• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用激光粉末床熔融工艺模拟数据对倾斜表面的形貌和粗糙度进行数值研究。

A Numerical Study of Topography and Roughness of Sloped Surfaces Using Process Simulation Data for Laser Powder Bed Fusion.

作者信息

Aydogan Beytullah, Chou Kevin

机构信息

Department of Industrial Engineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.

Bayburt University, Bayburt 69000, Turkey.

出版信息

Materials (Basel). 2024 Dec 5;17(23):5955. doi: 10.3390/ma17235955.

DOI:10.3390/ma17235955
PMID:39685391
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11643527/
Abstract

The simulation of additive manufacturing has become a prominent research area in the past decade. Process physics simulations are employed to replicate laser powder bed fusion (L-PBF) manufacturing processes, aiming to predict potential issues through simulated data. This study focuses on calculating surface roughness by utilizing 3D surface topology extracted from simulated data, as surface roughness significantly influences part quality. Accurately predicting surface roughness using a simulation remains a persistent challenge. To address this challenge, the L-PBF technique with two different cases (pre- and post-contouring) was simulated using two-step process physics simulations. The discrete element method was utilized to simulate powder spreading, followed by the Flow-3D melting simulation. Ten layers were simulated at three different linear energy density (LED) combinations for both cases, with samples positioned at a 30-degree angle to accommodate upskin and downskin effects. Furthermore, a three-dimensional representation of the melted region for each layer was generated using the thermal gradient output from the simulated data. All generated 3D layers were stacked and merged to consolidate a 3D representation of the overall sample. The surfaces (upskin, downskin, and side skins) were extracted from this merged sample. Subsequently, these surfaces were analyzed, and surface roughness (Sa values) was calculated using MATLAB. The obtained values were then compared with experimental results. The downskin surface roughness results from the simulation were found to be within the range of the experimental results. This alignment is attributed to the fact that the physics simulation primarily focuses on melt pool depth and width. These promising findings indicate the potential for accurately predicting surface roughness through simulation.

摘要

在过去十年中,增材制造模拟已成为一个突出的研究领域。过程物理模拟被用于复制激光粉末床熔融(L-PBF)制造过程,旨在通过模拟数据预测潜在问题。本研究重点是利用从模拟数据中提取的3D表面拓扑结构来计算表面粗糙度,因为表面粗糙度会显著影响零件质量。使用模拟准确预测表面粗糙度仍然是一个持续存在的挑战。为应对这一挑战,采用两步过程物理模拟对两种不同情况(轮廓处理前和轮廓处理后)的L-PBF技术进行了模拟。利用离散元方法模拟粉末铺展,随后进行Flow-3D熔凝模拟。两种情况下,在三种不同的线能量密度(LED)组合下模拟了十层,样品以30度角放置以适应上表面和下表面效应。此外,利用模拟数据输出的热梯度生成了每层熔融区域的三维表示。将所有生成的3D层堆叠并合并,以巩固整个样品的三维表示。从这个合并后的样品中提取表面(上表面、下表面和侧面)。随后,对这些表面进行分析,并使用MATLAB计算表面粗糙度(Sa值)。然后将获得的值与实验结果进行比较。发现模拟得到的下表面粗糙度结果在实验结果范围内。这种一致性归因于物理模拟主要关注熔池深度和宽度。这些有前景的发现表明通过模拟准确预测表面粗糙度具有潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7b6013cd9462/materials-17-05955-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/b113f6290f4e/materials-17-05955-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/249a2744db31/materials-17-05955-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/336e84eef9a6/materials-17-05955-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/50ba637196db/materials-17-05955-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/5bce9f2987ea/materials-17-05955-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/2418b3c9af7f/materials-17-05955-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/15ee0845e7e7/materials-17-05955-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/e20d2f6db4db/materials-17-05955-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/1bb40e0d06a6/materials-17-05955-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/675ba2bdb771/materials-17-05955-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/f7773623157f/materials-17-05955-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7fb07302ca2e/materials-17-05955-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7d7346295444/materials-17-05955-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/fe2bcf94bc8a/materials-17-05955-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/89bacd074c4b/materials-17-05955-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/98ba4bd4cdf6/materials-17-05955-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/2ded43367711/materials-17-05955-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/00d3b0f95c60/materials-17-05955-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/bcf8336f06cf/materials-17-05955-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7b6013cd9462/materials-17-05955-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/b113f6290f4e/materials-17-05955-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/249a2744db31/materials-17-05955-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/336e84eef9a6/materials-17-05955-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/50ba637196db/materials-17-05955-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/5bce9f2987ea/materials-17-05955-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/2418b3c9af7f/materials-17-05955-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/15ee0845e7e7/materials-17-05955-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/e20d2f6db4db/materials-17-05955-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/1bb40e0d06a6/materials-17-05955-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/675ba2bdb771/materials-17-05955-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/f7773623157f/materials-17-05955-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7fb07302ca2e/materials-17-05955-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7d7346295444/materials-17-05955-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/fe2bcf94bc8a/materials-17-05955-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/89bacd074c4b/materials-17-05955-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/98ba4bd4cdf6/materials-17-05955-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/2ded43367711/materials-17-05955-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/00d3b0f95c60/materials-17-05955-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/bcf8336f06cf/materials-17-05955-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/870d/11643527/7b6013cd9462/materials-17-05955-g020.jpg

