Optimization of Additive Manufacturing Process for High-Precision Metal Parts

Research Article
Open access

Optimization of Additive Manufacturing Process for High-Precision Metal Parts

Kunkun Zhang 1*
  • 1 Xi’an Technological University    
  • *corresponding author zkk02278@163.com
ACE Vol.162
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-157-0
ISBN (Online): 978-1-80590-158-7

Abstract

This study focuses on the optimization of additive manufacturing process for high-precision metal parts, and analyzes in-depth the problems of precision deviation, unstable material properties, and low production efficiency in the application of the current technology. Through the systematic study of key process parameters, material properties and post-processing technologies in the additive manufacturing process, this paper proposes a set of process optimization solutions that include innovative methods such as dynamic regulation of laser power, multi-angle scanning strategy and gradient heat treatment. The experimental results show that the optimized additive manufacturing process can improve the dimensional accuracy of metal parts by 28% and reduce the surface roughness to below Ra 3.2 μm, while the mechanical properties of the material reach more than 92% of those of the traditional manufacturing method. The research results provide theoretical basis and practical guidance for the additive manufacturing of high-precision metal parts, which is of great significance for promoting the application of advanced manufacturing technology in aerospace, medical devices and other high-end equipment manufacturing fields.

Keywords:

additive manufacturing, metal parts, process optimization, precision control, material properties

Zhang,K. (2025). Optimization of Additive Manufacturing Process for High-Precision Metal Parts. Applied and Computational Engineering,162,69-77.
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References

[1]. Delissen A, Boots E, Laro D, et al. Realization and assessment of metal additive manufacturing and topology optimization for high-precision motion systems[J]. Additive Manufacturing, 2022, 58: 103012.

[2]. Galati M, Calignano F, Viccica M, et al. Additive manufacturing redesigning of metallic parts for high precision machines[J]. Crystals, 2020, 10(3): 161.

[3]. Zhou H, Yang H, Li H, et al. Advancements in machine learning for material design and process optimization in the field of additive manufacturing[J]. China Foundry, 2024, 21(2): 101-115.

[4]. Paul R, Anand S. A combined energy and error optimization method for metal powder based additive manufacturing processes[J]. Rapid Prototyping Journal, 2015, 21(3): 301-312.

[5]. Loyda A, Arizmendi M, de Galarreta S R, et al. Meeting high precision requirements of additively manufactured components through hybrid manufacturing[J]. CIRP Journal of Manufacturing Science and Technology, 2023, 40: 199-212.

[6]. Zhu P, Zhang G, Teng X, et al. Investigation and process optimization for magnetic abrasive finishing additive manufacturing samples with different forming angles[J]. The International Journal of Advanced Manufacturing Technology, 2022: 1-17.

[7]. Getachew M T, Shiferaw M Z, Ayele B S. The current state of the art and advancements, challenges, and future of additive manufacturing in aerospace applications[J]. Advances in Materials Science and Engineering, 2023, 2023(1): 8817006.

[8]. Moradi A, Tajalli S, Mosallanejad M H, et al. Intelligent laser-based metal additive manufacturing: A review on machine learning for process optimization and property prediction[J]. The International Journal of Advanced Manufacturing Technology, 2024: 1-34.

[9]. Shaikh M O, Chen C C, Chiang H C, et al. Additive manufacturing using fine wire-based laser metal deposition[J]. Rapid Prototyping Journal, 2020, 26(3): 473-483.

[10]. Jin Q Y, Yu J H, Ha K S, et al. Multi-dimensional lattices design for ultrahigh specific strength metallic structure in additive manufacturing[J]. Materials & Design, 2021, 201: 109479.

[11]. Sossou G, Demoly F, Montavon G, et al. An additive manufacturing oriented design approach to mechanical assemblies[J]. Journal of Computational Design and Engineering, 2018, 5(1): 3-18.

[12]. Chalicheemalapalli Jayasankar D, Tröster T, Marten T. Optimizing Injection Molding Tool Design with Additive Manufacturing: A Focus on Thermal Performance and Process Efficiency[J]. Materials, 2025, 18(3): 571.

[13]. Chowdhury S, Mhapsekar K, Anand S. Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process[J]. Journal of Manufacturing Science and Engineering, 2018, 140(3): 031009.

[14]. Barik S, Bhandari R, Mondal M K. Optimization of wire arc additive manufacturing process parameters for low‐carbon steel and properties prediction by support vector regression model[J]. steel research international, 2024, 95(1): 2300369.

[15]. Joralmon D, Walling J, Rai A, et al. Optimized dispersion of inorganic metal salts in photocurable resins for high-precision continuous 3D printing of complex metal structures[J]. International Journal of Machine Tools and Manufacture, 2025, 206: 104259.

[16]. Chaudhary R, Akbari R, Antonini C. Rational design and characterization of materials for optimized additive manufacturing by digital light processing[J]. Polymers, 2023, 15(2): 287.

Cite this article

Zhang,K. (2025). Optimization of Additive Manufacturing Process for High-Precision Metal Parts. Applied and Computational Engineering,162,69-77.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN：978-1-80590-157-0(Print) / 978-1-80590-158-7(Online)
Editor：Anil Fernando
Conference website: https://2025.conffmce.org/glasgow.html
Conference date: 24 October 2025
Series: Applied and Computational Engineering
Volume number: Vol.162
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Delissen A, Boots E, Laro D, et al. Realization and assessment of metal additive manufacturing and topology optimization for high-precision motion systems[J]. Additive Manufacturing, 2022, 58: 103012.

[2]. Galati M, Calignano F, Viccica M, et al. Additive manufacturing redesigning of metallic parts for high precision machines[J]. Crystals, 2020, 10(3): 161.

[3]. Zhou H, Yang H, Li H, et al. Advancements in machine learning for material design and process optimization in the field of additive manufacturing[J]. China Foundry, 2024, 21(2): 101-115.

[4]. Paul R, Anand S. A combined energy and error optimization method for metal powder based additive manufacturing processes[J]. Rapid Prototyping Journal, 2015, 21(3): 301-312.

[5]. Loyda A, Arizmendi M, de Galarreta S R, et al. Meeting high precision requirements of additively manufactured components through hybrid manufacturing[J]. CIRP Journal of Manufacturing Science and Technology, 2023, 40: 199-212.

[6]. Zhu P, Zhang G, Teng X, et al. Investigation and process optimization for magnetic abrasive finishing additive manufacturing samples with different forming angles[J]. The International Journal of Advanced Manufacturing Technology, 2022: 1-17.

[7]. Getachew M T, Shiferaw M Z, Ayele B S. The current state of the art and advancements, challenges, and future of additive manufacturing in aerospace applications[J]. Advances in Materials Science and Engineering, 2023, 2023(1): 8817006.

[8]. Moradi A, Tajalli S, Mosallanejad M H, et al. Intelligent laser-based metal additive manufacturing: A review on machine learning for process optimization and property prediction[J]. The International Journal of Advanced Manufacturing Technology, 2024: 1-34.

[9]. Shaikh M O, Chen C C, Chiang H C, et al. Additive manufacturing using fine wire-based laser metal deposition[J]. Rapid Prototyping Journal, 2020, 26(3): 473-483.

[10]. Jin Q Y, Yu J H, Ha K S, et al. Multi-dimensional lattices design for ultrahigh specific strength metallic structure in additive manufacturing[J]. Materials & Design, 2021, 201: 109479.

[11]. Sossou G, Demoly F, Montavon G, et al. An additive manufacturing oriented design approach to mechanical assemblies[J]. Journal of Computational Design and Engineering, 2018, 5(1): 3-18.

[12]. Chalicheemalapalli Jayasankar D, Tröster T, Marten T. Optimizing Injection Molding Tool Design with Additive Manufacturing: A Focus on Thermal Performance and Process Efficiency[J]. Materials, 2025, 18(3): 571.

[13]. Chowdhury S, Mhapsekar K, Anand S. Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process[J]. Journal of Manufacturing Science and Engineering, 2018, 140(3): 031009.

[14]. Barik S, Bhandari R, Mondal M K. Optimization of wire arc additive manufacturing process parameters for low‐carbon steel and properties prediction by support vector regression model[J]. steel research international, 2024, 95(1): 2300369.

[15]. Joralmon D, Walling J, Rai A, et al. Optimized dispersion of inorganic metal salts in photocurable resins for high-precision continuous 3D printing of complex metal structures[J]. International Journal of Machine Tools and Manufacture, 2025, 206: 104259.

[16]. Chaudhary R, Akbari R, Antonini C. Rational design and characterization of materials for optimized additive manufacturing by digital light processing[J]. Polymers, 2023, 15(2): 287.

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