Alloy bush number control vehicle milling drill composite machine tool processing system
Patent Information
- Application Number
- CN202611261984.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-19
- Publication Date
- 2026-09-25
AI Technical Summary
[0004]本发明的目的就是为了弥补现有技术的不足，提供了一种合金衬套数控车铣钻复合机床加工系统，本发明通过采集机床运行的能耗数据与工艺参数数据并做同步处理后集中存储，同步留存各类历史加工数据、机床参数范围、材料与工序相关数据，利用存储的历史数据搭建加工能耗对应关系，依托能耗对应关系以能耗、表面粗糙度两项指标为目标求解得到多组工艺参数组合，摒弃依靠人工经验选定加工参数的模式，加工形成的工艺参数完成生产后自动存入存储内容内，累积的数据持续为后续同类产品的工艺计算提供数据源，解决了原有技术数据分散、历史加工数据不能复用、工艺选取依赖人工经验的缺陷
一、本发明通过采集机床运行的能耗数据与工艺参数数据并做同步处理后集中存储，同步留存各类历史加工数据、机床参数范围、材料与工序相关数据，利用存储的历史数据搭建加工能耗对应关系，依托能耗对应关系以能耗、表面粗糙度两项指标为目标求解得到多组工艺参数组合，摒弃依靠人工经验选定加工参数的模式，加工形成的工艺参数完成生产后自动存入存储内容内，累积的数据持续为后续同类产品的工艺计算提供数据源，解决了原有技术数据分散、历史加工数据不能复用、工艺选取依赖人工经验的缺陷。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of CNC machining technology, specifically to a CNC turning, milling and drilling composite machine tool machining system for alloy bushings. Background Technology
[0002] Alloy bushings are commonly used wear-resistant components in engineering machinery and hydraulic equipment. They are mostly formed and processed in one piece using CNC milling and turning composite equipment. At present, in the field of parts manufacturing, CNC composite machine tools have become the mainstream production equipment for mass production of bushings. In the scenario of large-scale mass production, similar bushing products will continue to be produced using fixed processing methods for a long time. With the gradual implementation of the concept of refined management in the manufacturing industry, in addition to controlling the forming quality of workpieces, the energy consumption management of machine tool processing has been gradually incorporated into the scope of daily production management. Various parts processing plants have widely configured CNC composite processing equipment for the mass production of alloy bushings. The processing conditions of different factories and different grades of alloy raw materials are significantly different. Under the long-term extensive processing mode, the selection of processing parameters mostly relies on the on-site experience of operators. The industry as a whole has long suffered from insufficient refined mass production management, which has also prompted relevant production enterprises to continuously explore intelligent parameter management solutions suitable for composite processing of alloy bushings.
[0003] However, existing CNC composite machining control technologies for alloy bushings lack a systematic data collection and parameter optimization architecture. Most of the data generated during the machining process is scattered and cannot be centrally collected and archived for reference in subsequent machining. Traditional machining modes rely on manual experience to select machining configurations, which cannot establish a correlation between energy consumption and workpiece quality based on historical production data. It is difficult to simultaneously address both energy consumption control and finished product quality requirements. Conventional technologies cannot rely on historical production data to perform correlation deduction of machining energy consumption, nor can they automatically select multiple suitable machining configurations based on actual production data. At the same time, there is a lack of processing logic to select the final configuration from multiple alternative configurations. After the machining configuration is selected, there is a lack of real-time verification and dynamic adjustment mechanisms during the machining process. The effective machining configurations after machining are completed cannot be automatically archived and saved, making it impossible to continuously enrich the historical database content and continuously optimize the machining selection for subsequent batches based on historical production data. This results in the inability to reuse historical production resources. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a CNC turning, milling, and drilling composite machine tool processing system for alloy bushings. This invention collects and centrally stores energy consumption data and process parameter data of the machine tool operation, processes them synchronously, and retains various historical processing data, machine tool parameter ranges, and material and process-related data. It uses the stored historical data to build a processing energy consumption correspondence, and based on the energy consumption correspondence, it solves multiple sets of process parameter combinations with energy consumption and surface roughness as the two objectives. It abandons the mode of selecting processing parameters based on manual experience. The process parameters formed after processing are automatically stored in the storage content after production is completed. The accumulated data continuously provides a data source for the process calculation of subsequent similar products, solving the defects of the original technology such as scattered data, inability to reuse historical processing data, and reliance on manual experience for process selection.
