CN110889231B - Metal milling parameter optimization method - Google Patents
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Abstract
The invention discloses a metal milling parameter optimization method, which comprises the following steps: firstly, the method comprises the following steps: processing a plurality of metal workpieces under different milling parameters; II, secondly: measuring surface roughness and residual stress; thirdly, the method comprises the following steps: establishing a first primary regression equation of milling parameters and surface roughness; establishing a second primary regression equation of the surface roughness and the residual stress; fourthly, the method comprises the following steps: and substituting the milling parameters and the correspondingly measured surface roughness into a first preliminary regression equation to obtain a first regression equation, substituting the measured residual stress and the measured surface roughness into a second preliminary regression equation to obtain a second regression equation, and performing the following steps: solving a third regression equation of the residual stress and the milling parameter according to the first regression equation and the second regression equation; sixthly, the method comprises the following steps: selecting proper residual stress to substitute into a third regression equation; seventhly, the method comprises the following steps: selecting milling parameters to substitute into a third regression equation; eighthly: and calculating a third regression equation, if the equation is established, determining the equation as the preferred milling parameter, and if the equation is not established, returning to seven. The milling parameters obtained by the method can meet the surface roughness and generate smaller residual stress.
Description
Technical Field
The invention belongs to the field of metal processing, and particularly relates to a method for optimizing metal milling parameters.
Background
Metal milling is a common metal processing means, and various milling parameters are often selected in metal milling to meet various processing quality requirements.
In order to meet the industrial requirements for metal surface roughness, a predictive mathematical model of the surface roughness (manufacturing technology and machine tool, 2006, 8 th, P65) is established for analyzing the milling parameter conditions.
However, with the development of industrial technology, certain requirements are made on the residual stress of the milled material, and the conventional simple surface roughness prediction model cannot continuously meet the more accurate technical requirements in the market, so that the problem that the smaller residual stress is generated while the surface roughness is met is urgently solved.
Disclosure of Invention
In order to solve the problems, the technical problem to be solved by the invention is to provide a metal milling parameter optimization method, and the milling parameters obtained by the method can generate smaller residual stress while meeting the surface roughness.
The technical scheme of the invention is as follows:
a metal milling parameter optimization method comprises the following steps:
the method comprises the following steps: milling a plurality of metal workpieces with the same material by using a cutter under different milling parameters;
step two: measuring the plurality of machined metal workpieces to obtain a plurality of groups of values of surface roughness and residual stress;
step three: establishing a multivariate non-linear first preliminary regression equation of the milling parameters and the surface roughness:
establishing a multivariate nonlinear second preliminary regression equation of the surface roughness and the residual stress: r a ＝AlnF+B；
In the formula, R a -surface roughness; v. of c -milling speed; f, feeding amount of each tooth; a is p -milling depth; k-coefficient determined by material and processing conditions; l, m, n-the influencing factor of each variable; f, residual stress; A. b-parameter factors affecting variables; d, milling width;
step four: performing regression analysis, substituting all the milling parameter values and all the correspondingly measured surface roughness values into the first preliminary regression equation to obtain a plurality of equations, solving the equation set, and solving unknown coefficients: k. l, m, n, obtaining a first regression equation:
substituting all the measured residual stress values and the surface roughness values into the second preliminary regression equation to obtain a plurality of equations, solving the equation set, and solving unknown coefficients: A. b, obtaining a second regression equation: r is a ＝7.8211-1.212lnF；
Step five: solving an equation set according to the first regression equation and the second regression equation to obtain a third regression equation of the residual stress and the milling parameter:
step six: selecting a proper residual stress value to substitute into the third regression equation under the condition of meeting the surface roughness constraint condition;
step seven: randomly selecting the milling parameter value to substitute into the third regression equation under the condition of meeting the actual application condition;
step eight: and calculating the third regression equation, if the equation is established, the milling parameter is the preferred milling parameter, and if the equation is not established, returning to the seventh step.
