CN113433891B - Optimization method of cutting path - Google Patents
Optimization method of cutting path Download PDFInfo
- Publication number
- CN113433891B CN113433891B CN202110796161.6A CN202110796161A CN113433891B CN 113433891 B CN113433891 B CN 113433891B CN 202110796161 A CN202110796161 A CN 202110796161A CN 113433891 B CN113433891 B CN 113433891B
- Authority
- CN
- China
- Prior art keywords
- cutting
- idle running
- ant
- sequence set
- running stroke
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/35—Nc in input of data, input till input file format
- G05B2219/35349—Display part, programmed locus and tool path, traject, dynamic locus
Landscapes
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Catching Or Destruction (AREA)
Abstract
The invention discloses a method for optimizing a cutting path, which comprises the steps of determining the origin of a cutter and the cutting point of a cutting piece; obtaining an initial path of a cutting point of the cutter which finishes all the cut pieces from the original point of the cutter by using a greedy algorithm, and establishing a cutting sequenceAn order set, and a first idle running stroke L is calculated according to the order of elements in the cutting order set 0 (ii) a Obtaining the current path of the cutting point of the cutting piece where the ants finish all the cutting pieces from the original point of the cutter by utilizing an ant algorithm, establishing an ant access sequence set, and calculating a second idle running stroke L according to the sequence of elements in the ant access sequence set k (ii) a When the second idle running stroke L k Less than the first empty running stroke L 0 Time, second idle running stroke L k Covering the corresponding ant access sequence set with the cutting sequence set to form a new cutting sequence set; when the second idle running stroke L k Greater than or equal to the first idle running stroke L 0 When the cutting sequence set is not changed; the optimization method of the cutting path solves the problem that the optimal cutting path cannot be selected by the existing method.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to a cutting path optimization method.
Background
In the cutting process of the cutter, the total processing time of the cut pieces is equal to the sum of the cutter idle running time and the time for cutting the whole cut pieces, wherein the cutter idle running time is related to the idle running path, so that the optimization of the idle running path is very important for improving the total processing time of the cut pieces. However, the existing cutting path selection method has certain limitations, and cannot select the optimal cutting path, so that the total processing time of the cut pieces cannot be further shortened, and the production and processing cost is higher.
Disclosure of Invention
Aiming at the defects, the invention aims to provide a cutting path optimization method, which utilizes a greedy algorithm and an ant algorithm to solve the problems that the existing method cannot select the optimal cutting path, cannot further shorten the total processing time of cut pieces and has high production and processing cost.
In order to achieve the purpose, the invention adopts the following technical scheme: a method of optimizing a cutting path, comprising the steps of:
step A: determining an origin of the cutter and a cutting point of a cutting piece;
and B, step B: obtaining an initial path of a cutting point of the cutter which runs all cut pieces from the original point of the cutter by using a greedy algorithm, establishing a cutting sequence set, and calculating a first idle running stroke L according to the sequence of elements in the cutting sequence set 0 ；
Step C: obtaining the current path of the cutting points of all the cutting pieces from the origin of the cutter by using an ant algorithm, establishing an ant access sequence set, and calculating a second idle running stroke L according to the sequence of elements in the ant access sequence set k ；
Step D: comparing the first idle running stroke L 0 And said second empty running stroke L k The size of (d);
when the second idle running stroke L k Less than said first idle running stroke L 0 Then, the second idle running stroke L k Covering the corresponding ant access sequence set with the cutting sequence set to form a new cutting sequence set;
when the second idle running stroke L k Greater than or equal to the first idle running stroke L 0 The cutting order set is not changed;
step E: outputting the cutting sequence set as a cutting path.
It should be noted that the step B includes:
step B1: establishing a cutting starting point of each cut piece, and forming a cutting starting point set P, P = { P = { (P) 1 ,P 2 ,...P n In which P is 1 As starting point of the cut of the first cut segment, P 2 As a starting point of cutting of the second cut piece, P n The cutting starting point of the nth cut piece is defined;
and step B2: calculating the original point P of the tool 0 Starting point P of cutting with the kth cut piece k Distance d (P) k ,P 0 ) And calculating the tool origin P 0 Starting point P of cutting with the (k + 1) th cut piece k+1 Distance d (P) k+1 ,P 0 ) Where k is n, k +1 is nn；
And step B3: comparison of d (P) k ,P 0 ) And d (P) k+1 ,P 0 ) When d (P) is large k ,P 0 ) Greater than d (P) k+1 ,P 0 ) When P is exchanged k And P k+1 At the position in the cutting start point set P, when d (P) k ,P 0 ) D (P) is less than or equal to k+1 ,P 0 ) When P is present k And P k+1 The position in the cutting start point set P is unchanged.
