CN116551475A - Grinding processing method and system for hardware tool - Google Patents
Grinding processing method and system for hardware tool Download PDFInfo
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- CN116551475A CN116551475A CN202310808948.9A CN202310808948A CN116551475A CN 116551475 A CN116551475 A CN 116551475A CN 202310808948 A CN202310808948 A CN 202310808948A CN 116551475 A CN116551475 A CN 116551475A
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- 238000000227 grinding Methods 0.000 title claims abstract description 470
- 238000003672 processing method Methods 0.000 title description 3
- 230000003746 surface roughness Effects 0.000 claims abstract description 97
- 238000000034 method Methods 0.000 claims abstract description 30
- 238000004088 simulation Methods 0.000 claims abstract description 24
- 238000003754 machining Methods 0.000 claims description 45
- 238000001514 detection method Methods 0.000 claims description 21
- 230000008859 change Effects 0.000 claims description 19
- 238000012216 screening Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 8
- 238000013210 evaluation model Methods 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 22
- 230000000694 effects Effects 0.000 abstract description 6
- 238000012549 training Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
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- 229910052782 aluminium Inorganic materials 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B1/00—Processes of grinding or polishing; Use of auxiliary equipment in connection with such processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
- B24B49/006—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation taking regard of the speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
- B24B49/14—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation taking regard of the temperature during grinding
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B51/00—Arrangements for automatic control of a series of individual steps in grinding a workpiece
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P70/00—Climate change mitigation technologies in the production process for final industrial or consumer products
- Y02P70/10—Greenhouse gas [GHG] capture, material saving, heat recovery or other energy efficient measures, e.g. motor control, characterised by manufacturing processes, e.g. for rolling metal or metal working
Abstract
The invention discloses a grinding method and a grinding system for hardware tools, which relate to the technical field of data processing, and the method comprises the following steps: receiving a grinding conceptual model and a surface expected roughness to obtain a first simulation model of a hardware workpiece; comparing the grinding conceptual model with the first simulation model of the hardware workpiece, obtaining a to-be-ground identification area, optimizing grinding control parameters of a grinding machine tool, evaluating roughness according to the optimizing result of the grinding control parameters, and grinding the to-be-ground hardware workpiece when the surface roughness predicting result is smaller than or equal to the surface expected roughness. The invention solves the technical problem of high processing cost caused by insufficient intelligent control of the grinding roughness of the hardware tool in the prior art, and achieves the technical effects of improving the product yield and reducing the processing cost by intelligently controlling the grinding roughness of the hardware tool.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a grinding method and a grinding system for hardware tools.
Background
Hardware tools are the general names of various metal devices manufactured by forging, calendaring, cutting and other physical processing of metals such as iron, steel, aluminum and the like. The raw materials are processed into various tools according to drawings or samples by using machines such as lathes, milling machines, drilling machines and polishing machines, and the control of the grinding roughness of the traditional hardware tool is realized by quality detection quantitative analysis after the processing is finished, so that the processing cost is higher.
Disclosure of Invention
The application provides a grinding method and system for hardware tool for solve among the prior art because hardware tool grinding roughness's management and control is intelligent inadequately, lead to the high technical problem of processing cost.
In a first aspect of the present application, there is provided a grinding method for a hardware tool, the method comprising: receiving grinding task information of a client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness; when a to-be-ground hardware workpiece enters a preset area, starting an image acquisition device to acquire hardware workpiece basic information, wherein the hardware workpiece basic information comprises a first simulation model of the hardware workpiece; comparing the grinding conceptual model with the first simulation model of the hardware workpiece to obtain a to-be-ground identification area; optimizing grinding control parameters of the grinding machine tool based on the identification area to be ground, and obtaining a grinding control parameter optimizing result; performing roughness assessment according to the grinding control parameter optimizing result to obtain a surface roughness predicting result; and when the surface roughness prediction result is smaller than or equal to the surface expected roughness, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
In a second aspect of the present application, there is provided a grinding system for a hardware tool, the system comprising: the grinding task information receiving module is used for receiving the grinding task information of the client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness; the hardware workpiece basic information acquisition module is used for starting the image acquisition device to acquire hardware workpiece basic information when a hardware workpiece to be ground enters a preset area, wherein the hardware workpiece basic information comprises a hardware workpiece first simulation model; the to-be-ground identification area acquisition module is used for comparing the grinding conceptual model with the first simulation model of the hardware workpiece to acquire an to-be-ground identification area; the grinding control parameter optimizing result obtaining module is used for optimizing the grinding control parameters of the grinding machine tool based on the to-be-ground identification area to obtain a grinding control parameter optimizing result; the surface roughness prediction result acquisition module is used for carrying out roughness assessment according to the grinding control parameter optimizing result to acquire a surface roughness prediction result; and the grinding module is used for grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result when the surface roughness predicting result is smaller than or equal to the surface expected roughness.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides a grinding processing method for hardware tools, which relates to the technical field of data processing and obtains a first simulation model of a hardware workpiece by receiving a grinding conceptual model and a surface expected roughness; the method comprises the steps of comparing a grinding conceptual model with a first simulation model of a hardware workpiece, obtaining a to-be-ground identification area, optimizing grinding control parameters of a grinding machine tool, carrying out roughness assessment according to a grinding control parameter optimizing result, and carrying out grinding processing on the to-be-ground hardware workpiece when a surface roughness predicting result is smaller than or equal to a surface expected roughness.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a grinding method for hardware tools according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining a grinding control parameter optimizing result in a grinding method for a hardware tool according to an embodiment of the present application;
fig. 3 is a schematic flow chart of grinding a hardware workpiece to be ground according to a grinding control parameter optimizing result in the grinding method for a hardware tool according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of a grinding system for hardware tools according to an embodiment of the present application.
