Relative density and surface roughness prediction for Inconel 718 by selective laser melting: central composite design and multi-objective optimization

Lu, Cuiyuan; Shi, Jing

doi:10.1007/s00170-021-08388-2

Cited by 20 publications

(9 citation statements)

References 59 publications

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“…Therefore, 10 articles dealing with metal additive manufacturing were identified. Interestingly, an equal number of articles dealing with the relatively new wire-arc additive manufacturing (WAAM) [10][11][12][13][14] and more conventional laser powder bed fusion processes for metal (LPBF-M) [15][16][17][18][19] could be identified. From the analyzed research, the use of machine learning in roughness prediction should be highlighted.…”

Section: Surfacementioning

confidence: 99%

“…From the analyzed research, the use of machine learning in roughness prediction should be highlighted. Lu and Shi [16] used a design-of-experiment approach with a central composite design for predicting surface roughness in LPBF-M, which led to an accuracy R 2 of 74.36%. Furthermore, Kumar and Jain [10] deployed a k-nearest neighbor algorithm in WAAM with a prediction error ranging from −5.8% to 2.3%.…”

Section: Surfacementioning

confidence: 99%

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Surface Properties and Tribological Behavior of Additively Manufactured Components: A Systematic Review

et al. 2023

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Innovative additive manufacturing processes for resilient and sustainable production will become even more important in the upcoming years. Due to the targeted and flexible use of materials, additive manufacturing allows for conserving resources and lightweight design enabling energy-efficient systems. While additive manufacturing processes were used in the past several decades mainly for high-priced individualized components and prototypes, the focus is now increasingly shifting to near-net-shape series production and the production of spare parts, whereby surface properties and the tribological behavior of the manufactured parts is becoming more and more important. Therefore, the present review provides a comprehensive overview of research in tribology to date in the field of additively manufactured components. Basic research still remains the main focus of the analyzed 165 papers. However, due to the potential of additive manufacturing processes in the area of individualized components, a certain trend toward medical technology applications can be identified for the moment. Regarding materials, the focus of previous studies has been on metals, with stainless steel and titanium alloys being the most frequently investigated materials. On the processing side, powder bed processes are mainly used. Based on the present literature research, the expected future trends in the field of tribology of additively manufactured components can be identified. In addition to further basic research, these include, above all, aspects of process optimization, function integration, coating, and post-treatment of the surfaces.

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Section: Surfacementioning

confidence: 99%

Section: Surfacementioning

confidence: 99%

Surface Properties and Tribological Behavior of Additively Manufactured Components: A Systematic Review

et al. 2023

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“…In the first stage, the appropriate SLM process condition for CoCrFeMnNi on the particular https://doi.org/10.36922/msam. 42 SLM system was to be obtained. For this purpose, laser power (P), hatch spacing (H), and layer thickness (T) were fixed at 100W, 60 μm, and 20 μm, respectively, while the laser scanning speed (V) was varied from 200 to 800 mm/s.…”

Section: Slm Experimentsmentioning

confidence: 99%

“…Cubic specimens obtained in the first stage of SLM experiment were analyzed by measuring their density and by visually analyzing their polished side whose normal is perpendicular to DD. Density of these cubes were measured with Archimedes method according to ASTM B962 [42] and compared to the bulk density of 8.05 g/cm 3 for CoCrFeMnNi [36] . Furthermore, the optical micrographs of polished cubic specimens were processed by the software ImageJ, and thus the porosity of obtained materials was obtained.…”

Section: Materials Characterizationmentioning

confidence: 99%

Laser additive manufacturing of magnesium alloys and its biomedical applications

Liu¹,

Ling²,

Chen³

et al. 2022

Mater Sci Add Manuf

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Biomedical magnesium (Mg) alloy with unique biodegradability and excellent biocompatibility is one of the most sought after materials in medical field for orthopedics applications. Nevertheless, the high corrosion rate and inadequate mechanical properties hinder its development. Apart from that, to obtain the best surgical result, the size and shape of the fixation implant need to be adapted to the individual case. Thus, additive manufacturing (AM) processes, such as laser powder bed fusion (LPBF), are used to overcome these issues. This work reviews the recent advancements in biodegradable Mg-based alloys prepared by LPBF for biomedical applications. The influence of feedstock features and manufacturing parameters on the formability and quality is delineated in detail. The mechanical performances, degradation behaviors, and biological behavior of the LPBF-processed parts are discussed. Furthermore, we also made some suggestions for the challenges of Mg alloys in LPBF processing and applications in biomedical.

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“…The prediction model could input processing parameters to achieve the surface roughness prediction. Lu et al [4] studied the effect of four process parameters on the surface roughness of Inconel 718 machined by SLM. Surface roughness model in reduced linear form showed good prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

Qin

et al. 2023

Int J Adv Manuf Technol

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For the problem of difficulty in measuring the surface roughness of large-pitch internal threads, predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in scope of use. In this paper, the homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and RBF neural network, which are suitable for researches with small sample sizes, from the machine learning algorithm were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. The SVM model was verified to have absolute errors within 0.05μm and relative errors within 4% for turning both left and right threaded surface, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness are difficult to measure due to structural characteristics.

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Relative density and surface roughness prediction for Inconel 718 by selective laser melting: central composite design and multi-objective optimization

Cited by 20 publications

References 59 publications

Surface Properties and Tribological Behavior of Additively Manufactured Components: A Systematic Review

Surface Properties and Tribological Behavior of Additively Manufactured Components: A Systematic Review

Laser additive manufacturing of magnesium alloys and its biomedical applications

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

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