CN115994619A - Intelligent generation method for part machining process route - Google Patents
Intelligent generation method for part machining process route Download PDFInfo
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Abstract
The invention discloses an intelligent generation method of a part machining process route, which comprises the following steps: acquiring part processing information; constructing a part processing feature set to form a processing process chain set to be selected; establishing a part characteristic processing process chain matching model by adopting a fuzzy comprehensive evaluation method, wherein the method comprises the steps of establishing a process chain matching evaluation index, establishing a processing process chain matching evaluation function and obtaining a matching processing process chain set; constructing a comprehensive evaluation function of the processing step sequence cost, and constructing a part processing process route planning model by considering the processing sequence constraint; planning a part processing process route by adopting a discrete artificial seagull optimization algorithm to obtain an optimized part processing process route; the method can realize intelligent generation of the part processing process route, improve the process route generation efficiency and ensure the quality of the process route generation.
Description
Technical Field
The invention relates to the technical field of part machining, in particular to an intelligent generation method of a part machining process route.
Background
At present, materials of mechanical products are diversified, functions are enriched, structures are complex, and higher requirements are put on decision of processing methods of mechanical parts. The existing method mainly comprises the steps of firstly extracting different local processing characteristics of the existing part, classifying the different local processing characteristics, establishing a corresponding characteristic processing method library aiming at the processing characteristics of different classifications, judging local structural characteristics on a target part through a characteristic identification method, searching the corresponding processing method aiming at the identified local characteristics in the library, and finally further reasoning and deciding through directly reusing the processing method of an identification object or combining characteristic processing requirement constraints, thereby obtaining certain achievements.
The process step ordering in the process route planning is one of the key factors affecting the design level of the overall process route, and the decision process is very complex due to the influence of many factors, such as the diversity of part features and processing methods, the experience of process decisions, and the complexity of the production environment. Moreover, for many manufacturing enterprises, the production and processing modes with high efficiency, low cost and high quality have great influence on the survival, competition and development of the enterprises, so that the method is also the focus of researches of students.
The prior art research on process route generation has focused mainly on the problem of ordering the process steps. There is less focus on how to match processing chains for processing features, and the only few ways to mention processing chain matching are simply by processing feasibility to select a chain. In addition, there is little research on the expression and the generation method of the sequence constraint between the steps.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent generation method of a part machining process route, which acquires part machining information, and acquires a matching process chain set by constructing a characteristic machining process chain matching algorithm based on a fuzzy comprehensive evaluation method; constructing a comprehensive evaluation function of the processing step sequence cost, and constructing a part processing process route planning model by considering the processing sequence constraint; and planning a part machining process route based on a discrete artificial seagull optimization algorithm, realizing intelligent generation of the part machining process route, and obtaining an optimized part machining process route. Compared with the traditional manual programming process route, the intelligent generation of the part processing process route is realized, the generation efficiency of the processing process route is improved, the generation quality of the processing process route is ensured, and the processing and time cost of the processing process is reduced.
The aim of the invention can be achieved by the following technical scheme:
an intelligent generation method of a part machining process route comprises the following steps:
s1: acquiring part processing information according to the part three-dimensional model, wherein the part processing information comprises part geometric shape information and part process requirement information;
s2: constructing a part processing feature set according to the part geometric shape information and the part process requirement information; forming a processing technology chain set to be selected according to the part processing characteristics in the part processing characteristic set;
s3: establishing a part characteristic processing process chain matching model by adopting a fuzzy comprehensive evaluation method, wherein the method comprises the steps of establishing a process chain matching evaluation index, establishing a processing process chain matching evaluation function and obtaining a matching processing process chain set;
s4: summarizing and uniformly numbering all the processing steps of the matched processing process chains in the matched processing process chain set, arranging the processing steps according to a specific sequence to form a processing step sequence, arranging the processing sequence of each processing step in the processing step sequence as a decision variable, constructing a comprehensive evaluation function of the processing step sequence cost by taking the lowest cost of the processing step sequence into an optimization target, and constructing a part processing process route planning model by considering the constraint of the processing sequence;
S5: based on a processing technology route planning model, a discrete artificial seagull optimization algorithm is adopted to plan a part processing technology route, a discrete strategy is added in the seagull optimization algorithm, two variation strategies, namely a Sine mapping strategy and a neighbor learning strategy, are added in a seagull population, and the optimized part processing technology route is obtained.
Further, any part to be machined includes a plurality of machining features, which form a part machining feature set, and the part machining feature set is shown in formula (1):
F＝{f 1 ,f 2 ,…,f n } (1)
in the formula (1), f i Representing the ith machined feature of the part to be machined, n being the total number of machined features of the part to be machined, the ith machined feature f of the part to be machined i As shown in formula (2):
f i ＝{x i ,y i ,z i } (2)
in the formula (2), x i Representing the machining precision of the ith machining feature of the part, describing the degree of coincidence of the actual size of the machined feature with the size specified in the drawing, y i Machined surface roughness representing the ith machined feature of a partDescribing the surface smoothness degree after feature processing, z i Other machining requirements, which represent the ith machining feature of the part, describe requirements specific to certain machining features, such as pore size of the pore feature, whether the planar feature is an end face, etc.
