An artificial intelligence-based workpiece drilling path generation method and system

Abstract

The present invention relates to the technical field of data processing. Specifically, it relates to a method and system for generating a drilling path of a workpiece based on artificial intelligence. The method includes: obtaining a minimum action set according to the hole position information to be processed and the drill bit information on the drill package; using the ant colony algorithm to sort the drilling actions in the minimum action set to obtain a first sorting result; using the genetic algorithm to sort the drilling actions in the minimum action set to obtain a second sorting result; comparing the total processing duration corresponding to the first sorting result with the total processing duration corresponding to the second sorting result, selecting the sorting result with the smaller total processing duration as the optimal sorting result, and using the movement path of the machine head corresponding to the optimal sorting result as the workpiece drilling path. By adopting the method for generating a workpiece drilling path based on artificial intelligence of the present invention, the efficiency of workpiece drilling can be greatly improved, and the energy consumption of the drilling equipment can be greatly reduced.

Description

Technical Field

[0001] The present invention generally relates to the technical field of data processing. More specifically, the present invention relates to a method and system for generating a drilling path of a workpiece based on artificial intelligence. Background Art

[0002] With the continuous improvement of people's living standards, the requirements for the living environment and furniture have also increased accordingly. Many newly built residential communities by developers are delivered as fully furnished houses, and almost all use customized furniture with unified standards. Therefore, the customized panel furniture industry has developed vigorously. A panel furniture is often assembled from multiple processed boards. Therefore, when manufacturing panel furniture, it is usually necessary to drill holes in each board to facilitate the splicing between the boards or the installation of other components.

[0003] Currently, the way to drill holes in a board is to use a drilling device for drilling. As Figure 1 shown, the existing drilling device includes a support frame 3 for horizontally placing the board, a clamp 4 for fixing the board when drilling the board, a number of gantries 1 arranged above the support frame, a number of machine heads 2 arranged on the gantries, a drill package is arranged on each machine head, and a drill package is composed of multiple groups of drill bits. Different groups of drill bits are used to drill different surfaces of the board; when drilling, through the movement of the gantry and the drill package, the drill bits are aligned with the corresponding hole positions, so as to complete the drilling action; with the surge in orders from panel furniture manufacturers and the continuous growth of production scale, higher and higher requirements are put forward for the drilling accuracy and efficiency of the board. In order to improve the drilling efficiency, the common practice is to optimize the drilling path. The existing optimization method is to first obtain all possible drilling actions based on the hole position information and the drill bit configuration information, and use the greedy algorithm to screen the drilling actions, that is, to preferentially select the drilling actions with more corresponding hole positions. However, using this method will not reduce the total number of drillings to the minimum, and the processing efficiency is still low. Summary of the Invention

[0004] To solve the technical problem of low drilling efficiency when using the existing drilling path optimization method for drilling, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a method for generating a drilling path of a workpiece based on artificial intelligence, including:

[0006] Obtaining a minimum action set according to the hole position information to be processed and the drill bit information on the drill package; the minimum action set refers to a set composed of drilling actions with the fewest number of drilling actions and covering all the holes to be processed;

[0007] Use the ant colony algorithm to sort the drilling operations in the minimum action set so that the total processing time of the drilling equipment head is the shortest after all the drilling operations in the minimum action set are completed, and obtain the first sorting result; use the genetic algorithm to sort the drilling operations in the minimum action set so that the total processing time of the drilling equipment head is the shortest after all the drilling operations in the minimum action set are completed, and obtain the second sorting result;

[0008] Compare the total processing time of the drilling equipment head corresponding to the first sorting result with the total processing time of the drilling equipment head corresponding to the second sorting result, select the sorting result with the smaller total processing time as the optimal sorting result, and use the moving path of the head corresponding to the optimal sorting result as the workpiece drilling path; the total processing time of the drilling equipment head is equal to the sum of the total time of gantry movement, the total time of the head moving on the gantry, the total time of gripper movement, and the total drilling time of the drilling operations.

[0009] The beneficial effects are as follows: When obtaining the workpiece drilling path, the method of the present invention reduces the drilling operations on the basis of ensuring coverage of all holes to be processed by obtaining the minimum action set, thereby improving the drilling efficiency; after obtaining the minimum action set, the ant colony algorithm and the genetic algorithm are respectively used to sort each drilling operation, and the best sorting result is selected as the final sorting result, so as to determine the best drilling path, so that after the drilling equipment head drills all the holes, the total processing time of the drilling equipment head is the shortest, thereby further improving the drilling efficiency and reducing the energy consumption of the drilling equipment. Therefore, the method for generating a workpiece drilling path based on artificial intelligence of the present invention can greatly improve the workpiece drilling efficiency and greatly reduce the energy consumption of the drilling equipment.

[0010] In one embodiment, for a certain drilling operation sorting result, the method for determining the total gripper movement time includes:

[0011] Generate the gripper safety area for each drilling operation; the gripper is used to clamp the plate during drilling to fix it;

[0012] Use the gripper safety area corresponding to each drilling operation to continuously intersect the gripper safety areas of adjacent drilling operations in the sorting result of this drilling operation to obtain each optimal gripper safety area;

[0013] Take the optimal gripper safety area as the gripper layout position corresponding to the corresponding drilling operation;

[0014] Calculate the total gripper movement time according to each gripper layout position and the gripper movement speed.

[0015] The beneficial effects are as follows: By determining the optimal gripper safety area and setting the gripper in the optimal gripper safety area during drilling, it is possible to avoid the collision between the drill bit and the gripper, prevent damage to the gripper, ensure the safety of the drilling operation, and reduce the number of gripper position replacements, thereby improving the processing efficiency.

