CN113419489A - Multi-cavity component rough machining cutter optimization selection method based on genetic algorithm - Google Patents
Multi-cavity component rough machining cutter optimization selection method based on genetic algorithm Download PDFInfo
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Abstract
The invention provides an optimized selection method of a multi-groove-cavity component rough machining cutter based on a genetic algorithm, and provides an automatic optimized selection method of the multi-groove-cavity component rough machining cutter based on machining domain identification. Firstly, calculating a single domain element channel, and narrowing the tool selection range according to the residual ratio constraint; obtaining an optional tool set by intersecting the tool library and the tool selection range; then, according to a tool milling process machining time model under the current technological conditions, the shortest machining time is taken as an optimization target, and the optimal selection of the optimal tool combination is realized by utilizing a genetic algorithm; and finally, operating a cutter allocation algorithm on the global cutter to perform local cutter allocation. The method effectively solves the problem of automatic cutter selection of complex components with less than more than ten slots, more than tens of slots and even hundreds of slots, can remarkably reduce the labor burden of the interactive programming of a technician, realizes the maximum processing efficiency based on cutter resources and improves the numerical control programming efficiency.
Description
Technical Field
The invention relates to the field of machining, in particular to a genetic algorithm-based optimization selection method for a rough machining cutter of a multi-groove-cavity component.
Background
Tool selection is an important content of numerical control machining programming of the multi-cavity component. Whether the cutter is reasonably selected directly influences the production efficiency and the processing cost of the complex component. The selection of too small or too large a tool diameter can result in reduced machining efficiency; the former increases the processing time, and the latter increases the workload of the subsequent process steps. However, the rational selection of the tool size is a complex and arduous task that requires not only extensive process knowledge and experience, but also complex mathematical calculations. At present, in the process of compiling numerical control machining programs of multi-cavity components, manual cutter selection and labor are wasted, so that the process is single, the quality of cutter selection is completely determined by the experience of a technician, and the optimized selection of machining resources is lacked. Currently, tool selection research mainly aims at carrying out optimal selection of a tool on a single characteristic object. For complex structural members with dozens of grooves in small number, dozens of grooves in large number or hundreds of grooves in large number, the optimized selection of the cutters for each characteristic sequentially and independently causes the problems of large quantity of cutters, low cutter selection efficiency and the like.
In order to improve the numerical control machining efficiency of the multi-groove-cavity component, the invention provides an optimization selection method of a multi-groove-cavity component rough machining cutter based on a genetic algorithm.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an optimized selection method of a rough machining cutter of a multi-cavity component based on a genetic algorithm.
The invention provides a genetic algorithm-based optimized selection method for a rough machining cutter of a multi-groove-cavity component, which comprises the following steps:
and a work domain channel calculation step: channel calculation of groove cavity processing is realized by adopting a layering method, and the maximum channel and the minimum channel of the work area element are solved by a layer tangent plane intersection ring channel;
and a cutter selection range calculation step: limiting the tool selection range based on the tool use condition and the cutting ratio condition, and determining the tool selection range;
the tool magazine and range intersection step: the selectable tools in the tool library are intersected with the tool selection range to obtain a selectable tool group;
genetic algorithm optimization steps: performing genetic algorithm optimization on the selectable cutter group through coding, initializing population selection, calculating the current population fitness, genetic operation words and convergence judgment to determine a global optimal cutter group;
a cutter distribution step: and determining a tool allocation formula of the local optimal tool set of each work area element according to the global optimal tool set.
Preferably, the genetic algorithm optimizing step comprises:
and (3) encoding: carrying out binary coding on the cutters in the selectable cutter group according to the selection state;
initializing population selection: initializing population selection, and presetting an initial value of a global cutter;
calculating the current population fitness: calculating the current population fitness by taking the processing time of the cutter combined milling process as a fitness function;
genetic manipulation steps: genetic operation, selection, crossing and variation operation methods in the population evolution process;
a convergence judgment step: convergence judgment, namely judging termination conditions;
and (3) decoding: and (5) determining a global optimal cutter by using a binary coding result analysis method.
