CN109814503B - Management and production system for high-precision machining - Google Patents
Management and production system for high-precision machining Download PDFInfo
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- CN109814503B CN109814503B CN201910033890.9A CN201910033890A CN109814503B CN 109814503 B CN109814503 B CN 109814503B CN 201910033890 A CN201910033890 A CN 201910033890A CN 109814503 B CN109814503 B CN 109814503B
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Abstract
The invention relates to a management and production system for high-precision machining, which solves the technical problem that various devices cannot be effectively managed and optimal devices cannot be provided for machining.
Description
Technical Field
The invention relates to the field of machining, in particular to a management and production system for high-precision machining.
Background
Machining refers to a process of changing the physical dimensions or properties of a workpiece by a mechanical device. The difference in the machining method can be divided into cutting machining and pressing machining. The production process of a machine refers to the whole process of making a product from raw materials (or semi-finished products). For machine production, the raw material transportation and preservation, production preparation, blank manufacturing, part processing and heat treatment, product assembly and debugging, painting and packaging and the like are included. The content of the production process is very wide, modern enterprises organize production and guide production by using the principle and method of system engineering, and the production process is regarded as a production system with input and output. In the production process, all the processes of changing the shape, size, position, property, etc. of the production object into a finished product or a semi-finished product are called as a process. It is an integral part of the production process. The processes can be divided into casting, forging, stamping, welding, machining, assembling and other processes, the mechanical manufacturing process generally refers to the sum of the machining process of the parts and the assembling process of the machine, and other processes are called auxiliary processes, such as transportation, storage, power supply, equipment maintenance and the like. The technological process consists of one or several steps in sequence.
The current automobile management and production system cannot effectively manage various devices and give optimal devices for processing. In order to solve the technical problems, the invention provides a high-precision machining management and production system.
Disclosure of Invention
The technical problem to be solved by the invention is that various devices cannot be effectively managed and the optimal devices cannot be provided for processing in the prior art. Provided is a new high-precision machining management and production system having a feature of high cost efficiency.
In order to solve the technical problems, the technical scheme is as follows:
a management and production system of high-precision machining is characterized by comprising a central server, a machining management unit, a detection management unit and a maintenance management unit, wherein the machining management unit, the detection management unit and the maintenance management unit are connected with the central server;
the processing management unit is used for receiving processing parameters and controlling processing equipment to perform mechanical processing;
the detection management unit is used for receiving the processing parameters to generate a detection flow and controlling the detection equipment to finish processing detection according to the detection flow;
the maintenance management unit is used for generating an efficiency function of corresponding equipment according to the received equipment use data by using the established maintenance model, and determining the priority selected by the equipment in the same kind of equipment according to the efficiency function;
and the central server calls the corresponding equipment in sequence according to the priority level selected by the equipment calculated by the maintenance management unit.
In the foregoing solution, for optimization, further generating an efficiency fault function of a corresponding device using the established maintenance model includes:
step 1, extracting equipment use data of the same equipment, which is acquired by a central server and comprises equipment running time parameters, equipment fault records and equipment efficiency functions, from the central server;
step 2, randomly extracting training data and test data from the data in the step 1, and establishing a training data set and a test data set;
step 3, carrying out data preprocessing on the training data set and the test data set to obtain clustering 5 indexes:
the life of the equipment is the total time of the equipment running in the past, is defined as L,
the last failure of the device, i.e., the end time of the last failure of the device-the start time of the last failure of the device, is defined as R,
the total number of faults in the device life is equal to the number of faults of the device in the operation of the device in the past, which is defined as F,
the total weighted cost, which is the total cost of the past equipment fault repair, is defined as W,
the total loss is the equipment efficiency reduction value after the equipment failure of the previous time and is defined as M;
step 4, according to the preprocessed data in the step 3, carrying out data standardization, carrying out cluster analysis on the equipment, dividing the equipment into five classes and the like, establishing a corresponding equipment group, and classifying all the equipment into the corresponding equipment group; analyzing the characteristics of each equipment group, carrying out priority sequencing on the 5 equipment groups according to the fault value, and adding a weight value;
and 5, adding an efficiency function to the 5 equipment groups, and calculating an output value of the efficiency fault function, namely the priority corresponding to the equipment groups, adding a weight value and an efficiency function value.