Further, the established part feature processing technology chain matching model adopts a fuzzy comprehensive evaluation method, evaluation analysis is carried out on the processing technology chain matching process of the single processing feature, and a matched processing technology chain is selected from a processing technology chain set to be selected for each processing feature of the part;
The specific steps for acquiring the matched processing technology chain set comprise:
according to each machining characteristic in the part machining characteristic set of the part to be machined, a corresponding set of to-be-selected machining process chains is constructed, and the set of to-be-selected machining process chains is shown in a formula (3):
PC i ＝{pc 1 ,pc 2 ,…,pc k ,…,pc m },k＝1,2,...,m (3)
in formula (3), PC i Representing the ith machined feature f according to the desired machined part i A set of constructed candidate processing chains, wherein pc k Representing the kth processing chain in the set of processing chains to be selected, as shown in formula (4):
in the formula (4), o q Representation pc k In the q-th processing step, p k Representation pc k The total number of processing steps;
based on a part processing feature set of a part to be processed, constructing a process chain matching evaluation index according to processing manufacturability requirements, thereby establishing a processing process chain matching evaluation function, wherein the process chain matching evaluation index comprises a processing feature level evaluation index and a material type evaluation index, the processing feature level evaluation index comprises processing precision, processing surface roughness and other processing requirements, and the material type evaluation index comprises a material type;
wherein the machining feature level evaluation index is related to machining features of the part, and a machining feature evaluation factor set is constructed according to machining precision, machining surface roughness and other machining requirements in the machining feature level evaluation index, as shown in formula (5):
U＝{u 1 ,u 2 ,u 3 } (5)
In the formula (5), u g (g=1, 2, 3) represents each influencing factor in the processing characteristic evaluation factor set, where u 1 Indicating the machining precision, u 2 Indicating the roughness of the machined surface, u 3 Representing other processing requirements;
constructing a machining characteristic evaluation each influence factor membership function, wherein the machining characteristic evaluation comprises a machining precision membership function, a machining surface roughness membership function and other machining requirement membership functions, and calculating each influence factor membership index value respectively;
the machining precision membership function is shown as (6):
in formula (6), x i Representing the ith machining feature f of the part to be machined i The number of required machining precision grades is set,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a processing accuracy membership function of +.>
Representing a processing chain pc to be selected k Maximum level of precision that can be processed, +.>
Representing a processing chain pc to be selected k The lowest precision grade that can process, the higher the precision grade, the more numerical valueSmall, i.e.)>
The processing surface roughness membership function is shown as (7):
in formula (7), y i Representing the ith machining feature f of the part to be machined i The desired value of the roughness of the machined surface,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a function of the membership of the machined surface roughness, +.>
Representing a processing chain pc to be selected k Maximum surface roughness value that can be processed, +.>
Representing a processing chain pc to be selected k The minimum surface roughness value that can be processed, the greater the surface roughness, the greater the roughness value, i.e. +.>
Other processing requirement membership functions are shown in formula (8):
in formula (8), z i Representing the ith machining feature f of the part to be machined i Is used for the production of the steel sheet,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Membership functions of other processing requirements;
the material type evaluation index is used for evaluating the matching degree between the processable material type of the processing chain to be selected and the material type of the part to be processed;
constructing a material type membership function according to the material type in the material type evaluation index, wherein the membership function is shown in a formula (9):
in the formula (9), ma represents the type of material required for the processing of the part,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k The membership function of the material type of (a) when the ith processing feature f is to be processed for the part to be processed i The constructed kth alternative processing technology chain pc k When the processable material type comprises ma, the processing is feasible, the membership function value is 1, and when the ith processing characteristic f of the part to be processed is obtained i The constructed kth alternative processing technology chain pc k When the type of the processable material does not comprise ma, the processing is not feasible, and the membership function value is 0;
and according to the machining precision membership function, the machining surface roughness membership function, other machining requirement membership functions and material type membership functions, carrying out weighted summation according to a formula (10), and constructing a machining process chain matching evaluation function:
in the formula (10), the amino acid sequence of the compound,
representation ofIth machined feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a machining precision membership index value, +.>
Representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a processed surface roughness membership index value +.>
Representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a membership index value of other processing requirements, +.>
Is to represent the ith machining characteristic f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a material type membership index value, w 1 ,w 2 ,w 3 ,w 4 Representation->
Corresponding weight coefficient, and w 1 +w 2 +w 3 +w 4 ＝1；
For the processing chain set PC to be selected i All the processing chains in the process are constructed as shown in a formula (10) to obtain a corresponding processing chain matching evaluation function value set { Score } 1 ,...,Score k ,...,Score m The elements in the processing technology chain matching evaluation function value set are sequenced from the big to the small, and the processing technology chain corresponding to the maximum element in the processing technology chain matching evaluation function value set is the ith processing feature f of the needed processed part i Is matched with a processing technology chain;
repeating the steps of constructing the processing technology chain matching evaluation function for other processing characteristics in the part processing characteristic set of the part to be processed to obtain matching processing technology chains of all n processing characteristics of the part to be processed, so as to form a matching processing technology chain set.
Further, the processing steps of all the matched processing process chains in the matched processing process chain set are summarized and arranged according to a specific sequence to form a processing step sequence, the processing step sequence AJS is shown as a formula (11),
AJS＝{Job 1 |〈a 1 ,b 1 〉,Job 2 |〈a 2 ,b 2 〉,…,Job N |〈a N ,b N 〉} (11)
in the formula (11), N represents the total number of steps in the processing step sequence AJS, job h Representing the h step of the part machining, two-tuple (a) h ,b h > represents Job h Processing feature priority a of corresponding step h And processing step priority b h ；
In the process of part machining, the machining characteristics are divided into three types: finish reference machining characteristics, attachment machining characteristics and common machining characteristics; the fine reference machining feature refers to a feature which needs to be machined first as a machining precision reference of other machining features, the attached machining feature refers to a feature attached to the other machining feature, the attached machining feature needs to be machined after machining of the attached machining feature is finished, and the common machining feature refers to a feature except the fine reference machining feature and the attached machining feature;
the processing feature priority a h Comprising the following steps: the processing features should be processed according to the sequence of the fine reference processing features, the common processing features and the attached processing features;
if step Job h Belonging to the precision reference machining feature, the corresponding machining feature priority a h =1, if Job h Belonging to the common processing characteristics, the corresponding processing characteristics have priority a h =2, if Job h Belonging to the dependent processing feature, the corresponding processing feature priority a h ＝3；
The processing step priority b h Comprising the following steps: for each processing step of the processing characteristics, the processing steps should be according to rough processing and semi-finish processing Finishing and superfinishing in sequence;
if step Job h For rough machining, the corresponding machining step priority b h =1, if Job h Semi-finishing, then its corresponding processing step priority b h =2, if Job h For finishing, its corresponding processing step priority b h =3, if Job h For superfinishing, then its corresponding process step priority b h ＝4。
The decision variables of the part processing process route planning problem are as follows: each machining step Job in the machining step sequence AJS of the required machined part h H=1, 2, where, N is arranged in the processing sequence.
In the process of machining the part, if equipment such as a machine tool, a cutter, a clamp and the like are frequently replaced and the machining method is frequently replaced under the condition that the machining process equipment and the machining method are determined, the machining time and the machining cost are increased, and the machining precision is not guaranteed, so that the sequence of the machining steps of the part is aimed at generating the machining step sequence with the minimum replacement times of the machining process equipment and the machining method under the condition that the constraint condition of the machining sequence is met, and the machining step sequence is used as an optimized part machining process route of the required machined part. In the processing of parts, when the processing methods used in the two processing steps and the machine tools, jigs, etc. coincide, it is necessary to arrange them together as much as possible for processing, so as to reduce the replacement of the processing methods and the processing equipment.
Further, the construction processing step sequence cost comprehensive evaluation function is shown as a formula (12):
in the formula (12), MCC represents the machine tool replacement cost, FCC represents the machining jig replacement cost, CCC represents the machining tool replacement cost, PCC represents the machining method replacement cost, ω 1 ,ω 2 ,ω 3 ,ω 4 Respectively represent the weight coefficient corresponding to MCC, FCC, CCC, PCC and has omega 1 +ω 2 +ω 3 +ω 4 ＝1；
In the part machining process, if machining equipment such as a machine tool, a cutter, a clamp and the like and a machining method are frequently replaced, the machining time and the machining cost are increased, and the guarantee of the machining precision is not facilitated, so that the number of times of changing the machining equipment and the machining method should be reduced as much as possible;
the processing sequence constraint of the processing step sequence in the formula (12) means that each processing step in the processing step sequence must satisfy the processing sequence constraint of the processing feature priority and the processing step priority;
the replacement cost MCC of the processing machine tool is shown as (13):
in formula (13), mcc h Job is the h processing step of the part h N is the total number of steps included in the sequence of processing steps;
mcc h the calculation is shown in formula (14):
in the formula (14), mc h To process the h processing step Job h The processing machine tool is adopted;
In the process route planning, the more the number of times of replacing a processing clamp used in the part processing process is, the more the consumed time is, and the longer the time for completing the part processing step sequence is, so that the number of times of replacing the processing clamp is reduced as much as possible;
the machining jig replacement cost FCC is as shown in formula (15):
in formula (15), fcc h Is the h of the partJob step h The machining jig replacement cost of (2) is calculated as shown in formula (16):
in formula (16), fc h To process the h processing step Job h The processing clamp is adopted in the process;
in the process route planning, the more the number of times of replacing a machining tool used in the part machining process is, the more the time consumed is, and the longer the time for completing the part machining step sequence is, so that the number of times of replacing the machining tool should be reduced as much as possible;
the machining tool replacement cost CCC is represented by formula (17):
in formula (17), ccc h Job is the h processing step of the part h The machining tool replacement cost of (2) is calculated as shown in formula (18):
in formula (18), cut h To process the h processing step Job h The processing tool is adopted;
in the process route planning, the more the number of times of changing the processing method used in the processing process of the part is, the more the consumed time is, and the longer the time for completing the processing step sequence of the part is, so that the number of times of changing the processing method should be reduced as much as possible;
The replacement cost PCC of the processing method is shown in a formula (19):
in the formula (19), the wc h Job is the h processing step of the part h The replacement cost of the processing method is calculated as shown in a formula (20):
in the formula (20), pc h To process the h processing step Job h The processing method is adopted.
Further, the processing technology route planning model is shown in a formula (21):
minf(Seq)＝mincost(Seq) (21)
in the formula (21), seq represents all the processing step sequence sets to be selected, cost (Seq) represents the processing step sequence cost calculated according to the processing step sequence cost comprehensive evaluation function, and the processing process route planning model selects the processing step sequence with the minimum processing step sequence cost from all the processing step sequence sets to be selected as the optimized part processing process route.
The discrete artificial seagull optimization algorithm is an intelligent optimization algorithm inspired by biology, and the main inspiration of the algorithm is from the migration and attack behaviors of seagulls in the nature.
Further, the gull l in the discrete artificial gull optimization algorithm is defined as a sequence X of processing steps to be selected for the required processing part l (t) initializing an N-dimensional vector, N being the total number of process steps of the process step sequence, the vector representation of seagull l being as shown in formula (22):
X l (t)＝(x l1 (t),x l2 (t),…,x lN (t)) (22)
In the formula (22), x lj (t) represents a sequence of processing steps X to be selected l Processing steps in a certain sequence in (t), t=0, 1,2, …, iter max Represents the current evolution iteration times and iter of the discrete artificial seagull optimization algorithm max And the maximum evolution iteration times preset by the discrete artificial seagull optimization algorithm are represented.
Further, the specific steps of obtaining the optimized part processing process route by adopting a discrete artificial seagull optimization algorithm comprise the following steps:
s81: discrete artificial seagull optimization algorithm parameter setting and population initialization, setting population quantity p and maximum evolution iteration number item max Setting X according to sea gull attack curve parameters uc, vc best Recording the seagull with the minimum processing step sequence cost in the current evolution iterative search process;
s82: generating a sequence of processing steps of continuous value codes of the sea-gull population of the initialized population quantity p by adopting a Sine mapping strategy, discretizing the sequence of processing steps of continuous value codes of the initialized sea-gull population by using a discrete strategy to generate a sequence of processing steps of the sea-gull population with evolution search of the initial t=0, and calculating the sequence of processing steps X corresponding to each sea-gull in the initialized population one by one l Process step sequence cost (X) l (t)) and take as X the seagull with the minimum processing step sequence cost best ；
S83: the discrete artificial seagull optimization algorithm evolution iteration specifically comprises the following steps:
s831: the migratory behavior of the gull population is performed, and the migratory behavior of the processing step sequence represented by the gull l is performed by the formulas (23) to (27):
C l (t)＝A l (t)×X l (t) (23)
B l (t)＝2×(A l (t)) 2 ×rd l (25)
M l (t)＝B l (t)×(X best (t)-X l (t)) (26)
D l (t)＝|C l (t)+M l (t)| (27)
in the formulas (23) to (27), C l (t) shows a sequence of processing steps represented by a seagull that does not collide with other seagulls, A l (t) TableShowing the moving behavior of seagull in evolving search solution space, f c Is a function of linear attenuation for controlling A l The frequency of movement of (t), the initial value of which is generally set to 2, B l (t) is a random movement behavior in which rd l Is a random number, and has a value of 0,1]Between D l (t) represents the gull X with the minimum processing step sequence cost in the gull population of the current evolution iteration of the gull l best (t) approaching;
s832: the attack behavior of the seagull population is carried out by the following formulas (28) to (32):
x' l (t)＝r l (t)×cos(kc(t)) (28)
y' l (t)＝r l (t)×sin(kc(t)) (29)
z' l (t)＝rc l ×kc(t) (30)
r l (t)＝uc×e kc(t)·vc ×I (31)
X l (t+1)＝(D l (t)⊙x' l (t)⊙y' l (t)⊙z' l (t))+X best (t) (32)
in the formulae (28) to (32), x' l (t)，y' l (t) and z' l (t) is the attack behavior of the seagull in the x, y and z dimensions, and rc is the spiral attack l For the radius of the helix, kc (t) ε [0,2π]Is a constant defining the spiral shape, I is an N-dimensional vector with element values of 1, uc and vc are gull attack curve parameters set in S81;
S833: updating the gull population after the migration and attack actions are executed by using a neighbor learning strategy;
s84: the processing step sequence of continuous value code of the seagull population updated by using the neighbor learning strategy is discretized by using the discrete strategy to obtain the processing step sequence of the seagull population, and the processing step sequence X corresponding to each seagull in the seagull population is calculated one by one l (t+1) processing step sequence cost (X l (t+1))；
S85: according to the processing step sequence cost (X) l (t+1)), and updating the gull X with minimum process step sequence cost during the course of the part process route best The specific updating rule is as follows: if the seagull X with the minimum processing step sequence cost in the seagull population of the current evolution iteration best (t+1) ratio X best The cost value of the processing step sequence is small, let X best ＝X best (t+1), otherwise, not updating X best ；
S86: current evolution iteration number t<Maximum evolution iteration number iter max Turning to S83, otherwise, the part machining process route planning algorithm is stopped and a machining step sequence X with minimum machining step sequence cost is output best As a route for optimizing the processing technology of the parts.
Further, the discrete strategy encodes a sequence of processing steps X of continuous value code generated in the calculation of the discrete artificial seagull optimization algorithm l The element values in (t) are arranged in order from small to large, if the element values of the elements are equal in size, the current element is arranged in the sequence of processing steps in the front-back order, and the result is ordered according to the element values, each processing step sequence X l The processing step elements of (t) correspondingly obtain an integer serial number, and the integer serial number is used for replacing the element value of the original position, namely a processing step sequence with discrete values is generated, wherein each element x lj (t), j=1, 2,..n represents a part machining step number in the corresponding machining step sequence;
the Sine mapping is a chaotic mapping, which can make the distribution of the gull population initially generated by the discrete artificial gull optimization algorithm more uniform, and the expression is shown as a formula (33):
x l(v+1) (init)＝μsin(πx lv (init)). v＝1,2,…,N-1；l＝1,2,…,p； (33)
in the formula (33), mu E [0,4 ]]Generally, 0.99, p is the number of sea gull population, x lv (init) is a sequence X of processing steps represented by a sea gull l generated by random initialization l The v-th processing step of (t) has a value of [0,1 ]]Processing steps with continuous value codingNumbering, x l(v+1) (init) is the sequence X of processing steps represented by the sea gull l after being mapped by the Sine l A process step number of the v-th process step of (t) encoded with a continuous value;
the neighbor learning strategy is as shown in formulas (34) to (37):
R l (t)＝||X l (t+1)-X l (t)|| (34)
N l (t)＝{X ne (t)|||X l (t)-X ne (t)||≤R l (t)} (35)
In the formulae (34) to (37), R l (t) represents the nearest neighbor radius, X of the continuous value-encoded processing step sequence represented by the current first seagull ne (t) a neighbor sequence of a continuous value-encoded processing step sequence represented by the current first seagull, N l (t) a neighbor sequence set representing a sequence of continuous value-encoded processing steps represented by the current first seagull, rand (N) l (t)) means randomly selecting a neighbor sequence from a set of neighbor sequences XNE l (t+1) represents a sequence of processing steps, X, of continuous value codes represented by the first seagull to be selected obtained from neighbor sequence learning l (t+1) is a sequence of processing steps for continuous value encoding represented by the first seagull updated using the neighbor learning strategy.
Compared with the prior art, the invention has the following technical effects:
(1) According to the processing technology requirements, the processing characteristics of the parts are considered, a fuzzy evaluation method is adopted to establish a part characteristic processing technology chain matching model, and a processing technology chain matching evaluation function is established to evaluate and analyze the processing technology chain matching process of the processing characteristics by establishing a processing characteristic level evaluation index and a material type evaluation index, so that an optimal processing technology chain is matched for each processing characteristic of the parts;
(2) According to the processing technology requirements, respectively obtaining matching processing technology chains of all processing characteristics of the part to be processed to form a matching processing technology chain set, and generating a processing step sequence to be selected; in order to optimize the sequence of the processing steps of the processing step sequence to be selected, the replacement cost of processing equipment and a processing method required to be used in the processing process of the part is considered, and meanwhile, under the constraint of the processing sequence, a comprehensive evaluation function of the cost of the processing step sequence is constructed; on the basis, a processing technology route planning model is constructed, and a processing step sequence with the minimum processing step sequence cost is selected from all processing step sequence sets to be selected to be used as an optimized part processing technology route;
(3) Providing and designing a discrete artificial seagull optimization algorithm, adding a discrete strategy in the seagull optimization algorithm, adding two variation strategies of a Sine mapping strategy and a neighbor learning strategy in seagull population evolution, and applying the discrete artificial seagull optimization algorithm to the problem of part processing process route planning; the discrete artificial seagull optimization algorithm can improve the population diversity of the seagull algorithm and the searching efficiency of population evolution iteration, and improve the computing performance and efficiency of the optimal or near optimal optimized part processing process route output by the algorithm.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a three-dimensional model diagram of a part "double-disk adapter tube";
fig. 3 is a diagram of a process chain matching evaluation index composition structure of a processing feature.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, modifications, equivalents, improvements, etc., which are apparent to those skilled in the art without the benefit of this disclosure, are intended to be included within the scope of this invention.
The core thought of the technical scheme of the invention is as follows: constructing a to-be-selected processing technology chain set according to the processing characteristics of the parts required by the parts, adopting a fuzzy comprehensive evaluation method, and selecting a matching processing technology chain for each processing characteristic of the parts by constructing a technology chain matching evaluation index, so as to select the matching processing technology chain for all the processing characteristics of the parts to form the matching processing technology chain set of the parts required to be processed; further summarizing all the matched processing steps of the processing technology chain into a processing step sequence, arranging the processing sequence of each processing step in the processing step sequence as a decision variable, taking into consideration the replacement cost of processing equipment and a processing method required by processing and the constraint of the processing sequence, and establishing a part processing technology route planning model by constructing a comprehensive evaluation function of the processing step sequence cost; and finally, performing iterative search by adopting a discrete artificial seagull optimization algorithm evolution, and obtaining a processing step sequence with the minimum processing step sequence cost as an optimized part processing process route, thereby realizing intelligent generation of the part machining process route, obtaining reasonable optimized sequencing of the processing steps in the processing process route, improving the generation efficiency of the processing process route, reducing the processing time consumption and the processing cost of the processing process route, and ensuring the quality and the efficiency of the generation of the processing process route.
The algorithm is described in detail below in connection with the actual part processing requirements and models.
As shown in fig. 1, the intelligent generation method of the part machining process route comprises the following steps:
s1: acquiring part processing information according to the part three-dimensional model, wherein the part processing information comprises part geometric shape information and part process requirement information;
s2: constructing a part processing feature set according to the part geometric shape information and the part process requirement information; forming a processing technology chain set to be selected according to the part processing characteristics in the part processing characteristic set;
s3: establishing a part characteristic processing process chain matching model by adopting a fuzzy comprehensive evaluation method, wherein the method comprises the steps of establishing a process chain matching evaluation index, establishing a processing process chain matching evaluation function and obtaining a matching processing process chain set;
s4: summarizing and uniformly numbering all the processing steps of the matched processing process chains in the matched processing process chain set, arranging the processing steps according to a specific sequence to form a processing step sequence, arranging the processing sequence of each processing step in the processing step sequence as a decision variable, constructing a comprehensive evaluation function of the processing step sequence cost by taking the lowest cost of the processing step sequence into an optimization target, and constructing a part processing process route planning model by considering the constraint of the processing sequence;
S5: based on a processing technology route planning model, a discrete artificial seagull optimization algorithm is adopted to plan a part processing technology route, a discrete strategy is added in the seagull optimization algorithm, two variation strategies, namely a Sine mapping strategy and a neighbor learning strategy, are added in a seagull population, and the optimized part processing technology route is obtained.
As shown in fig. 2, the process characteristic information and the process chain information of each process characteristic of the part "double-disk adapter tube" required to be processed in this embodiment are obtained as follows:
as shown in fig. 3, based on a part machining feature set of a part to be machined, a process chain matching evaluation index is constructed according to machining manufacturability requirements, thereby establishing a machining process chain matching evaluation function, wherein the process chain matching evaluation index comprises a machining feature level evaluation index and a material type evaluation index, the machining feature level evaluation index comprises machining precision, machining surface roughness and other machining requirements, and the material type evaluation index comprises a material type.
Refinement of S2:
for a part "double-disk adapter," part tooling characteristic information is first obtained as shown in table 1.
TABLE 1 part "double disk adapter" processing feature set information
For each processing feature of the part "double-disk adapter tube" in table 1, corresponding set information of the processing chains to be selected, summarized by the process personnel, can be obtained, including tables 2 and 3. Wherein table 2 is the set information of the processing technique chains to be selected corresponding to the outer circle surface characteristics, and table 3 is the set information of the processing technique chains to be selected corresponding to the hole characteristics.
TABLE 2 information on the set of process chains to be selected for the outer cylindrical surface features
TABLE 3 candidate processing chain set information for hole characteristics
Refinement of S3:
and after the information of the set of processing technique chains to be selected shown in the table 2 and the table 3 is respectively formed according to the processing characteristics of the outer circular surface of the part double-disc joint pipe, the processing characteristics of the hole and the like, matching the processing technique chains with the optimal processing characteristics of the part double-disc joint pipe by adopting a fuzzy comprehensive evaluation method.
Taking the characteristic of the outer circular surface of F06 as an example, the characteristic is marked as a processing characteristic F 6 This is expressed as:
f 6 ＝{x 6 ,y 6 ,z 6 }
wherein x is 6 Representing the machining characteristic f 6 Is x 6 ＝8；y 6 Representing the machining characteristic f 6 Preferably y 6 ＝1.2；z 6 Representing the machining characteristic f 6 Is preferably z 6 No, meaning no other processing requirements; furthermore, machining feature f 6 The type m of the used material is low-carbon cast steel;
f 6 Set PC corresponding to 10 candidate processing process chains shown in Table 2 6 Expressed as:
PC 6 ＝{pc 1 ,pc 2 ,...,pc 10 } = {101,102,..110 }, where 101,102 …,110 are the numbers of the alternative processing chain.
By processing features f 6 For the 1 st processing chain pc to be selected 1 Machining process chain matching evaluation function value calculation illustration of=101;
respectively processing precision x 6 Machined surface roughness y =8 6 =1.2, other processing requires z 6 =none and m is low carbon cast steel, substituting into the calculation formula of each influence factor membership function in the machining characteristic evaluation factor set, including machining precision membership function
Machining surface roughness membership function>
Membership function for other processing requirements
Material type membership function +.>
Calculating to obtain a processing characteristic f 6 For the 1 st processing chain pc to be selected 1 Each membership function value of =101:
machining precision membership function:
processing a surface roughness membership function: />
OthersProcessing requirement membership function: />
Material type membership function: />
In the same way, the obtained processing characteristics f 6 The membership function values corresponding to the 10 processing chains to be selected are shown in table 4:
TABLE 4 processing characteristics f 6 Each membership function value corresponding to 10 process chains to be selected
Processing characteristics f were determined based on the membership function values of the influence factors in the processing characteristics evaluation factor set shown in Table 4 6 Constructing a processing technology chain matching evaluation function of a processing technology chain to be selected:
for the current computing instance, w 1 ＝w 2 ＝w 3 ＝w 4 ＝0.25；
For processing characteristic f 6 The calculation results of the processing chain matching evaluation function value Score of each of the candidate processing chains are shown in table 5:
table 5 processing chain matching evaluation function values for each of the candidate processing chains
Selecting one to-be-selected processing technology chain with the largest processing technology chain matching evaluation function value from 10 to-be-selected processing technology chains as processing characteristics f 6 Is a matched processing chain, i.e. processing chain 103'Rough turning-semi-finish turning).
The matching processing process chains can be respectively screened out for other processing characteristics of the part 'double-disc joint pipe' according to the method, and a matching processing process chain set of the part is obtained, and the result is shown in Table 6:
table 6 matching process chain set of parts
Refinement of S4:
establishing a part machining process route planning model for preparing a machining process route planning of a part 'double-disk adapter pipe', wherein the model comprises the steps of summarizing, uniformly numbering and arranging all machining steps of matched machining process chains in a matched machining process chain set of the part according to a specific sequence to form a machining step sequence AJS, and matching machining equipment (a machining machine tool, a cutter and a clamp) and a machining method required by machining characteristics in each machining step in the machining step sequence AJS; calculating a machining step sequence comprehensive cost evaluation function of the machining step sequence AJS according to the machining machine tool replacement cost, the machining fixture replacement cost, the machining tool replacement cost, the machining method replacement cost and the machining sequence constraint; and (3) taking the processing sequence arrangement of each processing step in the processing step sequence as a decision variable, constructing a part processing process route planning model, and selecting the processing step sequence with the minimum processing step sequence cost from all the processing step sequence sets to be selected as an optimized part processing process route.
Firstly, a processing step sequence AJS is constructed, the processing steps of all matched processing process chains of processing features F01-F20 in the table 6 are summarized and numbered uniformly, and the processing step sequence AJS is formed by arranging according to a specific sequence, and then the processing step sequence AJS is expressed as follows:
AJS＝{Job 1 |〈a 1 ,b 1 〉,Job 2 |〈a 2 ,b 2 〉,...,Job h |〈a h ,b h 〉,...,Job 68 |〈a 68 ,b 68 〉}
wherein, each processing step Job h H=1, 2..68 attribute values are given in table 7:
TABLE 7 Attribute values for the processing steps
Job for each processing step h H=1, 2..68 the machining equipment (machining tool, machining fixture) and machining method used are shown in table 8:
table 8 processing equipment and processing method for each processing step
The comprehensive cost evaluation function of the processing step sequence is calculated, and the processing step sequence AJS1 is taken as an example;
firstly, a processing machine tool number sequence { mc ] corresponding to a processing step sequence AJS1 is obtained h Sequence of { fc } and machining jig numbers h Sequence of { cut } and machining tool number h Sequence { pc } of processing method numbers h Calculating a machine tool change cost sequence { mcc } h Sequence { fcc } machining jig replacement cost h Sequence of tool change costs { ccc }, machining tool change costs { ccc h Sequence { wc } and processing method replacement cost h -as in table 9-table 17:
table 9 processing step sequence AJS1
Table 10 processing step sequence AJS1 processing machine tool number sequence { mc ] h }
Table 11 processing step sequence AJS1 processing tool Change cost sequence { mcc } h }
Table 12 machining jig number sequence { fc ] of machining step sequence AJS1 h }
Table 13 machining jig replacement cost sequence { fcc ] for machining step sequence AJS1 h }
Table 14 machining tool number sequence { cut ] of machining step sequence AJS1 h }
Table 15 machining step sequence AJS1 machining tool replacement cost sequence { ccc h }
Table 16 processing method number sequence { pc ] of processing step sequence AJS1 h }
Table 17 processing step sequence AJS1 processing method replacement cost sequence { pcb h }
According to the data in each table, calculating the relative values of the replacement cost of the processing equipment, the replacement cost of the processing method and the like of the processing step sequence AJS1, and the method comprises the following steps: machine tool replacement cost mcc=2, tooling fixture replacement cost fcc=2, tooling tool replacement cost ccc=34, and tooling method replacement cost pcc=49.
Omega is taken out 1 ＝0.5，ω 2 ＝0.2，ω 3 =0.2 and ω 4 =0.1, and the machining sequence Cost comprehensive evaluation function cost=13 of the available machining sequence AJS1 is calculated in combination with the machining sequence constraint.1。
Based on the comprehensive evaluation function of the processing step sequence cost, the processing sequence of each processing step in the processing step sequence is used as a decision variable, a part processing process route planning model is constructed, and the processing step sequence with the minimum processing step sequence cost is selected from all the processing step sequence sets to be selected as an optimized part processing process route.
Refinement of S5:
the part processing technology route planning method based on the discrete artificial seagull optimization algorithm is adopted, the processing technology route planning of the part double-disk socket pipe is taken as an example, and the calculation process is as follows.
The initializing parameter setting in the discrete artificial seagull optimization algorithm comprises the following steps: setting the gull population size p=50 and the maximum evolution iteration number iter max The gull attack curve parameters uc=1, vc=1, =100.
Generating a sequence of processing steps for initializing continuous value codes of a gull population by adopting a Sine mapping strategy, as shown in table 18:
table 18 set mapping strategy generated processing step sequence for initializing continuous value codes of seagull population
Discretizing the sequence of processing steps for initializing continuous value codes of the seagull population by using a discretization strategy to generate a sequence of processing steps for generating an evolution search seagull population with the primary t=0, as shown in table 19:
table 19 shows a sequence of processing steps corresponding to the discretized gull population
Through continuous evolution iteration of the artificial seagull population, migration and attack behavior updating are carried out in each generation of seagull population evolution, and updating is carried out by using a neighbor learning strategy; when the maximum evolution iteration number iter is completed max After =100, the evolution iterative planning algorithm is stopped, and the machining step sequence with the minimum machining step sequence cost is output as the optimized part machining process route.