[0016] In one embodiment, the sorting of the drilling operations in the minimum action set by using the ant colony algorithm includes: Selecting a starting point, respectively enabling N ants to traverse all nodes, and counting the best performance of the ant colony; the pheromone matrix of the ant colony algorithm is of order N, and the nodes passed by the ants when searching for food are the head position coordinates of each drilling operation; the value of N is the total number of actions in the minimum action set; the head position coordinate of a drilling operation refers to the position coordinate of the head during the drilling operation; the best performance of the ant colony refers to the performance corresponding to the ant with the shortest total processing time of the drilling equipment head.

[0017] If the best performance of the ant colony does not meet the standard, change the path of traversing all nodes, re-count the best performance of the ant colony, and update the pheromone matrix; otherwise, obtain the first sorting result according to the crawling path of the ant corresponding to the best performance.

[0018] The beneficial effects are as follows: When sorting the drilling operations in the minimum action set by using the ant colony algorithm, by simultaneously sending N ants to search for paths, the efficiency of obtaining the first sorting result can be greatly improved, and further the generation efficiency of the workpiece drilling path can be improved.

[0019] In one embodiment, the sorting of the drilling operations in the minimum action set by using the genetic algorithm includes:

[0020] According to the minimum action set, an initial population is generated according to a preset generation rule. The initial population includes at least two individuals. A drilling operation sorting scheme is an individual, and each drilling operation sorting scheme includes all drilling operations; calculate the fitness value of each individual in the population.

[0021] Iteratively perform selection, crossover, and mutation operations on the individuals in the initial population until the termination condition is met; after terminating the iteration, output the individual with the highest fitness, and use the drilling operation sorting scheme corresponding to the individual with the highest fitness as the drilling operation sorting result.

[0022] In one embodiment, the generation rule includes at least one of the following rules:

[0023] Generation rule one: Fix the first action among all the drilling operations required for the workpiece, and then randomly select subsequent actions in turn until all actions are selected.

[0024] Generation rule two: Fix the first action among all the drilling actions required for the workpiece, and then use the greedy algorithm to sequentially select subsequent actions until all actions are selected;

[0025] Generation rule three: Randomly sort all the drilling actions required for the workpiece;

[0026] Generation rule four: Randomly select the first action among all the drilling actions required for the workpiece, and then use the greedy algorithm to sequentially select subsequent actions until all actions are selected.

[0027] In one embodiment, the obtaining of the minimum action set includes:

[0028] Generate a hole position - adapted drill bit correspondence table based on the hole position information to be processed and the drill bit information on the drill package. The hole position - adapted drill bit correspondence table is used to represent all the drill bits adapted to each hole position to be processed;

[0029] Merge the hole positions with the same drill package coordinates in the hole position - adapted drill bit correspondence table into the same drilling action, thereby obtaining an action set table;

[0030] Simplify the action set table by using the minimum set covering problem, thereby obtaining the minimum action set.

[0031] The beneficial effects are as follows: When obtaining the minimum action set, first obtain the correspondence between the hole position and the adapted drill bit, and merge the drilling actions according to the correspondence between the hole position and the adapted drill bit, thereby greatly reducing the number of drilling actions. Finally, use the minimum set covering problem to screen the drilling actions and remove the redundant drilling actions, thereby further reducing the number of drilling actions to improve the drilling efficiency.

[0032] In one embodiment, the simplifying of the action set table by using the minimum set covering problem includes:

[0033] S701: If only the Y - th column is 1 and other columns are all 0 in the M - th row of the table, then the Y - th column is a member of the optimal solution, and lock this column and the rows corresponding to the 1s in this column; if a certain column is all 1s, then this column is a member of the optimal solution; the table is a simplified action - hole position correspondence table obtained based on the action set table;

[0034] S702: If a certain column contains the largest number of 1s, select this column as a member of the optimal solution, and lock this column and the rows corresponding to the 1s in this column;

[0035] S703: Repeat steps S701 and S702 until the set composed of all the optimal solutions covers all the hole positions. The set composed of the optimal solutions is denoted as the minimum action set.

[0036] In one embodiment, the method for obtaining the simplified action - hole position correspondence table includes:

[0037] Check for duplicate entries in the action set table. If there are several actions with exactly the same hole positions in the action set table, perform a deduplication operation;

[0038] Take the hole positions to be processed as rows and the drilling actions in the action set table as columns to obtain an initial action-hole position correspondence table. For a certain column in this table, if the hole position corresponding to a certain row in this column is included in the hole positions of the drilling action corresponding to this column, set the value of this row to 1; otherwise, set the value of this row to 0;

[0039] If all the 1s in the M-th column appear in the N-th column, delete the M-th column; if all the 1s in the X-th row appear in the Y-th row, delete the Y-th row.

[0040] In one embodiment, the generation of the hole position - adapted drill bit correspondence table includes:

[0041] Read the attribute information of each hole position to be processed from an external processing file and generate a hole position table. The attribute information of the hole position to be processed includes the working surface where the hole position is located, the hole position type, the hole diameter, the hole depth, and whether a specific tool is specified;

[0042] Read the attribute information of each drill bit from the equipment table to generate a drill bit table. The attribute information of the drill bit includes the drill bit type, the relative position coordinates of the drill bit on the drill pack, the drill bit type, the working surface of the drill bit, the maximum drilling depth, and the limit of the stroke coordinates of the drill bit;

[0043] For each hole position to be processed, obtain a drill bit that matches the working surface where the hole position is located, the hole position type, the hole diameter, and the hole depth according to the hole position table and the drill bit table, so as to generate a hole position - adapted drill bit correspondence table.