[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a CNC turning, milling and drilling composite machine tool processing system for alloy bushings, the system comprising: a data acquisition module, a process database module, an energy consumption modeling module, a multi-objective optimization module and a process generation module; The data acquisition module is used to collect energy consumption data of various electrical equipment during machine tool operation and process parameter data of the current processing process in real time, and transmits all collected data after synchronous processing. The process database module is used to receive and store the synchronized collected data, including historical energy consumption data, historical process parameter data, machine tool rated parameter range, machining accuracy requirement data, alloy bushing material characteristic data, and machining process requirement data. The energy consumption modeling module is used to retrieve synchronized collected data, historical energy consumption data, and historical process parameter data to construct a comprehensive energy consumption model for the composite machining of alloy bushings by turning, milling, and drilling. The multi-objective optimization module is used to retrieve the comprehensive energy consumption model, machine tool rated parameter range and machining accuracy requirements data. With the objectives of minimizing machining energy consumption and minimizing machining surface roughness, it performs multi-objective solutions to obtain multiple sets of process parameter combinations that take into account both energy consumption and machining quality. The process generation module retrieves the material property data and processing procedure requirements data of the alloy bushing to be processed, selects the final process parameter combination from multiple sets of process parameter combinations, generates CNC machining instructions and transmits them to the machine tool control unit, and then transmits the process parameter combination for this processing to the process database module.
[0006] Furthermore, the data acquisition module includes a power sensor unit and a process parameter acquisition unit. The power sensor unit is installed in the power supply circuits of the machine tool's spindle motor, X-axis feed motor, Y-axis feed motor, Z-axis feed motor, and cutting fluid pump, respectively, to collect the instantaneous power data of each electrical device in real time, and calculate the energy consumption data for the corresponding time period through integration. The process parameter acquisition unit reads the process parameter data of the spindle speed, feed rate, depth of cut, and width of cut in real time during the current machining process through the communication interface of the machine tool's CNC unit. After synchronizing the energy consumption data with the process parameter data using timestamps, the synchronized acquisition data is obtained.
[0007] Furthermore, the process database module adopts a MySQL relational database structure, establishing four data tables: material information, process information, process parameters, and processing effects. The material information table stores alloy bushing material property data, including the alloy material grade, hardness, tensile strength, and thermal conductivity. The process information table stores processing process requirements and processing accuracy requirements, including the processing content, dimensional tolerances, and surface roughness requirements for turning, milling, and drilling processes. The process parameter table stores historical process parameter data and synchronized process parameter data, including the corresponding spindle speed, feed rate, depth of cut, and width of cut. The processing effect table stores historical energy consumption data and synchronized energy consumption data, including the actual processing energy consumption, surface roughness, and processing time under the corresponding process parameter data. It also stores the rated parameter ranges of the machine tool, including the rated range of spindle speed, feed rate, depth of cut, and width of cut. It also supports fuzzy retrieval based on grade and processing process.
[0008] Furthermore, when constructing the comprehensive energy consumption model, the energy consumption modeling module preprocesses historical energy consumption data, historical process parameter data, and synchronized collected data; it uses a multivariate nonlinear regression method to fit the mapping relationship between single process parameters and energy consumption in turning, milling, and drilling processes, establishes basic energy consumption sub-models for each process, and then introduces parameter interaction terms to analyze the impact of the coupling effect between different process parameters on energy consumption, and integrates them.
[0009] Furthermore, in the energy consumption modeling module, the expression for the comprehensive energy consumption model is: Where E is the processing energy consumption value, and n is the number of processing steps. Main spindle speed For feed rate, For cutting depth, For cutting width, Let be a constant term for the i-th process, corresponding to the no-load energy consumption of the machine tool for that process. Let be the coefficient of the linear term of the j-th parameter in the i-th process. Let be the coefficient of the interaction term between the j-th and k-th parameters in the i-th process. Let be the random error term of the i-th process, which follows a normal distribution with a mean of 0. , , All results were obtained through multivariate nonlinear regression fitting of historical energy consumption data and historical process parameter data.