Reference numerals illustrate: the grinding device comprises a grinding task information receiving module 11, a hardware workpiece basic information acquiring module 12, a to-be-ground identification area acquiring module 13, a grinding control parameter optimizing result acquiring module 14, a surface roughness predicting result acquiring module 15 and a grinding processing module 16.
Detailed Description
The application provides a grinding method for hardware tool for solve among the prior art because hardware tool abrasive machining roughness's management and control is intelligent inadequately, lead to the high technical problem of processing cost.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a grinding method for a hardware tool, the method comprising:
s100: receiving grinding task information of a client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness;
specifically, the grinding task information of the hardware workpiece from the client of the target user is received through a network, the grinding task information comprises a grinding conceptual model of the target hardware workpiece required by the target user and a surface expected roughness, the grinding conceptual model refers to a virtual model of the target hardware workpiece created by utilizing three-dimensional modeling software, and is also an ideal state model of the target hardware workpiece, the model comprises information such as the geometric shape, the geometric size, the material and the like of the target hardware workpiece, the most basic processing data and the workpiece effect can be provided in an intuitive form, the surface roughness refers to the smaller spacing and the unevenness of tiny peaks and valleys on the surface of a processed object, the distance between two peaks or two valleys of the surface roughness is small, the distance between two peaks or valleys is small, the micro geometric shape error is caused, and the surface roughness is smaller, and the surface is smoother. The surface expected roughness refers to the surface roughness of the target hardware workpiece required by a target user, and can be used as a control parameter of the surface roughness of the target hardware tool.
S200: when a to-be-ground hardware workpiece enters a preset area, starting an image acquisition device to acquire hardware workpiece basic information, wherein the hardware workpiece basic information comprises a first simulation model of the hardware workpiece;
specifically, the transmission is carried out through the grinding machine tool conveyor belt, when the to-be-ground hardware workpiece is transmitted to a preset area of the grinding machine tool, the image acquisition device is started to acquire basic information of the hardware workpiece, the to-be-ground hardware workpiece is a hardware workpiece which is preliminarily machined by the machine according to the grinding conceptual model, the shape and the size of the to-be-ground hardware workpiece are rough, the difference between the to-be-ground hardware workpiece and the grinding conceptual model is large, the to-be-ground hardware workpiece is required to be further ground through the grinding machine tool, therefore, an image acquisition area is preset at the front end of the grinding machine tool, the geometric size and the appearance condition of the to-be-ground hardware workpiece are acquired through the image acquisition device such as a camera, a scanner and the like which are installed in advance, namely, the basic information of the to-be-ground hardware workpiece is generated by the basic information of the hardware workpiece, and the first simulation model of the hardware workpiece is a virtual model of the to-be-ground hardware workpiece, and the ideal state of the target hardware workpiece is greatly different, and the to be compared with the grinding conceptual model, and the area to be optimized for the to be-ground hardware workpiece is found.
S300: comparing the grinding conceptual model with the first simulation model of the hardware workpiece to obtain a to-be-ground identification area;
specifically, the grinding concept model and the first simulation model of the hardware workpiece are led into the same project file, namely, the hardware workpiece and the first simulation model are placed in the same coordinate system, the two models are placed in superposition through a center point or any one reliable point as a fixed point, the area where the first simulation model of the hardware workpiece and the grinding concept model are inconsistent is screened out for identification, the size of the first simulation model of the hardware workpiece to be cut or polished is calculated, and the size is used as an identification area to be ground and can be used as reference data for optimizing grinding control parameters of a grinding machine tool subsequently.
S400: optimizing grinding control parameters of the grinding machine tool based on the identification area to be ground, and obtaining a grinding control parameter optimizing result;
specifically, the identification area to be ground is used as an optimizing object, and grinding control parameters of a currently used grinding machine tool are optimized, wherein the grinding machine tool is a machine tool for grinding the surface of a workpiece by using a grinding tool, and the machine tool is a machine for manufacturing machines and machines. And finding out an optimal parameter interval of the currently used grinding machine tool for processing the to-be-ground identification area, and taking the optimal parameter interval as a grinding control parameter optimizing result, wherein the optimal parameter interval can be used for subsequent roughness evaluation or as a grinding processing parameter.