Optionally, the step B further includes a step B4;
the step B4 is as follows: and repeating the step B2 and the step B3 until all the elements in the cutting starting point set P are calculated, and forming a cutting sequence set which takes the cutting starting point of the cut piece closest to the origin of the cutter as the first element and is arranged in an ascending order.
Specifically, the step C includes:
step C1: by using
The probability that the kth ant transfers from the ith panel to the jth panel at the time t is represented by the following formula:
among them, allowed k Alpha is an information heuristic factor, beta is an expected heuristic factor, and tau is allowed to be selected for the kth ant in the next step ij (t) pheromone transferred from ith to jth panel at time t, η ij (t) is the heuristic function for the transition from the ith panel to the jth panel at time t, η ij (t) is the reciprocal of the distance between the ith cut piece and the jth cut piece at the moment t, S is the terminal cut piece where the kth ant stays, and tau is (t) pheromone, η, transferred from the ith panel to the end panel at time t is (t) is a heuristic function of the transition from the ith panel to the end panel at time t;
and step C2: according to probability
Selecting the jth cut piece until the kth ant accesses all the cut pieces, establishing an ant access sequence set according to the sequence of the kth ant accessing the cut pieces, and calculating a second idle running stroke L of the cutter according to the sequence of elements in the ant access sequence set k 。
Preferably, a step F is further included between the step D and the step E;
the step F is as follows: comparing the second idle running stroke L corresponding to the kth ant k And a second idle running stroke L of the (k + 1) th ant k+1 The size of (d);
when the second idle running stroke L k+1 Less than the second empty running stroke L k Then, the second idle running stroke L k+1 The corresponding ant access sequence set covers the cutting sequence set to become a new cutting sequence set;
when the second idle running stroke L k+1 Greater than or equal to the second idle running stroke L k The set of cutting orders is unchanged.
It is worth mentioning that a step G is further included between the step F and the step E;
the step G is as follows: after all ants have visited all cut pieces, the pheromone is updated, and the formula is as follows:
τ ij (t+c)＝ρ.τ ij (t)+Δτ ij
wherein, tau ij (t + c) is pheromone transferred from the ith cutting piece to the jth cutting piece at the moment of t + c, rho is information volatilization coefficient, rho is more than 0 and less than or equal to 1, and delta tau is ij For the single-length track pheromone left on the track edge transferred from the ith panel to the jth panel,
for the kth ant from theThe unit length track pheromone left by the transfer of the i cut pieces to the track edge of the jth cut piece, and m is the total number of ants.
Optionally, in the process of updating pheromones in step G, pheromones added by the optimal path are added, and the formula is as follows:
wherein e is weight, delta b (i, j) is pheromone added when the ith cut piece is transferred to the jth cut piece, and L b Is the optimal path.
Specifically, in the process of updating pheromone in step G, compensation pheromone is added to make the pheromone left on the path where the ant passes through the path with higher number less, and the formula is as follows:
wherein Q (t) is a compensation pheromone,
r is the number of ants transferred from the ith panel to the jth panel, and R is the number of ants passing through the ith panel.
Preferably, between the step G and the step E, a step H is further included;
the step H is as follows: and C, setting a rated iteration number, and repeating the steps C, F and G until the iteration number is equal to the rated iteration number.
It is worth mentioning that the first empty running stroke L 0 And said second empty running stroke L k Obtained by the following formula:
wherein L is p Is the first idle running stroke L 0 Or a second empty run stroke L k ，l s Is the distance from the origin of the tool to the cutting point of the first cut segment, l e The distance from the cutting point of the nth cut segment to the origin of the cutter,
the empty running path from the ith cut piece to the (i + 1) th cut piece is represented by i =1 as the first cut piece and n-1 as the (n-1) th cut piece.