[0010] Furthermore, the specific steps of the multi-objective optimization module in solving the multi-objective problem are as follows: determine the two solution objectives of minimizing machining energy consumption and minimizing machining surface roughness, then retrieve the rated parameter range of the machine tool, generate multiple sets of initial process parameter combinations within the rated parameter range of the machine tool, calculate the machining energy consumption value and machining surface roughness value corresponding to each set of initial process parameter combinations, then screen out the process parameter combinations that simultaneously meet the machining accuracy requirements, and iteratively optimize the screened process parameter combinations to obtain multiple sets of process parameter combinations that take into account both energy consumption and machining quality.
[0011] Furthermore, in the multi-objective optimization module, the initial combination of process parameters is generated by taking values uniformly at fixed intervals within the rated range of each process parameter and then performing a full permutation; the processing energy consumption value is obtained by calling the comprehensive energy consumption model, and the processing surface roughness value is obtained by the mapping relationship between historical process parameter data and corresponding surface roughness; the processing accuracy requirements include dimensional tolerance requirements and surface roughness upper limit requirements; the termination condition of iterative optimization is that the number of iterations reaches the preset upper limit or the performance of the initial combination of process parameters no longer improves.
[0012] Furthermore, the process generation module pre-loads a general process template for alloy bushing processing. When a processing task for an alloy bushing to be processed is received, it retrieves the material property data and processing procedure requirements data of the corresponding alloy bushing. Based on the material type and processing procedure type, it filters out a preliminary matching combination of process parameters from multiple sets of process parameter combinations. Then, it calculates a comprehensive score for the preliminary matching combination of process parameters using a comprehensive scoring formula, selecting the combination with the highest comprehensive score as the final process parameter combination. The comprehensive scoring formula is as follows: Where S is the overall score. This is the energy consumption weighting coefficient, with a value ranging from 0.5 to 0.7. This is the surface roughness weighting coefficient, with a value ranging from 0.3 to 0.5. The weighting coefficients are preset according to processing requirements, and E is the processing energy consumption value corresponding to this combination of process parameters. This represents the maximum energy consumption value among multiple combinations of process parameters. Ra represents the minimum energy consumption value among multiple combinations of process parameters, and Ra represents the surface roughness value corresponding to that set of process parameters. This represents the maximum surface roughness value among multiple combinations of process parameters. It represents the minimum surface roughness value among multiple combinations of process parameters.
[0013] Furthermore, in the process generation module, based on the final process parameter combination obtained through screening, CNC machining instructions are generated and transmitted to the machine tool control unit. During the machining process, real-time energy consumption data is acquired and compared with the machining energy consumption value of the comprehensive energy consumption model. When the real-time energy consumption exceeds 10% of the machining energy consumption value, secondary optimization is performed, and the adjusted final process parameter combination is regenerated. After the machining is completed, the process parameter combination of this machining is transmitted to the process parameter table of the process database module for storage.
[0014] Compared with existing technologies, this CNC turning, milling and drilling composite machine tool processing system for alloy bushings has the following advantages: I. This invention collects and centrally stores energy consumption data and process parameter data of machine tool operation, processes them synchronously, and retains various historical processing data, machine tool parameter ranges, and material and process-related data. It uses the stored historical data to build a correspondence between processing energy consumption and process parameters. Based on the energy consumption correspondence, it solves multiple combinations of process parameters with energy consumption and surface roughness as the two objectives. It abandons the mode of selecting processing parameters based on manual experience. The process parameters formed after processing are automatically stored in the storage content after production is completed. The accumulated data continuously provides a data source for the process calculation of subsequent similar products, which solves the defects of the original technology data being scattered, historical processing data not being reusable, and process selection relying on manual experience.
[0015] Second, this invention selects the final combination of process parameters from multiple combinations of process parameters, generates machining instructions usable by the machine tool based on the final combination of process parameters, collects energy consumption data in real time during production and compares it with the energy consumption calculation results, and re-optimizes the process parameters after the values exceed the predetermined range. After the machining is completed, the process parameters used in this operation are archived and stored, and the existing data is continuously replenished. The subsequent parameter selection criteria are optimized based on the newly added data. This solves the problems of the original technology, such as the lack of parameter quantitative selection methods, the inability to automatically adjust for abnormal machining conditions, and the inability to continuously iterate and improve the stored data.