Preferably, the domain meta channel calculation includes:
layered processing confirmation: extracting a processing mode, and determining a cutter shaft control mode and whether layering processing is carried out;
layer section sequencing: creating layer tangent planes according to a first rule, and sequencing the layer tangent planes;
a step of intersection: intersecting each layered surface with the work area element in sequence to generate an intersecting surface and an intersecting ring;
obtaining a cross-loop large channel and a cross-loop small channel: carrying out offset processing on the intersection surface of the intersection ring, and solving the size channel of the intersection ring according to a second rule;
a work area element size channel obtaining step: and solving the size channel of the work domain element according to a third rule.
Preferably, the first rule is: if the work area element is used for fixed-axis rough machining, the layer cutting surface is a plane vertical to the direction of the cutter shaft; if the field element is subjected to variable-axis rough machining, the layer tangent plane is a curved surface parallel to the bottom surface.
Preferably, the second rule is: an offset curve is arranged on the using surface of the intersecting ring on the intersecting surface, and if the offset curve just intersects, the distance value is the minimum channel value of the intersecting ring; if the offset curve degenerates to a point, the distance value is the maximum channel value of the intersection ring.
Preferably, the third rule is: if single-layer processing is carried out, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the minimum value of the maximum channel of the intersecting rings is the maximum channel; and if the layered processing is carried out, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the maximum value of the maximum channel of the intersecting rings is the maximum channel.
Preferably, the blade selection range calculation comprises:
and a work domain element characteristic calculation step: inputting a current work area element, reading the allowance set by the process template and calculating the feature of the work area element;
selecting: removing the small groove working area elements, and taking the rest working area elements as the tool selection basis for rough machining of the groove cavity;
initial value setting step: taking the maximum value of the maximum channel as an initial value and a local cutting ratio;
a calculation step: calculating the cutting ratio of the current work area element;
a judging step: judging whether the cutting ratio condition is met, if so, determining the diameter of the current tool with the tool range unit and carrying out the tool range unit judgment step; if the judgment result is negative, reducing the diameter, and skipping to the calculation step;
and a work area element judgment step: judging whether a next domain element exists, if so, entering a similar judgment step; if the judgment result is negative, skipping to solve the output step;
and (3) a similar judgment step: judging whether the calculated work domain elements are similar, if so, determining the current work domain element, and skipping to the work domain element judging step; if the judgment result is negative, jumping to the step of calculating the characteristics of the domain elements;
and solving and outputting: solving the maximum value and the minimum value of all the domain elements to be respectively used as the feasible tool diameters of all the domain elements, and outputting results.
Preferably, the total number of partial tools of each domain element is less than or equal to 3.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a processing time model, which provides a basis for evaluating the processing efficiency of the cutter selection;
2. according to the geometric characteristics such as the cross surface profile of the project area metalayer, the channel distance and the like, and the combination of the tool library and the cutting database, the selection process of the rough machining tool of the structural member is optimized, and the maximum machining efficiency based on tool resources is realized;
3. the invention provides a tool combination optimization selection method facing a processing time model on the basis of a genetic algorithm, effectively solves the automatic tool selection problem of complex components with less than more than ten slots and more than dozens or even hundreds of slots, obviously reduces the labor burden of the interactive programming of a technician and improves the programming efficiency of a numerical control program.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of the optimized selection process of a rough machining tool for a multi-cavity component based on a genetic algorithm.
Fig. 2(a) is a schematic view of a hierarchical channel solution layer tangent plane.
FIG. 2(b) is a schematic diagram of the cross-channel generated by the layer cutting plane.
FIG. 3 is a process flow of domain meta-channel computation.
FIG. 4 is a flow chart of blade selection range calculation.
Fig. 5(a) is a contour of a region to be processed of the field element.
FIG. 5(b) shows the possible ranges of the tools t1 to t3 with respect to FIG. 2 (a).
FIG. 5(c) is a schematic view of the amount of work that can be performed by different tool sets.
Fig. 6 is a schematic diagram of the cutting volume per unit time tm.
FIG. 7 is a schematic diagram of pairwise pairing.
Fig. 8 is a schematic diagram of a single-point cross.
Fig. 9 is a directed diagram of a possible solution for tool allocation.
Fig. 10 is a flow chart of a tool assignment algorithm.
FIG. 11 is a schematic view of a test part.