Further, the detection apparatus includes: three-coordinate measuring machine, gantry measuring machine cantilever measuring machine and non-contact optical scanning measurement.
Further, the processing equipment comprises laser welding, a self-piercing riveting device and rotary tapping riveting.
Further, the detection process includes warehousing inspection, and the warehousing inspection includes:
step A, confirming the appearance, the quantity and the state, including on-site unpacking inspection and surface appearance inspection before warehousing;
step B, unpacking inspection and file submission confirmation, including purchase list consistency inspection, sample file complete inspection, DRE and SQE signature inspection;
step C, identification confirmation, including complete label check, label content integrity check and label content correctness check;
and D, warehousing the storeroom, including monitoring the storage and transportation processes.
Further, the processing management unit is configured to receive the processing parameter and control the processing device to perform the machining, including performing acceleration and deceleration control on the processing device, and specifically includes:
setting a time slice t1 acceleration process and a time slice t2 acceleration process which run in parallel by using an SpTa algorithm in an FPGA (field programmable gate array), and calculating the number of steps which need to run in the deceleration process in advance through the time slice t2 in the acceleration process when the acceleration process of the processing equipment is not finished;
wherein t1 > t2, t1 and t2 are positive integer time values;
the triggering of the deceleration process is that a stop command is received or the number of steps of uniform motion is completed to enter deceleration stop. The efficiency of price acceleration and deceleration control is improved by a mode of calculation in advance, and the algorithm is realized by using the parallel calculation characteristic of the FPGA.
The invention has the beneficial effects that: the invention analyzes the calling priority of all equipment by constructing the efficiency fault model of the same equipment used for processing, thereby realizing the maximum calling of the efficiency of the equipment. And high-efficiency and high-precision machining management and production are realized.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a flow chart of model building in example 1.
FIG. 2 is a schematic view of a maintenance model in example 1.
Fig. 3, radar mapping results in example 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides a management and production system for high-precision machining, which is characterized by comprising a central server, a machining management unit, a detection management unit and a maintenance management unit, wherein the machining management unit, the detection management unit and the maintenance management unit are connected with the central server;
the processing management unit is used for receiving processing parameters and controlling processing equipment to perform mechanical processing;
the detection management unit is used for receiving the processing parameters to generate a detection flow and controlling the detection equipment to finish processing detection according to the detection flow;
the maintenance management unit is used for generating an efficiency function of corresponding equipment according to the received equipment use data by using the established maintenance model, and determining the priority selected by the equipment in the same kind of equipment according to the efficiency function;
and the central server calls the corresponding equipment in sequence according to the priority level selected by the equipment calculated by the maintenance management unit.
As shown in FIG. 1, the model is firstly simulated, the effectiveness and the authenticity of the model are determined, and the deficiency of the model is found and optimized through the simulation of the model.
As shown in fig. 2, generating the efficiency failure function of the corresponding device using the established maintenance model includes:
step 1, extracting equipment use data of the same equipment, which is acquired by a central server and comprises equipment running time parameters, equipment fault records and equipment efficiency functions, from the central server;
step 2, randomly extracting training data and test data from the data in the step 1, and establishing a training data set and a test data set;
step 3, carrying out data preprocessing on the training data set and the test data set to obtain clustering 5 indexes:
the life of the equipment is the total time of the equipment running in the past, is defined as L,
the last failure of the device, i.e., the end time of the last failure of the device-the start time of the last failure of the device, is defined as R,
the total number of faults in the device life is equal to the number of faults of the device in the operation of the device in the past, which is defined as F,
the total weighted cost, which is the total cost of the past equipment fault repair, is defined as W,
the total loss is the equipment efficiency reduction value after the equipment failure of the previous time and is defined as M;
step 4, according to the preprocessed data in the step 3, performing data standardization, performing cluster analysis on the equipment, dividing the equipment into five classes and establishing corresponding equipment groups by using a radar drawing method as shown in fig. 3, and classifying all the equipment into the corresponding equipment groups; analyzing the characteristics of each equipment group, carrying out priority sequencing on the 5 equipment groups according to the fault value, and adding a weight value;
and 5, adding an efficiency function to the 5 equipment groups, and calculating an output value of the efficiency fault function, namely the priority corresponding to the equipment groups, adding a weight value and an efficiency function value.
