Uncertainty analysis of curing-induced dimensional variability of composite structures utilizing physics-guided Gaussian process meta-modeling

Zhou, Kai; Enos, Ryan; Zhang, Dianyun; Tang, Jiong

doi:10.1016/j.compstruct.2021.114816

Cited by 18 publications

(4 citation statements)

References 56 publications

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“…We found that model-based UE can not only improve the success rate of welding but also save labor and time costs of manual repair welding. {"contextName": "Welding", "BoundedContext": { "entityList": [ {"Weldingmanipulator3": { "type": "machine", "namespace": "weld.welding", "code": "Wm3", "UQ":{ "inference": "True", "modelName": "BN05100322" }, "input variable": [8], "output concrens": [{ "name": "cracking in the weld metal", "value": "", "passing value": "0" }], "userAttr": [6], "state": [4] }}, … ] }} ALGORITHM 1: An implementation of the constructed model. 10 IET Software which illustrates the generalizability of our proposed UQ framework.…”

Section: Solution and Resultsmentioning

confidence: 99%

“…In the actual manufacturing production, many factors, such as equipment states, material properties, environmental changes, and manufacturing tolerances, will lead to uncertainty in the process execution, and the process defects caused by uncertainty will bring high labor costs and time costs to the manufacturer, in some cases, even some small changes can affect the regular operation of the subsequent equipment [2,3]. Therefore, uncertainty in manufacturing scenarios has attracted widespread attention, and it is necessary to evaluate and eliminate uncertainty before process execution [4].…”

Section: Introductionmentioning

confidence: 99%

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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: Solution and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

Shi,

Liu,

Pan

2024

IET Software

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“…This is since it is crucial to have certainty in understanding the behaviour of load bearing structures and although high safety factors could be used to pass safety requirements, this detracts from the philosophy of lightweighting [10]. For load bearing usage of CFRP within production vehicles to become more commonplace, significant research has been invested into understanding how these defects generate during manufacture as well as understanding the impact on mechanical performance [11]. For the purpose of this paper, only the impact porosity has on mechanical performance is investigated.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-scale modelling approach to predict the reduction of transverse strength due to porosity in composite materials

Fisher

Eaton

Pullin

2023

Composite Structures

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“…Regarding the model interpretability methods, the SHapley Additive exPlanations (SHAP) method has gained increasing popularity, which explains how each feature influences the model and allows local and global analysis [41]. SHAP analysis has offered valuable insights into the disciplinary fields of composite performance analysis [19,37,42,43] and sensor fault detection [44], as well as manufacturing processes [45].…”

Section: Introductionmentioning

confidence: 99%

A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

Zhao,

Chen,

Jian

2023

Polymers

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This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving R2 values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.

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Uncertainty analysis of curing-induced dimensional variability of composite structures utilizing physics-guided Gaussian process meta-modeling

Cited by 18 publications

References 56 publications

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

A novel multi-scale modelling approach to predict the reduction of transverse strength due to porosity in composite materials

A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

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