[0044] In a second aspect, the present invention provides an artificial intelligence-based workpiece drilling path generation system, including a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the artificial intelligence-based workpiece drilling path generation method of the present invention is implemented. Description of the Drawings

[0045] By referring to the accompanying drawings and reading the detailed description below, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, where:

[0046] Figure 1 is a schematic diagram showing a drilling device in the prior art;

[0047] Figure 2Schematic diagram showing the process of the artificial intelligence-based workpiece drilling path generation method according to an embodiment of the present invention Figure 1 ;

[0048] Figure 3 Schematic diagram showing the process of the artificial intelligence-based workpiece drilling path generation method according to an embodiment of the present invention Figure 2 ;

[0049] Figure 4 Schematic diagram showing the sorting of drilling operations according to an embodiment of the present invention;

[0050] Figure 5 Schematic diagram showing the structure of the artificial intelligence-based workpiece drilling path generation system according to an embodiment of the present invention. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Next, the specific implementation manners of the present invention will be described in detail with reference to the accompanying drawings.

[0053] Embodiment of the artificial intelligence-based workpiece drilling path generation method:

[0054] As Figure 2 shown, the artificial intelligence-based workpiece drilling path generation method of the present invention includes:

[0055] S101. Obtain the minimum action set, specifically: according to the hole position information to be processed and the drill bit information on the drill package, obtain the minimum action set; the minimum action set refers to a set composed of drilling actions with the least number of drilling actions and covering all the hole positions to be processed.

[0056] The hole position information to be processed includes the working surface where the hole position to be processed is located, the hole position type, the hole diameter, the hole depth, and whether a specific tool is specified. The drill bit information on the drill package includes the drill bit type, the relative position coordinates of the drill bit on the drill package, the drill bit type, the drill bit working surface, the maximum drilling depth, and the travel coordinate limit of the drill bit. According to the hole position information to be processed and the drill bit information on the drill package, the drill bits corresponding to each hole position to be processed can be obtained. The drill bits corresponding to the hole positions to be processed refer to the drill bits that can be used to process the hole positions. For example, if the drill bits that can be used to process hole position A are drill bit a, drill bit b, and drill bit c, then the drill bits corresponding to hole position A are drill bit a, drill bit b, and drill bit c.

[0057] Under normal circumstances, a workpiece has six sides. There are multiple groups of drill bits on the same drill package. A group of drill bits includes multiple drill bits. Different groups of drill bits are used to process the hole positions on different sides of the workpiece. After determining the drill bits corresponding to each hole position to be processed, the hole position combinations that can be processed simultaneously under the same drill package can be screened out. One hole position combination corresponds to one drilling action. For example: Hole position 1, hole position 2, and hole position 3 of the workpiece can be processed by drill bit a, drill bit b, and drill bit c respectively. Then, hole position 1, hole position 2, and hole position 3 can be processed simultaneously, and they can be combined into one drilling action. During processing, drill bit a, drill bit b, and drill bit c can be drilled simultaneously.

[0058] After screening out the hole position combinations that can be processed simultaneously under the same drill package, the combination with the fewest number and covering all the hole positions to be processed can be screened out from all the hole position combinations, so as to obtain the minimum action set. For example: Suppose the hole position combinations include: {hole 1, hole 2, hole 3}, {hole 2, hole 3}, {hole 4}. Then, the combination with the fewest number and covering all the hole positions to be processed is: {hole 1, hole 2, hole 3}, {hole 4}.

[0059] S102. Obtain the first sorting result and the second sorting result. Specifically: Use the ant colony algorithm to sort the drilling actions in the minimum action set so that the total processing time of the drilling equipment head is the shortest after all the drilling actions in the minimum action set are completed, and obtain the first sorting result; Use the genetic algorithm to sort the drilling actions in the minimum action set so that the total processing time of the drilling equipment head is the shortest after all the drilling actions in the minimum action set are completed, and obtain the second sorting result.

[0060] In nature, when ants are looking for food, they will release pheromones on the paths they pass through. This is a volatile chemical substance. Other ants behind will choose paths according to the concentration of pheromones left by the leading ants on the road. The increase in pheromone concentration will attract more ants to choose this path, forming a positive feedback loop, and finally all ants will choose the same shortest path. The ant colony algorithm is a bionic algorithm that simulates the above ant foraging behavior. By simulating the pheromones left by ants during the process of looking for food to guide the behavior of other ants to choose paths. The earlier the ants choose paths, the more random they are. Longer paths require more time. Pheromones volatilize over time. Later ants are more inclined to choose paths with higher pheromone concentrations. This behavior causes pheromones to gradually accumulate on the shortest path over time, and finally the algorithm obtains the optimal path. The characteristics of the ant colony algorithm include parallelism, self-organization, robustness, positive feedback, etc. These characteristics make the ant colony algorithm perform well in solving combinatorial optimization problems.

[0061] When applying the principle of the ant colony algorithm to solve the problem of the optimal drilling path of a machine, the head coordinate position of each drilling action needs to be regarded as the path point (or called city) when an ant is looking for food. According to the different design characteristics of different machines, it is stipulated whether the starting point and the ending point of the ant foraging are fixed or must conform to certain rules, such as whether it is necessary to return to the starting point to form a closed trajectory after processing is completed, etc. Each ant must traverse all cities before the foraging process ends. During this process, each ant will release pheromone in the cities it passes through. Subsequently, while other ants tend to choose paths according to the concentration of pheromone, they also have a certain degree of random selectivity. The pheromone volatilizes according to the set rules. The increase in the cumulative pheromone concentration in the city will attract more later ants to choose this city, forming a positive feedback loop, and finally enabling the algorithm to obtain an optimal path composed of a sequence of cities with the highest pheromone concentration. This path is the processing sequence of the optimal drilling action.