One of the above technical solutions has the following beneficial effects: in the cutting path optimization method, the cut pieces to be cut are closed graphs, and the efficiency can be improved by solving the hybrid traveler problem and the ant colony algorithm. Firstly, a greedy algorithm is used for generating a cutting initial point of a nearest origin, and then an ant colony algorithm is used for solving an accurate solution, so that an optimized cutting path can be obtained. The known idle running process accords with a traveler problem, a greedy algorithm is firstly used for determining the initial cutting point of each cut piece, the path optimization problem is converted into the traveler problem, after the initial cutting sequence is determined, the optimized path is calculated by using an ant algorithm and then compared, the optimal cutting path can be obtained, so that the total processing time of the cut pieces is further shortened, and the production and processing cost is reduced.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
A method of optimizing a cutting path, comprising the steps of:
step A: determining the origin of the cutter and a cutting point of the cutting piece;
and B: obtaining an initial path of a cutting point of the cutter which runs all cut pieces from the original point of the cutter by using a greedy algorithm, establishing a cutting sequence set, and calculating a first idle running stroke L according to the sequence of elements in the cutting sequence set 0 ；
And C: obtaining the current path of the cutting points of all cut pieces which are completely walked by ants from the original point of the cutter by utilizing an ant algorithm, establishing an ant access sequence set, and calculating a second idle running stroke L according to the sequence of elements in the ant access sequence set k ；
Step D: comparing the first idle running stroke L 0 And said second empty running stroke L k The size of (d);
when the second idle running stroke L k Less than the first empty running stroke L 0 While, the second idle running stroke L k Covering the corresponding ant access sequence set with the cutting sequence set to form a new cutting sequence set;
when the second idle running stroke L k Greater than or equal to the first idle running stroke L 0 The cutting order set is not changed;
step E: outputting the cutting sequence set as a cutting path.
In the cutting path optimization method, the cut pieces to be cut are closed graphs, and the efficiency can be improved by solving the hybrid traveler problem and the ant colony algorithm. Firstly, a greedy algorithm is used for generating a cutting initial point of a nearest origin, and then an ant colony algorithm is used for solving an accurate solution, so that an optimized cutting path can be obtained. The known idle running process accords with a traveling salesman problem, a greedy algorithm is firstly used for determining the cutting initial point of each cut piece, the path optimization problem is converted into the traveling salesman problem, the optimized path is calculated by using an ant algorithm after the initial cutting sequence is determined, and then the optimized path is compared, so that the optimal cutting path can be obtained, the total processing time of the cut pieces is further shortened, and the production and processing cost is reduced.
For the known cutting point set for processing the cut pieces, due to the problem of travelers, people propose various solutions for the problem of the travelers, and typical heuristic search algorithms comprise local optimization, genetic algorithm, simulated annealing and the like, and compared with the heuristic search algorithms, the ant colony algorithm is obviously superior to other algorithms. The ant colony algorithm has stronger robustness. Compared with other algorithms, the ant colony algorithm has low requirement on the initial route, namely the solving result of the ant colony algorithm does not depend on the selection of the initial route, and manual adjustment is not needed in the searching process. And secondly, the number of parameters of the ant colony algorithm is small, the setting is simple, and the ant colony algorithm is easy to be applied to solving other combined optimization problems. Ants can disperse pheromones to be distributed in the foraging process, and the shorter the path is, the thicker the pheromones are, and the shorter the pheromones are, the shortest path is gradually gathered under the guidance of a positive feedback mechanism.
In some embodiments, the step B comprises:
step B1: establishing a cutting starting point of each cut piece, and forming a cutting starting point set P, P = { P = { (P) 1 ,P 2 ,...P n In which P is 1 As starting point of the cut of the first cut segment, P 2 As a starting point of cutting of the second cut piece, P n A cutting starting point of the nth cut piece is set;
and step B2: calculating the original point P of the tool 0 Starting point P of cutting with the kth cut piece k Distance d (P) k ,P 0 ) And calculating the tool origin P 0 Starting point P of cutting with the (k + 1) th cut piece k+1 Distance d (P) k+1 ,P 0 ) Wherein k belongs to n, and k +1 belongs to n;
and step B3: comparison d (P) k ,P 0 ) And d (P) k+1 ,P 0 ) When d (P) is k ,P 0 ) Is greater than d (P) k+1 ,P 0 ) When P is exchanged k And P k+1 At a position in the cutting start point set P when d (P) k ,P 0 ) D (P) is less than or equal to k+1 ,P 0 ) When is, P k And P k+1 The position in the cutting start point set P is unchanged.
