scholarly journals Special Issue on Uncertainty Quantification and Management in Additive Manufacturing

2021 ◽  
Author(s):  
Zhen Hu ◽  
Saideep Nannapaneni ◽  
Sankaran Mahadevan
Keyword(s):  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Special Issue

Abstract N/A

Preface to the special issue ‘Additive Manufacturing’

2021 ◽  
Vol 191 ◽  
pp. 110330
Author(s):  
H.L. Wei ◽  
T. Mukherjee ◽  
T. DebRoy
Keyword(s):  
Additive Manufacturing ◽  
Special Issue

A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bing Zhang ◽  
Raiyan Seede ◽  
Austin Whitt ◽  
David Shoukr ◽  
Xueqin Huang ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Process Optimization ◽  
Powder Bed Fusion ◽  
Assessment Framework ◽  
Laser Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Analytical Thermal Model

Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

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.

scholarly journals Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019)

Materials ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2133
Author(s):  
Eva María Rubio ◽  
Ana María Camacho
Keyword(s):  
Applied Sciences ◽  
Additive Manufacturing ◽  
3D Printing ◽  
Manufacturing Industry ◽  
Green Manufacturing ◽  
Manufacturing Processes ◽  
New Materials ◽  
Special Issue ◽  
Engineering Society ◽  
Manufacturing Engineering

The Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) has been launched as a joint issue of the journals “Materials” and “Applied Sciences”. The 29 contributions published in this Special Issue of Materials present cutting-edge advances in the field of manufacturing engineering focusing on additive manufacturing and 3D printing, advances and innovations in manufacturing processes, sustainable and green manufacturing, manufacturing of new materials, metrology and quality in manufacturing, industry 4.0, design, modeling, and simulation in manufacturing engineering and manufacturing engineering and society. Among them, these contributions highlight that the topic “additive manufacturing and 3D printing” has collected a large number of contributions in this journal because its huge potential has attracted the attention of numerous researchers over the last years.

scholarly journals Preface to Special Issue on “Establishment and Validation of the Base for 3D Design & Additive Manufacturing Standing on the Concept “Anisotropy” & “Customization” ”

Materia Japan ◽  
2015 ◽  
Vol 54 (10) ◽  
pp. 491-492
Author(s):  
Tomoyuki Kakeshita ◽  
Toshihiro Tanaka ◽  
Takayoshi Nakano ◽  
Hideki Araki ◽  
Masaharu Furutera ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Special Issue ◽  
3D Design
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