相似文献

1
A Numerical Study of Topography and Roughness of Sloped Surfaces Using Process Simulation Data for Laser Powder Bed Fusion.利用激光粉末床熔融工艺模拟数据对倾斜表面的形貌和粗糙度进行数值研究。
Materials (Basel). 2024 Dec 5;17(23):5955. doi: 10.3390/ma17235955.
2
Influence of Pre- and Post-Contouring Strategies to Downskin Sloped Surfaces in Laser Powder-Bed Fusion (L-PBF) Additive Manufacturing.激光粉末床熔融(L-PBF)增材制造中预轮廓和后轮廓策略对下表皮倾斜表面的影响。
Materials (Basel). 2024 May 30;17(11):2639. doi: 10.3390/ma17112639.
3
Downskin Surface Roughness Prediction with Machine Learning for As-Built CM247LC Fabricated Via Powder Bed Fusion with a Laser Beam.基于机器学习的激光粉末床熔融制造的竣工CM247LC材料的蒙皮表面粗糙度预测
3D Print Addit Manuf. 2024 Aug 20;11(4):1510-1522. doi: 10.1089/3dp.2022.0365. eCollection 2024 Aug.
4
Stability of a Melt Pool during 3D-Printing of an Unsupported Steel Component and Its Influence on Roughness.无支撑钢构件3D打印过程中熔池的稳定性及其对粗糙度的影响
Materials (Basel). 2020 Feb 10;13(3):808. doi: 10.3390/ma13030808.
5
Roughness and Near-Surface Porosity of Unsupported Overhangs Produced by High-Speed Laser Powder Bed Fusion.高速激光粉末床熔融制造的无支撑悬垂部分的粗糙度和近表面孔隙率
3D Print Addit Manuf. 2022 Aug 1;9(4):288-300. doi: 10.1089/3dp.2020.0097. Epub 2022 Aug 3.
6
Predictive Simulation of Process Windows for Powder Bed Fusion Additive Manufacturing: Influence of the Powder Bulk Density.粉末床熔融增材制造工艺窗口的预测模拟:粉末堆积密度的影响
Materials (Basel). 2017 Sep 22;10(10):1117. doi: 10.3390/ma10101117.
7
Optimization of Surface Roughness and Density of Overhang Structures Fabricated by Laser Powder Bed Fusion.激光粉末床熔融制造悬垂结构的表面粗糙度和密度优化
3D Print Addit Manuf. 2023 Aug 1;10(4):732-748. doi: 10.1089/3dp.2021.0180. Epub 2023 Aug 9.
8
Spattering mechanism of laser powder bed fusion additive manufacturing on heterogeneous surfaces.激光粉末床熔融增材制造在异质表面上的飞溅机制
Sci Rep. 2022 Nov 27;12(1):20384. doi: 10.1038/s41598-022-24828-9.
9
Development and Validation of Empirical Models to Predict Metal Additively Manufactured Part Density and Surface Roughness from Powder Characteristics.基于粉末特性预测金属增材制造零件密度和表面粗糙度的经验模型的开发与验证
Materials (Basel). 2022 Jul 5;15(13):4707. doi: 10.3390/ma15134707.
10
Laser Polishing of Additive Manufactured Aluminium Parts by Modulated Laser Power.通过调制激光功率对增材制造铝部件进行激光抛光
Micromachines (Basel). 2021 Oct 30;12(11):1332. doi: 10.3390/mi12111332.

本文引用的文献

1
Influence of Pre- and Post-Contouring Strategies to Downskin Sloped Surfaces in Laser Powder-Bed Fusion (L-PBF) Additive Manufacturing.激光粉末床熔融(L-PBF)增材制造中预轮廓和后轮廓策略对下表皮倾斜表面的影响。
Materials (Basel). 2024 May 30;17(11):2639. doi: 10.3390/ma17112639.
2
Optimization of Surface Roughness and Density of Overhang Structures Fabricated by Laser Powder Bed Fusion.激光粉末床熔融制造悬垂结构的表面粗糙度和密度优化
3D Print Addit Manuf. 2023 Aug 1;10(4):732-748. doi: 10.1089/3dp.2021.0180. Epub 2023 Aug 9.
3
Biomechanical performances of PCL/HA micro- and macro-porous lattice scaffolds fabricated via laser powder bed fusion for bone tissue engineering.
通过激光粉末床熔合制造用于骨组织工程的 PCL/HA 微孔和大孔晶格支架的生物力学性能。
Mater Sci Eng C Mater Biol Appl. 2021 Sep;128:112300. doi: 10.1016/j.msec.2021.112300. Epub 2021 Jul 9.
4
A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations.激光粉末床熔融模型与模拟中的模型误差和参数不确定性综述
J Manuf Sci Eng. 2019;141. doi: 10.1115/1.4042789.