[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0018] Figure 1 A flowchart of a CNC milling and drilling composite machine tool processing system for alloy bushings; Figure 2 A framework diagram for constructing a comprehensive energy consumption model in a CNC turning, milling, and drilling composite machine tool system for alloy bushings; Figure 3 This is a flowchart illustrating the combination of multiple process parameters in a CNC milling and turning composite machine tool machining system for alloy bushings. Detailed Implementation
[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0020] In this embodiment, a CNC turning, milling, and drilling composite machine tool is used to batch process Cr12MoV alloy bushings for hydraulic valve bodies in a small-to-medium-sized hydraulic component manufacturing plant. The workshop is equipped with three integrated composite machining machines of the same model, which have been carrying out mass production of bushings of the same specifications for a long time. In this embodiment, the CNC turning, milling, and drilling composite machine tool processing system for alloy bushings is used in the bushing mass production workshop. It consists of a data acquisition module, a process database module, an energy consumption modeling module, a multi-objective optimization module, and a process generation module. The components are interconnected and transmitted in real time through the workshop's industrial Ethernet. The system uses a unified system clock to ensure the consistency of data timing throughout the process. The processed workpieces are all Cr12MoV alloy bushings. The forming process consists of three continuous operations: external turning, internal drilling, and external keyway milling. Batch processing is carried out around the clock according to the factory's established production plan.
[0021] Firstly, the data acquisition module completes the data acquisition and timing synchronization of equipment operation. This data acquisition module has a built-in power sensor unit and a process parameter acquisition unit. The power sensor unit installs Hall-type power acquisition elements on the power supply lines of the spindle drive motor, X-axis feed motor, Y-axis feed motor, Z-axis feed motor, and cutting fluid pump, respectively. The sampling frequency is fixed at 100Hz, and a set of instantaneous active power values is collected every 0.01s. Through discrete integration calculation, the energy consumption for the corresponding time period is calculated using the collected instantaneous power. Specifically, the energy consumption within a single sampling interval is equal to the product of the instantaneous power and the sampling duration. The energy consumption values corresponding to all intervals within the calculation cycle are accumulated item by item. The accumulated result is the actual energy consumption of the corresponding equipment during that period. In this way, instantaneous power data is converted into storable and modelable quantitative energy consumption data. The process parameter acquisition unit connects to the machine tool's built-in CNC unit via an RS485 communication cable to continuously read the four process parameters of the machine tool's real-time operation: spindle speed, feed rate, depth of cut, and width of cut. After the acquisition by both types of units is completed, all energy consumption data and process parameter data are bound to a timestamp generated at the same time. A one-to-one correspondence is completed according to the timestamp, and data synchronization processing is completed. After processing, the synchronized acquired data is transmitted, such as... Figure 1 As shown.
[0022] The process database module receives and stores the synchronized collected data. This module relies on a MySQL relational database for its storage architecture and internally comprises four types of tables: material information table, process information table, process parameter table, and processing effect table. The material information table records the inherent material properties of the Cr12MoV alloy used in this batch of processing, such as its grade, room temperature hardness, tensile strength, and thermal conductivity. The process information table records the work content corresponding to the three processing steps of the bushing, dimensional tolerances indicated on drawings, and the upper limit of the allowable surface roughness of the finished product, as well as process requirements and precision constraints. The process parameter table stores historical process parameters retained from past production at the factory. The system stores data on the number of data points and the synchronously processed process parameters, with a reserved independent write channel for storing new process parameters transmitted back after subsequent processing. The processing effect data table uniformly stores historical production energy consumption and synchronously processed energy consumption data. Each energy consumption record is bound to the corresponding workpiece measured roughness, single-piece processing time, and other supporting data under the corresponding process. In addition to the four types of data tables, a separate storage partition is set up to record the rated parameter range of the three processing equipment as specified by the factory. The content includes the upper and lower limits of the spindle speed, the feed rate range of each axis, and the allowable processing range of the cutting depth and cutting width. The system also has a built-in search engine that allows for fuzzy searching by entering the alloy grade and the name of the processing procedure to quickly retrieve the required stored data.