Further, as shown in fig. 2, step S400 in the embodiment of the present application further includes:
s410: the grinding control parameters comprise grinding frequency, grinding depth and grinding angle;
s420: acquiring basic information of a grinding machine, wherein the basic information of the grinding machine comprises model information of the grinding machine;
s430: determining a grinding frequency constraint interval, a grinding depth constraint interval and a grinding angle constraint interval according to the model information of the grinding machine tool;
s440: based on the to-be-ground identification region, assigning values of the grinding frequency, the grinding depth and the grinding angle according to the grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval, and obtaining a plurality of groups of grinding control parameter assignment results;
s450: traversing the multiple groups of grinding control parameter assignment results to perform grinding temperature analysis, and obtaining multiple grinding prediction temperatures;
s460: and deleting assignment results of the plurality of grinding predicted temperatures which do not belong to the grinding temperature prediction interval, and obtaining the grinding control parameter optimizing result.
Specifically, the grinding machine tool is a machine tool for grinding the surface of a workpiece by using a grinding tool, the grinding control parameters comprise the grinding frequency, the grinding depth and the grinding angle of the grinding machine tool, basic information of the grinding machine tool is obtained by reading the nameplate of the grinding machine tool which is currently used, the model information of the grinding machine tool is included, the grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval of the grinding machine tool are inquired by the model information of the grinding machine tool which is currently used, the grinding frequency is the rotation frequency of the grinding wheel of the grinding machine tool and is the number of turns rotated in any 1s on the excircle surface of the grinding wheel, the grinding depth is the size of a hardware workpiece which is ground by the grinding wheel of the grinding machine tool and the machining plane of the hardware workpiece to be machined at each time, and the grinding angle is the angle formed by the grinding wheel of the grinding machine tool and the machining plane of the hardware workpiece to be machined. The grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval are grinding parameter adjustable ranges of the grinding machine tool. And selecting a plurality of groups of randomly combined grinding frequency values, grinding depth values and grinding angle values from the grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval based on the geometric characteristics of the to-be-ground identification area of the to-be-ground hardware workpiece, and taking the grinding frequency values, the grinding depth values and the grinding angle values as a plurality of groups of grinding control parameter assignment results. Because the grinding speed of the grinding process is high, the temperature of the grinding area is usually high and can reach 800-1000 ℃, and the local burn of the surface of the workpiece is easily caused by the overhigh temperature, so that the workpiece can be deformed and even cracks are generated on the surface of the workpiece. Therefore, it is necessary to perform grinding simulation based on the to-be-ground identification area of the to-be-ground hardware workpiece by using the multiple sets of grinding control parameter assignment results, predict temperatures possibly generated during machining, obtain multiple grinding prediction temperatures, compare the multiple grinding prediction temperatures with a grinding temperature prediction interval, delete assignment results of the grinding prediction temperatures not in the grinding temperature prediction interval, and reserve assignment results of the grinding prediction temperatures in the grinding temperature prediction interval, wherein the grinding temperature prediction interval is a grinding temperature interval which can ensure normal machining effects and cannot damage the workpiece, and take assignment results of the grinding prediction temperatures in the grinding temperature prediction interval as optimization results of the grinding control parameters, so that the grinding prediction temperatures can be used for subsequent roughness evaluation or as grinding parameters.
S500: performing roughness assessment according to the grinding control parameter optimizing result to obtain a surface roughness predicting result;
further, step S500 in the embodiment of the present application further includes:
s510: according to the grinding control parameter optimizing result, the hardware workpiece model information to be ground and the grinding machine tool model information, carrying out networking retrieval to obtain surface roughness detection data;
s520: when the data volume of the surface roughness detection data is larger than or equal to a first data volume threshold value, evaluating a surface roughness concentration value according to the surface roughness detection data to obtain the surface roughness prediction result;
s530: and when the data volume of the surface roughness detection data is smaller than the first data volume threshold, inputting the grinding control parameter optimizing result into a surface roughness evaluation model embedded in a server to obtain the surface roughness prediction result.
Specifically, the grinding control parameter optimizing result, the type information of the hardware workpiece to be ground and the type information of the grinding machine tool are used as search conditions, internet big data are used for information search, a plurality of groups of surface roughness detection data of finished products are obtained after the grinding control parameter optimizing result is used as a control parameter and the grinding machine tools of the same type are used for grinding the hardware workpiece to be ground of the same type. When the data amount of the surface roughness detection data of a certain parameter combination in the grinding control parameter optimizing result is larger than or equal to a first data amount threshold, the data amount of the surface roughness detection data of the group of parameters is large enough and has high reliability, the first data amount threshold is a data amount minimum value set according to the reliability requirement of the data, when the data amount of the surface roughness detection data of a certain parameter combination meets the first data amount threshold, the data in the surface roughness detection data is subjected to centralized value evaluation, and the average value calculation can be performed after the maximum value and the minimum value are removed, and the obtained average value is used as the surface roughness prediction result of the group of parameters.
