Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

2021 ◽  
Author(s):  
Sankaran Mahadevan ◽  
Paromita Nath ◽  
Zhen Hu
Keyword(s):  
Decision Making ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Process Optimization ◽  
Data Driven ◽  
Future Research ◽  
Multi Scale ◽  
Uncertainty Sources ◽  
Optimization And Control ◽  
And Control

Abstract This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and minimize the variability of the AM product. However, before using these models for decision-making, a fundamental question that needs to be answered is to what degree the models can be trusted, and consider the various uncertainty sources that affect their prediction. Uncertainty quantification (UQ) in AM is not trivial because of the complex multi-physics, multi-scale phenomena in the AM process. This article reviews the literature on UQ methodologies focusing on model uncertainty, discusses the corresponding activities of calibration, verification and validation, and examines their applications reported in the AM literature. The extension of current UQ methodologies to additive manufacturing needs to address multi-physics, multi-scale interactions, increasing presence of data-driven models, high cost of manufacturing, and complexity of measurements. The activities that need to be undertaken in order to implement verification, calibration, and validation for AM are discussed. Literature on using the results of UQ activities towards AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. Future research needs both in terms of UQ and decision-making in AM are outlined.

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 On the generation of validated manufacturing process optimization and control schemes

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 57-62
Author(s):  
Alexios Papacharalampopoulos ◽  
Harry Bikas ◽  
Christos Michail ◽  
Panagiotis Stavropoulos
Keyword(s):  
Process Optimization ◽  
Manufacturing Process ◽  
Control Schemes ◽  
Optimization And Control ◽  
And Control

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.

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

scholarly journals A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

2020 ◽  
Vol 279 ◽  
pp. 115834
Author(s):  
Usman Ali ◽  
Mohammad Haris Shamsi ◽  
Mark Bohacek ◽  
Karl Purcell ◽  
Cathal Hoare ◽  
...  
Keyword(s):  
Decision Making ◽  
Building Energy ◽  
Energy Modeling ◽  
Data Driven ◽  
Building Energy Modeling ◽  
Multi Scale ◽  
Data Driven Approach ◽  
Support Decision Making

A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Zhuo Wang ◽  
Pengwei Liu ◽  
Yaohong Xiao ◽  
Xiangyang Cui ◽  
Zhen Hu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Optimization ◽  
Robust Design ◽  
Parameters Optimization ◽  
Optimization Under Uncertainty ◽  
Data Driven ◽  
Robust Design Optimization ◽  
Beam Power ◽  
Property Control ◽  
Noise Factors

The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.

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