Step B4 is: and repeating the step B2 and the step B3 until all the elements in the cutting starting point set P are calculated, and forming a cutting sequence set which takes the cutting starting point of the cut piece closest to the origin of the cutter as the first element and is arranged in an ascending order.
Greedy algorithms, which do not take into account global optimality, which result in a locally optimal solution, mean that the selection that seems best at the present time is always made when solving the problem. The greedy algorithm can make optimal selection in the aspect of problem solving, and the solving rate is high. And determining the cutting initial point of the cutter by a greedy algorithm, so that the path can be optimized simply and effectively.
Through traversing all cut-parts, arrange all the distance between the cutting initial point of cut-parts and the cutter initial point according to the mode of ascending order after comparing, the distance between the cutting initial point of the cut-parts that the element that cuts in the cutting order set that leans forward corresponds and the cutter initial point will always be less than the distance between the cutting initial point of the cut-parts that the element that leans back corresponds and the cutter initial point to realize that the cutting path is from preliminary optimization.
It is worth mentioning that the step C includes:
step C1: by using
Represents the probability that the kth ant transfers from the ith panel to the jth panel at the time t, and the formula is as follows:
among them, allowed k Alpha is an information heuristic factor, beta is an expected heuristic factor and tau is the next allowable selection of the kth ant ij (t) pheromone transferred from ith panel to jth panel at time t, η ij (t) is the heuristic function for the transition from the ith panel to the jth panel at time t, η ij (t) is the reciprocal of the distance between the ith cut piece and the jth cut piece at the moment t, S is the terminal cut piece where the kth ant stays, and tau is (t) pheromone, η, transferred from ith panel to end panel at time t is (t) is a heuristic function of the transition from the ith panel to the end panel at time t;
and step C2: according to probability
Selecting the jth cut piece until the kth ant visits all the cut pieces, establishing an ant visit sequence set according to the sequence of the kth ant visiting the cut pieces, and calculating a second idle running stroke L of the cutter according to the sequence of elements in the ant visit sequence set k 。
The ant algorithm is a probabilistic algorithm for finding an optimized path. And a positive feedback mechanism is adopted, so that the search process is continuously converged and finally approaches to an optimal solution. Each ant individual can change the surrounding environment by releasing the pheromone, and each ant individual can sense the real-time change of the surrounding environment, and the individuals are indirectly communicated through the environment. The searching process adopts a distributed computing mode, and a plurality of ant individuals simultaneously perform parallel computing, so that the computing power and the operating efficiency of the algorithm are greatly improved. The heuristic probability search mode is not easy to fall into local optimum and is easy to find out the global optimum solution.
Optionally, a step F is further included between the step D and the step E;
the step F is as follows: comparing the second idle running journey L corresponding to the kth ant k And a second idle running route L of the (k + 1) th ant k+1 The size of (d);
when the second idle running stroke L k+1 Less than the second empty running stroke L k Then, the second idle running stroke L k+1 Covering the cutting sequence set by the corresponding ant access sequence set to form a new cutting sequence set;
when the second idle running stroke L k+1 Greater than or equal to the second idle running stroke L k The set of cutting orders is unchanged.
And after all ants traverse all the cut pieces, second idle running strokes corresponding to the number of the ants are formed, and the cutting path is optimized by comparing the sizes of the second idle running strokes and extracting the smallest second idle running stroke. The larger the number of ants, the more second empty runs are formed, and thus the more the optimal solution can be approached.
In some embodiments, a step G is further included between said steps F and E;
the step G is as follows: after all ants have visited all cut pieces, the pheromone is updated, and the formula is as follows:
τ ij (t+c)＝ρ.τ ij (t)+Δτ ij
wherein, tau ij (t + c) is pheromone transferred from the ith cutting piece to the jth cutting piece at the moment of t + c, rho is an information volatilization coefficient, rho is more than 0 and less than or equal to 1, and delta tau ij For the single-length track pheromone left on the track edge transferred from the ith panel to the jth panel,
the unit length trace pheromone left on the trace edge for the k-th ant to transfer from the ith cut piece to the jth cut piece, and m is the total number of ants.