Further, when the data amount of the surface roughness detection data of a certain set of parameters is smaller than the first data amount threshold, it is indicated that the retrieved data amount of the surface roughness detection data is too small to have reliability and cannot be directly used as the surface roughness prediction result. And then using the retrieved multiple groups of surface roughness detection data based on different parameter combinations in the grinding control parameter optimizing result as sample data, randomly dividing the sample data into a training data set, a verification data set and a test data set, constructing a surface roughness assessment model by combining the framework of the BP neural network model, and training, verifying and testing the surface roughness assessment model by using the training data set, the verification data set and the test data set until the model converges and reaches the preset accuracy requirement, thereby completing the training of the surface roughness assessment model. The BP neural network is a multi-layer feedforward neural network trained according to an error reverse propagation algorithm, a mathematical equation of a mapping relation between input and output is not required to be determined in advance, a certain rule is learned only through self training, and a result closest to an expected output value is obtained when an input value is given. The surface roughness assessment model is embedded in the server and is used for predicting the surface roughness of the hardware workpiece by taking the optimizing result of the grinding control parameter as input data and outputting the surface roughness predicting result, and the surface roughness predicting result can be used for judging whether the currently obtained optimizing result of the grinding control parameter can be used for carrying out subsequent grinding processing.
S600: and when the surface roughness prediction result is smaller than or equal to the surface expected roughness, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
Specifically, when the surface roughness prediction result is smaller than or equal to the surface expected roughness, it is indicated that the surface roughness prediction result meets the surface expected roughness requirement of a customer, the grinding parameters in the grinding control parameter optimizing result can be used for grinding the hardware workpiece to be ground, the surface roughness of a finished product can be ensured to meet the customer requirement, the yield of the product is improved, and further the production cost is reduced.
Further, as shown in fig. 3, step S600 in the embodiment of the present application further includes:
s610: acquiring a controllable grinding precision threshold value;
s620: performing controllable precision analysis according to the grinding control parameter optimizing result to obtain minimum machining precision;
s630: and when the minimum machining precision is greater than or equal to the controllable grinding precision threshold, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
Specifically, a controllable grinding precision threshold of the grinding machine tool, namely a parameter precision threshold of the grinding machine tool, is obtained through model information of the currently used grinding machine tool, wherein the controllable grinding precision threshold comprises a minimum variable quantity of a controllable grinding wheel direction, a minimum variable quantity of a controllable grinding wheel cutting depth, and an exemplary minimum variable quantity of the grinding wheel direction of the grinding machine tool of a certain model is 3 degrees, and a minimum variable quantity of the grinding wheel cutting depth is 1 millimeter. According to the grinding control parameter optimizing result, controllable precision analysis is carried out, namely, the minimum machining precision required by grinding machining according to the grinding control parameter optimizing result is found, then the minimum machining precision is compared with the controllable grinding precision threshold, when the minimum machining precision is larger than or equal to the controllable grinding precision threshold, the precision threshold of the grinding machine tool can meet the minimum precision requirement of the grinding control parameter optimizing result, for example, the minimum adjustment angle required by grinding machining according to the grinding control parameter optimizing result is 3mm, the minimum adjustment angle of the grinding machine tool is 2mm, the precision requirement can be met, the grinding control parameter optimizing result can be used for grinding machining the hardware workpiece to be ground, and if the minimum machining precision is smaller than the controllable grinding precision threshold, the parameter adjustment precision of the selected grinding control parameter optimizing result is excessively fine and is not in the grinding precision range which can be achieved by the currently used machine tool, and proper grinding control parameters need to be reselected.
Further, step S620 in the embodiment of the present application further includes:
s621: determining a grinding direction change sequence according to a grinding angle optimizing result of the grinding control parameter optimizing result;
s622: determining a grinding depth change sequence according to a grinding depth optimizing result of the grinding control parameter optimizing result;
s623: screening a minimum value of the direction change degree according to the grinding direction change sequence, and obtaining minimum accuracy of direction adjustment;
s624: screening a minimum value of the depth variation according to the grinding depth variation sequence, and obtaining minimum accuracy of depth adjustment;
s625: and adding the direction adjustment minimum precision and the depth adjustment minimum precision to the minimum machining precision.