Fig. 12 is a diagram of an iterative process of a genetic algorithm.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
FIG. 1 shows an optimized selection process of a rough machining tool for a multi-cavity component based on a genetic algorithm, which is proposed by the present invention, wherein firstly, a single domain meta-channel is calculated, and the tool selection range is narrowed according to the constraint of a residual ratio; obtaining an optional tool set by intersecting the tool library and the tool selection range; then, according to a machining time model in the cutter milling process under the current technological condition, the shortest machining time is taken as an optimization target, and the optimal selection of the optimal cutter combination is realized by utilizing a genetic algorithm; and finally, operating a cutter allocation algorithm on the global cutter to perform local cutter allocation. Further, the optimized selection method of the rough machining cutter of the multi-groove cavity component based on the genetic algorithm can be implemented according to the following steps:
a) calculating a work domain channel;
b) calculating the range of knife selection;
c) intersecting the tool magazine with the range;
d) optimizing a genetic algorithm;
e) and (6) distributing the cutter.
According to an embodiment of the invention, in the step 101), channel calculation for slot cavity processing is performed by adopting a layering method aiming at the channel calculation of the work area element, and the maximum channel and the minimum channel of the work area element are solved by using a layer tangent plane intersection ring channel.
The layering method intersects the work domain element with the layer tangent plane to form an intersection plane { F1,F2,...,FnThe layer tangent plane is schematically shown in FIG. 2(a), and the boundary of the intersection plane is an intersection ring { C }1,C2,...,Cn}. Is provided with
Is an intersecting ring Ci(i 1.., n) is added to the first point,
is the central axis of the intersecting ring.
Note the book
Then D is called the intersection point
The channel at the position, referred to as the cross-loop channel for short, the cross-loop channel being the intersectionThe basis of the noodle selection knife.
The minimum value of the internal element is called an interlinking minimum channel; the maximum is called the cross-loop maximum channel, which is schematically shown in FIG. 2 (b).
According to the idea of the hierarchical method, the domain meta-channel is calculated from the intersection ring channel and is represented as
Wherein f is a work domain element channel operator, channel operator for short. For the same domain element, if different processing strategies are adopted, the channel operators are different. Domain cell channel SpIs referred to as the maximum channel, SpIs simply referred to as the minimum channel.
The process of computing the domain meta channel, as shown in fig. 3, includes the following steps:
step 1: extracting a processing mode, and determining a cutter shaft control mode and whether layering processing is carried out;
step 2: creating layer tangent planes according to the rule 1, and sequencing the layer tangent planes;
step 3: intersecting each layered surface with the work area element in sequence to generate an intersecting surface and an intersecting ring;
step 4: performing offset processing on the intersection surface of the intersection ring, and solving the size channel of the intersection ring according to the rule 2;
step 5: and (4) solving the size channel of the work domain element according to the rule 3.
According to the rule 1, if the tool area element is subjected to fixed-axis rough machining, the layer cutting surface is a plane perpendicular to the direction of the tool axis; if the field element is subjected to variable-axis rough machining, the layer tangent plane is a curved surface parallel to the bottom surface.
In the rule 2, an offset curve is applied to the intersection use surface on the intersection surface, and if the offset curve just intersects, the distance value is the minimum intersection channel value; if the offset curve degenerates to a point, the distance value is the maximum channel value of the intersection ring.
In rule 3, if a single layer is processed, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the minimum value of the maximum channel of the intersecting rings is the maximum channel. And if the layered processing is carried out, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the maximum value of the maximum channel of the intersecting rings is the maximum channel.
Based on the above rule, channel calculation is carried out on each work domain element, and the big and small channels form a sequence couple (D)min,Dmax)。
According to one embodiment of the invention, the conditions for restricting the tool selection in step 102) include a tool use condition C1And cutting ratio condition C2。
C1:DL(t)＜DmaxWork area element giSelected tool set TLi(t1,t2,..) maximum diameter D of the cutterL(t) requires less than the maximum channel D of the work cellmax；
C2:DS(t)≤DAccMinimum diameter DS(t) the diameter D of the tool to be worked is less than or equal to the diameter D of the working elementAcc。
The ratio of the machining area volume of the rough machining cutter set T to the part P to the volume to be machined in the rough machining stage of the machining area element is called rough machining cutting volume ratio, cutting ratio for short, and is marked as rho-VmV, the diameter of the cutter satisfying the current cutting ratio of the work area element is DAcc. The range of knife selection is that the using condition C of the knife is satisfied simultaneously1And cutting ratio condition C2The tool diameter range of (D) is marked as theta (DA)min,DAmax)。
The knife selection range calculation method comprises the following steps: according to rule 4, after eliminating the small groove work area elements, all the work area elements are constrained by the cutting ratio rho, and the diameter D of the feasible cutter of all the work area elementsAccIs taken as the lower limit DA of the knife selection diametermin，DAccMaximum value of (D) is taken as the upper limit DA of the blade selection diametermaxAnd the blade selection range calculation flow is shown in fig. 4, and the steps are as follows:
step1, inputting the current work area element, reading the allowance set by the process template and calculating the characteristics of the work area element, such as a size channel, the height of the work area element and the material amount;
step2, eliminating the small groove work area elements, and taking the rest work area elements as the tool selection basis for rough machining of the groove cavity.
Step3 takes the maximum value of the maximum channel as DAccTaking rho as a local cutting ratio;
step4, calculating the cutting ratio of the current tool area element;
step5 judges whether the cutting ratio condition is satisfied? Is to determine the feasible tool diameter D of the current work area elementAccCarrying out the next step; otherwise, reduce DAccJump Step 4;
step6 judges whether there is a next domain cell? If yes, carrying out the next step; if not, jumping to Step 8;
step7 determines whether the calculated domain elements are similar? Is, determine the current domain element DAccJump Step 6; if not, jumping to Step 1;
step8 solving all the domain elements DAccRespectively as DA of all work area elementsmax、DAminAnd output.
The rule 4 is a small groove judgment rule, and the content is a work area element g in the part PiIf the selected post-stage side wall finish machining tool can completely remove the material area to be machined of the tool area element, g is judgediIs a small slot domain element.
According to an embodiment of the present invention, the step 103) is to process the selectable tool for the flute cavity rough machining preset by the user in the tool library L, and to obtain the selectable tool group by intersecting the selectable tool with the tool selection range θ
Theoretically according to the knife selection range, the upper limit DA of the knife selection range thetamaxAnd the lower limit DA of the knife selectionminCorresponding tools can be found in the tool library L, but the same is not necessarily true in practice, and the maximum and minimum tool selection formulas in the intersection process are as follows:
in the formulaN, N is the number of tools in the tool magazine L (in order from small to large),
the ith is a rough machining tool. If D ═ DAminThen t ismin＝t*(ii) a If D ═ DAmaxThen t ismax＝t*. Let L internally conform to tmin≤t≤tmaxThe cutters t are arranged from big to small
Wherein t is1＝tmax、tm＝tminM is
The number of inner cutters.
According to an embodiment of the present invention, the step 104) process performs encoding, initial population selection, current population fitness calculation, genetic opwords and convergence judgment step by step.
And (4) coding, namely performing binary coding on the cutters in the selectable cutter group according to the selection state. Alternative sets of tools are known
tiCorresponding to the selected cutter diameter D (t)i) Wherein the number of cutters is m. Using binary coding, expressed in 0-1 variable
Inner cutter ti(1 < i.ltoreq.m):
when n is 1, the global and local optimal tools are optional tool sets without using an optimization algorithm
Minimum knife t ofmThe machining time is the tool tmThe processing time of (2); when n is more than 1 and less than or equal to m, one global optimal cutter is the minimum cutter tmTherefore, the global optimal cutter selection belongs to the n-1 dimension optimization problem, and the selectable cutter domain of the optimized variable is
Then
The internal cutter can be expressed as a selection state genome according to the selection state
The genome is n-1 in length. Integrating a status into a genome
Represents one individual, and a plurality of individuals constitute a population omega.
Selecting an initial population, including the number n (omega) of the population and individuals
The selection of the cutter selection method can be based on the process experience of the cutter selection, the number of the overall cutters can be preset to be 2-3, namely, the population with the genome length of 1 or 2 accounts for a certain proportion (more than 50%) of n (omega), and the population selection of the rest proportion is completely random, so that the efficiency and the accuracy of the algorithm are improved.
And calculating the current population fitness by taking the processing time of the cutter combined milling process as a fitness function. The calculation process of the machining time in the cutter milling process is as follows:
a certain work cell g in the part PiThe processing process comprises
The working amount d of the jth cutterjCan be expressed as:
wherein
Represents the j-th tool to the work area element giThe workable field of (1). Working area element giThe variable amount of the tool changes when different tool combinations are used as shown in fig. 5.
By constant cutting volume method, while considering the constraint P of cutting powerc≤[Pc]To determine the actual cutting depth apAnd cutting width ae. As shown in fig. 6, unit time tm(min) schematic diagram of the internal cutting volume, which can be expressed as
The cutting volume in unit time is kept constant, the optimal cutting volume of the cutter is determined through a few groups of cutting experiments, the cutting depth and the cutting width of an experimental cutter or other unexperienced cutters can be calculated, and the cutting database of all cutters in the cutter library is easy to form and is used as a reference for cutter selection.
Machining time T of tool milling processpIncluding the actual cutting time TmTool lifting time T in the work area unitrInter-unit linkage tool advance and retreat time TlAnd time of changing the tool, i.e.
Tp＝Tm+Tr+Tl+n(TG)*ο，
Wherein T ism、TrAnd calculating the approximate time according to the current work area element process scheme. T islO is a constant value,
f1as a function of the number of 1's in the genome. Machining time T of combined milling process by toolpAs evaluation criteria, individual individuals in the population Ω were calculated
T ofp，TpThe smaller the individual gene, the more superior the probability of being leftLarge, TpAs a global tool TGFunction of (2)
Genetic manipulation, selection, crossing and mutation manipulation are carried out to make the population evolve continuously. Classifying the population into selective retention individuals, cross individuals, variant individuals and selective rejection individuals according to the proportion, wherein the corresponding ratio is omegar、ωc、ωv、ωaAnd ω isr+ωc+ωv+ωa＝1。
The selection operation is adopted for discarding the individuals with low fitness in the population, namely the individuals with low fitness in the population are discarded according to the proportion omegaaSelecting individuals and directly abandoning; directly reserving individuals with high fitness in the population, namely proportionally omega from the populationrIndividual direct retention was selected. Cross-over was performed using pairwise pairings of individuals, as shown in FIG. 7, followed by gene point crossing, as shown in FIG. 8. Mutation operations are random inversions of gene values within the genome, i.e., 1 → 0 or 0 → 1.
And (4) convergence judgment, namely judging whether a termination condition is reached or not, and judging whether the fitness variation of the optimal individual is lower than a given threshold value or not so as to determine whether termination is carried out or not.
Decoding, analyzing the binary coding result according to the inverse operation of the coding, and determining the selected global cutter.
According to one embodiment of the invention, said step 105) tool assignment. And determining a local optimal tool set of each work area element by the global optimal tool set.
The feasible solutions for tool allocation are combined into a directed graph structure, and as shown in FIG. 9, the constraint is to select a path from t1To tjThe nodal lines of (a) minimize the total cutting time. The number of local tool sets influences the number of tool changes of the global tool according to the principle of less tool changes, so for simplification, the total number of local tools of each domain element is not more than 3.
The tool assignment algorithm flow, fig. 10. When j ≧ 3, only the feasible solution { t ≧ 32,t3,...,tj-2,tj-1Phi, selecting a tool by calculating the actual cutting time T of the toolmComparing T in the feasible solutionmThe smallest corresponding toolWith a result of selection of 2 or 3 cutters, i.e. TLi(t1,tj) Or TLi(t1,tk,tj) Wherein k is 2, 3. When j is 2, the result is 2 cutters, i.e. TLi(t1,tj). When j equals 1, the result is 1 tool, i.e. TLi(tj)。
As shown in FIG. 11, the number of the groove cavities is 82 and the number of the small grooves is 1 in the test part of the embodiment, so that the number of the effective groove cavities selected by the roughing tool is 81, and the optimization result data is shown in Table 1.
TABLE 1 optimized data records
FIG. 12 shows that the processing time varies with the number of iterations of the genetic algorithm population, and the time variation is greatly reduced after 5 generations, and the genetic algorithm can be considered to converge after 10 generations.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in such a manner as to implement the same functions in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (8)
1. A multi-cavity component rough machining cutter optimization selection method based on a genetic algorithm is characterized by comprising the following steps:
and a work domain channel calculation step: channel calculation of groove cavity processing is realized by adopting a layering method, and a maximum channel and a minimum channel of a power domain element are solved by a layer tangent plane intersection ring channel;
and a cutter selection range calculation step: limiting the tool selection range based on the tool use condition and the cutting ratio condition, and determining the tool selection range;
the tool magazine and range intersection step: the selectable tools in the tool library are intersected with the tool selection range to obtain a selectable tool group;
genetic algorithm optimization steps: performing genetic algorithm optimization on the selectable cutter group through coding, initializing population selection, calculating the current population fitness, genetic operation words and convergence judgment to determine a global optimal cutter group;
a cutter distribution step: and determining a cutter allocation method of the local optimal cutter group of each work area element by the global optimal cutter group.
2. The genetic algorithm-based optimized selection method for a roughing tool of a multi-fluted cavity member according to claim 1 wherein the genetic algorithm optimization step comprises:
and (3) encoding: carrying out binary coding on the cutters in the selectable cutter group according to the selection state;
initializing population selection: initializing population selection, and presetting an initial value of a global cutter;
calculating the current population fitness: calculating the current population fitness by taking the processing time of the cutter combined milling process as a fitness function;
genetic manipulation steps: genetic operation, selection, crossing and variation operation methods in the population evolution process;
a convergence judgment step: convergence judgment, namely judging termination conditions;
and (3) decoding: and (5) determining a global optimal cutter by using a binary coding result analysis method.
3. The genetic algorithm-based optimized selection method for the rough machining tool of the multi-flute cavity component according to claim 1, wherein the calculation of the tool domain meta-channel comprises:
layered processing confirmation: extracting a processing mode, and determining a cutter shaft control mode and whether layering processing is carried out;
layer section sequencing: creating layer tangent planes according to a first rule, and sequencing the layer tangent planes;
a step of intersection: intersecting each layered surface with the work area element in sequence to generate an intersecting surface and an intersecting ring;
obtaining a cross-loop large channel and a cross-loop small channel: carrying out offset processing on the intersection surface of the intersection ring, and obtaining the size channel of the intersection ring according to a second rule;
a work area element size channel obtaining step: and solving the size channel of the work domain element according to a third rule.
4. The genetic algorithm-based optimized selection method for a rough machining tool for a multi-fluted chamber member according to claim 3, wherein the first rule is: if the work area element is used for fixed-axis rough machining, the layer cutting surface is a plane vertical to the direction of the cutter shaft; if the field element is subjected to variable-axis rough machining, the layer tangent plane is a curved surface parallel to the bottom surface.
5. The genetic algorithm-based optimized selection method for a rough machining tool for a multi-fluted chamber member according to claim 3, wherein the second rule is: an offset curve is arranged on the using surface of the intersecting ring on the intersecting surface, and if the offset curve just intersects, the distance value is the minimum channel value of the intersecting ring; if the offset curve degenerates to a point, the distance value is the maximum channel value of the intersection ring.
6. The genetic algorithm-based optimized selection method for a rough machining tool for a multi-fluted chamber member according to claim 3, wherein the third rule is: if single-layer processing is carried out, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the minimum value of the maximum channel of the intersecting rings is the maximum channel; and if the layered processing is carried out, the minimum value of the minimum channel of each layer of the intersecting rings is the minimum channel, and the maximum value of the maximum channel of the intersecting rings is the maximum channel.
7. The genetic algorithm-based optimized selection method for the rough machining tool of the multi-flute cavity component according to claim 1, wherein the tool selection range calculation comprises:
and a work domain element characteristic calculation step: inputting a current work area element, reading the allowance set by the process template and calculating the property of the work area element;
selecting: removing the small groove working area elements, and taking the rest working area elements as the tool selection basis for rough machining of the groove cavity;
initial value setting step: taking the maximum value of the maximum channel as an initial value and a local cutting ratio;
a calculation step: calculating the cutting ratio of the current work area element;
a judging step: judging whether the cutting ratio condition is met, if so, determining the diameter of the current feasible tool of the tool domain element, and performing a tool domain element judgment step; if the judgment result is negative, reducing the diameter, and skipping to the calculation step;
and a work area element judgment step: judging whether a next domain element exists, if so, entering a similar judgment step; if the judgment result is negative, skipping to solve the output step;
and (3) a similar judgment step: judging whether the calculated work domain elements are similar, if so, determining the current work domain element, and skipping to the work domain element judging step; if the judgment result is negative, jumping to the step of calculating the characteristics of the domain elements;
and solving and outputting: and solving the maximum value and the minimum value of all the domain elements to respectively serve as the feasible tool diameters of all the domain elements, and outputting the result.
8. The genetic algorithm-based optimized selection method for rough machining tools for multi-fluted chamber members according to claim 1, wherein the total number of local tools per field element is less than or equal to 3.
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