An optimization method for energy consumption in fused deposition modeling contour printing

Abstract

This invention provides an optimization method for energy consumption in fused deposition modeling contour printing, comprising the following steps: S1, reading the part contour; S2, determining the contour center point; S3, sorting the initial points; S4, determining the starting point position and printing sequence; S5, optimizing the starting point position; and S6, forming the final printing path. This optimization method, when printing multi-contour model parts, optimizes the printing path while ensuring printing requirements are met, thereby reducing idle strokes and lowering printing energy consumption.

Description

Technical Field

[0001] This invention relates to the field of FDM 3D printing, and more particularly to a method for optimizing energy consumption in fused deposition modeling contour printing. Background Technology

[0002] Manufacturing is a fundamental industry that provides people with daily necessities and services, and it has been revitalized in recent years. At the same time, "low-carbon" living has been widely promoted and gradually accepted by the public. As an important future development technology in manufacturing and other fields, FDM 3D printing has become an important way to promote the sustainable development of manufacturing and alleviate the energy crisis through energy consumption optimization.

[0003] FDM 3D printing is a crucial process in modern manufacturing, and the FDM 3D printing path problem has been a constant companion to the development of 3D printing technology, remaining key to improving printing efficiency and saving energy. FDM 3D printing comprises four processes: modeling, slicing, printing, and post-processing. The slicing process involves using slicing software to divide the model parts into layers, planning a specific path for printing. This method reduces manual labor and has a certain degree of robustness. In recent years, with in-depth research into path planning, improved shortest distance methods and improved nearest neighbor methods have been applied to optimize the starting point position for printing, thereby optimizing the energy consumption of contour printing. Simultaneously, the development of computer technology and the application of artificial intelligence, such as ant colony algorithms, genetic algorithms, and fusion algorithms, can achieve very ideal printing paths. However, these methods have not yet solved the problem of slicing software getting trapped in local optima when establishing the starting point position by following the shortest distance principle. The current printing method follows the principle of selecting the nearest starting point for printing the current contour after the printhead completes printing the current contour. It only selects the nearest point each time without backtracking. However, this often only minimizes the path for that segment, not the overall path, leading to a local optimum in the printing path length. Therefore, it often fails to achieve good results when dealing with multiple contours within a single layer slice, impacting production efficiency, increasing time costs, and adding unnecessary energy consumption.

[0004] In summary, given the existence of multiple contours in existing single-layer slices, it is essential to design an FDM 3D slice contour path planning method to reduce the energy consumption of contour printing. Summary of the Invention

[0005] To address the problem of existing slicing software easily getting trapped in local optima of path length when planning multiple contours, this invention provides an optimization method for energy consumption in fused deposition modeling contour printing. Combining the advantages of fast convergence speed of genetic algorithms and the ability of simulated annealing algorithms to accept poor solutions, a contour path planning method based on genetic simulated annealing algorithm is established. This avoids the problem of increased path length caused by slicing software following the shortest path principle. Under the condition of meeting printing requirements, the printing path reaches its optimum.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for optimizing energy consumption in fused deposition modeling contour printing includes the following steps:

[0008] S1. Part Contour Reading

[0009] Read each contour from a single-layer slice of the model part;

[0010] S2. Determining the center point of the contour

[0011] Determine the center point of each polygon in the single-layer slice outline, and form an initial point set with the printing origin;

[0012] S3, Initial Point Sort

[0013] The initial points are sorted using a genetic simulated annealing algorithm, and the shortest distance between all initial points is calculated.

[0014] S4. Determining the starting point position and printing order.

[0015] The first point of each contour traversed by the shortest path between the initial points obtained in S3 is taken as the starting point of that contour, and the printing starting point of each contour is determined in turn; at the same time, the order in which the starting points are determined is recorded as the printing order, and the printing order is determined accordingly.

[0016] S5. Optimization of starting point position

[0017] Optimize the position of the starting point to form the optimal trajectory;

[0018] S6. Form the final print path.

[0019] Furthermore, the determination of the contour center point described in step S2 involves taking the outermost contour of the model part as a separate contour, selecting one vertex of the contour as the printing origin, and taking the center points of other polygonal contours inside and the printing origin as the initial point set; here, for irregular polygons, the centroid of the set of contours can be selected as the center point of the contour.