According to an embodiment of the invention, in the first step, a plurality of groups of milling processes are performed on one metal workpiece by using a cutter under different milling parameters, and in the second step, the surface roughness and the residual stress value are obtained by measuring the milling process of each group.
According to an embodiment of the invention, the number of the plurality of groups of milling processes is three.
According to an embodiment of the present invention, the milling process of one of the plurality of sets of milling processes is a plurality of milling processes under the same milling parameter, and the values of the surface roughness and the residual stress in the second step are average values of the values measured by the plurality of milling processes.
According to an embodiment of the present invention, the number of the multiple milling processes is three.
According to an embodiment of the present invention, the milling parameters are milling speed, feed per tooth, milling depth, and milling width.
According to one embodiment of the invention, the milling speed is 120-190 m/min, the feed per tooth is 0.05-0.1 mm/z, the milling depth is 0.5-2 mm, and the milling width is 1mm.
According to an embodiment of the invention, the milling process adopts a vertical dry milling process.
According to an embodiment of the invention, the metal workpieces are quenched and tempered hot-work die steel with the hardness of 45HRC, and the cutter is a hard alloy steel end mill coated with a TiAlSiN coating.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
(1) In the first to eighth steps of the embodiment of the invention, the optimized milling parameter can be obtained by using a regression analysis method, and the milling parameter can generate smaller residual stress while meeting the surface roughness, so that the processed workpiece has better quality and meets the use requirement of the workpiece.
(2) In one embodiment of the invention, a metal workpiece is subjected to multiple groups of milling processing by using the cutter under different milling parameters to obtain multiple groups of data, so that the same metal workpiece can be repeatedly utilized to save materials.
(3) In one embodiment of the invention, the surface roughness and the residual stress are average values of values measured by multiple times of milling, so that the errors of the measured surface roughness and the measured residual stress are smaller, and the influence of accidental errors on data is reduced.
Drawings
Embodiments of the invention will be described in further detail below with reference to the accompanying drawings, in which:
FIG. 1 is a line graph of surface roughness and residual stress for a preferred method of metal milling parameters of the present invention.
Detailed Description
The following describes a preferred method for milling metal parameters according to the present invention in further detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise ratio for the purpose of facilitating and distinctly aiding in the description of the embodiments of the invention.
Referring to fig. 1, the present invention includes a method for optimizing milling parameters of metal, milling a workpiece, performing regression analysis on related parameters, and calculating a regression equation to obtain optimized milling parameters by performing multiple analysis and screening, wherein the optimized milling parameters can solve the problem of non-selection of milling parameters in the process of satisfying surface roughnessAnd properly, a large residual stress is generated. The method specifically comprises the following steps: the method comprises the following steps: milling a plurality of metal workpieces with the same material by using a cutter under different milling parameters; step two: measuring a plurality of processed metal workpieces to obtain and record a plurality of groups of numerical values of surface roughness and residual stress; step three: the step can be carried out before or simultaneously with the step two, and a multivariate nonlinear first primary regression equation of the milling parameters and the surface roughness is established:
the equation is obtained according to the existing experimental research, for example, the research in 2019, 11 th stage of the tool technology shows that the feed amount is the most main factor influencing the surface roughness, and the first preliminary regression equation is established based on the research and the practical application of the method; establishing a surface roughness and residual stress multivariate nonlinear second preliminary regression equation: r a = AlnF + B, this equation is based on the existing research, i.e. the relation between residual stress and feed available from experimental models presents an approximate curve of y = alnx + B, where x, y represent variable parameters; a. b represents constants (mechanical engineer, 2019, 10 th period), and residual stress generated in the milling process has a concomitant relation with surface roughness, so that a second primary regression equation of the constants is established; in the formula, R a -surface roughness; v. of c -milling speed; f, feeding amount of each tooth; a is p -milling depth; k-coefficient determined by material and processing conditions; l, m, n-the influencing factor of each variable; f, residual stress; A. b-parameter factors affecting variables; d, milling width; step four: carrying out regression analysis, substituting all milling parameter values and all correspondingly measured surface roughness values into the first primary regression equation to obtain a plurality of groups of equations, solving the equation set, and solving unknown coefficients: k. l, m and n to obtain a first regression equation:
substituting all measured residual stress values and surface roughness values into a second preliminary regression equation to obtain multiple sets of equations andsolving the equation set to obtain unknown coefficients: A. b, obtaining a second regression equation: r a =7.8211-1.212lnF; step five: solving an equation set according to the first regression equation and the second regression equation to obtain a third regression equation of the residual stress and the milling parameter:
step six: selecting a proper residual stress value to substitute into a third regression equation under the condition of meeting the surface roughness constraint condition; step seven: randomly selecting a milling parameter value to substitute into a third regression equation under the condition of meeting the actual application condition; step eight: and calculating a third regression equation, wherein if the equation is established, the milling parameter is the optimized milling parameter, and if the equation is not established, returning to the seventh step. The milling in the first step adopts a vertical milling machine, the milling adopts a vertical dry type forward milling process, the measured data of the process is more accurate, and a plurality of metal workpieces are quenched and tempered hot-work DIEVAR die steel with the hardness of 45 HRC. The regression analysis method can be used for obtaining the optimal milling parameters, and the milling parameters can meet the requirements of surface roughness and simultaneously generate smaller residual stress, so that the quality of the processed workpiece is better, and the use requirements of the workpiece are met.
Furthermore, in the step one, a plurality of groups of milling processing are carried out on one metal workpiece by using a cutter under different milling parameters, and in the step two, the surface roughness and the residual stress value are obtained by measuring the milling processing of each group. Therefore, a metal workpiece can be subjected to multiple groups of milling processing by using the cutter under different milling parameters to obtain multiple groups of data, so that the same metal workpiece can be repeatedly utilized to save materials.
Furthermore, the number of the plurality of groups of milling processing is three. Is a preferred amount.
Furthermore, the milling of one of the plurality of groups of milling is a plurality of times of milling under the same milling parameter, and the surface roughness and the residual stress value in the second step are average values of the values measured by the plurality of times of milling. The measured surface roughness and residual stress errors are smaller, and the influence of accidental errors on data is reduced.
Further, the number of the multiple milling processes is three. Is a preferred amount
Further, the milling parameters comprise milling speed, feed per tooth, milling depth and milling width.
Furthermore, the milling speed is 120-190 m/min, the feed per tooth is 0.05-0.1 mm/z, the milling depth is 0.5-2 mm, and the milling width is 1mm. Is a preferred amount
Further, the milling process adopts a vertical dry milling process. The processing technology is more representative.
Furthermore, the metal workpieces are quenched and tempered hot-work die steel with the hardness of 45HRC, and the cutter is a hard alloy steel end mill coated with a TiAlSiN coating.
The following further illustrates the specific experimental procedures of the present invention:
in the experiment, five groups of milling processing are adopted to quickly and accurately obtain wider experiment results, each group is milled by using one metal workpiece, the same metal workpiece is processed for three times to obtain three groups of data, and different milling parameters are adopted each time, so that the same workpiece can be used for multiple times, and materials are saved; after the first milling is finished, another metal workpiece is replaced, after the first milling of the other metal workpiece is finished, the previous metal workpiece is replaced again for the second milling, and the like, so that the same metal workpiece can be re-installed for many times, and the installation error can be further reduced while the material is saved; before the milling parameters are selected, a surface roughness range is appointed according to past experience, then the corresponding milling parameters are estimated according to the range, and then the surface roughness value is measured after milling is finished, so that the finally measured surface roughness is vertically distributed in a wider range, and experimental data are more representative.
In the experiment, the milling speeds are four, namely 120m/min, 150m/min, 170m/min and 190m/min, the feeding amount of each tooth is four, namely 0.05mm/z, 0.065mm/z, 0.085mm/z and 0.1mm/z, the milling depths are four, namely 0.5mm, 1mm, 1.5mm and 2mm, and the default milling widths are all 1mm.
