Optimization of Material Mechanical Properties and Processing Parameters in High Performance Mechanical Manufacturing Technology

Material properties are one of the key factors in the design and manufacturing process of mechanical parts. Common materials include metal materials, plastic materials and composite materials. Among them, metal materials are the most commonly used in mechanical parts. The properties of metal materials mainly include mechanical properties, physical properties and chemical properties [1–4]. Mechanical properties refer to the mechanical behavior of the material under the action of external forces, including strength, hardness, toughness and so on. Physical properties involve the thermal properties, magnetic properties, electrical properties, etc. of the material. Chemical properties are related to the material's corrosion resistance, chemical stability, etc. [5–8]. At the same time, there are differences in the properties of different metal materials. In the design and manufacture of mechanical parts, according to the specific requirements of the selection of appropriate material properties, can improve the reliability and performance of parts [9–11].

Machinability is the plasticity, machinability and process performance of the material in the machining process. The machinability of materials directly affects the molding and machining efficiency of parts [12]. There are obvious differences in the machining properties of different materials. Therefore, when selecting materials, the matching of machining properties and machining process needs to be fully considered. In addition, machinability is also related to the internal structure and organization of materials [13–15]. For example, the size and distribution of grains affect the mechanical properties and machining properties of materials. Therefore, in the process of material processing, through the optimization of heat treatment and microstructure control, it can improve the processing performance of the material to achieve better processing results [16–18].

This paper first carries out experiments on the mechanical properties of high-performance materials, realizes the research on the mechanical properties of high-performance materials, and further constructs an optimization model of high-performance material machining parameters by combining the characteristics of the mechanical properties of materials, which provides an effective way to improve the machining efficiency and quality. With the help of NSGA-II algorithm, the optimal solution set of Pareto is obtained, from which the best parameter scheme is selected through screening, and the optimization variables are constructed with a number of constraints, such as spindle speed, milling depth, etc. The optimal solution set is selected from the Pareto front end. The process parameters that meet the machining requirements are selected from the Pareto front end to optimize the parameters. Establish the multi-objective optimization genetic algorithm, complete the parameter coding settings through MATLAB software, use the linear weighted combination method to realize the single-objective function of the fusion of the name, complete the selection operator, crossover operator, variation operator and the calculation and setting of the operating parameters. The high CF/UHMWPE fiber hybrid composites used in the experiments on mechanical properties of high-performance materials are still used as the main body of the study to carry out processing parameter optimization experiments and test the effect of processing parameter optimization.

2

Experiments on the mechanical properties of high-performance materials

With the rapid development of high-performance manufacturing technology, people in high-performance mechanical manufacturing used in the mechanical properties of high-performance materials put forward more urgent needs, which is also a high-performance materials to carry out the necessary conditions for the optimization of processing parameters.

This study will take the high-performance composite material - CF/UHMWPE fiber hybrid composite material as the main research body of this paper's high-performance material mechanical properties experiment, to carry out the material mechanical properties of the pleated core test.

2.1

Preparation of V-type pleated sandwich panels

Four unidirectional laminates of CF/UHMWPE fiber hybrid composites with different blending ratios and a thickness of 1 mm were prepared.The specific experimental procedures are as follows. 1)

Preparation. Cut the carbon fiber prepreg sheets needed for the preparation of laminates and the unidirectional tape prepregs needed for the preparation of UHMWPE inserts, with the size of 265 mm×255 mm. Clean the experimental bench with anhydrous ethanol before the experiment to ensure that no foreign matter or impurities enter the prepregs during the lay-up period. Clean the working surface of the mold and coat the mold release agent on the working surface of the mold.

2)

Layup design. Calculate the number of layers of carbon fiber prepreg tape and ultra-high molecular weight polyethylene fiber inserts required for the preparation of 1 mm plywood in accordance with the set mixing ratio, and the specific design is shown in Table 1. Fiber blending is divided into intra-ply and inter-ply blending, and this paper adopts symmetric inter-ply blending.

3)

Interlayer preparation: UHMWPE fiber inserts were prepared by orthogonal lay-up [0°/90°] using cut UHMWPE fiber unidirectional prepreg tapes, and hot-pressing lamination. The interlayer was made with waterborne polyurethane resin that contained 15%.

