Advances in Numerical Simulation of Selective Laser Melting (SLM) Forming Processes

Selective laser melting (SLM) technology has gradually become an important supplement to traditional processing methods in the industrial field due to its high precision, ability to manufacture complex structures, and efficient material utilization. However, during the SLM forming process, the high-speed movement of the laser heat source makes the evolution of the instantaneous temperature gradient and thermal stress (such as residual stress and deformation cracking) difficult to monitor in real time by experimental means, which limits process optimization and defect control. To address this challenge, this paper systematically reviews the latest research progress in numerical simulation technology for the SLM forming process, focusing on the coupling mechanism of the temperature and stress fields and their simulation methods, aiming to provide theoretical support for process parameter optimization through quantitative modeling. Research shows that numerical simulation technology based on adaptive heat source models and multiscale algorithms can significantly improve the accuracy of temperature gradient prediction (error less than 8 percent) and increase the computational efficiency of multilayer SLM models by more than 40 percent through equivalent interlayer heat-load strategies. When combined with data-driven machine learning methods, it is expected to support the development of a virtual-real process optimization framework. At the industrial application level, metal powder SLM technology has been used in the following scenarios: in the aerospace field, it is applied to the manufacturing of lightweight titanium alloy wing ribs and nickel-based high-temperature alloy engine nozzles; in the biomedical field, it is used to customize orthopedic implants and bionic devices with porous structures; and in the automotive field, it is used for rapid prototyping of complex functional parts such as aluminum alloy fuel cell bipolar plates. This review provides a methodological reference for reducing the process development costs of SLM technology and expanding its application in high-end manufacturing.

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Publication Details

Journal
Current Materials Science
Published
2026-03-16
DOI
https://doi.org/10.2174/0126661454399873251203183554
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
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article

Advances in Numerical Simulation of Selective Laser Melting (SLM) Forming Processes

Bingwei Gao, Jingyao Shi, Mingjing Qin, Zhonghui Sun et al.
Current Materials Science
Additive Manufacturing Materials and Processes
article

Advances in Numerical Simulation of Selective Laser Melting (SLM) Forming Processes

Bingwei Gao, Jingyao Shi, Mingjing Qin, Zhonghui Sun, Shishen Hou
article en

Abstract

Selective laser melting (SLM) technology has gradually become an important supplement to traditional processing methods in the industrial field due to its high precision, ability to manufacture complex structures, and efficient material utilization. However, during the SLM forming process, the high-speed movement of the laser heat source makes the evolution of the instantaneous temperature gradient and thermal stress (such as residual stress and deformation cracking) difficult to monitor in real time by experimental means, which limits process optimization and defect control. To address this challenge, this paper systematically reviews the latest research progress in numerical simulation technology for the SLM forming process, focusing on the coupling mechanism of the temperature and stress fields and their simulation methods, aiming to provide theoretical support for process parameter optimization through quantitative modeling. Research shows that numerical simulation technology based on adaptive heat source models and multiscale algorithms can significantly improve the accuracy of temperature gradient prediction (error less than 8 percent) and increase the computational efficiency of multilayer SLM models by more than 40 percent through equivalent interlayer heat-load strategies. When combined with data-driven machine learning methods, it is expected to support the development of a virtual-real process optimization framework. At the industrial application level, metal powder SLM technology has been used in the following scenarios: in the aerospace field, it is applied to the manufacturing of lightweight titanium alloy wing ribs and nickel-based high-temperature alloy engine nozzles; in the biomedical field, it is used to customize orthopedic implants and bionic devices with porous structures; and in the automotive field, it is used for rapid prototyping of complex functional parts such as aluminum alloy fuel cell bipolar plates. This review provides a methodological reference for reducing the process development costs of SLM technology and expanding its application in high-end manufacturing.

Current Materials ScienceVol. 19
Harbin University of Science and Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Additive Manufacturing Materials and Processes
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