The first group of processing, the material to be processed adopts quenched and tempered hot-work DIEVAR die steel with the hardness of 45HRC as a test material, and milling is carried out by a hard alloy steel end mill, and the first time is as follows: the milling speed is 120m/min, the feed per tooth is 0.1mm/z, the milling depth is 2mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 124.5MPa, and the surface roughness was measured to be 2.01. Mu.m. And (3) for the second time: the milling speed is 190m/min, the feed per tooth is 0.085mm/z, the milling depth is 1mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 246.7MPa, and the surface roughness was measured to be 1.29 μm. And thirdly: the milling speed is 150m/min, the feed per tooth is 0.05mm/z, the milling depth is 1mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 298.6MPa, and the surface roughness was measured to be 0.855. Mu.m.
And in the second group of processing, the material to be processed adopts quenched and tempered hot die steel DIEVAR with the hardness of 45HRC as a test material, and is milled by a hard alloy steel end mill, and the first time is as follows: the milling speed is 150m/min, the feed per tooth is 0.085mm/z, the milling depth is 2mm, and the cutting width is 1mm. The residual stress at the cutting was measured to be 137.5MPa, and the surface roughness was measured to be 2.45 μm. And (3) for the second time: the milling speed is 120m/min, the feed per tooth is 0.085mm/z, the milling depth is 1.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 210MPa, and the surface roughness was measured to be 1.28. Mu.m. And thirdly: the milling speed is 170m/min, the feed per tooth is 0.085mm/z, the milling depth is 0.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 366MPa, and the surface roughness was measured to be 0.84. Mu.m.
And in the third group of processing, the material to be processed adopts quenched and tempered hot-work die steel DIEVAR with the hardness of 45HRC as a test material, and a hard alloy steel end mill is used for milling, and the first time: the milling speed is 170m/min, the feed per tooth is 0.065mm/z, the milling depth is 2mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 110MPa, and the surface roughness was measured to be 2.536. Mu.m. And (3) for the second time: the milling speed is 170m/min, the feed per tooth is 0.05mm/z, the milling depth is 1.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 151.3MPa, and the surface roughness was measured to be 1.36. Mu.m. And thirdly: the milling speed is 120m/min, the feed per tooth is 0.05mm/z, the milling depth is 0.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 280MPa, and the surface roughness was measured to be 0.845. Mu.m.
And in the fourth group of processing, the material to be processed adopts quenched and tempered hot-work die steel DIEVAR with the hardness of 45HRC as a test material, and is milled by a hard alloy steel end mill, and the first time is as follows: the milling speed is 190m/min, the feed per tooth is 0.05mm/z, the milling depth is 2mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 86MPa, and the surface roughness was measured to be 2.465. Mu.m. And (3) for the second time: the milling speed is 190m/min, the feed per tooth is 0.1mm/z, the milling depth is 0.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 264MPa, and the surface roughness was 1.114. Mu.m. And thirdly: the milling speed is 190m/min, the feed per tooth is 0.065mm/z, the milling depth is 1.5mm, and the cutting width is 1mm. The residual stress at cutting was measured to be 141MPa, and the surface roughness was measured to be 0.91. Mu.m.
And a fifth group of processing, wherein the material to be processed adopts quenched and tempered hot-work die steel DIEVAR with the hardness of 45HRC as a test material, and a hard alloy steel end mill is used for milling, and the first time: the milling speed is 150m/min, the feed per tooth is 0.1mm/z, the milling depth is 1.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 198.3MPa, and the surface roughness was measured to be 2.11. Mu.m. And (3) for the second time: the milling speed is 170m/min, the feed per tooth is 0.05mm/z, the milling depth is 1.5mm, and the cutting width is 1mm. The residual stress of cutting was measured to be 133.3MPa, and the surface roughness was 1.38 μm. And thirdly: the milling speed is 120m/min, the feed per tooth is 0.065mm/z, the milling depth is 1mm, and the cutting width is 1mm. The residual stress at the time of cutting was measured to be 231.3MPa, and the surface roughness was measured to be 0.953. Mu.m.