4)

Paving hot pressing: the prepared UHMWPE insert and carbon fiber unidirectional prepreg are paved in accordance with the set mixing ratio and paving order, and the prepreg is paved and compacted layer by layer to reduce the interlayer bubbles and gaps, reduce the porosity of the laminated board, and ensure the flatness of the board. The process curve is hot pressed after the completion of layup, and the mold is removed for mold opening after the end of hot pressing. The prepared unidirectional plywood of high-performance fiber-reinforced composites with a laminate size of 265 mm × 255 mm × 1 mm.

Table 1.

Sample

Sample	Mixed volume ratio	Layer ratio	Carbon fiber prepreg	UHMWPE layer	Layer structure
1#	100%	25C	25	0	[C₂₅/U₀]
2#	88.80%	24CX2U	24	2	[C₈/U₁/C₈/U₁/C₈]
3#	75.80%	20CX4U	20	9	[C₄/U₁/C₄/U₁/C₄]
4#	43.50%	10CX9U	10	4	[C₁/U₁/C₁/U₁/C₁]

2.2

Experimental Tests

The compression test of V-type pleated sandwich panels was carried out on a 10-ton universal testing machine at the College of Aeronautics and Astronautics of Chongqing University.

According to the standard “Test Method for Flat Compression Performance of Sandwich Structure or Core (GB/T 1453-2005)” to use displacement loading, set the loading speed of 0.5mm/min, the experimental environmental conditions according to GB/T 1446-2005, the temperature of 25 °C, and the experimental process is videotaped, the testing machine records the load-displacement data.

The flat compressive strength and flat compressive modulus of elasticity of V-type pleated sandwich panels were calculated by equations (1) and (2): 1 $σ = \frac{P}{F}$

where σ is the core flat compressive strength in megapascals (MPa), P is the breaking load in newtons (N), and F is the specimen cross-sectional area in square millimeters (mm²): 2 $E_{c} = \frac{Δ P \times (h - 2 t_{f})}{Δ h \times F}$

where E_c is the core flat compressive modulus of elasticity in megapascals (MPa), ΔP is the value of the load increment on the straight line segment of the load-displacement curve in newtons (N), h is the specimen thickness in millimeters (mm), t_f is the thickness of the panel in millimeters (mm), and Δh is the incremental compressive deformation in millimeters (mm) corresponding to ΔP.

2.3

Analysis of experimental results

2.3.1

Tensile strength test

The stress-strain curves of unidirectional laminate specimens (1#) prepared by ultra-thin carbon fiber prepreg tape and unidirectional laminate specimens (2#, 3#, 4#) of hybrid composites prepared by ultra-thin UHMWPE fiber inserts are shown in Fig. 1. From the figure, it can be seen that the average tensile strength of specimen 1# is 1920 MPa, the average tensile strength of specimen 2# is 1684 MPa, the average tensile strength of specimen 3# is 883 MPa, and the average tensile strength of specimen 4# is 271 MPa. because of the decrease in CF volume fraction of 2#, 3#, and 4#, their strengths are lower than that of 1#, and the strengths of 2# are reduced by 12.29%, the strength of 3# strength decreased by 54.01% and 4# strength decreased by 85.89%. The larger decrease in strength of specimens 3# and 4# is mainly due to the fact that the tensile properties of the composites are closely related to the characteristics of the fibers and resin as well as the interfacial bond strength. With the increase of UHMWPE fiber content, the interfacial bonding strength of the prepared laminates decreased significantly, resulting in an aggravation of delamination of the laminates. As a result, the mechanical properties of specimens 3# and 4# decreased significantly. Specimen 1# completely failed when the strain reached 1.83%, while the corresponding strength of specimen 2# at the same strain still had 1572 MPa and did not fail. Specimen 3# has a strength of 686 MPa at the same strain, while the strength of 4# has dropped below 150 MPa. This is because different fiber components have different elongation at break.

2.3.2

Bending Performance Test

Based on the bending damage macro-morphology of four unidirectional laminates with different mixing ratios of CF/UHMWPE hybrid composites, specimens 1#, 2#, 3# and 4#, the bending strengths of each specimen are shown in Fig. 2. It can be seen that the average value of the bending strength of specimen 1# is 1320MPa, and the average value of the bending strength of specimen 2# is 689Mpa, and specimens 1# and 2# fracture in the middle position of the specimen. The average value of the bending strength of specimen 3# is 303Mpa, the average value of the bending strength of specimen 4# is 102Mpa, specimen 3# and 4# are deformed in the middle, and the bending damage of the specimen is more stable.