[0062] After the dispatched ants complete the path finding work, if the algorithm termination condition is not met, the next batch of ant colonies must be dispatched to repeat a new round of path finding work. This process is called iteration; the iteration termination conditions can be: the number of iterations reaches the maximum number of iterations, the ant colony algorithm reaches the longest algorithm usage time, the best result of the ant colony meets the preset conditions, or the difference between the best results of consecutive multiple iterations is within the set range.

[0063] The genetic algorithm is based on the natural selection of Darwin's theory of biological evolution and the biological evolution process of genetic mechanisms, thereby establishing a computational model. By simulating the process of continuously generating better offspring in natural evolution, the purpose of searching for the optimal solution to the problem is achieved. This algorithm, through mathematical means and using computer simulation operations, converts the problem-solving process into processes similar to gene crossover and mutation of chromosomes in biological evolution. When solving relatively complex combinatorial optimization problems, compared with some conventional optimization algorithms, it can usually obtain better optimization results relatively quickly. The genetic algorithm has been widely applied in fields such as combinatorial optimization, machine learning, signal processing, adaptive control, and artificial life.

[0064] When using the genetic algorithm to optimize the drilling path, when generating the initial population, each drilling action in the minimum action set needs to be numbered. Any sorting of all processing actions is a chromosome in the solution set, and one chromosome corresponds to an individual in the population. For example, if there are a total of 6 processing actions numbered sequentially from 1 to 6, then 325416 is regarded as a chromosome, and 623145 is also a chromosome.

[0065] The conditions mainly satisfied by the design of the fitness function include: 1. Single-valued, continuous, non-negative, and maximized; 2. Reasonable and consistent; 3. Small computational amount; 4. Strong versatility.

[0066] When using a genetic algorithm to optimize the drilling path of a workpiece, different emphases can be placed on evaluating the quality of a path. For example, the total distance is the shortest, or the total energy consumption is the lowest, or the total processing time is the shortest, etc. The shortest total distance means the shortest completion time and the highest efficiency. In addition, since the energy consumption of the gantry movement and the energy consumption of the drill head movement are different during the machining process, the movement path can be split into the gantry travel and the drill head travel according to the machining action sequence, and the total machining energy consumption can be calculated separately. Then, the path with the lowest energy consumption has the optimal sorting.

[0067] S103. Generate the drilling path of the workpiece, specifically: compare the total processing time of the drill head of the drilling equipment corresponding to the first sorting result with the total processing time of the drill head of the drilling equipment corresponding to the second sorting result, select the sorting result with the smaller total processing time as the optimal sorting result, and use the movement path of the drill head corresponding to the optimal sorting result as the drilling path of the workpiece; the total processing time of the drill head of the drilling equipment is equal to the sum of the total time of the gantry movement, the total time of the drill head moving on the gantry, the total time of the gripper movement, and the total drilling time of the drilling action.

[0068] Assume that the optimal sorting result is drilling action Q, drilling action W, and drilling action E. Then, the movement path of the drill head corresponding to the optimal sorting result is drilling action Q - drilling action W - drilling action E; when the drill head moves from the initial position to the position of the drill head corresponding to drilling action Q, the time for the drill head to move on the gantry is a, the time for the gantry to move is b, and the drilling time for drilling action Q is c; when the drill head moves from the position of the drill head corresponding to drilling action Q to the position of the drill head corresponding to drilling action W, the time for the drill head to move on the gantry is d, the time for the gantry to move is e, and the drilling time for drilling action W is f; when the drill head moves from the position of the drill head corresponding to drilling action W to the position of the drill head corresponding to drilling action E, the time for the drill head to move on the gantry is g, the time for the gantry to move is h, and the drilling time for drilling action E is i; after all drilling actions are completed, the total time for the gripper to move is j. Then, the corresponding total processing time of the drill head of the drilling equipment is equal to a + b + c + d + e + f + g + h + i + j.

[0069] Since the gripper is used to fix the workpiece during the drilling action, if a certain drilling action may touch the workpiece during execution, the position of the gripper needs to be changed before this drilling action is executed, and changing the position of the gripper takes time. The gripper movement time needs to be considered when calculating the total processing time of the drill head of the drilling equipment.

[0070] After obtaining the drilling path of the workpiece, drill the workpiece according to the drilling path, which can improve the drilling efficiency and reduce the energy consumption of the drilling equipment.

[0071] When the method of the present invention obtains the drilling path of the workpiece, by obtaining the minimum action set, on the basis of ensuring that all the holes to be processed are covered, the drilling actions are reduced, thereby improving the drilling efficiency; after obtaining the minimum action set, the ant colony algorithm and the genetic algorithm are respectively used to sort each drilling action, and the best sorting result is selected as the final sorting result, so as to determine the best drilling path, so that after the drill head of the drilling equipment drills all the holes, the total processing time of the drill head of the drilling equipment is the shortest, thereby further improving the drilling efficiency and reducing the energy consumption of the drilling equipment. Therefore, adopting the method for generating the drilling path of the workpiece based on artificial intelligence of the present invention can greatly improve the drilling efficiency of the workpiece and greatly reduce the energy consumption of the drilling equipment.

[0072] As Figure 3 shown, in one embodiment, for a certain drilling action sorting result, the method for determining the total clamping hand movement time includes:

[0073] S201. Generate the clamping hand safety area for each drilling action; the clamping hand is used to clamp the plate during drilling to fix it;

[0074] S202. Continuously intersect the clamping hand safety areas of adjacent drilling actions in the optimal sorting result by using the clamping hand safety area corresponding to each drilling action to obtain each optimal clamping hand safety area;

[0075] S203. Take the optimal clamping hand safety area as the clamping hand layout position corresponding to the corresponding drilling action, and calculate the total clamping hand movement time according to each clamping hand layout position and the clamping hand movement speed.