[0020] Furthermore, the algorithm used in the initial point sorting in step S3 is the genetic simulated annealing algorithm: starting from the origin, traversing all center points and returning to the origin, this process is regarded as solving the traveling salesman problem, and the algorithm is used to plan the shortest distance between the points in the initial point set.

[0021] Furthermore, the method for determining the starting point position and printing order in step S4 is as follows: starting from the origin and following the shortest path planned in step S3, the first point of each contour passed by the path is taken as the printing starting point of that contour, and the printing starting point of each contour is determined in turn; at the same time, the order in which the starting points are determined is recorded as the printing order, thereby determining the printing order.

[0022] Furthermore, the method for optimizing the starting point position in step S5 is as follows: First, group the three consecutive printing starting points together; second, determine whether the intermediate point is on the optimal path of the other two points. If not, find the point closer to the sum of the distances of the other two points as the updated position of the intermediate point. If there are multiple points with the closest sum of distances, select the point closer to the third point as the updated position of the intermediate point. Then, update all points sequentially according to the above method. If a point is updated, it is necessary to re-determine whether the preceding points need to be updated. For the last point, refer to the origin and the point before the last point. Finally, obtain the optimal printing path.

[0023] Furthermore, the criterion for determining the optimal printing path in step S5 is as follows: repeat step S5. If the positions of all points no longer change, then input to the next level. If they change, then return to the previous level to continue optimization and obtain a new path.

[0024] Compared with existing technologies, the present invention has the following advantages:

[0025] 1. This invention utilizes the characteristics of genetic simulated annealing algorithm to quickly determine the outline printing order by fast convergence and avoidance of premature convergence, thereby improving work efficiency and avoiding the problem of local optima in path length.

[0026] 2. The present invention further optimizes the starting point of the contour, thereby further reducing energy consumption. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the optimized energy consumption method for fused deposition modeling contour printing provided by the present invention;

[0028] Figure 2 This is a schematic diagram of a printed model part provided in an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram showing the positions of the center points of each contour in step 2 of the present invention.

[0030] Figure 4(a) is a schematic diagram of the genetic algorithm operation flow in step 3 provided in the embodiment of the present invention;

[0031] Figure 4 (b) is a schematic diagram of the simulated annealing algorithm operation flow in step 3 of the present invention;

[0032] Figure 4 (c) is a schematic diagram of the operation flow of returning to the genetic algorithm after the simulated annealing operation is completed in step 3 of the present invention, provided in an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the initial point sorting result determined by the genetic simulated annealing algorithm in step 3 of the present invention.

[0034] Figure 6 This is a schematic diagram of step 4, determining the starting point position and printing order, provided in an embodiment of the present invention.

[0035] Figure 7 This is a schematic diagram of the result after optimizing the starting point position in step 5 of the present invention.

[0036] Figure 8 The Cura slicing software provided in this embodiment of the invention is used for slicing. Figure 2 The diagram shows the contour path of the sliced ​​model part.

[0037] Figure 9 The slicing software provided in this embodiment of the invention is Simplify3D. Figure 2 The diagram shows the contour path of the model part after slicing. Detailed Implementation

[0038] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] A method for optimizing energy consumption in fused deposition modeling contour printing is presented. Referring to the flowchart shown in Figure 1, path planning is performed on the contours contained in the model part shown in Figure 2. The model part in the figure has a total of 7 contours. The outermost contour is defined as G1, and G1 contains 6 closed contours, denoted as G... i ={G2, G3,…,G ７ Each contour has a different number of vertices, and the center of each contour, except for the outermost contour, is {L2, L3, ..., L7}. In this embodiment, an LMG600 series power analyzer is used to collect power data during the contour printing process.

[0040] The specific optimization process is as follows:

[0041] Step 1: Reading the part outline

[0042] Based on the model part drawn in AutoCAD, read the outline contained in the model part to determine the outermost outline and the inner printing outline to be printed.

[0043] Step 2: Determining the center point of the contour

[0044] As shown in Figure 3, the outermost contour of the model part is taken as a separate contour, and one vertex of the contour is selected as the printing origin L1. The center points of other polygonal contours inside are selected and denoted as {L2, L3, ..., L7}, and together with the printing origin, they form the initial point set R = {L1, L2, L3, ..., L7}.