3

Optimization model for processing parameters of high-performance materials

In the previous chapter, this study takes high-performance composites - CF/UHMWPE fiber hybrid composites as the research object, and conducts experiments on material mechanical properties of high-performance materials applied in high-performance mechanical manufacturing technology. After understanding the mechanical properties of high-performance materials, how to select the most consistent process parameters that meet production needs, processing parameter optimization has become the next important issue. This chapter will focus on the optimization method for processing parameters of high-performance materials.

3.1

Parametric multi-objective optimization based on NSGA-II algorithm

Parameters are critical process parameters in machining. Reasonable parameters can ensure the quality of the workpiece while also improving machining efficiency and reducing production costs. Through this paper previously established to consider the tip offset heartbeat power model, milling cutter axis area life model can be seen, different cutting parameters on the robot will have different effects, for the processing needs of this paper, should limit the material and the robot's maximum deformation error, and at the same time to ensure high machining efficiency and long life of the material. However, high efficiency and long life are conflicting goals, so how to select parameters that balance deformation, efficiency, and life becomes a top priority. In this section, a parametric multi-objective optimization model based on the NSGA-II algorithm is established to obtain the Pareto optimal solution set, and then the best parameter scheme is selected from it after screening [19–20].

3.1.1

Optimization objectives

1)

Longitudinal cross-section area removal rate objective function

For the through-hole type workpiece processed in this paper, in order to improve the processing efficiency, there is no need to completely mill the material in the hole, only the contour of the hole is processed by the groove, and the center material can be unloaded. Therefore, the machining process of the end mill along the hole contour layer by layer slot formed by the processing trajectory for a column surface, each hole processing time should be by the area of the column surface and the unit time of the longitudinal cross-section area of the milling cutter cutting area of the ratio, that is, the area of the removal rate to determine the traditional optimization of the parameters of the metal removal rate selected. In order to improve machining efficiency and reduce labor and material costs, it is expected that the larger the area removal rate, the better, the formula is shown below, the optimization variables include spindle speed n, depth of cut a_p, feed per tooth f_z: 3 $w_{S} = n \cdot f_{z} \cdot N \cdot a_{p}$

2)

Surface roughness

Surface roughness is an important indicator for evaluating and controlling the quality of material processing, which directly affects the performance and life of the processed parts. Optimization of processing parameters to improve the surface roughness is important for improving the quality of material parts, extend their service life. Surface roughness also has a significant impact on the wear resistance, fatigue strength, corrosion resistance and sealing of material parts.

3.1.2

Optimization variables and constraints

Constructing optimization variables: 4 $x = (x_{1}, x_{2}, x_{3}), x_{1}, x_{2}, x_{3}$

The values of n, a_p and f_z are shown respectively. Due to the processing capability of the processing equipment and the material can withstand the load is limited, and the surface of the workpiece processing quality has certain requirements, therefore, certain constraints are needed to ensure that the selected optimization parameters can effectively correspond to the actual processing situation. 1)

Spindle speed n(r/min) machining system selected spindle for Jingyao XT40 mechanical boring and milling spindle head, motor power of 3.7kW, rated speed of 1500/r min, the minimum speed of 100/r min maximum speed of 4500/r min, so 100≤n≤4500. That is: 5 ${\begin{array}{l} g_{1} (x) = 100 - x_{1} \leq 0 \\ g_{2} (x) = x_{1} - 4500 \leq 0 \end{array}$

2)

depth of cut a_p(mm) when the depth of cut is too small, due to the machine's own positioning error compared to CNC machine tools, the depth of cut may be uneven, resulting in the cutting process is not stable, so set the minimum value of 0.5mm, the depth of cut is too large will result in too much force beyond the spindle head machining capacity, set the upper limit of 5mm, so 0.5≤a_p≤5, namely: 6 ${\begin{array}{l} g_{3} (x) = 0.5 - x_{2} \leq 0 \\ g_{4} (x) = x_{2} - 5 \leq 0 \end{array}$

3)

Feed per tooth f_z(mm) when the feed is too large when the cutting force increases significantly, there may be insufficient torque phenomenon, due to the processing is intermittent cutting, the impact on the cutting edge will increase significantly, easy to wear, chipping. And when the feed amount per tooth is too large, the thickness of the chip increases, which is not conducive to chip removal, the comprehensive consideration is 0.02≤f_z≤0.16. That is: 7 ${\begin{array}{l} g_{5} (x) = 0.02 - x_{3} \leq 0 \\ g_{6} (x) = x_{3} - 0.16 \leq 0 \end{array}$

4)

Total cutting force F_xzz(N) The cutting force is mainly limited by the spindle head power. According to the motor speed and torque calculation formula: 8 $T = 9550 \frac{P}{n}$

Where T - torque (N·m).