Specifically, the grinding angle optimizing result data in the grinding control parameter optimizing result is arranged according to a small order to generate a grinding direction change sequence, and similarly, the grinding depth optimizing result data in the grinding control parameter optimizing result is arranged according to a large order to generate a grinding depth change sequence. And screening out the minimum value of the grinding direction change degree from the grinding direction change sequence, taking the minimum value as the minimum direction adjustment precision, and screening out the minimum value of the grinding depth change degree from the grinding depth change sequence, and taking the minimum value of the grinding depth change degree as the minimum depth adjustment precision. And taking the minimum direction adjustment precision and the minimum depth adjustment precision as the minimum machining precision, wherein the minimum machining precision can be used for judging whether the parameter adjustment precision in the grinding control parameter optimizing result is within the grinding precision range which can be achieved by the currently used grinding machine tool.
Further, the embodiment of the present application further includes step S700, where step S700 further includes:
s710: when the surface roughness prediction result is greater than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold, performing secondary optimization on grinding control parameters of a grinding machine tool based on the identification area to be ground, and obtaining a secondary optimizing result of the grinding control parameters;
s720: and grinding the hardware workpiece to be ground according to the secondary optimizing result of the grinding control parameter.
Specifically, when the surface roughness prediction result is greater than the surface expected roughness, or the minimum machining precision is smaller than the controllable grinding precision threshold, or the surface roughness prediction result and the minimum machining precision do not meet the requirements of the surface expected roughness and the controllable grinding precision threshold, performing secondary optimization on the grinding control parameters of the grinding machine tool aiming at the to-be-ground identification area, reselecting the parameter combination that both the surface roughness prediction result and the minimum machining precision can meet the surface expected roughness and the controllable grinding precision threshold, and performing grinding processing on the to-be-ground hardware workpiece according to the secondary optimizing result of the grinding control parameters as a secondary optimizing result of the grinding control parameters, so that the grinding can be ensured by using a current grinding machine tool, the surface expected roughness of a finished hardware workpiece can be ensured to meet the requirements of customers, the product yield can be improved, and the processing cost can be reduced.
Further, step S710 in the embodiment of the present application further includes:
s711: constructing a first fitness function, wherein the first fitness function characterizes a first deviation value of the surface roughness prediction result and the surface expected roughness;
s712: constructing a second fitness function, wherein the second fitness function characterizes a second deviation value of the controllable grinding precision threshold and the minimum machining precision;
s713: traversing the grinding control parameter optimizing result according to the first fitness function to obtain a first deviation value set;
s714: traversing the grinding control parameter optimizing result according to the second fitness function to obtain a second deviation value set;
s715: screening the grinding control parameter optimizing result that the first deviation value is smaller than or equal to a first deviation threshold value and the second deviation value is smaller than or equal to a second deviation threshold value according to the first deviation value set and the second deviation value set, and setting the grinding control parameter optimizing result as an initial control parameter, wherein the first deviation threshold value corresponds to the first deviation value set, and the second deviation threshold value corresponds to the second deviation value set;
s716: according to a preset adjustment step length, adjusting the initial control parameters to obtain a first expansion result of the initial control parameters;
S717: repeating expansion when the surface roughness prediction result of the first expansion result of the initial control parameter is greater than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold;
s718: and when the surface roughness prediction result is smaller than or equal to the surface expected roughness and the minimum machining precision is larger than or equal to the controllable grinding precision threshold, acquiring a secondary optimizing result of the grinding control parameter.
Specifically, a first fitness function is constructed according to the difference relation between the surface roughness prediction result and the surface expected roughness, wherein the fitness function is a corresponding relation between all individuals in the problem and the fitness of the individuals, and is generally a real-value function. The first fitness function characterizes a first deviation value of the surface roughness prediction result and the surface expected roughness, the first fitness function can be used for calculating a difference value between the surface roughness prediction result and the surface expected roughness of a workpiece under different grinding control parameters, and the smaller the difference value is, the closer the surface roughness of the workpiece is to the surface expected roughness, and the better the selected grinding control parameters are. And similarly, constructing a second fitness function according to the difference relation between the controllable grinding precision threshold and the minimum machining precision, wherein the second fitness function characterizes a second deviation value of the controllable grinding precision threshold and the minimum machining precision. Through the first fitness function and the second fitness function, the grinding control parameters can be evaluated in quality, and the grinding control parameters can be conveniently screened. Extracting all grinding control parameter data in the grinding control parameter optimizing result, and a plurality of surface roughness predicting results and a plurality of surface expected roughness corresponding to the grinding control parameter optimizing result, then respectively inputting all the grinding control parameter data, a plurality of surface roughness predicting results and a plurality of surface expected roughness in the grinding control parameter optimizing result into the first fitness function, calculating to obtain a plurality of roughness deviation values, arranging the obtained deviation values into a deviation value data set to serve as a first deviation value set, and similarly, traversing all the data in the grinding control parameter optimizing result by the second fitness function to obtain a plurality of deviation values to serve as a second deviation value set. Further, the grinding control parameter optimizing result with the