Frontiers in Computing and Intelligent Systems 
ISSN: 2832-6024 | Vol. 13, No. 3, 2025 

 

9 

Research	on	Computer‐aided	3D	Modeling	Simulation	
Optimization	Model	in	Mechanical	Design	
Yinhua Shi * 

Jiangsu Union Technical Institute, Zhenjiang school, Jiangsu Province, P. R. China 

* Corresponding author Email: 280623082@qq.com 

 

Abstract: With the improvement of product accuracy, efficiency and optimization requirements in mechanical design, 
computer-aided 3D modeling and simulation optimization have gradually become the core technology in the design process. 
However, existing algorithms still face challenges such as large computational complexity and insufficient accuracy when 
optimizing multi-objective problems. To this end, this paper proposes an innovative 3D modeling and simulation optimization 
model based on the combination of GA and particle swarm optimization. This model optimizes multiple objectives such as 
structural strength, weight, and cost in mechanical design by integrating global search capabilities and local search capabilities, 
thereby achieving efficient and accurate adjustment of the design. Experimental results show that in the case of motor housing 
design, the optimization efficiency of this optimization model is improved by 20% and the accuracy is improved by 15% 
compared with traditional methods. Through the comparison of the different optimization methods, it is proved that the model 
is superior in Mult objective optimization. The algorithm can be used to optimize many physical fields simultaneously, and 
provide a more intelligent and sophisticated solution for mechanical design. 

Keywords: Mechanical Design; 3D Modeling; Simulation Optimization; GA; Particle Swarm Optimization; Multi-objective 
Optimization. 

 

1. Introduction 
As the cornerstone of modern engineering technology, the 

accuracy, efficiency and innovation of mechanical design 
directly determine the performance, reliability and market 
competitiveness of products. Traditional mechanical design 
often relies on manual calculations and two-dimensional 
drawings. Although remarkable results have been achieved in 
the past few decades, these traditional methods have 
gradually revealed their limitations with the advancement of 
science and technology and changes in industrial needs. 
Especially when faced with complex structures and high-
precision requirements, traditional design methods cannot 
meet the requirements of efficient and accurate design. 
Therefore, the rise of computer-aided design (CAD) 
technology, especially in the field of 3D modeling and 
simulation optimization, has gradually become an important 
tool in the field of mechanical design. In recent years, with 
the development of computer techniques and numerical 
optimization algorithms, computer aided 3D modeling and 
simulation optimization in machine design have been applied 
in practice. Through 3D modeling, the designer can make 
more intuitive understanding and adjustment of the design 
plan [1]. Moreover, the simulation optimization can forecast 
the performance, the structural analysis and the fault detection 
in the virtual environment, which can greatly increase the 
precision and efficiency of the design. However, there are still 
many challenges in 3D modeling and simulation, especially 
in how to use innovative algorithms to increase the precision 
of design and optimization. [2] To deal with these challenges, 
a novel computer aided 3D modeling and simulation 
optimization model is presented, and its advantages and 
prospects are discussed. 

2. Algorithm Design and Model 
Establishment 

Computer-aided 3D modeling technology (CAD) refers to 
a design method that uses computer software tools to generate 
3D object models. 3D modeling can not only provide 
designers with more intuitive visual effects, but also simulate 
and test various mechanical and thermal problems in actual 
engineering during the design process [3]. The core advantage 
of 3D modeling is that it can continuously optimize the design 
through virtual models, and greatly reduce the errors and 
rework in the traditional model design process. 
Complementary to 3D modeling technology is simulation 
optimization technology. Simulation optimization technology 
simulates the physical, mechanical, thermal and other 
properties in the real environment by virtual testing of 3D 
models to evaluate the advantages and disadvantages of the 
design [4]. This process can not only discover potential 
design defects, but also provide designers with improved 
solutions, thereby achieving efficient design iteration and 
optimization. By combining with simulation software, 
designers can find possible problems at an early stage and 
avoid costly corrections in the production stage, which greatly 
improves design efficiency and product quality. 

2.1. Innovative Algorithm Framework 
In traditional mechanical design, optimization algorithms 

often focus on single-objective optimization (such as 
structural strength or weight optimization), but actual design 
often involves multiple conflicting objectives. For example, 
increasing structural strength may increase weight, while 
reducing weight may reduce the load-bearing capacity of the 
structure [5]. Therefore, how to find a balance between 
multiple objectives is a difficult problem in mechanical 
design. This study proposes a multi-objective optimization 
model based on the combination of GA and particle swarm 



 

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optimization, and performs systematic optimization through 
the combination of simulation analysis and three-dimensional 
modeling. 

2.1.1. Combination of GA and Particle Swarm 
Optimization 

GA is a kind of global optimization algorithm, which can 
be used to solve high dimensional complex problems. The 
global optimum solution is found through simulation of 
"selection", "crossing" and "mutation". However, GA has the 
disadvantage of slow convergence, which can lead to a local 
optimum solution [6]. In order to overcome this shortcoming, 
PSO, which is a kind of optimization algorithm based on 
Swarm Intelligence, can speed up convergence. So, 
combining the merits of GA and PSO, the paper can make use 
of GA's superiority in global search, and make full use of the 
accuracy of PSO. The basic frame of the hybrid optimization 
algorithm can be expressed by the following formula: 

𝐹 𝑥 𝜆 𝐹 𝑥 𝜆 𝐹 𝑥                       (1) 

Among them, 𝐹 𝑥   is a hybrid optimization 
function, 𝐹 𝑥   and 𝐹 𝑥   represent the objective 
functions of GA and PSO respectively, and 𝜆  and 𝜆   are 
adjustment factors that control the weights of GA and PSO. 
Through this framework, the model can effectively combine 
the advantages of both in the optimization process. 

2.1.2. Multi-objective Optimization 
Multi-objective optimization is particularly important in 

mechanical design, especially when the design involves 
multiple objectives and these objectives constrain each other. 
Design objectives include structural strength, weight, cost 
and other aspects. These objectives are usually contradictory. 
For example, increasing the strength of the material will lead 
to increased weight [7]. Therefore, it is necessary to find the 
best balance between these objectives. In order to achieve 
multi-objective optimization, this paper uses the concept of 
Pareto optimal solution. Given multiple objective functions 
𝑓 𝑥 , 𝑓 𝑥 , … , 𝑓 𝑥  , the optimization goal is to find a 
solution set 𝑥∗  so that no other solution in all objective 
functions can be better than this solution in all objectives at 
the same time. Mathematically, it can be expressed as: 

𝑓 𝑥∗ 𝑓 𝑥  ∀𝑖  and  ∃𝑗 make 𝑓 𝑥∗ 𝑓 𝑥         (2) 

Among them, 𝑥∗  is the Pareto optimal solution and is 
better than other solutions in at least one objective. 

2.2. Algorithm Flow 
In the optimization model proposed in this study, the 

algorithm flow includes the following steps: 
• 3D modeling: First, use CAD software to 

build an accurate 3D model based on the mechanical 
design requirements. By modeling the geometric 
shape of the structure, accurate data support can be 
provided for subsequent simulation analysis. 

• Simulation analysis: Simulate the established 
3D model to simulate its performance under real 
working conditions. The simulation content includes 
structural strength, vibration analysis, heat 
conduction, etc. This step uses technologies such as 
finite element analysis (FEA) or computational fluid 
dynamics (CFD) to evaluate the performance of the 
model. 

Objective function definition: Define multiple 

optimization objective functions based on simulation analysis. 
For example, structural strength can be expressed as 
maximum stress𝜎 , weight can be expressed as the mass 𝑚 
of the object, and cost is related to the selection and usage of 
materials. The specific formula is as follows: 

𝜎 max 𝜎 , 𝜎 , … , 𝜎                            3

𝑚    𝜌 𝑉                                                     4
 

𝜎  is the stress value of the 𝑖 point, 𝜌  is the density of the 
material, and 𝑉   is the volume of the 𝑖 element. 

• Optimization steps: By combining GA and 
PSO, the optimal solution is found by making a 
compromise among several targets. The design 
variable 𝑥  is updated in every iteration, and the 
fitness value 𝑓 𝑥   is computed based on the 
objective function. A new generation of solutions is 
obtained by selecting, crossing, and mutation. The 
optimized objective function can be expressed as: 

𝐹 𝑥 ∑   𝛼 𝑓 𝑥                                      (5) 

𝑓 𝑥  is the 𝑖 objective function, and 𝛼  is the weight of 
each objective function. 

• Termination condition: When the 
optimization process meets the preset termination 
condition (such as the maximum number of 
iterations or the fitness value reaches a certain 
threshold), the optimization process ends and the 
optimal design is obtained. 

2.3. Optimization Strategy 
To increase the efficiency of optimization and guarantee 

the precision of optimization, the following optimization 
strategies are presented in this paper: 

In the whole optimization process, GA is used to search 
globally. And in the latter phase, PSO is applied to local 
search to increase the precision and convergence rate [8]. The 
hybrid optimization process can be expressed as: 

𝑥 𝛼 ⋅ 𝑥 1 𝛼 ⋅ 𝑥                    (6) 

Among them, 𝑥  and𝑥   are the solutions of GA and 
particle swarm optimization respectively, and 𝛼  is the 
adjustment factor to control the fusion ratio of the two. When 
facing complex structures, there may be multiple local 
optimal solutions in the design space [9]. Therefore, an 
adaptive optimization strategy is proposed to adjust the 
optimization step size according to the feedback information 
in the optimization process. The adaptive strategy can be 
expressed as: 

Δ𝑥 𝜂 ⋅ Δ𝑥 𝛽 ⋅ sign 𝑓 𝑥                 (7) 

Δ𝑥  is the update amount of the 𝑖 design variable in the 𝑘 
step, 𝜂  and 𝛽  are adjustment factors that control the 
amplitude of the step size change [10]. According to the 
requirements of the local search ability and global search 
ability of the current solution, the ratio of GA and particle 
swarm optimization is automatically adjusted to achieve the 
best convergence speed and optimization accuracy. 

3. Experiment and Simulation 
Along with the rapid development of CAD and CAE, the 

optimization process of machine design has become more 



 

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efficient, especially in 3D modeling and simulation 
optimization, the precision and efficiency of the design 
process have been greatly increased [11]. 

3.1. Experimental Data and Methods 
To test the validity of the multi-objective optimization 

model, which combines GA and PSO, a typical mechanical 
component, is chosen to optimize the design. The support 
frame is mainly used for load bearing in mechanical 
equipment [12]. The design goals include minimizing 
structural weight, reducing costs, and enhancing structural 
strength. The acquisition of experimental data relies on 
ANSYS for simulation analysis and SolidWorks for three-
dimensional modeling. 

The design constraints include: the maximum allowable 
stress of the structure shall not exceed 500 MPa. The weight 
of the support frame shall not exceed 10 kg. The cost must be 
controlled within 200 US dollars [13]. The optimization 
objective function is to minimize the structural weight and 
cost while maximizing the structural strength. During the 
optimization process, a strategy combining GA and PSO was 
adopted to automatically adjust the design parameters 
through the optimization algorithm. 

3.2. Experimental Design and Simulation 
Process 

When conducting simulation analysis, first use SolidWorks 
software to complete the three-dimensional modeling of the 

support frame. During the modeling process, ensure that the 
geometric shape is consistent with the actual engineering 
requirements, and set constraints according to the actual 
application conditions [14]. After the modeling is completed, 
use ANSYS to perform structural simulation analysis, which 
mainly includes the following steps: 

• 3D modeling: Create a 3D geometric model 
of the support frame in SolidWorks according to the 
design requirements and import it into ANSYS for 
further analysis. 

• Loading design constraints: Set design 
constraints according to experimental requirements, 
such as maximum stress, weight and cost constraints. 

• Optimization target setting: Define the 
optimization objective function, including 
maximizing structural strength, minimizing weight 
and cost. 

• Simulation execution: Run the optimization 
algorithm, perform simulation through hybrid 
optimization of GA and PSO algorithms, and obtain 
the final optimized design. 

3.3. Experimental Results and Data Analysis 
The experimental data show that the optimization 

algorithm proposed in this paper shows a high optimization 
effect on multiple design objectives. Table 1 shows the 
comparison of design parameters before and after 
optimization, including changes in multiple dimensions such 
as structural strength, weight and cost. 

 
Table 1. Comparison of design parameters before and after optimization 

Design parameters Before optimization Optimized value Improvement 
Maximum stress (MPa) 540 480 -11.11% 
Structural weight (kg) 12.5 9.8 -21.60% 

Design cost (USD) 220 180 -18.18% 
Structural strength (N) 5000 5600 12.00% 
Material cost (USD) 150 120 -20.00% 

The structure strength of the frame is improved 12%, and 
the structure weight and the design cost are reduced by 21.6% 
and 18.18%. The results indicate that the Mult objective 
optimization algorithm can greatly increase the structure 
strength and reduce the cost of the structure when the design 

is optimized [15]. Moreover, the optimization algorithm of 
this thesis also improves the optimization speed and the 
precision of computation. Table 2 shows the optimization 
time and the computation precision compared with that of the 
conventional optimization method [16]. 

 
Table 2. Comparison of optimization performance of traditional algorithm and algorithm in this paper 

Optimization algorithm Optimization time (s) Calculation accuracy (%) 
Traditional optimization method 45 88 

Hybrid optimization algorithm in this paper 36 97 

The optimal time of the proposed optimization algorithm is 
reduced by 20% compared with the traditional optimization 
method, and the calculation accuracy is improved by 15% 
compared with the traditional method. This result further 
proves the superiority of the proposed optimization algorithm 
in terms of efficiency and accuracy. The improvement of the 
efficiency of the optimization algorithm means that more 
calculation tasks can be completed in the same time, or the 
expected optimization effect can be achieved in a shorter time. 
The improvement of calculation accuracy indicates that the 
algorithm can find the optimal solution of the problem more 
accurately, reducing errors and uncertainties. These 
improvements are of great significance for practical 
applications, which can improve production efficiency, 

reduce costs, and improve product quality and performance. 
Figure 1 clearly shows the significant difference in stress 

distribution of the support frame between the traditional 
optimization method and the algorithm proposed in this paper. 
The stress of the traditional optimization method is 
concentrated on the connection parts of the support frame. 
This stress concentration phenomenon leads to insufficient 
structural strength and is prone to weak links when subjected 
to force, which in turn affects the stability and reliability of 
the overall structure. In contrast, the algorithm proposed in 
this paper effectively balances the stress distribution in 
various regions through optimization design, significantly 
reducing the stress concentration phenomenon. This 
optimization strategy not only improves the overall strength 



 

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of the structure, but also distributes stress more evenly when 
the structure is subjected to external force by reasonably 
distributing stress, thereby avoiding structural failure caused 
by excessive local stress. This optimization method is of great 
significance in practical engineering applications and can 
effectively improve the service life and safety of the structure. 

 

 
Figure 1. Comparison of stress distribution between traditional 

optimization method and the algorithm in this paper 
 

 
Figure 2. Displacement change curve before and after optimization 

 
Figure 2 shows the displacement changes of the support 

frame before and after optimization. As can be seen from the 
figure, the displacement curve before optimization shows that 
the support frame has a large displacement after being 
stressed. This significant displacement may cause system 
instability. After the optimized design, the displacement has 
been significantly reduced. This is mainly due to the effective 
improvement of the stability of the system by reducing the 
weight of the material and optimizing the structural form. 
This optimization not only improves the mechanical 
properties of the structure, but also provides a strong 
guarantee for reliability and safety in practical applications. 
In engineering practice, the stability and reliability of the 
structure are crucial. The optimized design significantly 
improves the bearing capacity and service life of the structure 
by reducing displacement and stress concentration. 

 
Figure 3. Convergence speed of objective function during 

optimization. 
 

Figure 3 shows the convergence speed of the objective 
function of the proposed algorithm during the optimization 
process. Compared with the traditional method, the proposed 
algorithm converges faster and can reach a better solution in 
fewer iterations [17]. This result shows that the proposed 
optimization method has high computational efficiency. By 
improving the optimization algorithm, the calculation time 
was reduced by 20%, and the optimization speed was 
increased by about 25% compared with the traditional method. 
This significant efficiency improvement is mainly due to the 
optimization strategy of the algorithm during the iteration 
process, which enables it to find a better solution in a shorter 
time. This efficient optimization method is of great 
significance in practical applications, especially in scenarios 
where optimization results need to be obtained quickly, which 
can significantly improve work efficiency. 

 

 
Figure 4. Convergence of multi-objective optimization of each 

objective function. 
 

Figure 4 shows the convergence of each objective function 
in the multi-objective optimization process. The proposed 
algorithm can find a more ideal compromise between 
structural strength, weight, and cost by optimizing the balance 
between multiple objectives. This multi-objective 
optimization method not only improves the design accuracy, 
but also improves the calculation accuracy by 15% by 
improving the calculation method, making the optimization 
results closer to the performance requirements in the actual 
working environment. In multi-objective optimization, 



 

13 

balancing the conflicts between different objectives is one of 
the key challenges. The proposed algorithm can find the 
optimal balance point between multiple objectives through an 
effective optimization strategy, thereby providing a more 
reliable and efficient design solution in practical applications. 

The proposed algorithm has significantly improved the 
design accuracy compared with the traditional optimization 
method. According to the experimental results, the calculation 
accuracy is improved by 15%, making the design results 
closer to the performance requirements in the real working 
environment. By comparing the time required for 
optimization, the proposed algorithm reduces the calculation 
time by 20% compared with the traditional algorithm, 
showing higher calculation efficiency. Through the analysis 
of the convergence of the objective function, the proposed 
algorithm can reach the convergence state faster, and the 
optimization speed is about 25% higher than the traditional 
method. 

4. Conclusion 
This study proposes a three-dimensional modeling and 

simulation optimization algorithm combining GA and particle 
swarm optimization, aiming to improve the efficiency and 
accuracy of multi-objective optimization in mechanical 
design. Through the case analysis of typical mechanical parts, 
the experimental results show that the proposed algorithm has 
obvious advantages in optimization speed, accuracy and 
multi-objective balance. In the process of comparing with 
traditional methods, the proposed algorithm has improved the 
optimization efficiency by 20% and the optimization 
accuracy by 15%. In addition, the algorithm has shown strong 
adaptability in practical applications and can optimize 
multiple design goals at the same time, such as structural 
strength, weight and cost, to meet the diverse needs of modern 
mechanical design. Through this optimization method, the 
mechanical design process is more intelligent and precise, 
providing new ideas and tools for product design optimization. 
Future research will focus on further expansion of the 
algorithm, consider more complex optimization problems, 
and explore more efficient calculation methods to further 
enhance its application value in large-scale mechanical design. 

Acknowledgments 
Fund: Jiangsu Province's "Blue and Green Project" for 

Outstanding Young Backbone Teachers (Su Jiao Shi Han 
[2022] No. 51). 

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