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.

Simulation-Based Process Optimization of Metallic Additive Manufacturing Under Uncertainty

2019 ◽  
Author(s):  
Zhuo Wang ◽  
Pengwei Liu ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Additive Manufacturing ◽  
Robust Design ◽  
Simulation Models ◽  
Scanning Speed ◽  
Parameters Optimization ◽  
Optimization Under Uncertainty ◽  
Data Driven ◽  
Robust Design Optimization ◽  
Beam Power ◽  
Noise Factors

Abstract 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-6A1-4V 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 multi-physics 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 largest impact on the microstructure variation.

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.

Gradient-based design robustness measure for robust geotechnical design

2014 ◽  
Vol 51 (11) ◽  
pp. 1331-1342 ◽  
Author(s):  
Wenping Gong ◽  
Sara Khoshnevisan ◽  
C. Hsein Juang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Cost Efficiency ◽  
Robust Design Optimization ◽  
System Response ◽  
Robustness Measure ◽  
Gradient Based ◽  
Geotechnical Design ◽  
Safety Constraint ◽  
Noise Factors

This paper presents a gradient-based robustness measure for robust geotechnical design (RGD) that considers safety, design robustness, and cost efficiency simultaneously. In the context of robust design, a design is deemed robust if the system response of concern is insensitive, to a certain degree, to the variation of noise factors (i.e., uncertain geotechnical parameters, loading parameters, construction variation, and model biases or errors). The key to a robust design is a quantifiable robustness measure with which the robust design optimization can be effectively and efficiently implemented. Based on the developed gradient-based robustness measure, a robust design optimization framework is proposed. In this framework, the design (safety) constraint is analyzed using advanced first-order second-moment (AFOSM) method, considering the variation in the noise factors. The design robustness, in terms of sensitivity index (SI), is evaluated using the normalized gradient of the system response to the noise factors, which can be efficiently computed from the by-product of AFOSM analysis. Within the proposed framework, robust design optimization is performed with two objectives, design robustness and cost efficiency, while the design (safety) constraint is satisfied by meeting a target reliability index. Generally, cost efficiency and design robustness are conflicting objectives and the robust design optimization yields a Pareto front, which reveals a tradeoff between the two objectives. Through an illustrative example of a shallow foundation design, the effectiveness and significance of this new robust design approach is demonstrated.

scholarly journals Parameters Optimization of Auxiliary Gas Process for Double-Wire SS316L Stainless Steel Arc Additive Manufacturing

Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 190
Author(s):  
Wei Wu ◽  
Jiaxiang Xue ◽  
Wei Xu ◽  
Hongyan Lin ◽  
Heqing Tang ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Evaluation System ◽  
Gas Flow ◽  
Interactive Effect ◽  
Parameters Optimization ◽  
Heat Accumulation ◽  
Auxiliary Process ◽  
Strength And Stiffness

Serious heat accumulation limits the further efficiency and application in additive manufacturing (AM). This study accordingly proposed a double-wire SS316L stainless steel arc AM with a two-direction auxiliary gas process to research the effect of three parameters, such as auxiliary gas nozzle angle, auxiliary gas flow rate and nozzle-to-substrate distance on depositions, then based on the Box–Behnken Design response surface, a regression equation between three parameters and the total score were established to optimized parameters by an evaluation system. The results showed that samples with nozzle angle of 30° had poor morphology but good properties, and increasing gas flow or decreasing distance would enhance the airflow strength and stiffness, then strongly stir the molten pool and resist the interference. Then a diverse combination of auxiliary process parameters had different influences on the morphology and properties, and an interactive effect on the comprehensive score. Ultimately the optimal auxiliary gas process parameters were 17.4°, 25 L/min and 10.44 mm, which not only bettered the morphology, but refined the grains and improved the properties due to the stirring and cooling effect of the auxiliary gas, which provides a feasible way for quality and efficiency improvements in arc additive manufacturing.

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