[0076] As Figure 4As shown in the figure, assume that the workpiece drilling operation includes five steps: drilling operation A1, drilling operation A2, drilling operation A3, drilling operation A4, and drilling operation A5. The sequence of drilling operations is drilling operation A1 - drilling operation A2 - drilling operation A3 - drilling operation A4 - drilling operation A5. In the figure, the circles represent the positions of the workpiece drilling operations, and the shaded areas represent the gripper safety zones (the gripper safety zones are used to represent the areas where the gripper can be set. Placing the gripper within the gripper safety zone can prevent the drill bit from colliding with the gripper). By finding the intersection of the gripper safety zones of drilling operation A1 and drilling operation A2, the intersection area B1 can be obtained (the intersection area B1 means that when the gripper is within the intersection area B1 and the machine head is performing drilling operation A1 and drilling operation A2, the drill bit will never drill on the gripper no matter what). By finding the intersection of the intersection area B1 and the gripper safety zone of drilling operation A3, the intersection area B2 can be obtained (the intersection area B2 means that when the gripper is within the intersection area B2 and the machine head is performing drilling operation A1, drilling operation A2, and drilling operation A3, the drill bit will never drill on the gripper no matter what. That is, the gripper can be arranged to be within the intersection area B2, so that when the machine head performs drilling operation A1, drilling operation A2, and drilling operation A3, the gripper position does not need to be changed). By finding the intersection of the intersection area B2 and the gripper safety zone of drilling operation A4, the intersection area B3 can be obtained. From Figure 4 It can be seen that the intersection area B3 is a blank area (the blank area means that it is impossible to find a gripper area that allows the machine head to complete drilling operation A1, drilling operation A2, and drilling operation A3 and still continue to complete drilling operation A4 without changing the gripper position. That is to say, the number of times the intersection area is a blank area can be used to determine the number of times the gripper position needs to be changed, and then determine the time to change the gripper). Therefore, at this time, the gripper position needs to be changed. At this time, the gripper safety zone of drilling operation A4 can be used as the intersection area B4, and the intersection is found with the gripper safety zone of drilling operation A5 to obtain the intersection area B5.

[0077] From Figure 4 It can be known that placing the gripper in the intersection area B2 can perform drilling operation A1, drilling operation A2, and drilling operation A3 without changing the gripper position, and placing the gripper in the intersection area B5 can perform drilling operation A4 and drilling operation A5 without changing the gripper position. Only one gripper change is required during the entire workpiece drilling process, that is, changing the gripper to the intersection area B5 after drilling operation A3 is completed. The total duration of the gripper movement can be determined based on the gripper movement speed and the path length of the gripper moving from the intersection area B2 to the intersection area B5.

[0078] When performing each drilling operation, the gripper is set in the optimal gripper safety zone to fix the workpiece, thereby completing the drilling operation.

[0079] By determining the optimal gripper safety area and setting the gripper within the optimal gripper safety area during drilling, it is possible to avoid collisions between the drill bit and the gripper, prevent damage to the gripper, ensure the safety of the drilling operation, reduce the number of gripper position replacements, and improve processing efficiency.

[0080] As can be seen from the above embodiments, the iteration termination condition can be that the best result of the ant colony meets a preset condition. In one embodiment, the step of using the ant colony algorithm to sort the drilling actions in the minimum action set includes:

[0081] S301. Select a starting point, and let N ants traverse all nodes respectively, and count the best result of the ant colony. The pheromone matrix of the ant colony algorithm is of order N, and the nodes passed by the ants when looking for food are the head position coordinates of each drilling action. The value of N is the total number of actions in the minimum action set. The head position coordinate of the drilling action refers to the position coordinate of the head when the drilling action is performed. The best result of the ant colony refers to the result corresponding to the ant with the shortest total processing time of the drilling equipment head.

[0082] When performing the drilling action, the head position remains stationary, and only the drill bit needs to be extended for drilling. The head position coordinate of the drilling action refers to the position where the head of the drilling equipment should be when the corresponding drilling action is performed.

[0083] S302. If the best result of the ant colony does not meet the standard, change the path of traversing all nodes, re-count the best result of the ant colony, and update the pheromone matrix; otherwise, obtain the first sorting result according to the ant crawling path corresponding to the best result.

[0084] At the same time, N ants are sent out for path finding, and the path finding calculation process of each ant is completely independent. Such an algorithm is suitable for efficient parallel operation in a multi-core or multi-threaded environment.

[0085] By sending out N ants for path finding at the same time, the efficiency of obtaining the first sorting result can be greatly improved, and further the efficiency of generating the workpiece drilling path can be improved.

[0086] For ant A, its path finding process includes:

[0087] (1) Select a fixed or random starting point;

[0088] (2) Generate a random number Q1. If the random number Q1 is less than the preset value Q0, then generate a random number Q2, and select the next node using the roulette wheel algorithm according to the random number Q2. If the random number Q1 is greater than or equal to the preset value Q0, select the next node by combining the distance from the current node to each remaining node and the pheromone amount of each remaining node.

[0089] Selecting the next node by combining the distances from the current node to each of the remaining nodes and the pheromone amounts of each of the remaining nodes includes calculating the probability of each node being selected. For a certain node, the calculation expression of its selection probability P1 is:

[0090]

[0091] In the formula, ρ represents the pheromone concentration of this node, and l represents the distance between this node and the current node.

[0092] (3) Matrix pheromone evaporation and local pheromone enhancement;

[0093] Pheromone evaporation: Every once in a while, the pheromone evaporates at the speed ratio set by the system.

