본문 바로가기
KERD
논문KCI 등재

반응표면모델을 통한 적층제조된 ZrH2 접종제 첨가AA7075 합금의 균열 밀도 예측

Prediction of Crack Density in additive manufactured AA7075 Alloy Reinforced with ZrH2 inoculant via Response Surface Method

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류금속공학

초록

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of highquality Al alloy components for various applications.

Abstract

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of highquality Al alloy components for various applications.

참고문헌 (18)

  1. Mojtaba Jalali, "SLM Additive Manufacturing of NiTi Porous Implants: A Review of Constitutive Models, Finite Element Simulations, Manufacturing, Heat Treatment, Mechanical, and Biomedical Studies", Metals and Materials International, 29(9), pp.2458~2491 (2023), 대한금속·재료학회[DOI]
  2. M. J. Sagong, , Mater. Sci. Eng, 847, pp.143318~ (2022)
  3. A. Paolini, , Addit. Manuf, 30, pp.100894~ (2019)
  4. Lingling Ren, "Effect of Sc Content on the Microstructure and Properties of Al–Mg–Sc Alloys Deposited by Wire Arc Additive Manufacturing", Metals and Materials International, 27(1), pp.68~77 (2021), 대한금속·재료학회[DOI]
  5. P. He, , Addit. Manuf, 61, pp.103356~ (2023)
  6. M. Ghasri-Khouzani, , Mater. Today Commun, 35, pp.105555~ (2023)
  7. A. Martin, , Mater. Charact, 183, pp.111650~ (2022)
  8. Q. Tan, , Addit. Manuf, 49, pp.102516~ (2022)
  9. M. Navaneetha Krishnan, "Image Texture Description, Surface-Grains Structure Morphology and Prediction of Wear Parameters for Mg", B4C Composites Using Response Surface Methodology, 2022, pp.28(5), 1195~1214[DOI]
  10. J. A. Lee, , J. Mater. Res. Technol, 27, pp.413~ (2023)
  11. J. A. Lee, , J. Mater. Sci, 58, pp.4780~ (2023)
  12. D. S. Shim, , J. Mater. Res. Technol, 15, pp.119~ (2021)
  13. R. Srinivasan, , Mater. Today: Proc, 27, pp.1827~ (2020)
  14. J. Choe, , Addit. Manuf, 62, pp.103370~ (2023)
  15. Erfan Maleki, "Influences of Shot Peening Parameters on Mechanical Properties and Fatigue Behavior of 316 L Steel: Experimental, Taguchi Method and Response Surface Methodology", Metals and Materials International, 27(11), pp.4418~4440 (2021), 대한금속·재료학회[DOI]
  16. Santanu Sardar, "Multi Tribo-Performance Optimization of AA7075–Al2O3 Composites by Grey Based Response Surface Methodology", Metals and Materials International, 27(6), pp.1859~1879 (2021), 대한금속·재료학회[DOI]
  17. W. Stopyra, , Addit. Manuf, 35, pp.101270~ (2020)
  18. G. Li, , Opt. Laser Technol, 147, pp.107621~ (2022)

관련 논문AI추천

이 간행물의 다른 논문 (9건)