Study of Microstructure and Properties of 316L with Selective Laser Melting Based on Multivariate Interaction Influence

Sun, Jianfeng; Shen, Luyan; Wang, Weiqiang; Liu, Zhu; Chen, Huaming; Duan, Jieli

doi:10.1155/2020/8404052

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…AISI 316L is an extensively studied metallic material in SLM research [6,7], due to the simplicity of its single-phase (γ Fe -austenite) microstructure [8,9]. The AM process is particularly advantageous for prototyping, aviation, aerospace and medicine [10] because of the design freedom, which allows for bionic structures [11], leading to a substantial reduction in the component's weight [12].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Energy Density for Selective Laser Melting on the Microstructure and Mechanical Properties of Stainless Steel

Donik

Kraner

Paulin

et al. 2020

Metals

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We have investigated the impact of the process parameters for the selective laser melting (SLM) of the stainless steel AISI 316L on its microstructure and mechanical properties. Properly selected SLM process parameters produce tailored material properties, by varying the laser’s power, scanning speed and beam diameter. We produced and systematically studied a matrix of samples with different porosities, microstructures, textures and mechanical properties. We identified a combination of process parameters that resulted in materials with tensile strengths up to 711 MPa, yield strengths up to 604 MPa and an elongation up to 31%, while the highest achieved hardness was 227 HV10. The correlation between the average single-cell diameter in the hierarchical structure and the laser’s input energy is systematically studied, discussed and explained. The same energy density with different SLM process parameters result in different material properties. The higher energy density of the SLM produces larger cellular structures and crystal grains. A different energy density produces different textures with only one predominant texture component, which was revealed by electron-backscatter diffraction. Furthermore, three possible explanations for the origin of the dislocations are proposed.

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Section: Introductionmentioning

confidence: 99%

Influence of the Energy Density for Selective Laser Melting on the Microstructure and Mechanical Properties of Stainless Steel

Donik

Kraner

Paulin

et al. 2020

Metals

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“…In addition, the optimal values of the printing parameters and the experimental values were slightly different, leading to differences between the measurements and predictions. However, in some previous studies (Li et al , 2021; Cao et al , 2021; Meng et al , 2020; Sun et al , 2020), these intervals of relative errors were accepted, taking into account the instrument measurement error and some accidental random factors that may occur during the experiment. Therefore, the optimal results presented herein are reliable for use in SLM manufacturing.…”

Section: Resultsmentioning

confidence: 99%

“…ultimate tensile stress, 515 MPa and elongation, 30%) were extracted from ASTM (F3184-16, 2016) and used as reference values in the problem formulation. The relative density and hardness were averaged according to the maximum values obtained in published studies (Deng et al , 2020; Wang, 2016; Sun et al , 2020; Gong et al , 2019; Agrawal et al , 2020; Dixit et al , 2018; Quinn et al , 2019). The hardness case was also used to compare the fact that a commercially obtained 316 L bar had an 80 HRB (Do et al , 2018): …”

Section: Methodsmentioning

confidence: 99%

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

La Fé-Perdomo,

Ramos-Grez,

Quiza

et al. 2023

RPJ

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Purpose 316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range. Design/methodology/approach Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem. Findings A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm3/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum. Originality/value To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

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“…Wang et al [27] determined the optimal process settings of speed function and focus offset that resulted in high RD (99%) and well-melted top-build surface SS316L parts, fabricated by electron beam melting. Deng et al [28] and Sun et al [29] used different experimental techniques to optimize process parameters and obtain the desired mechanical properties for SS316L parts. They produced high-quality parts in terms of tensile strength, density, and surface roughness that can further improve the fatigue properties of the material.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Multi-Performance Metrics and Process Parameter Optimization in Laser Powder Bed Fusion

Abdulla

Barsoum

et al. 2022

Metals

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This study aims to develop mathematical models to improve multi-performance metrics, such as relative density and operating costs, in laser powder bed fusion (LPBF), also known as selective laser melting, a metallic additive manufacturing technique, by optimizing the printing process parameters. The work develops a data-driven model for relative density based on measurements and an analytical model for operating costs related to the process parameters. Optimization models are formulated to maximize relative density or minimize operating costs by determining the optimal set of process parameters, while meeting a target level of the other performance metrics (i.e., relative density or operating costs). Furthermore, new metrics are devised to test the sensitivity of the optimization solutions, which are used in a novel robust optimization model to acquire less sensitive process parameters. The sensitivity analysis examines the effect of varying some parameters on the relative density of the fabricated specimens. Samples with a relative density greater than 99% and a machine operating cost of USD 1.00 per sample can be produced, utilizing a combination of low laser power (100 W), high scan speed (444 mm/s), moderate layer thickness (0.11 mm), and large hatch distance (0.4 mm). This is the first work to investigate the relationship between the quality of the fabricated samples and operating cost in the LPBF process. The formulated robust optimization model achieved less sensitive parameter values that may be more suitable for real operations. The equations used in the models are verified via 10-fold cross-validation, and the predicted results are further verified by comparing them with the experimental data in the literature. The multi-performance optimization models and framework presented in this study can pave the way for other additive manufacturing techniques and material grades for successful industrial-level implementation.

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Study of Microstructure and Properties of 316L with Selective Laser Melting Based on Multivariate Interaction Influence

Cited by 8 publications

References 24 publications

Influence of the Energy Density for Selective Laser Melting on the Microstructure and Mechanical Properties of Stainless Steel

Influence of the Energy Density for Selective Laser Melting on the Microstructure and Mechanical Properties of Stainless Steel

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

Mathematical Modeling of Multi-Performance Metrics and Process Parameter Optimization in Laser Powder Bed Fusion

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