[0094] Local pheromone enhancement: In the process of ant path finding, pheromone should be left at the destination node every step. The amount of pheromone accumulates according to the set value. The formula is as follows:

[0095] a i,j =a i ′ ,j +(1 - Rho)×Tau0(2)

[0096] In the formula, Rho is the local pheromone evaporation factor, (1 - Rho) is the residue amount, Tau0 is the pheromone increment, a i,j is the pheromone concentration from node i to node j, a i ′ ,j is the pheromone concentration from node i to node j before the current accumulation.

[0097] (4) In response to ant A traversing all nodes, count the current path length and end the path finding process.

[0098] In one embodiment, the using the genetic algorithm to sort the punching actions in the minimum action set includes:

[0099] S401. According to the minimum action set, generate an initial population according to a preset generation rule. The initial population includes at least two individuals. A punching action sorting scheme is an individual, and each punching action sorting scheme includes all punching actions; calculate the fitness value of each individual in the population.

[0100] The core of Darwin's theory of evolution is the survival of the fittest and the elimination of the unfit. Fitness represents an individual's ability to adapt to the environment. The more excellent an individual is, the more offspring it usually has, and the inferior individuals are gradually eliminated in the process of evolution. The fitness function of the genetic algorithm, also called the evaluation function, is an index used to evaluate the quality of each individual in the population, and it is evaluated according to the objective function of the problem to be solved. The fitness value is calculated based on the fitness function, and the design of the fitness function mainly meets the following conditions:

[0101] 1. Single-valued, continuous, non-negative, and maximized.

[0102] 2. Reasonable and consistent.

[0103] 3. Small computational amount.

[0104] 4. Strong versatility.

[0105] When using the genetic algorithm to optimize the drilling path, different emphases can be placed on evaluating the quality of a path. For example, the shortest total distance (i.e., the evaluation method with priority on efficiency), or the lowest total energy consumption (i.e., the evaluation method with priority on energy consumption), etc.; the shortest total distance means the shortest processing time and the highest efficiency; in addition, because the energy consumption of the gantry movement and the energy consumption of the drill package movement are different during the machining process, the movement path can be split into the gantry travel and the drill package travel according to the machining action sequence to separately calculate the total machining energy consumption, and the path with the lowest energy consumption has the best sorting.

[0106] If the evaluation method with priority on efficiency is selected, the expression of the fitness function is:

[0107]

[0108] If the evaluation method with priority on energy consumption is selected, the expression of the fitness function is:

[0109]

[0110] In formulas (3) to (4), f x represents the fitness of an individual, l1 represents the total length of the movement path of the head corresponding to the individual, l a represents the total length of the gantry movement path corresponding to the individual, E1 represents the unit energy consumption of the gantry movement, l b represents the total length of the drill package movement path, and E2 represents the unit energy consumption of the drill package movement.

[0111] S402. Iteratively perform selection, crossover, and mutation operations on the individuals in the initial population until the termination condition is met; after terminating the iteration, output the individual with the highest fitness, and use the drilling action sorting scheme corresponding to the individual with the highest fitness as the drilling action sorting result.

[0112] Selection: The process of selecting excellent individuals in a population for combination, pairing, and mating to produce better offspring is called selection. The purpose of selection is to directly inherit the optimized individuals (or solutions) to the next generation or generate new individuals through pairing and crossover and then inherit them to the next generation. The selection operation is based on the fitness evaluation of individuals in the population. The commonly used selection operators are as follows: fitness proportion method, stochastic universal sampling method, and local selection method. The process of selection can be relatively flexible. The method of "matchmaker" can be adopted to give the most excellent chromosomes more opportunities to pair with each other. At the same time, inbreeding should be avoided to prevent the offspring from converging and being unable to be better. Sometimes, in order to avoid destroying the current optimal chromosome, it can be directly arranged to enter the evolutionary process of the next generation.

[0113] In the process of biological evolution in nature, the recombination (i.e., mutation) of biological genetic genes plays a core role. Similarly, in genetic algorithms, the crossover operator of genetic operations plays a core role. Crossover refers to the operation of replacing and recombining part of the structures of two parent individuals to generate new individuals. Through crossover, the search ability of genetic algorithms is improved by leaps and bounds. During the long time from the emergence of genetic algorithms to the present, domestic and foreign researchers have been continuously studying the crossover operator, aiming at how to perform gene crossover of chromosomes for the same or similar problems, so that the algorithm can converge to the local optimal result faster and better. Currently, relatively good crossover methods are all obtained based on the statistical summary of experimental data on classical problems. In this embodiment, the ordered crossover method is selected when solving the problem of drilling path optimization.

[0114] The basic content of the mutation operator is to change the gene values at some gene loci of the individual strings in the population. According to the different individual coding representation methods, the mutation operator includes: real-value mutation and binary mutation.

[0115] In one embodiment, the generation rule can be any one of the following:

[0116] Generation rule one: Fix the first action among all the drilling actions required for the workpiece, and then randomly select the subsequent actions in turn until all actions are selected.

[0117] Generation rule two: Fix the first action among all the drilling actions required for the workpiece, and then use the greedy algorithm to select the subsequent actions in turn until all actions are selected.

[0118] Generation rule three: Randomly sort all the drilling actions required for the workpiece.

[0119] Generation rule four: Randomly select the first action among all the drilling actions required for the workpiece, and then use the greedy algorithm to select the subsequent actions in turn until all actions are selected.

[0120] The initial population can be quickly generated according to the generation rules in this embodiment.

[0121] In one embodiment, the obtaining of the minimum action set includes:

[0122] S501. Generate a correspondence table between hole positions and adapted drill bits, where the correspondence table between hole positions and adapted drill bits is used to represent all the drill bits adapted to each hole position to be machined;

[0123] S502. Merge the hole positions with the same drill package coordinates in the correspondence table between hole positions and adapted drill bits into the same drilling action, so as to obtain an action set table;

[0124] S503. Simplify the action set table by using the minimum set covering problem, so as to obtain the minimum action set.