And representing a feasible solution of the cutting path optimization problem by using the walking paths of the ants, wherein all paths of the whole ant group form a solution space of the cutting path optimization problem. The shorter ants release a larger amount of pheromone, and as time advances, the concentration of pheromone accumulated on the shorter paths gradually increases, and the number of ants selecting the paths also increases. Finally, the whole ant can be concentrated on the optimal path under the action of positive feedback, and the corresponding optimal solution of the problem to be optimized is obtained at the moment.
In another embodiment, in the process of updating pheromones in step G, pheromones added by the optimal path are added, and the formula is as follows:
wherein e is weight, delta b (i, j) is pheromone added when the ith cut piece is transferred to the jth cut piece, and L b Is the optimal path.
τ ij (t+c)＝ρ.τ ij (t)+Δτ ij
In the formula
On the basis, a means of strengthening pheromones is used for better guiding the searching direction of ants, and extra pheromone quantity is added to improve the efficiency of the algorithm.
In another embodiment, in the process of updating pheromones in step G, compensation pheromones are added to make the pheromones left on the paths where the ants pass through the higher number, and the formula is as follows:
wherein Q (t) is a compensation pheromone,
r is the number of ants transferred from the ith panel to the jth panel, R is the number of ants passing through the ith panel.
In the ant colony algorithm, in the searching process, ants release a constant Q pheromone on a path, so that a large number of pheromones are left on a suboptimal path, and the ant colony algorithm is easy to fall into a local optimal solution. Leaving fewer pheromones on the higher number of paths an ant has traveled can avoid losing global nature.
Preferably, between the step G and the step E, a step H is further included;
the step H is as follows: and C, setting a rated iteration number, and repeating the steps C, F and G until the iteration number is equal to the rated iteration number.
The more the set iteration times are, the more the times of all ants traversing all cut pieces are, and thus the optimal solution can be approached more.
It is worth mentioning that the first empty running stroke L 0 And said second empty running stroke L k Obtained by the following formula:
wherein L is p Is the first idle running stroke L 0 Or a second empty run stroke L k ，l s Is the distance from the origin of the tool to the cutting point of the first cut segment, l e The distance from the cutting point of the nth cut segment to the origin of the cutter,
the empty running path from the ith cut piece to the (i + 1) th cut piece is defined as i =1 as the first cut piece and n-1 as the (n-1) th cut piece.
By the formula
Calculating a first idle running stroke L 0 Or a second empty run stroke L k Thereafter, the first empty run L can be compared by the step D 0 And said second empty running stroke L k Of (c) is used.
The following disclosure provides many different embodiments or examples for implementing different configurations of embodiments of the invention. In order to simplify the disclosure of embodiments of the invention, the components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, embodiments of the invention may repeat reference numerals and/or reference letters in the various examples, which have been repeated for purposes of simplicity and clarity and do not in themselves dictate a relationship between the various embodiments and/or arrangements discussed.
In the description herein, references to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example" or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A method of optimizing a cutting path, comprising the steps of:
step A: determining an origin of the cutter and a cutting point of a cutting piece;
and B, step B: obtaining an initial path of a cutting point of the cutter which runs all cut pieces from the original point of the cutter by using a greedy algorithm, establishing a cutting sequence set, and calculating a first idle running stroke L according to the sequence of elements in the cutting sequence set 0 ；
The step B comprises the following steps:
step B1: establishing a cutting starting point of each cut piece, and forming a cutting starting point set P, P = { P = { (P) 1 ,P 2 ,...P n }，Wherein P is 1 As a starting point of cutting of the first cut segment, P 2 As starting point of the cut of the second cut segment, P n The cutting starting point of the nth cut piece is defined;
and step B2: calculating the original point P of the tool 0 From the cutting starting point P of the kth cut piece k Distance d (P) k ,P 0 ) And calculating the tool origin P 0 Starting point P of cutting with the (k + 1) th cut piece k+1 Distance d (P) k+1 ,P 0 ) Wherein k belongs to n, and k +1 belongs to n;
and step B3: comparison d (P) k ,P 0 ) And d (P) k+1 ,P 0 ) When d (P) is k ,P 0 ) Greater than d (P) k+1 ,P 0 ) When P is exchanged k And P k+1 At a position in the cutting start point set P when d (P) k ,P 0 ) D (P) is less than or equal to k+1 ,P 0 ) When P is present k And P k+1 The position in the cutting starting point set P is unchanged;
