This paper demonstrates the application of generative design to metal 3D printing using SLM to reduce print time and material costs. The study included a sample of 30 traditionally designed 3D models, to which generative design was applied, with the topology modified. The results demonstrate a significant reduction in 3D printing time (average 28,5%) and the weight of metal powder used (average 31,09%). The study also recorded an increase in support material requirements for efficient 3D printing, with an average increase of 38%, which is expected due to the nature of generative model generation. Despite these positive results, the effective implementation of generative design technologies in the additive manufacturing industry will require the development of a software market and the expansion of a highly qualified workforce to produce 3D models that meet 3D printing quality standards.

Introduction

In recent years, additive manufacturing, particularly metal 3D printing, has become a key player in manufacturing processes in a variety of industries, such as aerospace, automotive, energy and
mechanical engineering. These technologies provide high flexibility in design, the ability to create
complex geometries and reduced waste compared to traditional manufacturing methods [1]. However,
Despite their advantages, there are a number of challenges associated with the need to optimize production processes.

The aim of the study is to investigate the effectiveness of using generative design to optimize metal 3D printing processes in terms of reducing material consumption and printing time.

Generative design is an innovative approach that enables automation
design process using additive technologies and create optimized designs with minimal use of materials while maintaining strength characteristics [2]. This
is especially relevant in the context of growing demands for sustainable development and environmental responsibility. Generative design has great potential, but is not without its problems. In [3], problems with computational costs and the need for post-processing at various stages of production are noted.

Exploring the potential of generative design applications can help develop more sustainable and cost-effective manufacturing processes, an important goal for key sectors of the economy.

The concept of generative design

Generative design is a modern design method that uses algorithmic processes and computer technology to create optimized forms and structures from an initial model. This approach allows designers and engineers to specify specific parameters, such as
such as materials, dimensions, loads and manufacturing constraints, after which the system generates a variety of solution options that meet the specified criteria [4].

The process is based on algorithmic modeling that imitates the processes of natural selection, which allows us to find the most effective forms and structures that may not be obvious in traditional design. [5]. This method aims to optimize the design taking into account various factors such as strength, weight, cost and functionality. This helps to create lighter and stronger products, which is of additional importance in industries such as aerospace and automotive [6].

Generative design is a perfect fit with 3D printing technologies as it allows for the creation of complex
geometric shapes that are difficult or impossible to produce using traditional methods [7].

Generation of new product topology

In this study, generative design was implemented using Autodesk Inventor 2025 software. This tool allows algorithmic approaches to create optimized geometric shapes based on specified parameters and constraints (Fig. 1).

Fig. 1. Example of optimization of the topology of a part from a housing
research models

The process begins with defining product requirements such as mechanical loads, size and material constraints. Autodesk Inventor then generates multiple design options from which the most efficient one is selected.

After generating the models, the user is given the opportunity to evaluate each of them using built-in tools such as static and dynamic analysis, as well as assess the stability of structures [8]. This allows not only to identify optimal solutions, but also to understand how various parameters affect the efficiency of transformations.

During the study, the following parameters were identified for measurement:

  • Print time: The total time required to complete printing of each sample was measured, allowing the productivity of the process to be assessed;
  • Material volume: the amount of powder metal required to print each sample was measured, allowing us to assess the cost-effectiveness of the process and the potential for reducing material costs.

Testing samples before and after applying generative design

Testing of samples included several stages.

  • Sample preparation: 3D models with traditional designs were first selected, and new models were created using generative design in Autodesk Inventor. All 30 samples were designed for printing on an SLM Solution 280 v2 3D printer. The material used was AISI 440C steel.
  • The finite element method (FEM) was used to analyze the stresses and strains of the samples. This method allows modeling the behavior of materials under various loads, providing accurate predictions about the strength and stability of structures [9]. The models were created in the corresponding software, where stress analysis was performed for both types of samples (Fig. 2).

Fig. 2. Analysis of stresses and strains using FEM

  • The calculation of the parameters subject to change - printing time and material costs - was performed in the NetFabb program.

3. NetFabb calculates the total print time based on the following factors:

  • scanning time: determined by the laser speed and the number of passes required to fill each layer;
  • Travel Time: This takes into account the time it takes to move the laser between print areas;
  • total number of layers: the thickness of each layer and the total height of the model are taken into account.

The calculation of the amount of powder material required to print a model is based on:

  • volume of material: calculated based on the geometry of the model and the specified density of the material;
  • material losses: takes into account losses during cleaning and recycling of unused powder after printing.

4. Analysis of results: the change in the parameters of the models selected in the study after the application of generative design was assessed. Attention was paid to increasing the efficiency of material use and reducing energy consumption while maintaining strength characteristics.

Comparative analysis of product parameters before and after applying generative design

As a result of the study, indicators were calculated that make it possible to determine the effectiveness of the generative change of the original models (table).

Based on the data in the table, relative indicators of reduction in mass, volume and printing time were calculated:

  • savings in materials required for the production of products vary from 8,9 to 61,64% on the selected data set, with an average value of 31,09%;
  • the change in the volume of the 3D model varies from 3,3 to 64,55% with an average value of 35%;
  • Savings in 3D printing time required to produce products range from 3,33 to 60% with an average value of 28,5%.

In absolute terms, the use of generative design for the studied model bank allowed us to save 872 hours of printing and 30,8 kg of powder metal.

The study also yielded negative results for mass (6,5% increase) and print time (7,4% increase) in one of the 30 samples. Despite the reduction in mass of the product itself, the mass of the support material increased, leading to an overall negative result—both print time and the required amount of material. Therefore, the change in support mass using generative design was further analyzed (Figure 3).

Fig. 3. Change in the mass of the supporting material

Table. Results of calculations of indicators for traditional and generative samples

Detail number Original 3D models Models transformed by generative design
Volume,
sm3		 Mass, g Weight with sub. mat.1, g Time
3D printing, h		 Volume cm3 Mass, g Weight with pod.
mat., g		 Time
3D printing,
hour
1 121,3 929,0 968,1 26,5 74,5 570,7 618,7 17,3
2 787,5 6032,3 6476,5 179,0 279,2 2138,4 2889,6 90,3
3 304,0 2328,5 2474,0 74,0 217,5 1666,2 1834,7 57,3
4 370,4 2837,2 2965,2 87,0 228,5 1750,3 2101,4 64,5
5 489,7 3751,3 3884,5 111,0 301,8 2311,6 2712,9 80,5
.........
25 124,8 955,9 1001,9 27,5 86,1 659,2 703,6 19,5
26 188,4 1443,1 1501,4 40,5 113,4 869,0 911,3 25,0
27 11,5 87,7 112,6 6,0 9,3 71,3 101,9 5,8
28 1803,0 13810,7 14874,1 464,8 1137,3 8711,9 9705,1 303,0
29 395,0 3025,7 3069,1 83,0 203,5 1558,8 1647,4 46,0
30 48,4 370,7 398,8 12,0 29,6 226,8 252,7 8,0

In 16 of the 30 samples, the support mass increased by an average of 38%. This is due to the fact that generative design creates more intricate and complex shapes, requiring additional support to prevent deformation of the product during printing [10]. Nevertheless, despite the identified negative effect associated with the increase in support material, we consider the overall results of the study to be positive – generative design allowed us to reduce the mass, volume, and weight of the print by an average of one-third.

Conclusion

The practical significance of the obtained results lies in supporting decision-making in the process of managing production resources when using additive technologies in order to increase productivity, environmental sustainability and adaptation to market changes.

The experimental results obtained, indicating a reduction in printing time and the amount of material used, have a significant impact on production logistics. Reducing printing time allows for faster production cycles, which in turn facilitates a faster response to
changes in demand and improving overall production flexibility. In addition, reducing the amount of material used
material reduces raw material costs and improves the overall economic efficiency of the process within the production costs.

It should be noted that one of the challenges in using generative design technology in 3D printing is the limited software market and the lack of highly qualified personnel to prepare models that meet the requirements for high-quality 3D printing. We hope that the general trend towards the development of artificial intelligence technologies, as well as the growing demand of industries consuming additive technology products, will help overcome this barrier.

References

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Anna Nikolaevna Degtyareva is a postgraduate student at NUST MISIS and Deputy Director General for Development. Studia3D,
Sapelkin Maxim Eduardovich - postgraduate student of NUST MISIS, project manager of JSC RASU,
Golubev Oleg Valentinovich - PhD in Engineering, Associate Professor, NUST MISIS,
Kamonichkin Dmitry Tamashevich - General Director Studia3D.
E-mail: anvishnevskay@mail.ru