[0045] Step 3: Sort the initial points

[0046] Starting from the origin L1, traversing all center points and returning to the origin L1, this process is regarded as solving the traveling salesman problem. The shortest distance between the points in the initial set of points is planned by the genetic simulated annealing algorithm.

[0047] The specific steps are as follows:

[0048] The genetic algorithm operation is shown in Figure 4(a):

[0049] 1. Define initialization parameters. Define the optimal solution Sbest; set the population size N, iteration count MAXGEN, crossover probability Pc, and mutation probability Pm for the genetic algorithm; set the outer loop iteration count MaxOutIter, inner loop iteration count MaxInIter, initial temperature T0, and cooling factor alpha for the simulated annealing algorithm.

[0050] 2. Calculate the fitness value. The fitness value is the reciprocal of the objective function value. Therefore, the smaller the fitness value, the lower the probability that the offspring will be selected. The objective function proposed in this invention is the shortest path value, so the fitness value is:

[0051] Equation (1)

[0052] 3. Perform the selection operation. Based on the fitness value of each individual, a binary tournament is used to select the best individual from all path values ​​to enter the next generation.

[0053] 4. Crossover and mutation operations. Based on probabilities Pc and Pm, randomly determine crossover and mutation individuals. Then, randomly select two starting points from these individuals and perform swap, reverse, and insertion operations to generate new individuals.

[0054] After each run of the genetic algorithm, the top 30% of the best individuals in the offspring population undergo simulated annealing. Figure 4 As shown in (b):

[0055] 1. Generate a new solution. During the search, different weights are assigned to the exchange structure, the inversion structure, and the insertion structure. A roulette wheel is used to select which structure to use. Under the given neighborhood structure, a new path Snew is generated for the current path Scurr. The fitness values ​​g(Scurr) and g(Snew) of the two paths are compared.

[0056] 2. Determine whether to accept the new solution. If the fitness value of the new path is better than the current optimal path, then accept the new path; otherwise, accept the new path according to the Metropolis criterion with probability p:

[0057] Equation (2)

[0058] 3. Update the simulated annealing global optimal solution. Compare the objective function g(Snew) of the new path with the objective function g(Sbest1) of the current optimal solution to update the global optimal path.

[0059] 4. Update the current temperature. This is used to modify the temperature value in the outer loop, making the algorithm asymptotically convergent. The entire system converges to the global optimum with probability 1. The updated temperature is:

[0060] Equation (3)

[0061] 5. Output the optimal individual from the simulated annealing. When the outer loop reaches its maximum count, exit the simulated annealing algorithm and output the current shortest path.

[0062] Return the genetic algorithm, such as Figure 4 (c)

[0063] 1. Reorganize the new Chrom. Reorganize the individual Sbest1 output from each iteration of the simulated annealing algorithm into a new offspring population.

[0064] 2. Update the global optimum of the genetic algorithm. Compare the fitness values ​​of the best individual (bestChrom) in the recombined population (chrom) with the current best individual (Sbest) to update the global optimum.

[0065] 3. Output the global optimal solution. Repeat step 3 until the maximum number of iterations of the genetic algorithm is reached, and output the global optimal solution Sbest at this point, which is the shortest path under this algorithm, such as... Figure 5 As shown.

[0066] Step 4: Determine the starting point and printing order

[0067] Starting from the origin and following the shortest path planned in step S3, the first point of each contour traversed by this path is taken as the printing start point of that contour. The printing start points M={L1,S2, S3,…,S7} for each contour are determined sequentially. The order in which the start points are determined is recorded as the printing order, thus determining the printing order L1-S2-S3-…-S7. The result is as follows: Figure 6 As shown.

[0068] Step 5: Optimize the starting point position

[0069] After selecting the starting point set M, optimization is performed. First, select L1 and S3 from the starting point set M. Traverse all vertices of the contour containing S2, finding the point whose sum of distances is closer to L1 and S3, and update the point containing S2. If multiple vertices have the closest sum of distances, select the point closer to S3 as the updated S2. Update S3, S4, ..., S6 sequentially according to the above steps. During optimization, if a point is updated, it is necessary to re-evaluate whether previous points need updating. If an update is needed, continue until S2; otherwise, continue forward optimization. For the last point S... ７ Then, refer to S6 and L1 to update the points with the shortest distance. Finally, obtain the optimized starting point set M'={L1',S2',S3',…,S7'} and the printing order L1'-S2'-S3'…S7'-L1', thus obtaining... Figure 7 The outline printing path is shown.

