scholarly journals Multi-scale surface characterization in additive manufacturing using CT

2016 ◽  
pp. 271-280 ◽  
Author(s):  
Yann QUINSAT ◽  
Claire LARTIGUE ◽  
Christopher A. BROWN ◽  
Lamine HATTALI
Keyword(s):  
Additive Manufacturing ◽  
Surface Characterization ◽  
Multi Scale ◽  
Scale Surface

Multi-Scale Surface Characterization and Control Based High Density Measurements

2014 ◽  
Author(s):  
Xin Weng ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Surface Quality ◽  
Process Monitoring ◽  
Surface Characterization ◽  
Quality Evaluation ◽  
Surface Shape ◽  
Machined Surface ◽  
Multi Scale ◽  
And Control ◽  
Scale Surface ◽  
Machined Surfaces

It is widely observed that today’s engineering products demand increasingly strict tolerances. The shape of a machined surface plays a critical role to the desired functionality of a product. Even a small error can be the difference between a successful product launch and a major delay. Thus, it is important to develop measurement tools to ensure the quality and accuracy of products’ machined surfaces. The key to assessing the quality is robust measurement and inspection techniques combined with advanced analysis. However, conventional Geometrical Dimensioning and Tolerancing (GD&T) such as flatness falls short of characterizing the surface shape. With the advancements in metrology methodology utilizing digital holographic interferometry, large amount of surface data can be captured at high resolution and accuracy without changing platform or technique. This captured High Definition Data (HDD) enables the mining of more valuable information from machined surfaces that most current industry practice cannot achieve in a timely manner. Such new metrology system opens the torrent of observable events at plant floor and increases the transparency of machining processes. This presents great opportunities to characterize machined surface into a new level of details, which can be applied in production quality evaluation and process condition monitoring and control. This research work proposes a framework of a multi-scale surface characterization for surface quality evaluation and process monitoring. Case studies are presented to show how proposed metrics could be applied in surface quality evaluation and process monitoring.

scholarly journals Additive Manufacturing of Multi‐Scale Porous Soft Tissue Implants That Encourage Vascularization and Tissue Ingrowth

2021 ◽  
pp. 2100229
Author(s):  
Fergal B. Coulter ◽  
Ruth E. Levey ◽  
Scott T. Robinson ◽  
Eimear B. Dolan ◽  
Stefano Deotti ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Soft Tissue ◽  
Tissue Ingrowth ◽  
Multi Scale

Articulated 3D model matching using multi-scale histograms of shape features for customized additive manufacturing

2021 ◽  
Vol 132 ◽  
pp. 103520
Author(s):  
Xin Lin ◽  
Kunpeng Zhu ◽  
Min Zhou ◽  
Jerry Ying Hsi Fuh ◽  
Qing-guo Wang
Keyword(s):  
Additive Manufacturing ◽  
3D Model ◽  
Shape Features ◽  
Model Matching ◽  
Multi Scale

scholarly journals A multi-scale multi-physics modeling framework of laser powder bed fusion additive manufacturing process

2018 ◽  
Vol 73 (3) ◽  
pp. 151-157 ◽  
Author(s):  
Jing Zhang ◽  
Yi Zhang ◽  
Weng Hoh Lee ◽  
Linmin Wu ◽  
Hyun-Hee Choi ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Modeling Framework ◽  
Powder Bed ◽  
Multi Scale ◽  
Physics Modeling

Lubricant migration on stainless steel induced by bio-inspired multi-scale surface patterns

2018 ◽  
Vol 150 ◽  
pp. 55-63 ◽  
Author(s):  
Philipp G. Grützmacher ◽  
Andreas Rosenkranz ◽  
Adam Szurdak ◽  
Carsten Gachot ◽  
Gerhard Hirt ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Multi Scale ◽  
Surface Patterns ◽  
Lubricant Migration ◽  
Scale Surface

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

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