[0023] The energy consumption modeling module retrieves synchronized collected data, historical energy consumption data, and historical process parameter data from the process database module to build a comprehensive energy consumption model. First, a preprocessing operation is performed. The first step uses the 3σ criterion to eliminate outlier data, statistically analyzing all energy consumption data corresponding to the same parameters under the same machining process, calculating the average and standard deviation, and defining the effective data interval by adding or subtracting three times the standard deviation from the average. Data outside this effective interval is directly discarded. The second step uses extreme value normalization to process the remaining effective data, unifying physical data with different dimensions such as spindle speed, feed rate, and energy consumption to the 0-1 interval, eliminating calculation bias caused by different units. After preprocessing, the dataset is split according to the three processes of turning, milling, and drilling. A multivariate nonlinear regression algorithm is used to establish the correspondence between four process parameters and machining energy consumption within each process, obtaining the basic energy consumption sub-models for each of the three processes. The interaction between process parameters is additionally considered during modeling to quantify energy consumption fluctuations caused by parameter linkage changes. After all sub-models are built, they are integrated according to the sequence of processes to form a comprehensive energy consumption model adapted to Cr12MoV bushing machining. Figure 2 As shown; the expression for the comprehensive energy consumption model is: Where E is the processing energy consumption value, and n is the number of processing steps. Main spindle speed For feed rate, For cutting depth, For cutting width, Let be a constant term for the i-th process, corresponding to the no-load energy consumption of the machine tool for that process. Let be the coefficient of the linear term of the j-th parameter in the i-th process. Let be the coefficient of the interaction term between the j-th and k-th parameters in the i-th process. Let be the random error term of the i-th process, which follows a normal distribution with a mean of 0. , , All of these were obtained through multivariate nonlinear regression fitting of historical energy consumption data and historical process parameter data; the constant term, linear term coefficient, and parameter interaction term coefficient in the model formula were all automatically generated by regression calculation based on the historical data of the database inventory, and the random error term followed a normal distribution with a mean of 0.
[0024] After the comprehensive energy consumption model is constructed, the multi-objective optimization module retrieves the comprehensive energy consumption model, the rated parameter range of the equipment, and the preset processing accuracy indicators. It uses minimizing processing energy consumption and minimizing the surface roughness of the finished product as two optimization objectives to solve for the parameters. A fixed value step size is set according to the rated value range of each process parameter. Discrete parameter values are uniformly selected within the limited range. After all values are fully permuted and combined, a large batch of initial process parameter combinations are generated. These are substituted into the comprehensive energy consumption model to calculate the processing energy consumption value corresponding to each set of parameters. The estimated roughness of each set of parameters is obtained based on the historical process and roughness correspondence records in the process database module. Invalid parameter combinations that cannot meet the accuracy standards in terms of dimensional tolerance and surface roughness are screened out. The remaining parameters enter the iterative optimization process. Dual iteration termination conditions are set on-site: the total number of iterations reaches the upper limit of 500, or the energy consumption and roughness indicators corresponding to the parameters no longer show optimization changes after 15 consecutive iterations. The iteration calculation terminates when either condition is met, ultimately outputting multiple sets of process parameter combinations that balance energy consumption control and processing quality. Figure 3 As shown.
[0025] The process generation module pre-loads a general processing procedure template for chromium-based alloy bushings. After the workshop issues a single-piece or batch processing task, it retrieves Cr12MoV material property data and bushing-specific process requirements from the process database module. Combining material properties and processing procedure characteristics, it selects a preliminary matching combination of process parameters from multiple sets of process parameter combinations. Based on the workshop's production focus, the energy consumption weight coefficient is set to 0.6, and the surface roughness weight coefficient is set to 0.4. The sum of these two coefficients is fixed at 1. Normalization is performed using the extreme energy consumption and surface roughness values corresponding to each set of parameters. A comprehensive scoring formula is used to calculate the comprehensive score of each preliminary matching combination of process parameters. The process parameter combination with the highest comprehensive score is selected as the final process parameter combination, i.e., the final production process parameters. The comprehensive scoring formula is: Where S is the overall score. This is the energy consumption weighting coefficient, with a value ranging from 0.5 to 0.7. This is the surface roughness weighting coefficient, with a value ranging from 0.3 to 0.5. The weighting coefficients are preset according to processing requirements, and E is the processing energy consumption value corresponding to this combination of process parameters. This represents the maximum energy consumption value among multiple combinations of process parameters. Ra represents the minimum energy consumption value among multiple combinations of process parameters, and Ra represents the surface roughness value corresponding to that set of process parameters. This represents the maximum surface roughness value among multiple combinations of process parameters. The minimum surface roughness value among multiple combinations of process parameters is determined; based on the final combination of process parameters, G-code CNC machining instructions that can be recognized by the machine tool are generated and sent to the machine tool control unit to drive the equipment to start machining; throughout the entire workpiece machining process, energy consumption data collected on-site is continuously received, and the measured energy consumption is compared with the machining energy consumption value obtained from the comprehensive energy consumption model. Once the measured energy consumption exceeds the machining energy consumption value by 10%, secondary optimization is automatically initiated, and parameter optimization and machining instruction generation are completed again; after the entire batch of bushings is machined, the combination of process parameters actually used in this production is sent back to the process database module, stored in the process parameter data table for archiving and storage, and the data reserve is continuously expanded.