[0070] Step 6: Generate the final print path

[0071] Furthermore, this embodiment also utilizes Cura and Simplify 3D slicing software for... Figure 2 The model parts shown are sliced ​​to form Figure 8 and Figure 9 The slice outline path is shown. Figure 7 , Figure 8 and Figure 9 Printing experiments were conducted on the outline printing path shown, and the printing time and energy consumption information were collected, as shown in Table 1 below. Table 1 shows that the single-layer printing time of the path planned in this invention is 74.75 s, which is 10.21% and 11.80% shorter than the two slicing software methods, respectively. The single-layer printing energy consumption of the path planned in this invention is 2069.91 J, which is 12.87% and 14.73% lower than the two slicing software methods, respectively, under the same printing environment and bed and nozzle heating temperatures. Therefore, the method proposed in this invention significantly improves printing efficiency and significantly reduces printing energy consumption.

[0072] Table 1. Comparison of Printing Time and Energy Consumption

[0073] Path planning methods This article's method Cura Simplify3D Single-layer printing time / s 74.75 83.25 84.75 Single-layer printing energy consumption / J 2069.91 2375.69 2427.59

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing energy consumption in fused deposition modeling contour printing, characterized in that, Includes the following steps: S1. Part Contour Reading Read each contour from a single-layer slice of the model part; S2. Determining the center point of the contour Determine the center point of each polygon in the single-layer slice outline, and form an initial point set with the printing origin; S3, Initial Point Sort The initial points are sorted using a genetic simulated annealing algorithm, and the shortest distance between all initial points is calculated. S4. Determining the starting point position and printing order. The first point of each contour traversed by the shortest path between the initial points obtained in S3 is taken as the starting point of that contour, and the printing starting point of each contour is determined in turn; at the same time, the order in which the starting points are determined is recorded as the printing order, and the printing order is determined accordingly. S5. Optimization of starting point position Optimize the position of the starting point to form the optimal trajectory; The method for optimizing the starting point position in step S5 is as follows: First, group three consecutive printing starting points together; second, determine whether the middle point among these three printing starting points is on the shortest path of the other two points. If not, find the point with the closest sum of distances to the other two points among all vertices of the contour where the middle point is located, and use it as the updated middle point position. If there are multiple points with the closest sum of distances, select the point with the closest sum of distances to the third point among the three as the updated middle point position; then update all points in sequence according to the above method. If a point is updated, it is necessary to re-determine whether the preceding points need to be updated. For the last point, update it by referring to the origin and the point before the last point; finally, obtain the optimal printing path. S6. Form the final print path.

2. The method of claim 1, wherein, The determination of the contour center point described in step S2 involves taking the outermost contour of the model part as a separate contour, selecting one vertex of the contour as the printing origin, and taking the center points of other polygonal contours inside and the printing origin as the initial point set; here, for irregular polygons, the geometric centroid of the contour is selected as the center point of the contour.

3. The method of claim 1, wherein, The algorithm used in the initial point sorting in step S3 is the genetic simulated annealing algorithm: starting from the origin, traversing all center points and returning to the origin, this process is regarded as solving the traveling salesman problem, and the algorithm is used to plan the shortest distance between the points in the initial point set.

4. The method of claim 1, wherein, The method for determining the starting point position and printing order in step S4 is as follows: starting from the origin and following the shortest path planned in step S3, the first point of each contour passed by the path is taken as the printing starting point of that contour, and the printing starting point of each contour is determined in turn. At the same time, the order in which the starting points are determined is recorded as the printing order, thereby determining the printing order.

5. The method of optimizing energy consumption of fused deposition modeling contour printing according to any one of claims 1 to 4, characterized in that, The criterion for determining the optimal printing path in step S5 is as follows: repeat step S5. If the positions of all points no longer change, input to the next level. If they change, return to the previous level to continue optimization and obtain a new path.

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