Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

Mahadevan, Sankaran; Nath, Paromita; Hu, Zhen

doi:10.1115/1.4053184

Cited by 18 publications

(7 citation statements)

References 114 publications

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“…Additive manufacturing is an intelligent manufacturing process that adds materials layer by layer to produce 3D structural entities [45]. The complex layer-by-layer manufacturing process causes various uncertainties, such as natural changes in powder absorption rate, fluctuation in temperature boundary, and uncertainty in powder particle characteristics [46]. Metal 3D printing is a typical metal additive manufacturing process.…”

Section: Case Study Designmentioning

confidence: 99%

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

Shi,

Liu,

Pan

2024

IET Software

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Uncertainty exists widely in various fields, especially in industrial manufacturing. From traditional manufacturing to intelligent manufacturing, uncertainty always exists in the manufacturing process. With the integration of rapidly developing intelligent technology, the complexity of manufacturing scenarios is increasing, and the postdecision method cannot fully meet the needs of the high reliability of the process. It is necessary to research the pre-elimination of uncertainty to ensure the reliability of process execution. Here, we analyze the sources and characteristics of uncertainty in manufacturing scenarios and propose a meta-model architecture and uncertainty quantification (UQ) framework for uncertainty modeling. On the one hand, our approach involves the creation of a meta-model structure that incorporates various strategies for uncertainty elimination (UE). On the other hand, we develop a comprehensive UQ framework that utilizes quantified metrics and outcomes to bolster the UE process. Finally, a deterministic model is constructed to guide and drive the process execution, which can achieve the purpose of controlling the uncertainty in advance and ensuring the reliability of the process. In addition, two typical manufacturing process scenarios are modeled, and quantitative experiments are conducted on a simulated production line and open-source data sets, respectively, to illustrate the idea and feasibility of the proposed approach. The proposed UE approach, which innovatively combines the domain modeling from the software engineering field and the probability-based UQ method, can be used as a general tool to guide the reliable execution of the process.

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Section: Case Study Designmentioning

confidence: 99%

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

Shi,

Liu,

Pan

2024

IET Software

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“…AM has both aleatory and epistemic uncertainty in its process inputs [19]. As a result, there are substantial efforts to address the inherent variability of AM at the front end of production.…”

Section: Digital Twin Certified and Additive Manufacturing (Am)mentioning

confidence: 99%

Digital Twin Certified: Employing Virtual Testing of Digital Twins in Manufacturing to Ensure Quality Products

Grieves

2023

Machines

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Quality products are a main focus for manufacturers. Product users only determine a product to be a quality product if it performs in operation to the user’s perceived standard. Product manufactures need to take a product lifecycle quality (PLQ) perspective of quality and not simply focus on manufacturing quality control, which is more accurately specification control. Manufacturing is the key phase where products take their physical form. There are increasing costs and decreasing risks of different physical quality strategies. The information provided using digital twins and virtual testing promises to be both low risk and cost and has the potential to predict what the customer will experience in operation by testing products passively with data and actively with simulation to destruction. Digital Twin Certified (DTC) is proposed as the methodology for accomplishing this. DTC will be especially important for the adoption of additive manufacturing.

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“…In this article, the flexural strength of α-SiC and its uncertainty are modeled over a range of temperatures, 0 ℃ to 1600 ℃. A Bayesian uncertainty update demonstrates how different synthetic data sets influence the uncertainty [16]. In addition, the number of inferred model parameters is discussed.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Uncertainty Update to a Model of Flexural Strength of α-SiC

Walker,

Sun,

Chen

2024

Mater. Plus

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This article demonstrates a statistical method to update the uncertainty in the flexural strength of silicon carbide, α-SiC. The previously reported uncertainty for the flexural strength of α-SiC was a constant ±15%. However, this uncertainty should be adjusted as more data becomes available. A Bayesian approach is proposed to rapidly and precisely update the uncertainty. To validate the method, five scenarios are demonstrated. The first scenario assumes the experimental data is distributed as the model predicts. The second and third scenarios have the model underestimating and overestimating flexural strength, respectively. The fourth and fifth scenarios use data from a thermo-mechanical fracture model. The thermo-mechanical fracture model introduces a change in the temperature transition of flexural strength. The uncertainty decreased from 15% to a range between 8.3% and 13.4%. Two parameters are inferred in the fourth scenario while five are inferred in the fifth scenario. Inferring five parameters leads to more consistent uncertainty across temperature.

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Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

Cited by 18 publications

References 114 publications

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

Digital Twin Certified: Employing Virtual Testing of Digital Twins in Manufacturing to Ensure Quality Products

Bayesian Uncertainty Update to a Model of Flexural Strength of α-SiC

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