[0026] In summary, this embodiment demonstrates a complete implementation plan for the batch processing of Cr12MoV hydraulic bushings. It covers everything from energy consumption integral conversion of on-site data collection, multi-type data classification and warehousing, layered preprocessing of processing data, construction of energy consumption models for each process, generation and iterative screening of multi-parameter combinations, weighted parameter optimization, dynamic parameter adjustment for production anomalies, and automatic archiving of completed parameters. The entire operational process breaks away from the traditional processing model that relies on operator experience to select process parameters. Instead, it leverages historical data to autonomously optimize and select processes. Completed data from each batch is automatically added to the database, achieving closed-loop accumulation of production data. The entire operational logic is stable and reliable, and can be directly implemented in batches on CNC composite machining production lines for similar alloy bushings.
[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A CNC turning, milling, and drilling composite machine tool processing system for alloy bushings, characterized in that, The system includes: a data acquisition module, a process database module, an energy consumption modeling module, a multi-objective optimization module, and a process generation module; The data acquisition module is used to collect energy consumption data of various electrical equipment during machine tool operation and process parameter data of the current processing process in real time, and transmits all collected data after synchronous processing. The process database module is used to receive and store the synchronized collected data, including historical energy consumption data, historical process parameter data, machine tool rated parameter range, machining accuracy requirement data, alloy bushing material characteristic data, and machining process requirement data. The energy consumption modeling module is used to retrieve synchronized collected data, historical energy consumption data, and historical process parameter data to construct a comprehensive energy consumption model for the composite machining of alloy bushings by turning, milling, and drilling. The multi-objective optimization module is used to retrieve the comprehensive energy consumption model, machine tool rated parameter range and machining accuracy requirements data. With the objectives of minimizing machining energy consumption and minimizing machining surface roughness, it performs multi-objective solutions to obtain multiple sets of process parameter combinations that take into account both energy consumption and machining quality. The process generation module retrieves the material property data and processing procedure requirements data of the alloy bushing to be processed, selects the final process parameter combination from multiple sets of process parameter combinations, generates CNC machining instructions and transmits them to the machine tool control unit, and then transmits the process parameter combination for this processing to the process database module.
2. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 1, characterized in that, The data acquisition module includes a power sensor unit and a process parameter acquisition unit. The power sensor unit is installed in the power supply circuits of the machine tool's spindle motor, X-axis feed motor, Y-axis feed motor, Z-axis feed motor and cutting fluid pump, respectively, to collect the instantaneous power data of each electrical device in real time, and to obtain the energy consumption data for the corresponding time period through integration calculation. The process parameter acquisition unit reads the process parameter data of spindle speed, feed rate, depth of cut, and width of cut in real time through the communication interface of the machine tool CNC unit. After synchronizing the energy consumption data with the process parameter data using timestamps, the synchronized collected data is obtained.
3. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 1, characterized in that, The process database module adopts a MySQL relational database structure, establishing four data tables: material information, process information, process parameters, and processing results. The material information table stores alloy bushing material property data, including alloy material grade, hardness, tensile strength, and thermal conductivity. The process information table stores processing process requirements and processing accuracy requirements, including the processing content, dimensional tolerances, and surface roughness requirements for turning, milling, and drilling processes. The process parameter table stores historical process parameter data and synchronized process parameter data, including corresponding spindle speed, feed rate, depth of cut, and width of cut. The processing results table stores historical energy consumption data and synchronized energy consumption data, including actual processing energy consumption, surface roughness, and processing time under the corresponding process parameter data. It also stores the rated parameter ranges of the machine tool, including the rated range of spindle speed, feed rate, depth of cut, and width of cut.
4. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 1, characterized in that, When constructing the comprehensive energy consumption model, the energy consumption modeling module preprocesses historical energy consumption data, historical process parameter data, and synchronized collected data. It uses a multivariate nonlinear regression method to fit the mapping relationship between single process parameters and energy consumption in turning, milling, and drilling processes, establishes basic energy consumption sub-models for each process, and then introduces parameter interaction terms to analyze the impact of the coupling effect between different process parameters on energy consumption. Finally, it integrates the basic energy consumption sub-models of each process to obtain the comprehensive energy consumption model for alloy bushing turning, milling, and drilling composite machining.
5. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 4, characterized in that, In the energy consumption modeling module, the expression for the comprehensive energy consumption model is: Where E is the processing energy consumption value, and n is the number of processing steps. Main spindle speed For feed rate, For cutting depth, For cutting width, Let i be a constant term for the i-th process. Let be the coefficient of the linear term of the j-th parameter in the i-th process. Let be the coefficient of the interaction term between the j-th and k-th parameters in the i-th process. Let be the random error term for the i-th process.
6. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 1, characterized in that, The specific steps of the multi-objective optimization module in solving the multi-objective problem are as follows: determine the two solution objectives of minimizing machining energy consumption and minimizing machining surface roughness, then retrieve the rated parameter range of the machine tool, generate multiple sets of initial process parameter combinations within the rated parameter range of the machine tool, calculate the machining energy consumption value and machining surface roughness value corresponding to each set of initial process parameter combinations, then screen out the process parameter combinations that simultaneously meet the machining accuracy requirements, and iteratively optimize the screened process parameter combinations to obtain multiple sets of process parameter combinations that take into account both energy consumption and machining quality.
7. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 6, characterized in that, In the multi-objective optimization module, the initial combination of process parameters is generated by taking values uniformly at fixed intervals within the rated range of each process parameter and then performing a full permutation; the processing energy consumption value is obtained by calling the comprehensive energy consumption model, and the processing surface roughness value is obtained by mapping the historical process parameter data with the corresponding surface roughness; the processing accuracy requirements include dimensional tolerance requirements and surface roughness upper limit requirements; the termination condition of iterative optimization is that the number of iterations reaches the preset upper limit or the performance of the initial combination of process parameters no longer improves.
8. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 1, characterized in that, In the process generation module, a general process template for alloy bushing processing is pre-loaded. When a processing task for an alloy bushing to be processed is received, the module retrieves the material property data and processing procedure requirements data of the corresponding alloy bushing. Based on the material type and processing procedure type, a preliminary matching combination of process parameters is selected from multiple sets of process parameter combinations. Then, a comprehensive scoring formula is used to calculate the comprehensive score of the preliminary matching combination of process parameters, and the combination with the highest comprehensive score is selected as the final process parameter combination. The comprehensive scoring formula is as follows: Where S is the overall score, This is the energy consumption weighting coefficient, with a value ranging from 0.5 to 0.
7. This is the surface roughness weighting coefficient, with a value ranging from 0.3 to 0.
5. E represents the processing energy consumption value corresponding to this combination of process parameters. This represents the maximum energy consumption value among multiple combinations of process parameters. Ra represents the minimum energy consumption value among multiple combinations of process parameters, and Ra represents the surface roughness value corresponding to that set of process parameters. This represents the maximum surface roughness value among multiple combinations of process parameters. It represents the minimum surface roughness value among multiple combinations of process parameters.
9. The CNC turning, milling, and drilling composite machine tool processing system for alloy bushings according to claim 8, characterized in that, In the process generation module, CNC machining instructions are generated based on the final process parameter combination obtained through screening, and the CNC machining instructions are transmitted to the machine tool control unit. During the machining process, real-time energy consumption data is acquired and compared with the machining energy consumption value of the comprehensive energy consumption model. When the real-time energy consumption exceeds 10% of the machining energy consumption value, secondary optimization is performed, and the adjusted final process parameter combination is regenerated. After the machining is completed, the process parameter combination of this machining is transmitted to the process parameter table of the process database module for storage.