Specifically, the detection device includes: three-coordinate measuring machine, gantry measuring machine cantilever measuring machine and non-contact optical scanning measurement.
Specifically, the processing equipment comprises laser welding, a self-piercing riveting device and rotary tapping riveting.
Specifically, the detection process includes warehousing inspection, and the warehousing inspection includes:
step A, confirming the appearance, the quantity and the state, including on-site unpacking inspection and surface appearance inspection before warehousing;
step B, unpacking inspection and file submission confirmation, including purchase list consistency inspection, sample file complete inspection, DRE and SQE signature inspection;
step C, identification confirmation, including complete label check, label content integrity check and label content correctness check;
and D, warehousing the storeroom, including monitoring the storage and transportation processes.
The processing management unit is used for receiving the processing parameters and controlling the processing equipment to perform mechanical processing, and comprises acceleration and deceleration control on the processing equipment, and specifically comprises the following steps:
setting a time slice t1 acceleration process and a time slice t2 acceleration process which run in parallel by using an SpTa algorithm in an FPGA (field programmable gate array), and calculating the number of steps which need to run in the deceleration process in advance through the time slice t2 in the acceleration process when the acceleration process of the processing equipment is not finished;
wherein t1 > t2, t1 and t2 are positive integer time values;
the triggering of the deceleration process is that a stop command is received or the number of steps of uniform motion is completed to enter deceleration stop.
In this embodiment, the existing control method can be used for controlling the processing apparatus and the detection apparatus. The main improvement is the optional improvement of the equipment. The rest of the undisclosed part can adopt the prior art.
Although the illustrative embodiments of the present invention have been described above to enable those skilled in the art to understand the present invention, the present invention is not limited to the scope of the embodiments, and it is apparent to those skilled in the art that all the inventive concepts using the present invention are protected as long as they can be changed within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (5)
1. A management and production system of high-precision machining is characterized in that: the high-precision machining management and production system is characterized by comprising a central server, a machining management unit, a detection management unit and a maintenance management unit, wherein the machining management unit, the detection management unit and the maintenance management unit are connected with the central server;
the processing management unit is used for receiving processing parameters and controlling processing equipment to perform mechanical processing;
the detection management unit is used for receiving the processing parameters to generate a detection flow and controlling the detection equipment to finish processing detection according to the detection flow;
the maintenance management unit is used for generating an efficiency function of corresponding equipment according to the received equipment use data by using the established maintenance model, and determining the priority selected by the equipment in the same kind of equipment according to the efficiency function;
the central server calls the corresponding equipment in sequence according to the priority level selected by the equipment calculated by the maintenance management unit;
generating an efficiency fault function for the corresponding device using the established maintenance model comprises:
step 1, extracting equipment use data of the same equipment, which is acquired by a central server and comprises equipment running time parameters, equipment fault records and equipment efficiency functions, from the central server;
step 2, randomly extracting training data and test data from the data in the step 1, and establishing a training data set and a test data set;
step 3, carrying out data preprocessing on the training data set and the test data set to obtain clustering 5 indexes:
device life = total time of operation of the device, defined as L,
device last failure = end time of device last failure-start time of device last failure, defined as R,
the total number of faults in the device life = the number of faults of the device in the device operation of the previous time, defined as F,
total weighted cost = total cost of maintenance for past equipment failures, defined as W,
the total profit = the equipment efficiency drop value after the equipment failure of the previous time, which is defined as M;
step 4, according to the preprocessed data in the step 3, carrying out data standardization, carrying out cluster analysis on the equipment, dividing the equipment into five classes and the like, establishing a corresponding equipment group, and classifying all the equipment into the corresponding equipment group; analyzing the characteristics of each equipment group, carrying out priority sequencing on the 5 equipment groups according to the fault value, and adding a weight value;
and 5, adding an efficiency function to the 5 equipment groups, and calculating an output value of the efficiency fault function = priority corresponding to the equipment groups and additional weight values and efficiency function values.