Substituting each data of the five groups of processing into a first primary regression equation and a second primary regression equation to obtain a first regression equation
The second regression equation is Ra =7.8211-1.212ln F, and the third regression equation is obtained according to the first regression equation and the second regression equation
Wherein Ra-surface roughness (. Mu.m); v. of c -milling speed (m/min); f-feed per tooth (mm/z))；a p -milling depth (mm); f-residual stress (MPa). Milling parameter values which meet the surface roughness and generate small residual stress can be calculated according to the obtained first regression equation and the third regression equation, and fig. 1 is a line graph of the surface roughness and the residual stress obtained by five groups of processing.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the embodiments. Even if various changes are made to the present invention, it is still within the scope of the present invention if they fall within the scope of the claims of the present invention and their equivalents.
Claims (9)
1. A metal milling parameter optimization method is characterized by comprising the following steps:
the method comprises the following steps: milling a plurality of metal workpieces with the same material by using a cutter under different milling parameters;
step two: measuring the plurality of machined metal workpieces to obtain a plurality of groups of values of surface roughness and residual stress;
step three: establishing a multivariate nonlinear first preliminary regression equation of the milling parameters and the surface roughness:
establishing a multivariate nonlinear second preliminary regression equation of the surface roughness and the residual stress: r is a ＝AlnF+B；
In the formula, R a -surface roughness; v. of c -milling speed; f, feeding amount of each tooth; a is p -milling depth; k is a coefficient determined by the material and the processing conditions; l, m, n-the influencing factor of each variable; f, residual stress; A. b-parameter factors affecting variables; d, milling width;
step four: performing regression analysis, substituting all the milling parameter values and all the correspondingly measured surface roughness values into the first preliminary regression equation to obtain a plurality of equations, solving the equation set, and solving unknown coefficients: k. l, m, n to giveA regression equation:
substituting all the measured residual stress values and the surface roughness values into the second preliminary regression equation to obtain a plurality of equations, solving the equation set, and solving unknown coefficients: A. b, obtaining a second regression equation: r is a ＝7.8211-1.212lnF；
Step five: solving an equation set according to the first regression equation and the second regression equation to obtain a third regression equation of the residual stress and the milling parameter:
step six: selecting a proper residual stress value to substitute into the third regression equation under the condition of meeting the surface roughness constraint condition;
step seven: randomly selecting the milling parameter values to substitute into the third regression equation under the condition of meeting the actual application condition;
step eight: and calculating the third regression equation, if the equation is established, the milling parameter is the preferred milling parameter, and if the equation is not established, returning to the seventh step.
2. The method for optimizing metal milling parameters according to claim 1, wherein in the first step, a plurality of sets of milling processes are performed on one metal workpiece by using a cutter under different milling parameters, and in the second step, the surface roughness and the residual stress value are measured for each set of milling processes.
3. The metal milling parameter optimizing method according to claim 2, wherein the plurality of milling machining sets are three.
4. The method for optimizing metal milling parameters according to claim 2, wherein the milling process of one of the plurality of sets of milling processes is a plurality of milling processes under the same milling parameter, and the values of the surface roughness and the residual stress in the second step are average values of the measured values of the plurality of milling processes.
5. The metal milling parameter optimizing method according to claim 4, characterized in that the number of the milling processes is three.
6. The metal milling parameter optimizing method according to claim 1, wherein the milling parameters are milling speed, feed per tooth, milling depth and milling width.
7. The metal milling parameter optimizing method according to claim 6, characterized in that the milling speed is 120-190 m/min, the feed per tooth is 0.05-0.1 mm/z, the milling depth is 0.5-2 mm, and the milling width is 1mm.
8. The metal milling parameter optimizing method according to claim 1, wherein the milling process adopts a vertical dry milling process.
9. The method for optimizing metal milling parameters according to claim 1, wherein the metal workpieces are quenched and tempered hot-work die steels with the hardness of 45HRC, and the tool is a hard alloy steel end mill coated with TiAlSiN coating.
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