[0125] When obtaining the minimum action set, first obtain the correspondence between hole positions and adapted drill bits, and merge the drilling actions according to the correspondence between hole positions and adapted drill bits, so as to greatly reduce the number of drilling actions. Finally, use the minimum set covering problem to screen the drilling actions, remove the redundant drilling actions, so as to further reduce the number of drilling actions, in order to improve the drilling efficiency.

[0126] In one embodiment, simplifying the action set table by using the minimum set covering problem includes:

[0127] S701. Check the single row and the all-1 column, including: if only the Y-th column in the M-th row of the table is 1 and the other columns are all 0, then the Y-th column is a member of the optimal solution, and lock this column and the rows corresponding to the 1s in this column; if a certain column is all 1, then this column is a member of the optimal solution; the table is a simplified action-hole position correspondence table obtained according to the action set table;

[0128] The actions in the simplified action-hole position correspondence table cover all the holes to be machined and the number of actions is the least.

[0129] S702. Use the greedy algorithm to find the optimal solution: if a certain column contains the largest number of 1s, take this column as a member of the optimal solution, and lock this column and the rows corresponding to the 1s in this column;

[0130] S703. Repeat steps S701 and S702 until the set composed of all the optimal solutions covers all the hole positions, and the set composed of the optimal solutions is denoted as the minimum action set.

[0131] In one embodiment, the method for obtaining the simplified action-hole position correspondence table includes:

[0132] S801. Check for duplicates in the action set table. If there are several actions with exactly the same hole positions in the action set table, perform a duplicate removal operation;

[0133] S802. Take the holes to be machined as rows and the drilling actions in the action set table as columns to obtain an initial action-hole position correspondence table. For a certain column in this table, if the hole position corresponding to a certain row in this column is included in the holes of the drilling action corresponding to this column, set the value of this row to 1; otherwise, set the value of this row to 0.

[0134] For example: Suppose there are three drilling actions. Among them, the holes to be machined corresponding to drilling action 1 are hole 1 and hole 2, the holes to be machined corresponding to drilling action 2 are hole 1, hole 2 and hole 3, and the hole to be machined corresponding to drilling action 3 is hole 4. Then the initial action-hole position correspondence table formed is as shown in the following table:

[0135] Table 1

[0136] Punching action 1 Punching action 2 Punching action 3 Hole 1 1 1 0 Hole 2 1 1 0 Hole 3 0 1 0 Hole 4 0 0 1

[0137] S803. Perform column reduction and row reduction on the matrix, including: if all 1s in the Mth column appear in the Nth column, delete the Mth column; if all 1s in the Xth row appear in the Yth row, delete the Yth row.

[0138] In one embodiment, the generation of the hole position - adapted drill bit correspondence table includes:

[0139] S601. Read the attribute information of each hole to be machined from an external machining file and generate a hole position table. The attribute information of the hole to be machined includes the working surface where the hole is located, the hole type, the hole diameter, the hole depth, and whether a specific tool is specified.

[0140] S602. Read the attribute information of each drill bit from the equipment table and generate a drill bit table. The attribute information of the drill bit includes the drill bit type, the relative position coordinates of the drill bit on the drill package, the drill bit type, the working surface of the drill bit, the maximum drilling depth, and the stroke coordinate limit of the drill bit.

[0141] S603. For each hole to be machined, obtain a drill bit that matches the working surface where the hole is located, the hole type, the hole diameter, and the hole depth according to the hole position table and the drill bit table, so as to generate a hole position - adapted drill bit correspondence table.

[0142] In one embodiment, if the drilling equipment has multiple drill packages, the attribute information of the drill bit further includes the drill package number of the drill bit. Before simplifying the action set table, it further includes: merging actions that meet the drill package safety distance into one action.

[0143] In one embodiment, if the drilling equipment has multiple drill packages and multiple gantries, after merging actions that meet the drill package safety distance into one action, it further includes: merging actions that meet the gantry safety distance into one action.

[0144] Embodiment of an Artificial Intelligence-Based Workpiece Drilling Path Generation System:

[0145] The present invention also provides an artificial intelligence-based workpiece drilling path generation system. As Figure 5 shown, the artificial intelligence-based workpiece drilling path generation system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the artificial intelligence-based workpiece drilling path generation method in the above embodiment of the artificial intelligence-based workpiece drilling path generation method is implemented.

[0146] The artificial intelligence-based workpiece drilling path generation system further includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0147] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium. For instance, resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise held by such a computer-readable medium.

[0148] In the description of this specification, "a plurality of" and "several" mean at least two, such as two, three, or more, etc., unless otherwise clearly and specifically defined.