P - Motor rated power (kW).

n - motor rated speed (r/min).

The power of the spindle head motor is 3.7kW, and the rated speed is 1500r/min. It is known that the rated torque of the motor is 23.56N·m. Within the range of the parameters in the text, the parameters corresponding to the generation of the maximum force are a_p = 5mm, a_e = 25mm, f_z = 0.16mm, λ = 333.307°, ρ = 0.0116mm.

5)

Bending deformation of material w_D(mm)

The bending deformation of the material during the process can be broken down into sub-deformations in the feed direction x and normal direction y. Too much deformation in the feed direction will lead to insufficient space for the material to escape, and the toolbar will come into contact with the workpiece, which is easy to be dangerous. Since the diameter of the toolpost is 24mm and the diameter of the cutterhead is 25mm, i.e., the material avoidance space is 0.5mm, for safety reasons, when the material deformation reaches 0.1mm, it is considered to have exceeded the critical value. Normal deformation affects the workpiece machining error. The total deformation of the material in the process is jointly composed of deformation in the x and y directions, which is greater than either deformation, and it may be desirable to limit the combined force in the x and y directions. The input variables of the material bending deformation model include the peak radial force F_{xy max} in the x and y directions, the material diameter (i.e., cutting width a_e), and the material overhang L. Through the previous analysis in Part 4, it can be seen that, under the consideration of the eccentricity of the tool tip, the maximum peak radial force within the selected parameter range reaches F_{xy max} = 725N. When the diameter of the material of 25mm overhangs 130mm, according to the material bending deformation model established in Chapter 2, it can be calculated that at this time, the material bending deformation model can be calculated. Bending deformation model can be calculated at this time the material end deflection reaches 0.124 mm, the deformation exceeds 0.1 mm. therefore, the need for material with different overhang lengths, the size of the bending deformation is limited, so 0≤w_D≤0.1. that is: 9 ${\begin{array}{l} g_{7} (w_{D}) = w_{D} \geq 0 \\ g_{8} (w_{D}) = w_{D} - 0.1 \leq 0 \end{array}$

3.2

Genetic algorithm for multi-objective optimization of processing

Genetic algorithm is an algorithm that simulates biological evolution and optimizes the global search of a population. The four elements of parameter code setting, design of fitness function, genetic operation and control parameter setting are the core elements of genetic algorithm. In this paper, we use MATLAB software and utilize the University of Sheffield Genetic Algorithm Toolbox as the algorithm base code for the establishment of multi-objective genetic algorithms. The setting process is described below respectively. 1)

Parameter code setting. Since the genetic algorithm is established by imitating the genetic process, it needs to encode the individual parameters according to a certain law to generate strings during its operation. The selection of the encoding method determines the arrangement order of the individual chromosome strings, and also has a certain impact on the selection operator, crossover operator, and mutation operator settings.

In this paper, we use the binary coding method, using the bs2rv command in the Genetic Algorithm Toolbox to encode the individual into a binary string, and then carry out the genetic algorithm operation on it, and when the operation is finished, decode the binary string to obtain the optimal individual.

2)

Fitness function. The basic logic of the genetic algorithm is to obtain the individual with the largest fitness by continuously eliminating the individuals with low fitness, i.e., retaining the individual with the largest fitness in each generation. The fitness function directly affects the parameter optimization results, so it is especially important to set the fitness function reasonably.

In this paper, we utilize the linear weighted combination method to fuse the single-objective functions and calculate the fitness of individuals in each generation [21].

3)

Selection operator. The elimination probability of an individual in the genetic algorithm is determined by the size of its fitness value, and the role of the selection operator is to assign the elimination probability to each individual based on the fitness value, and the individuals with high fitness values are eliminated with less probability, which results in higher fitness values for the next generation of individuals.

The selection operator is set as in equation (10), if the larger the value of individual fitness, the higher the probability that an individual in the group will be selected, assuming that the size of a group is M, where the ind individual has a fitness value of W_i, then the probability that the individual will be selected is P, then: 10 $P = \frac{W_{i}}{\sum_{i = 1}^{M} W_{i}} (i = 1, 2, 3, \dots \dots, M)$

4)

Crossover operator. The main way to generate new individuals in the next generation of the genetic algorithm is to set up the crossover operator, whose function is to cross reorganize the encoded strings of the two individuals of the previous generation, while ensuring that the length of the encoding remains unchanged. The crossover operator can keep the genetic algorithm from falling into local solutions.

The program compiled in this paper uses the px command in the genetic algorithm toolbox to set the crossover probability to 0.7 and calls the crossover operator with the recombin command.

5)

Mutation operator. Genetic algorithms, in addition to generating new individuals by means of crossover recombination, will also be a position on the chromosome code with a certain probability of conversion to the position of the other allele code characters, which is designed to mimic the process of mutation of species in nature, and further save the genetic algorithm from falling into a local solution to make the results of the calculations more accurate, and the probability of mutation is usually between 0.01 and 0.1.

Considering the complexity of the fitness function and pursuing the efficiency of the algorithm operation, the program compiled in this paper utilizes the pm command in the Genetic Algorithm Toolbox to set the probability of variance to 0.02, and calls the crossover operation using the mut command.

6)

Running parameters. In addition to the mutation probability P_m and crossover probability P_x already set in the previous section, the parameters that need to be set in advance for the normal operation of the genetic algorithm include the individual coding length L, the population size M, and the number of iterations T, which are three parameters. Individual coding length L has different selection strategies according to different coding methods, because this paper uses binary coding method for coding, so this program uses the PRECI command in the Genetic Algorithm Toolbox to set L = 20. Population size M, that is, the number of individuals contained in the population, directly affects the amount of calculations of the genetic algorithm, if the size of the population is selected too small will affect the population's full evolution, but if the population size is selected too large will greatly lengthen the time of the genetic algorithm, and the number of iterations will be increased. If the population size is too small, it will affect the full evolution of the population, but if the population size is too large, it will greatly prolong the operation time of the genetic algorithm, this program uses the NIND command in the genetic algorithm toolbox to set M = 40. The number of iterations T, that is, the number of cycles of the genetic algorithm from the beginning of the run to the end of the run, indicates that when the population iterates to the T th generation, it stops running, and outputs the individual with the highest fitness in the current population. Generally, the number of iterations is selected between 100 and 500, and this program uses the MAXGEN command in the genetic algorithm toolbox to set T = 100.

4

Experiments on optimization of processing parameters for high-performance materials

In the previous section, the performance mechanical property experiments were carried out using a high-performance composite material, CF/UHMWPE fiber hybrid composite, as the research subject of the high-performance material mechanical property experiments in this paper. The focus of the study in this chapter will remain on CF/UHMWPE fiber hybrid composites, and the optimization of processing parameters will be achieved through multi-objective optimization.

The determination of the processing parameters of CF/UHMWPE fiber hybrid composites is mainly influenced by the performance of machine tools, material parameters, experimental environment, and processing technology. In this paper, these constraints are simplified in the research process, and the discussion mainly focuses on the parameters that affect the cutting performance of the material.

4.1

Optimization results and analysis of machining parameters

According to the high-performance material processing parameter optimization model that has been established in this paper, the multi-objective parameter optimization algorithm based on genetic algorithm is used to solve the problem, and the algorithm initially sets the number of populations to be 50, and the number of iterations to be 100. After the algorithm iteratively seeks for the optimum and forms the result of multi-objective optimization as shown in Fig. 3, and (a) and (b) are the points of the hollow triangle in the figure that represent the optimized frontier points, which are uniformly distributed on the curve on the curve, and the different positions reflect the conflict and compromise of the two optimization objectives. The red star-marked points in Fig. (a) represent the sample point sets, which are uniformly distributed in the sample space. In this paper, the optimization objectives are minimum surface roughness and maximum material removal rate, so the optimization front is concentrated in the lower left corner of the picture, and the conflicting objectives make the optimization front in the shape of the figure.

The values of the decision variables in the Pareto front are shown specifically in Fig. 4. In the figure, axis x₁ represents spindle speed, axis x₂ represents feed rate and axis x₃ represents radial feed. The graph shows that the value of spindle speed x₁ ranges from 9400 to 9600 r/min, and the higher spindle speed of the surface has a significant effect on reducing the surface roughness of the workpiece machining. The value of feed speed x₂ is in the range of 598~600mm/min, which indicates that the feed speed of material cutting can be appropriately increased under the state of high rotational speed cutting, and the feed speed has a greater impact on the efficiency of material machining.