first deviation value smaller than or equal to a first deviation threshold value and the second deviation value smaller than or equal to a second deviation threshold value is selected from the first deviation value set and the second deviation value set, and is used as an initial control parameter, namely a control parameter before non-optimization. The first deviation threshold value corresponds to the first deviation value set and is a preset roughness deviation threshold value used for screening out grinding control parameters with larger deviation between the predicted roughness and the surface expected roughness, and the second deviation threshold value corresponds to the second deviation value set and is a preset precision deviation threshold value used for screening out grinding control parameters with higher fineness requirements. Presetting an adjustment step length for each grinding control parameter, expanding the initial control parameter upwards or downwards based on the preset adjustment step length, taking the adjusted initial control parameter as a first expansion result of the initial control parameter, for example, the preset adjustment step length of a grinding angle is 1 degree, carrying out primary adjustment on the initial control parameter based on the preset adjustment step length, for example, expanding the grinding angle once to obtain an adjusted initial control parameter, namely, a first expansion result of the initial control parameter, and repeating the above parameter expansion steps until the surface roughness prediction result is smaller than or equal to the surface desired roughness and the minimum machining precision is larger than or equal to the controllable grinding precision threshold, stopping parameter expansion, taking the grinding control parameter which is finally adjusted as a second optimizing result of the grinding parameter, and carrying out secondary optimizing on the workpiece by using the grinding control parameter when one of the surface roughness prediction result of the initial control parameter and the minimum machining precision does not meet the surface desired roughness or the controllable grinding precision threshold, so that the product machining rate is further ensured.
In summary, the embodiments of the present application have at least the following technical effects:
the method comprises the steps of receiving grinding task information of a client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness; acquiring basic information of a hardware workpiece, wherein the basic information comprises a first simulation model of the hardware workpiece; comparing the grinding conceptual model with a first simulation model of the hardware workpiece to obtain a to-be-ground identification area; optimizing grinding control parameters of the grinding machine tool based on the identification area to be ground; and carrying out roughness assessment according to the grinding control parameter optimizing result, and carrying out grinding processing on the hardware workpiece to be ground when the surface roughness predicting result is smaller than or equal to the surface expected roughness.
The technical effects of improving the product yield and reducing the processing cost by intelligently controlling the roughness of the grinding processing of the hardware tool are achieved.
Example two
Based on the same inventive concept as one of the grinding methods for hardware tools in the previous embodiments, as shown in fig. 4, the present application provides a grinding system for hardware tools, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
A grinding task information receiving module 11, wherein the grinding task information receiving module 11 is used for receiving grinding task information of a client, and the grinding task information comprises a grinding concept model and a surface expected roughness;
the hardware workpiece basic information acquisition module 12 is used for starting the image acquisition device to acquire hardware workpiece basic information when a hardware workpiece to be ground enters a preset area, wherein the hardware workpiece basic information comprises a hardware workpiece first simulation model;
the to-be-ground identification area acquisition module 13 is used for comparing the grinding conceptual model with the first simulation model of the hardware workpiece to acquire an to-be-ground identification area;
the grinding control parameter optimizing result obtaining module 14, wherein the grinding control parameter optimizing result obtaining module 14 is used for optimizing the grinding control parameters of the grinding machine tool based on the to-be-ground identification area to obtain the grinding control parameter optimizing result;
the surface roughness prediction result obtaining module 15, where the surface roughness prediction result obtaining module 15 is configured to perform roughness assessment according to the grinding control parameter optimizing result, and obtain a surface roughness prediction result;
And the grinding module 16 is used for grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result when the surface roughness predicting result is smaller than or equal to the surface expected roughness.
Further, the grinding control parameter optimizing result obtaining module 14 is further configured to perform the following steps:
the grinding control parameters comprise grinding frequency, grinding depth and grinding angle;
acquiring basic information of a grinding machine, wherein the basic information of the grinding machine comprises model information of the grinding machine;
determining a grinding frequency constraint interval, a grinding depth constraint interval and a grinding angle constraint interval according to the model information of the grinding machine tool;
based on the to-be-ground identification region, assigning values of the grinding frequency, the grinding depth and the grinding angle according to the grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval, and obtaining a plurality of groups of grinding control parameter assignment results;
traversing the multiple groups of grinding control parameter assignment results to perform grinding temperature analysis, and obtaining multiple grinding prediction temperatures;
and deleting assignment results of the plurality of grinding predicted temperatures which do not belong to the grinding temperature prediction interval, and obtaining the grinding control parameter optimizing result.
Further, the surface roughness prediction result obtaining module 15 is further configured to perform the following steps:
according to the grinding control parameter optimizing result, the hardware workpiece model information to be ground and the grinding machine tool model information, carrying out networking retrieval to obtain surface roughness detection data;
when the data volume of the surface roughness detection data is larger than or equal to a first data volume threshold value, evaluating a surface roughness concentration value according to the surface roughness detection data to obtain the surface roughness prediction result;
and when the data volume of the surface roughness detection data is smaller than the first data volume threshold, inputting the grinding control parameter optimizing result into a surface roughness evaluation model embedded in a server to obtain the surface roughness prediction result.
Further, the grinding module 16 is further configured to perform the following steps:
acquiring a controllable grinding precision threshold value;
performing controllable precision analysis according to the grinding control parameter optimizing result to obtain minimum machining precision;
and when the minimum machining precision is greater than or equal to the controllable grinding precision threshold, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
Further, the grinding module 16 is further configured to perform the following steps:
determining a grinding direction change sequence according to a grinding angle optimizing result of the grinding control parameter optimizing result;
determining a grinding depth change sequence according to a grinding depth optimizing result of the grinding control parameter optimizing result;
screening a minimum value of the direction change degree according to the grinding direction change sequence, and obtaining minimum accuracy of direction adjustment;
screening a minimum value of the depth variation according to the grinding depth variation sequence, and obtaining minimum accuracy of depth adjustment;
and adding the direction adjustment minimum precision and the depth adjustment minimum precision to the minimum machining precision.
Further, the system further comprises:
the grinding control parameter secondary optimizing result obtaining module is used for carrying out secondary optimization on the grinding control parameters of the grinding machine tool based on the identification area to be ground when the surface roughness prediction result is greater than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold value, so as to obtain a grinding control parameter secondary optimizing result;
The grinding module is used for grinding the hardware workpiece to be ground according to the secondary optimizing result of the grinding control parameter;
further, the system further comprises:
the first fitness function construction module is used for constructing a first fitness function, wherein the first fitness function represents a first deviation value between the surface roughness prediction result and the surface expected roughness;
the second fitness function construction module is used for constructing a second fitness function, wherein the second fitness function represents a second deviation value between the controllable grinding precision threshold and the minimum machining precision;
the first deviation value set acquisition module is used for traversing the grinding control parameter optimizing result according to the first fitness function to acquire a first deviation value set;
the second deviation value set acquisition module is used for traversing the grinding control parameter optimizing result according to the second fitness function to acquire a second deviation value set;
The initial control parameter setting module is used for screening the grinding control parameter optimizing result that the first deviation value is smaller than or equal to a first deviation threshold value and the second deviation value is smaller than or equal to a second deviation threshold value according to the first deviation value set and the second deviation value set, and setting the grinding control parameter optimizing result as an initial control parameter, wherein the first deviation threshold value corresponds to the first deviation value set, and the second deviation threshold value corresponds to the second deviation value set;
the first expansion result acquisition module is used for adjusting the initial control parameters according to a preset adjustment step length to acquire first expansion results of the initial control parameters;
the repeated expansion module is used for repeatedly expanding when the surface roughness predicted result of the first expansion result of the initial control parameter is larger than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold;
the secondary optimizing result obtaining module is used for obtaining the secondary optimizing result of the grinding control parameter when the surface roughness predicted result is smaller than or equal to the surface expected roughness and the minimum machining precision is larger than or equal to the controllable grinding precision threshold.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. A grinding method for hardware tools, the method comprising:
receiving grinding task information of a client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness;
when a to-be-ground hardware workpiece enters a preset area, starting an image acquisition device to acquire hardware workpiece basic information, wherein the hardware workpiece basic information comprises a first simulation model of the hardware workpiece;
comparing the grinding conceptual model with the first simulation model of the hardware workpiece to obtain a to-be-ground identification area;
optimizing grinding control parameters of the grinding machine tool based on the identification area to be ground, and obtaining a grinding control parameter optimizing result;
performing roughness assessment according to the grinding control parameter optimizing result to obtain a surface roughness predicting result;
and when the surface roughness prediction result is smaller than or equal to the surface expected roughness, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
2. The method of claim 1, wherein when the predicted surface roughness is less than or equal to the desired surface roughness, grinding the hardware workpiece to be ground based on the optimized grinding control parameter results, further comprising:
Acquiring a controllable grinding precision threshold value;
performing controllable precision analysis according to the grinding control parameter optimizing result to obtain minimum machining precision;
and when the minimum machining precision is greater than or equal to the controllable grinding precision threshold, grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result.
3. The method as recited in claim 2, further comprising:
when the surface roughness prediction result is greater than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold, performing secondary optimization on grinding control parameters of a grinding machine tool based on the identification area to be ground, and obtaining a secondary optimizing result of the grinding control parameters;
and grinding the hardware workpiece to be ground according to the secondary optimizing result of the grinding control parameter.
4. The method of claim 1, wherein optimizing the grinding control parameters of the grinding machine based on the identified region to be ground, obtaining the grinding control parameter optimizing results, comprises:
the grinding control parameters comprise grinding frequency, grinding depth and grinding angle;
acquiring basic information of a grinding machine, wherein the basic information of the grinding machine comprises model information of the grinding machine;
Determining a grinding frequency constraint interval, a grinding depth constraint interval and a grinding angle constraint interval according to the model information of the grinding machine tool;
based on the to-be-ground identification region, assigning values of the grinding frequency, the grinding depth and the grinding angle according to the grinding frequency constraint interval, the grinding depth constraint interval and the grinding angle constraint interval, and obtaining a plurality of groups of grinding control parameter assignment results;
traversing the multiple groups of grinding control parameter assignment results to perform grinding temperature analysis, and obtaining multiple grinding prediction temperatures;
and deleting assignment results of the plurality of grinding predicted temperatures which do not belong to the grinding temperature prediction interval, and obtaining the grinding control parameter optimizing result.
5. The method of claim 1, wherein performing roughness assessment based on the grinding control parameter optimizing result to obtain a surface roughness prediction result comprises:
according to the grinding control parameter optimizing result, the hardware workpiece model information to be ground and the grinding machine tool model information, carrying out networking retrieval to obtain surface roughness detection data;
when the data volume of the surface roughness detection data is larger than or equal to a first data volume threshold value, evaluating a surface roughness concentration value according to the surface roughness detection data to obtain the surface roughness prediction result;
And when the data volume of the surface roughness detection data is smaller than the first data volume threshold, inputting the grinding control parameter optimizing result into a surface roughness evaluation model embedded in a server to obtain the surface roughness prediction result.
6. The method of claim 2, wherein performing a controllable accuracy analysis based on the grinding control parameter optimizing result to obtain a minimum machining accuracy comprises:
determining a grinding direction change sequence according to a grinding angle optimizing result of the grinding control parameter optimizing result;
determining a grinding depth change sequence according to a grinding depth optimizing result of the grinding control parameter optimizing result;
screening a minimum value of the direction change degree according to the grinding direction change sequence, and obtaining minimum accuracy of direction adjustment;
screening a minimum value of the depth variation according to the grinding depth variation sequence, and obtaining minimum accuracy of depth adjustment;
and adding the direction adjustment minimum precision and the depth adjustment minimum precision to the minimum machining precision.
7. A method according to claim 3, wherein when the surface roughness prediction result is greater than the surface desired roughness, or/and the minimum machining precision is less than the controllable grinding precision threshold, performing secondary optimization on a grinding control parameter of a grinding machine based on the identification area to be ground, and obtaining a secondary optimization result of the grinding control parameter comprises:
Constructing a first fitness function, wherein the first fitness function characterizes a first deviation value of the surface roughness prediction result and the surface expected roughness;
constructing a second fitness function, wherein the second fitness function characterizes a second deviation value of the controllable grinding precision threshold and the minimum machining precision;
traversing the grinding control parameter optimizing result according to the first fitness function to obtain a first deviation value set;
traversing the grinding control parameter optimizing result according to the second fitness function to obtain a second deviation value set;
screening the grinding control parameter optimizing result that the first deviation value is smaller than or equal to a first deviation threshold value and the second deviation value is smaller than or equal to a second deviation threshold value according to the first deviation value set and the second deviation value set, and setting the grinding control parameter optimizing result as an initial control parameter, wherein the first deviation threshold value corresponds to the first deviation value set, and the second deviation threshold value corresponds to the second deviation value set;
according to a preset adjustment step length, adjusting the initial control parameters to obtain a first expansion result of the initial control parameters;
Repeating expansion when the surface roughness prediction result of the first expansion result of the initial control parameter is greater than the surface expected roughness or/and the minimum machining precision is smaller than the controllable grinding precision threshold;
and when the surface roughness prediction result is smaller than or equal to the surface expected roughness and the minimum machining precision is larger than or equal to the controllable grinding precision threshold, acquiring a secondary optimizing result of the grinding control parameter.
8. A grinding system for a hardware tool, the system comprising:
the grinding task information receiving module is used for receiving the grinding task information of the client, wherein the grinding task information comprises a grinding conceptual model and a surface expected roughness;
the hardware workpiece basic information acquisition module is used for starting the image acquisition device to acquire hardware workpiece basic information when a hardware workpiece to be ground enters a preset area, wherein the hardware workpiece basic information comprises a hardware workpiece first simulation model;
the to-be-ground identification area acquisition module is used for comparing the grinding conceptual model with the first simulation model of the hardware workpiece to acquire an to-be-ground identification area;
The grinding control parameter optimizing result obtaining module is used for optimizing the grinding control parameters of the grinding machine tool based on the to-be-ground identification area to obtain a grinding control parameter optimizing result;
the surface roughness prediction result acquisition module is used for carrying out roughness assessment according to the grinding control parameter optimizing result to acquire a surface roughness prediction result;
and the grinding module is used for grinding the hardware workpiece to be ground according to the grinding control parameter optimizing result when the surface roughness predicting result is smaller than or equal to the surface expected roughness.
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