The sequence of processing steps corresponding to the seagull population after evolution iteration to the 100 th generation is shown in table 20:
table 20 evolves and iterates to the sequence of processing steps corresponding to the gull population after passage 100
Finally, a process step sequence X with the smallest process step sequence cost is output best As an optimized part machining processThe route is shown in Table 21, and the part processing process route X is optimized best Corresponding process step sequence cost (X best )＝10.6。
Optimized part processing process route X output by table 21 planning algorithm best
Examples of the process route planning by the part 'double-disc joint pipe' include: firstly, all processing characteristics of a part 'double-disc adapter tube', such as a circular ring plane, an outer circular surface, a hole and the like, are acquired; secondly, taking the outer circular surface characteristics and the hole characteristics as examples respectively, constructing a corresponding processing technology chain set to be selected; thirdly, taking the outer circle surface feature F06 as an example, constructing a processing technology chain matching evaluation function by adopting a fuzzy comprehensive evaluation method, calculating a technology chain matching evaluation function value of a to-be-selected processing technology chain, screening out a process of matching the processing technology chain for the feature, and respectively screening out the matching processing technology chain for other processing features of the part double-disc bearing pipe to obtain a matching processing technology chain set of the part; then, establishing a part machining process route planning model, namely summarizing, uniformly numbering and arranging machining steps of all matched machining process chains in a part matched machining process chain set to form a machining step sequence according to a specific sequence, and matching machining equipment (a machining machine tool, a machining tool and a machining clamp) and a machining method required by machining characteristics in each machining step in the machining step sequence; calculating a machining step sequence comprehensive cost evaluation function of a machining step sequence according to the machining machine tool replacement cost, the machining fixture replacement cost, the machining tool replacement cost, the machining method replacement cost and the machining sequence constraint; the processing sequence arrangement of each processing step in the processing step sequence is used as a decision variable, a part processing process route planning model is constructed, and the processing step sequence with the minimum processing step sequence cost is selected from all processing step sequence sets to be selected to be used as an optimized part processing process route; and finally, planning the processing process route of the part 'double-disc bearing pipe' by adopting a part processing process route planning method based on a discrete artificial seagull optimization algorithm to obtain a processing step sequence with minimum processing step sequence cost, and taking the processing step sequence as an optimized part processing process route of the part 'double-disc bearing pipe'. The details are not described in detail.
Likewise, the processing route of other mechanical parts can be planned by adopting the method provided herein, and the method is not particularly exemplified.
In summary, the method provided by the invention is feasible, practical, and can well realize intelligent generation of the mechanical processing process route of the part, so that the reasonable optimized sequencing of the processing steps in the processing process route is obtained, the generation efficiency of the processing process route is improved, the processing time consumption and cost of the processing process route are reduced, and the technical effects of the quality and efficiency of the generation of the processing process route are ensured.
Claims (9)
1. The intelligent generation method of the part machining process route is characterized by comprising the following steps of:
s1: acquiring part processing information according to the part three-dimensional model, wherein the part processing information comprises part geometric shape information and part process requirement information;
s2: constructing a part processing feature set according to the part geometric shape information and the part process requirement information; forming a processing technology chain set to be selected according to the part processing characteristics in the part processing characteristic set;
s3: establishing a part characteristic processing process chain matching model by adopting a fuzzy comprehensive evaluation method, wherein the method comprises the steps of establishing a process chain matching evaluation index, establishing a processing process chain matching evaluation function and obtaining a matching processing process chain set;
S4: summarizing and uniformly numbering all the processing steps of the matched processing process chains in the matched processing process chain set, arranging the processing steps according to a specific sequence to form a processing step sequence, arranging the processing sequence of each processing step in the processing step sequence as a decision variable, constructing a comprehensive evaluation function of the processing step sequence cost by taking the lowest cost of the processing step sequence into an optimization target, and constructing a part processing process route planning model by considering the constraint of the processing sequence;
s5: based on a processing technology route planning model, a discrete artificial seagull optimization algorithm is adopted to plan a part processing technology route, a discrete strategy is added in the seagull optimization algorithm, two variation strategies, namely a Sine mapping strategy and a neighbor learning strategy, are added in a seagull population, and the optimized part processing technology route is obtained.
2. The intelligent generation method of a part machining process route according to claim 1, wherein any part to be machined comprises a plurality of machining features to form a part machining feature set, and the part machining feature set is shown in formula (1):
F＝{f 1 ,f 2 ,…,f n } (1)
in the formula (1), f i Representing the ith machined feature of the part to be machined, n being the total number of machined features of the part to be machined, the ith machined feature f of the part to be machined i As shown in formula (2):
f i ＝{x i ,y i ,z i } (2)
in the formula (2), x i Representing the machining precision of the ith machining feature of the part, describing the degree of coincidence of the actual size of the machined feature with the size specified in the drawing, y i Representing the roughness of the machined surface of the ith machined feature of the part, describing the degree of surface smoothness after machining of the feature, z i Other machining requirements, which represent the ith machining feature of the part, describe requirements specific to certain machining features, such as pore size of the pore feature, whether the planar feature is an end face, etc.
3. The method for intelligently generating a part machining process route according to claim 2, wherein the specific step of establishing the part feature machining process chain matching model and obtaining a matching machining process chain set comprises the following steps:
s31: according to each machining characteristic in the part machining characteristic set of the part to be machined, a corresponding set of to-be-selected machining process chains is constructed, and the set of to-be-selected machining process chains is shown in a formula (3):
PC i ＝{pc 1 ,pc 2 ,…,pc k ,…,pc m },k＝1,2,...,m (3)
in formula (3), PC i Representing the ith machined feature f according to the desired machined part i A set of constructed candidate processing chains, wherein pc k Representing the kth processing chain in the set of processing chains to be selected, as shown in formula (4):
in the formula (4), o q Representation pc k In the q-th processing step, p k Representation pc k The total number of processing steps;
s32: based on a part processing feature set of a part to be processed, constructing a process chain matching evaluation index according to processing manufacturability requirements, thereby establishing a processing process chain matching evaluation function, wherein the process chain matching evaluation index comprises a processing feature level evaluation index and a material type evaluation index, the processing feature level evaluation index comprises processing precision, processing surface roughness and other processing requirements, and the material type evaluation index comprises a material type;
s33: according to the machining precision, the machining surface roughness and other machining requirements in the machining feature level evaluation index, a machining feature evaluation factor set is constructed, as shown in a formula (5):
U＝{u 1 ,u 2 ,u 3 } (5)
in the formula (5), u g (g=1, 2, 3) represents each influencing factor in the processing characteristic evaluation factor set, where u 1 Indicating the machining precision, u 2 Indicating the roughness of the machined surface, u 3 Representing other processing requirements;
s34: constructing a machining characteristic evaluation each influence factor membership function, wherein the machining characteristic evaluation comprises a machining precision membership function, a machining surface roughness membership function and other machining requirement membership functions, and calculating each influence factor membership index value respectively;
The machining precision membership function is shown as (6):
in formula (6), x i Representing the ith machining feature f of the part to be machined i The number of required machining precision grades is set,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a processing accuracy membership function of +.>
Representing a processing chain pc to be selected k Maximum level of precision that can be processed, +.>
Representing a processing chain pc to be selected k The lower the precision grade, the smaller the value, i.e. +.>
The processing surface roughness membership function is shown as (7):
in formula (7), y i Representing the ith machining feature f of the part to be machined i The desired value of the roughness of the machined surface,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a function of the membership of the machined surface roughness, +.>
Representing a processing chain pc to be selected k Maximum surface roughness value that can be processed, +.>
Representing a processing chain pc to be selected k The minimum surface roughness value that can be processed is the greater the surface roughness, the greater the roughness value, i.e
Other processing requirement membership functions are shown in formula (8):
in formula (8), z i Representing the ith machining feature f of the part to be machined i Is used for the production of the steel sheet,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Membership functions of other processing requirements;
s35: constructing a material type membership function according to the material type in the material type evaluation index, wherein the membership function is shown in a formula (9):
in the formula (9), ma represents the type of material required for the processing of the part,
representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k The membership function of the material type of (a) when the ith processing feature f is to be processed for the part to be processed i The constructed kth alternative processing technology chain pc k When the processable material type comprises ma, the processing is feasible, the membership function value is 1, and when the ith processing characteristic f of the part to be processed is obtained i The constructed kth alternative processing technology chain pc k When the type of the processable material does not comprise ma, the processing is not feasible, and the membership function value is 0;
s36: and according to the machining precision membership function, the machining surface roughness membership function, other machining requirement membership functions and material type membership functions, carrying out weighted summation according to a formula (10), and constructing a machining process chain matching evaluation function:
in the formula (10), the amino acid sequence of the compound, 
Representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a machining precision membership index value, +.>
Representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a processed surface roughness membership index value +.>
Representing the ith machining feature f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a membership index value for other processing requirements,/>
is to represent the ith machining characteristic f of the part to be machined i The constructed kth alternative processing technology chain pc k Is a material type membership index value, w 1 ,w 2 ,w 3 ,w 4 Representation->
Corresponding weight coefficient, and w 1 +w 2 +w 3 +w 4 ＝1；
S37: for the processing chain set PC to be selected i All the processing chains in the process are used for constructing a processing chain matching evaluation function according to the method as in S36, and a corresponding processing chain matching evaluation function value set { Score } 1 ,...,Score k ,...,Score m The elements in the processing technology chain matching evaluation function value set are sequenced from the big to the small, and the processing technology chain corresponding to the maximum element in the processing technology chain matching evaluation function value set is the ith processing feature f of the needed processed part i Is matched with a processing technology chain;
s38: and S31-S37 are repeated, and matching processing process chains of n processing characteristics of the part to be processed are respectively obtained to form a matching processing process chain set.
4. The method for intelligently generating a machining process route for a part according to claim 3, wherein the machining steps of all the matching machining process chains in the matching machining process chain set are summarized, numbered in a unified manner and arranged in a specific order to form a machining step sequence, the machining step sequence AJS is shown in formula (11),
AJS＝{Job 1 |<a 1 ,b 1 >,Job 2 |<a 2 ,b 2 >,…,Job N |<a N ,b N >} (11)
in the formula (11), N represents the total number of steps in the processing step sequence AJS, job h Representing the number of the part processing step corresponding to the h processing step of the part processing, and a binary group<a h ,b h >Representing Job h Processing feature priority a of corresponding step h And processing step priority b h ；
In the process of part machining, the machining characteristics are divided into three types: finish reference machining characteristics, attachment machining characteristics and common machining characteristics; the fine reference machining feature refers to a feature which needs to be machined first as a machining precision reference of other machining features, the attached machining feature refers to a feature attached to the other machining feature, the attached machining feature needs to be machined after machining of the attached machining feature is finished, and the common machining feature refers to machining features except the fine reference machining feature and the attached machining feature;
the processing feature priority a h Comprising the following steps: the processing features should be processed according to the sequence of the fine reference processing features, the common processing features and the attached processing features;
if step Job h Belonging to the precision reference machining feature, the corresponding machining feature priority a h =1, if Job h Belonging to the common processing characteristics, the corresponding processing characteristics have priority a h =2, if Job h Belonging to the dependent processing feature, the corresponding processing feature priority a h ＝3；
The processing step priority b h Comprising the following steps: for the processing steps of each processing feature, the processing should be performed in the order of rough processing, semi-finishing, finishing and superfinishing;
if step Job h For rough machining, the corresponding machining step priority b h =1, if Job h Semi-finishing, then its corresponding processing step priority b h =2, if Job h For finishing, its corresponding processing step priority b h =3, if Job h For superfinishing, then its corresponding process step priority b h ＝4。
5. The method of intelligent generation of a part machining process route according to claim 4, wherein the construction of the machining step sequence cost comprehensive evaluation function is as shown in formula (12):
in the formula (12), MCC represents the machine tool replacement cost, FCC represents the machining jig replacement cost, CCC represents the machining tool replacement cost, PCC represents the machining method replacement cost, ω 1 ,ω 2 ,ω 3 ,ω 4 Respectively represent the weight coefficient corresponding to MCC, FCC, CCC, PCC and has omega 1 +ω 2 +ω 3 +ω 4 In the machining process of the part, if machining equipment such as a machine tool, a cutter, a clamp and the like and a machining method are frequently replaced, the machining time and the machining cost are increased, and the guarantee of the machining precision is not facilitated, so that the number of times of changing the machining equipment and the machining method should be reduced as much as possible;
the processing sequence constraint of the processing step sequence in the formula (12) means that each processing step in the processing step sequence must satisfy the processing sequence constraint of the processing feature priority and the processing step priority;
the replacement cost MCC of the processing machine tool is shown as (13):
in formula (13), mcc h Job is the h processing step of the part h N is the total number of steps included in the sequence of processing steps;
mcc h the calculation is shown in formula (14):
in the formula (14), mc h To process the h processing step Job h The processing machine tool is adopted;
the machining jig replacement cost FCC is as shown in formula (15):
in formula (15), fcc h Job is the h processing step of the part h The machining jig replacement cost of (2) is calculated as shown in formula (16):
in formula (16), fc h To process the h processing step Job h The processing clamp is adopted in the process;
The machining tool replacement cost CCC is represented by formula (17):
in formula (17), ccc h Job is the h processing step of the part h The machining tool replacement cost of (2) is calculated as shown in formula (18):
in formula (18), cut h To process the h processing step Job h The processing tool is adopted;
the replacement cost PCC of the processing method is shown in a formula (19):
in the formula (19), the wc h Job is the h processing step of the part h The processing method of (2) is replaced byThe calculation is shown in the formula (20):
in the formula (20), pc h To process the h processing step Job h The processing method is adopted.
6. The intelligent generation method of a part machining process route according to claim 5, wherein the machining process route planning model is as shown in formula (21):
in the formula (21), seq represents all the processing step sequence sets to be selected, cost (Seq) represents the processing step sequence cost calculated according to the processing step sequence cost comprehensive evaluation function, and the processing process route planning model selects the processing step sequence with the minimum processing step sequence cost from all the processing step sequence sets to be selected as the optimized part processing process route.
7. The method of claim 6, wherein the gull l of the discrete artificial gull optimization algorithm is defined as a sequence of processing steps X to be selected for the desired part to be processed l (t) initializing an N-dimensional vector, N being the total number of process steps of the process step sequence, the vector representation of seagull l being as shown in formula (22):
X l (t)＝(x l1 (t),x l2 (t),…,x lN (t)) (22)
in the formula (22), x lj (t) represents a sequence of processing steps X to be selected l Processing steps in a certain sequence in (t), t=0, 1,2, …, iter max Represents the current evolution iteration times and iter of the discrete artificial seagull optimization algorithm max Indicating separationThe maximum evolution iteration number preset by the scattered artificial seagull optimization algorithm.
8. The method for intelligently generating a part machining process route according to claim 7, wherein the specific step of obtaining the optimized part machining process route by adopting a discrete artificial seagull optimization algorithm comprises the following steps:
s81: discrete artificial seagull optimization algorithm parameter setting and population initialization, setting population quantity p and maximum evolution iteration number item max Setting X according to sea gull attack curve parameters uc, vc best Recording the seagull with the minimum processing step sequence cost in the current evolution iterative search process;
s82: generating a sequence of processing steps of continuous value codes of the sea-gull population of the initialized population quantity p by adopting a Sine mapping strategy, discretizing the sequence of processing steps of continuous value codes of the initialized sea-gull population by using a discrete strategy to generate a sequence of processing steps of the sea-gull population with evolution search of the initial t=0, and calculating the sequence of processing steps X corresponding to each sea-gull in the initialized population one by one l Process step sequence cost (X) l (t)) and take as X the seagull with the minimum processing step sequence cost best ；
S83: the discrete artificial seagull optimization algorithm evolution iteration specifically comprises the following steps:
s831: the migratory behavior of the sea gull population is carried out, and the processing step sequence X represented by the sea gull l l The migration behavior of (t) is performed by formulas (23) to (27):
C l (t)＝A l (t)×X l (t) (23)
B l (t)＝2×(A l (t)) 2 ×rd l (25)
M l (t)＝B l (t)×(X best (t)-X l (t)) (26)
D l (t)＝|C l (t)+M l (t)| (27)
in the formulas (23) to (27), C l (t) shows a sequence of processing steps represented by a seagull that does not collide with other seagulls, A l (t) represents the movement behavior of seagull in the evolution search solution space, f c Is a function of linear attenuation for controlling A l The frequency of movement of (t), the initial value of which is generally set to 2, B l (t) is a random movement behavior in which rd l Is a random number, and has a value of 0,1]Between D l (t) represents the gull X with the minimum processing step sequence cost in the gull population of the current evolution iteration of the gull l best (t) approaching;
s832: the attack behavior of the seagull population is carried out by the following formulas (28) to (32):
x' l (t)＝r l (t)×cos(kc(t)) (28)
y' l (t)＝r l (t)×sin(kc(t)) (29)
z' l (t)＝rc l ×kc(t) (30)
r l (t)＝uc×e kc(t)·vc ×I (31)
X l (t+1)＝(D l (t)⊙x' l (t)⊙y' l (t)⊙z' l (t))+X best (t) (32)
in the formulae (28) to (32), x' l (t)，y' l (t) and z' l (t) is the attack behavior of the seagull in the x, y and z dimensions, and rc is the spiral attack l For the radius of the helix, kc (t) ε [0,2π]Is a constant defining the spiral shape, I is an N-dimensional vector with element values of 1, uc and vc are gull attack curve parameters set in S81;
s833: updating the gull population after the migration and attack actions are executed by using a neighbor learning strategy;
s84: for continuous values of gull population updated using neighbor learning strategyThe coded processing step sequence is discretized by using a discretization strategy to obtain the processing step sequence of the seagull population, and the processing step sequence X corresponding to each seagull in the seagull population is calculated one by one l (t+1) processing step sequence cost (X l (t+1))；
S85: according to the processing step sequence cost (X) l (t+1)), and updating the gull X with minimum process step sequence cost during the course of the part process route best The specific updating rule is as follows: if the seagull X with the minimum processing step sequence cost in the seagull population of the current evolution iteration best (t+1) ratio X best The cost value of the processing step sequence is small, let X best ＝X best (t+1), otherwise, not updating X best ；
S86: current evolution iteration number t<Maximum evolution iteration number iter max Turning to S83, otherwise, the part machining process route planning algorithm is stopped and a machining step sequence X with minimum machining step sequence cost is output best As a route for optimizing the processing technology of the parts.
9. The method of claim 8, wherein the discrete strategy encodes a sequence of process steps X of the continuous value code generated in the discrete artificial gull optimization algorithm calculation l The element values in (t) are arranged in order from small to large, if the element values of the elements are equal in size, the current element is arranged in the sequence of processing steps in the front-back order, and the result is ordered according to the element values, each processing step sequence X l The processing step elements of (t) correspondingly obtain an integer serial number, and the integer serial number is used for replacing the element value of the original position, namely a processing step sequence with discrete values is generated, wherein each element x lj (t), j=1, 2,..n represents a part machining step number in the corresponding machining step sequence;
the Sine mapping is a chaotic mapping, which can make the distribution of the gull population initially generated by the discrete artificial gull optimization algorithm more uniform, and the expression is shown as a formula (33):
x l(v+1) (init)＝μsin(πx lv (init))·v＝1，2，·…，N-1；l＝1，2，·…，p； (33)
in the formula (33), mu E [0,4 ]]Generally, 0.99, p is the number of sea gull population, x lv (init) is a sequence X of processing steps represented by a sea gull l generated by random initialization l The v-th processing step of (t) has a value of [0,1 ] ]Process step number, x, of continuous value code between l(v+1) (init) is the sequence X of processing steps represented by the sea gull l after being mapped by the Sine l A process step number of the v-th process step of (t) encoded with a continuous value;
the neighbor learning strategy is as shown in formulas (34) to (37):
R l (t)＝||X l (t+1)-X l (t)|| (34)
N l (t)＝{X ne (t)|||X l (t)-X ne (t)||≤R l (t)} (35)
in the formulae (34) to (37), R l (t) represents the nearest neighbor radius, X of the continuous value-encoded processing step sequence represented by the current first seagull ne (t) a neighbor sequence of a continuous value-encoded processing step sequence represented by the current first seagull, N l (t) a neighbor sequence set representing a sequence of continuous value-encoded processing steps represented by the current first seagull, rand (N) l (t)) means randomly selecting a neighbor sequence from a set of neighbor sequences XNE l (t+1) represents a sequence of processing steps, X, of continuous value codes represented by the first seagull to be selected obtained from neighbor sequence learning l (t+1) continuous value coding represented by the first seagull updated using the neighbor learning strategyIs a sequence of processing steps.
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| CN117742270A (en) * | 2023-12-25 | 2024-03-22 | 哈尔滨工业大学（威海） | Re-optimizing bacterial foraging algorithm oriented to characteristic family processing scheme selection |
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| CN116681266A (en) * | 2023-08-02 | 2023-09-01 | 广东台正精密机械有限公司 | Production scheduling method and system of mirror surface electric discharge machine |
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| CN117742270A (en) * | 2023-12-25 | 2024-03-22 | 哈尔滨工业大学（威海） | Re-optimizing bacterial foraging algorithm oriented to characteristic family processing scheme selection |
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