2. The management and production system of high precision machining according to claim 1, characterized in that: the detection apparatus includes: three-coordinate measuring machine, gantry measuring machine cantilever measuring machine and non-contact optical scanning measurement.
3. The management and production system of high precision machining according to claim 1, characterized in that: the processing equipment comprises laser welding, a self-piercing riveting device and rotary tapping riveting.
4. The management and production system of high precision machining according to claim 1, characterized in that: the detection process comprises warehousing inspection, and the warehousing inspection comprises the following steps:
step A, confirming the appearance, the quantity and the state, including on-site unpacking inspection and surface appearance inspection before warehousing;
step B, unpacking inspection and file submission confirmation, including purchase list consistency inspection, sample file complete inspection, DRE and SQE signature inspection;
step C, identification confirmation, including complete label check, label content integrity check and label content correctness check;
and D, warehousing the storeroom, including monitoring the storage and transportation processes.
5. The management and production system of high precision machining according to claim 1, characterized in that: the processing management unit is used for receiving the processing parameters and controlling the processing equipment to perform mechanical processing, and comprises acceleration and deceleration control on the processing equipment, and specifically comprises the following steps:
setting a time slice t1 acceleration process and a time slice t2 acceleration process which run in parallel by using an SpTa algorithm in an FPGA (field programmable gate array), and calculating the number of steps which need to run in the deceleration process in advance through the time slice t2 in the acceleration process when the acceleration process of the processing equipment is not finished;
wherein t1 > t2, t1 and t2 are positive integer time values;
the triggering of the deceleration process is that a stop command is received or the number of steps of uniform motion is completed to enter deceleration stop.
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Citations (3)
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| CN104111642A (en) * | 2014-06-11 | 2014-10-22 | 华中科技大学 | Equipment preventive maintenance and flexible job shop scheduling integrated optimization method |
| CN106651139A (en) * | 2016-11-21 | 2017-05-10 | 哈尔滨理工大学 | Asymmetric multi-workshop integrated dispatching method with consideration of same-kind-of-equipment process |
| CN108181885A (en) * | 2017-12-29 | 2018-06-19 | 安徽中凯信息产业股份有限公司 | A kind of automation control method |
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| US20190018729A1 (en) * | 2017-04-14 | 2019-01-17 | Microsoft Technology Licensing, Llc | Anomaly remediation using device analytics |
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|---|---|---|---|---|
| CN104111642A (en) * | 2014-06-11 | 2014-10-22 | 华中科技大学 | Equipment preventive maintenance and flexible job shop scheduling integrated optimization method |
| CN106651139A (en) * | 2016-11-21 | 2017-05-10 | 哈尔滨理工大学 | Asymmetric multi-workshop integrated dispatching method with consideration of same-kind-of-equipment process |
| CN108181885A (en) * | 2017-12-29 | 2018-06-19 | 安徽中凯信息产业股份有限公司 | A kind of automation control method |
Non-Patent Citations (2)
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| 基于FPGA的SpTa自适应电机控制算法;王岩;《科技资讯》;20180910(第25期);第17-18页 * |
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Inventor after: Shao Yongqing Inventor after: Ye Xinrong Inventor after: Zhang Aiwen Inventor after: Xie Xiaojuan Inventor after: Zhu Caihua Inventor before: Ye Xinrong Inventor before: Zhang Aiwen Inventor before: Xie Xiaojuan Inventor before: Zhu Caihua |
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Effective date of registration: 20220221 Address after: 315000 Room 201, building 14, Fanyu village, Zhonggongmiao street, Yinzhou District, Ningbo City, Zhejiang Province Applicant after: Shao Yongqing Address before: 241002 No.189, Jiuhua South Road, Wuhu City, Anhui Province Applicant before: Ye Xinrong |
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Effective date of registration: 20221226 Address after: Room L302, Building 2, No. 188 Jinghua Road (Yonggang Modern Minglou), High tech Zone, Ningbo City, Zhejiang Province 315000 Patentee after: Ningbo Aikai Machinery Co.,Ltd. Address before: 315000 Room 201, building 14, Fanyu village, Zhonggongmiao street, Yinzhou District, Ningbo City, Zhejiang Province Patentee before: Shao Yongqing |