step C: obtaining the current path of the cutting points of all cut pieces which are completely walked by ants from the original point of the cutter by utilizing an ant algorithm, establishing an ant access sequence set, and calculating a second idle running stroke L according to the sequence of elements in the ant access sequence set k ；
The step C comprises the following steps:
step C1: by using
The probability that the kth ant transfers from the ith panel to the jth panel at the time t is represented by the following formula:
among them, allowed k Alpha is an information heuristic factor, beta is an expected heuristic factor, and tau is allowed to be selected for the kth ant in the next step ij (t) pheromone transferred from ith panel to jth panel at time t, η ij (t) the ith cut-part at time tHeuristic function transferred to jth panel, η ij (t) is the reciprocal of the distance between the ith cut piece and the jth cut piece at the moment t, S is the end point cut piece where the kth ant stays, and tau is is (t) pheromone, η, transferred from ith panel to end panel at time t is (t) is a heuristic function of the transition from the ith panel to the end panel at time t;
and step C2: according to probability
Selecting the jth cut piece until the kth ant visits all the cut pieces, establishing an ant visit sequence set according to the sequence of the kth ant visiting the cut pieces, and calculating a second idle running stroke L of the cutter according to the sequence of elements in the ant visit sequence set k ；
Step D: comparing the first idle running stroke L 0 And said second empty running stroke L k The size of (d);
when the second idle running stroke L k Less than said first idle running stroke L 0 Then, the second idle running stroke L k Covering the corresponding ant access sequence set with the cutting sequence set to form a new cutting sequence set;
when the second idle running stroke L k Greater than or equal to the first idle running stroke L 0 When the cutting sequence set is not changed;
step E: outputting the cutting sequence set as a cutting path;
a step F is further included between the step D and the step E;
the step F is as follows: comparing the second idle running journey L corresponding to the kth ant k And a second idle running route L of the (k + 1) th ant k+1 The size of (d);
when the second idle running stroke L k+1 Less than the second empty running stroke L k Then, the second idle running stroke L k+1 Covering the cutting sequence set by the corresponding ant access sequence set to form a new cutting sequence set;
when the second idle running stroke L k+1 Greater than or equal to the second idle running stroke L k The cutting order set is not changed;
a step G is further included between the step F and the step E;
the step G is as follows: after all ants have visited all cut pieces, the pheromone is updated, and the formula is as follows:
τ ij (t+c)＝ρ.τ ij (t)+Δτ ij
wherein, tau ij (t + c) is pheromone transferred from the ith cutting piece to the jth cutting piece at the moment of t + c, rho is an information volatilization coefficient, rho is more than 0 and less than or equal to 1, and delta tau ij The unit length trace pheromone left on the trace edge for the transition from the ith panel to the jth panel,
the unit length trace pheromone left on the trace edge for transferring the kth ant from the ith cut piece to the jth cut piece, and m is the total number of ants.
2. A method of optimizing a cutting path as claimed in claim 1, wherein: the step B also comprises a step B4;
the step B4 is as follows: and repeating the step B2 and the step B3 until all the elements in the cutting starting point set P are calculated, and forming a cutting sequence set which takes the cutting starting point of the cut piece closest to the origin of the cutter as the first element and is arranged in an ascending order.
3. A method for optimizing a cutting path as claimed in claim 1, wherein: and G, adding pheromones added by the optimal path in the process of updating the pheromones in the step G, wherein the formula is as follows:
wherein e is weight, delta b (i, j) is pheromone added when the ith cut piece is transferred to the jth cut piece, and L b Is the optimal path.
4. A method for optimizing a cutting path as claimed in claim 1, wherein: in the process of updating pheromones in the step G, adding compensation pheromones to ensure that fewer pheromones are left on the path where the ants pass through the higher number, wherein the formula is as follows:
wherein Q (t) is a compensation pheromone,
r is the number of ants transferred from the ith panel to the jth panel, and R is the number of ants passing through the ith panel.
5. The method of optimizing a cutting path of claim 4, wherein: between the step G and the step E, a step H is also included;
the step H is as follows: and C, setting a rated iteration number, and repeating the steps C, F and G until the iteration number is equal to the rated iteration number.