The Pareto optimal solution set obtained after optimization is shown in Table 2. It can be selected from the Pareto optimal solution set according to the machining requirements during the machining process. Among them, when the maximum material removal rate is the main objective, it can be selected among the solutions with smaller surface roughness, such as 2, 10, 16, etc. When surface roughness is the primary objective, it can be chosen from the solutions with smaller material removal rates, such as 7, 9, 15, and so on. When there is no clear requirement for workpiece machining, a compromise optimal solution can be chosen. Through multi-objective parameter optimization, it can provide more basis for the reasonable selection of cutting parameters in production practice and avoid blindness in cutting parameter selection.

Table 2

Pareto optimal solution set

Number	Spindle speedn (r/min)	Feed speedfv (mm/min)	Radial depthea (mm)	Surface roughness (μm)	Material division rate (mm²/min)
1	9450.45	600	0.5808	0.8264	158.249
2	9476.22	600	0.5778	1.4465	340.9081
3	9536.43	600	0.5648	1.3039	289.3583
4	9469.29	600	0.5788	1.2157	274.9616
5	9538.6	600	0.5778	0.8944	181.4063
6	9466.43	600	0.5668	0.8756	172.1069
7	9518.01	600	0.5698	0.7481	134.9363
8	9460.93	600	0.5838	1.0704	218.2345
9	9458.84	600	0.5768	0.8191	148.1827
10	9492.34	600	0.5658	1.3924	325.7098
11	9487.75	600	0.5718	1.3098	297.1774
12	9386.57	600	0.5808	1.3218	296.8388
13	9440.64	600	0.5738	0.9708	205.2253
14	9463.16	600	0.5728	0.8705	155.0067
15	9454.07	600	0.5728	0.7741	137.8664
16	9521.76	600	0.5798	1.4859	358.5064
17	9474.46	600	0.5788	1.27	278.818
18	9445.93	600	0.5848	1.0734	230.2345
19	9459.03	600	0.5668	0.8382	149.866
20	9411.7	600	0.5678	1.2122	277.7674

4.2

Experimental verification of machining parameter optimization

In order to prove the validity of the multi-objective optimization results, the optimization results obtained in the previous section are verified, and 10 groups of experimental data are randomly selected from the above optimization results for experimental verification. The experimental verification only needs to measure the surface roughness of the workpiece, and the real values measured experimentally are compared with the multi-objective optimization values, and the results are shown in Fig. 5. The results show that the highest prediction accuracy of the workpiece surface roughness is 98.66%, the minimum prediction accuracy is 93.12%, and the average prediction accuracy is 95.86%, which indicates that the multi-objective optimization of machining parameters proposed in this paper is reliable, and it has a guiding significance for the machining parameters selection in the actual production process. The experimental data and the optimization results of the trend in value change are consistent, with a high degree of coincidence.

5

Conclusion

This study takes CF/UHMWPE fiber hybrid composites, a material in high-performance mechanical manufacturing technology, as the main research body of material mechanical property experiment, to carry out high-performance material mechanical property experiments and complete an in-depth study of its mechanical properties. And further develop the optimization model for processing parameters of high-performance materials. In order to verify the effectiveness of the multi-objective parameter optimization method proposed in this paper, the optimization experiment of processing parameters of high-performance materials is carried out. To realize the Pareto frontier decision variables, the spindle speed x1 value range of 9500~9700r/min, feed speed value range in 598~600mm/min, in the value range to achieve parameter optimization and get the Pareto optimal solution set. To optimize the results of the random 10 groups of experimental data as the content, compared with the experimental measurement of the true value of this paper and multi-objective optimization of the value, can be obtained the highest prediction accuracy of the surface roughness of the workpiece up to 98.66%, the minimum prediction accuracy is also 93.12%, the average prediction accuracy of 95.86%, and the two trends are consistent with high degree of consistency.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Optimization of Material Mechanical Properties and Processing Parameters in High Performance Mechanical Manufacturing Technology

Linyuan Tian

Shutong Tian

Xue Zhou

Published Online: Mar 19, 2025

Received: Oct 21, 2024

Accepted: Feb 12, 2025

DOI: https://doi.org/10.2478/amns-2025-0436

KeywordsHigh performance materials, Material mechanical properties, NSGA-II algorithm, Parameter optimization

© 2025 Linyuan Tian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
High performance materials, Material mechanical properties, NSGA-II algorithm, Parameter optimization