering the batch number when switching product batches. It automatically adapts to the measurement mode without requiring manual reset, and allows for local fine-tuning and optimization of template parameters. It integrates batch quality analysis functions, automatically compares the dimensional deviation data of different workpieces within the same batch with the dimensional deviation distribution between different batches, identifies the causes of batch quality fluctuations, generates batch quality analysis reports and process optimization suggestions, and is compatible with direct parsing of CAD design files and process files, automatically extracting key dimensional parameters to generate adapted measurement and processing schemes.
10. The intelligent high-precision height gauge and CNC lathe collaborative measurement and machining system according to claim 1, characterized in that, It also includes a measurement path optimization module, which uses the A* algorithm to combine the workpiece 3D model with the actual processing scene to plan the collision-free optimal measurement path. It monitors sudden interference generated during the processing through a vision sensor and adjusts the measurement path in real time. It supports multi-probe collaborative path planning and dynamically adjusts the path node density according to the measurement accuracy requirements. Users can choose between efficiency-first, accuracy-first, or balanced modes according to production needs. The path planning results can be previewed through a visual interface, and manual drag-and-drop adjustment of path nodes is supported. It also has a path simulation function to simulate the movement trajectory of the measurement probes to verify the rationality of the path and generate a path planning report.