[0149] Although this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, modifications, and alternative approaches will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. A method for generating a workpiece drilling path based on artificial intelligence, characterized in that: include: A hole position and matching drill bit correspondence table is generated based on the hole position information to be processed and the drill bit information on the drill package, and the hole position and matching drill bit correspondence table is used to characterize all drill bits that are compatible with each hole position to be processed; the hole positions with the same drill package coordinates in the hole position and matching drill bit correspondence table are merged into the same drilling action, thereby obtaining an action set table; the action set table is checked for duplicates, and if the action set table contains several actions with exactly the same hole positions, deduplication is performed; the hole positions to be processed are taken as rows, and the drilling actions in the action set table are taken as columns, thereby obtaining an initial action-hole position correspondence table, and for a certain column of the table, if the hole position corresponding to a certain row of the column is the hole position included in the drilling action corresponding to the column, the value of the row is set to 1, otherwise the value of the row is set to 0; If all 1s in the Mth column appear in the Nth column, delete the Mth column; if all 1s in the Xth row appear in the Yth row, delete the Yth row; S701, if only the Yth column in the Mth row of the table is 1, and all other columns are 0, then the Yth column is a member of the optimal solution, and the column and the row corresponding to the 1 in the column are locked; if all 1s in a certain column, then the column is a member of the optimal solution; S702, if a certain column contains the largest number of 1s, take the column as a member of the optimal solution, and lock the column and the row corresponding to the 1 in the column; S703, repeat steps S701 and S702 until the set composed of all optimal solutions covers all hole positions, and the set composed of the optimal solutions is recorded as the minimum action set; the minimum action set refers to the set composed of drilling actions with the least number of drilling actions and covering all hole positions to be processed; The drilling actions in the minimum action set are sorted by using an ant colony algorithm so that the total processing time of the drilling equipment head after each drilling action in the minimum action set is completed is the shortest, and a first sorting result is obtained; the drilling actions in the minimum action set are sorted by using a genetic algorithm so that the total processing time of the drilling equipment head after each drilling action in the minimum action set is completed is the shortest, and a second sorting result is obtained; Compare the total processing time of the drilling equipment head corresponding to the first sorting result with the total processing time of the drilling equipment head corresponding to the second sorting result, select the sorting result with the smaller total processing time as the optimal sorting result, and use the movement path of the head corresponding to the optimal sorting result as the workpiece drilling path; the total processing time of the drilling equipment head is equal to the sum of the total time of the gantry movement, the total time of the head moving on the gantry, the total time of the clamping hand moving and the total drilling time of the drilling action.

2. The method for generating a workpiece drilling path based on artificial intelligence as claimed in claim 1, characterized in that: For a drilling action sorting result, the method for determining the total time duration of the clamping hand movement includes: Generate a gripper safety zone for each drilling action; the gripper is used to grip the plate to fix it during drilling; Using the hand-clamping safety area corresponding to each drilling action, the hand-clamping safety areas of adjacent drilling actions in the drilling action sorting results are continuously intersected to obtain the optimal hand-clamping safety areas. The optimal gripper safety area is used as the gripper layout position corresponding to the corresponding drilling action, and the total gripper movement time is calculated based on each gripper layout position and gripper movement speed.

3. The method for generating a workpiece drilling path based on artificial intelligence as claimed in claim 1, characterized in that: The method of using the ant colony algorithm to sort the drilling actions in the minimum action set includes: selecting a starting point, respectively making N ants traverse all nodes, and counting the best results of the ant colony; the pheromone matrix of the ant colony algorithm is of N order, and the nodes that the ants pass through when searching for food are the position coordinates of the machine heads of each drilling action; the value of N is the total number of actions in the minimum action set; the position coordinates of the machine head of the drilling action refer to the position coordinates of the machine head when the drilling action is performed; the best result of the ant colony refers to the result corresponding to the ant with the shortest total processing time of the drilling equipment machine head; If the best score of the ant colony does not meet the standard, the path of traversing all nodes is changed, the best score of the ant colony is recalculated, and the pheromone matrix is ​​updated; otherwise, the first ranking result is obtained according to the ant crawling path corresponding to the best score.

4. The method for generating a workpiece drilling path based on artificial intelligence as claimed in claim 1, characterized in that: The using of a genetic algorithm to sort the drilling actions in the minimum action set comprises: According to the minimum action set, an initial population is generated according to a preset generation rule, wherein the initial population includes at least two individuals, one drilling action sorting scheme is one individual, and each drilling action sorting scheme includes all drilling actions; and the fitness value of each individual in the population is calculated; Iteratively perform selection, crossover and mutation operations on individuals in the initial population until a termination condition is met; after the iteration is terminated, the individual with the highest fitness is output, and the drilling action sorting scheme corresponding to the individual with the highest fitness is used as the drilling action sorting result.

5. The method for generating a workpiece drilling path based on artificial intelligence as claimed in claim 4, characterized in that: The generation rules include at least one of the following rules: Generation rule 1: the first action among all drilling actions required to fix the workpiece is selected, and then subsequent actions are randomly selected in sequence until all actions are selected; Generate rule 2: select the first action among all drilling actions required to fix the workpiece, and then use the greedy algorithm to select subsequent actions in sequence until all actions are selected; Generation rule three: randomly order all drilling actions required for the workpiece; Generation rule 4: randomly select the first action among all drilling actions required for the workpiece, and then use the greedy algorithm to select subsequent actions in sequence until all actions are selected.

6. The method for generating a workpiece drilling path based on artificial intelligence as claimed in claim 1, characterized in that: The generating of the table of correspondence between hole positions and matching drill bits comprises: Read the attribute information of each hole to be processed from the external processing file and generate a hole table. The attribute information of the hole to be processed includes the working surface where the hole is located, the hole type, the hole diameter, the hole depth, and whether a specific tool is specified; Read the attribute information of each drill bit from the equipment table to generate a drill bit table, wherein the attribute information of the drill bit includes the drill bit type, the relative position coordinates of the drill bit on the drill bag, the drill bit type, the drill bit working surface, the maximum drilling depth and the travel coordinate limit of the drill bit; For each hole to be processed, a drill bit that matches the working surface, hole type, hole diameter and hole depth of the hole is obtained according to the hole table and the drill bit table, thereby generating a corresponding table of hole positions and matching drill bits.

7. A workpiece drilling path generation system based on artificial intelligence, comprising a memory and a processor, wherein the memory stores computer program instructions, characterized in that: When the computer program instructions are executed by the processor, the method for generating a workpiece drilling path based on artificial intelligence as described in any one of claims 1 to 6 is implemented.

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