6. The method of claim 1, wherein the cutting path is optimizedCharacterized in that: the first empty running stroke L 0 And said second empty running stroke L k Obtained by the following formula:
wherein L is p Is the first idle running stroke L 0 Or a second empty run stroke L k ，l s Is the distance from the origin of the tool to the cutting point of the first cut segment, l e The distance from the cutting point of the nth cut segment back to the origin of the cutter,
the empty running path from the ith cut piece to the (i + 1) th cut piece is represented by i =1 as the first cut piece and n-1 as the (n-1) th cut piece.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110796161.6A CN113433891B (en) | 2021-07-14 | 2021-07-14 | Optimization method of cutting path |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110796161.6A CN113433891B (en) | 2021-07-14 | 2021-07-14 | Optimization method of cutting path |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113433891A CN113433891A (en) | 2021-09-24 |
| CN113433891B true CN113433891B (en) | 2022-12-27 |
Family
ID=77760359
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110796161.6A Active CN113433891B (en) | 2021-07-14 | 2021-07-14 | Optimization method of cutting path |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113433891B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115246050B (en) * | 2022-09-23 | 2022-12-20 | 江苏跃格智能装备有限公司 | Laser cutting path optimization control method based on machine vision |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101451305B (en) * | 2008-12-18 | 2010-12-08 | 浙江工业大学 | The Control Method of Tool Empty Travel Path in Clothing Cloth and Sample Cutting Process |
| CN111753350A (en) * | 2020-07-07 | 2020-10-09 | 武汉久同智能科技有限公司 | Greedy algorithm-based cutting path generation method |
| CN112596464B (en) * | 2020-10-22 | 2022-09-30 | 北京工业大学 | Numerical control machining feed path optimization method for large complex cabin structural part |
-
2021
- 2021-07-14 CN CN202110796161.6A patent/CN113433891B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113433891A (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109102124B (en) | Decomposition-based dynamic multi-target multi-path induction method, system and storage medium | |
| CN104331743B (en) | Electric vehicle travel planning method based on multi-target optimization | |
| CN114020045B (en) | Unmanned aerial vehicle track planning method based on improved ant colony algorithm | |
| CN106503789A (en) | Loop-free shortest path searching method based on Di Jiesitela and minimax ant colony | |
| CN113433891B (en) | Optimization method of cutting path | |
| CN112033403A (en) | Unmanned aerial vehicle optimal path searching method based on potential field improved ant colony algorithm | |
| CN106845703B (en) | A time-varying K shortest path search method for urban road network considering turning delay | |
| CN110488842A (en) | A kind of track of vehicle prediction technique based on two-way kernel ridge regression | |
| CN108519737B (en) | Unmanned equipment path planning method considering energy supply | |
| CN110375759A (en) | Multi-robots Path Planning Method based on ant group algorithm | |
| CN112418514B (en) | Method for optimizing campus bus route planning by using ant colony system | |
| CN110836675A (en) | Decision tree-based automatic driving search decision method | |
| CN109540165B (en) | Heuristic search expressway network constraint routing algorithm | |
| CN109211242A (en) | A kind of three-dimensional space multi-goal path planing method merging RRT and ant group algorithm | |
| CN109002923A (en) | A kind of intercity multimode travel route planing method | |
| CN114217607A (en) | Takeout delivery path planning method, system and storage medium | |
| Pylyavskyy et al. | A reinforcement learning hyper-heuristic for the optimisation of flight connections | |
| CN109840625B (en) | Courier group path navigation method | |
| CN114253215B (en) | A Path Planning Method for Automatic Drilling and Riveting of Civil Aircraft Cabin Doors Based on Improved Ant Colony Algorithm | |
| CN118857324A (en) | Path planning method for collaborative operation of unmanned sanitation vehicles | |
| WO2019119457A1 (en) | Model factor calculation-based method and device for dynamic vehicle pickup-and-delivery route planning | |
| Guo et al. | A multi-destination daily carpooling problem and an ant colony based resolution method | |
| Yu et al. | Dynamic reproductive ant colony algorithm based on piecewise clustering | |
| CN117478518A (en) | Map convolution neural network assisted big data storage forwarding scheduling method | |
| Xiao | Optimal travel path planning and real time forecast system based on ant colony algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |