Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems

Topic Information

Dear Colleagues,

There are various uncertainty sources affecting the design, manufacturing, and maintenance of complex engineering systems. In recent decades, uncertainty quantification has demonstrated great potential for scientifically analyzing how the uncertainties affect the performance of products. On the one hand, the uncertainty factors stemming from design, manufacturing, and operation are expected to be thoroughly quantified and considered when designing new products, which is the so-called design under uncertainty. On the other hand, the uncertainty sources contained in the manufacturing process and in the prediction of operational performance are expected to be comprehensively quantified and included for decision making under uncertainty during manufacturing and operation. The purpose of this topic is to present the latest advancements in the field of uncertainty quantification in design, manufacturing, and maintenance. The topic includes but is not limited to:

• Uncertainty quantification and reduction;
• Design under uncertainty;
• Decision making under uncertainty;
• Uncertainty modeling and analysis;
• Model calibration, verification, and validation;
• Risk and reliability analysis;
• Robust/reliability-based design optimization;
• Uncertainty-aware machine learning models;
• Uncertainty quantification in additive manufacturing;
• Confidence-based remaining useful life estimation;
• Uncertainty-aware diagnostics and prognostics;
• Uncertainty-aware battery management systems;
• Probabilistic and non-probabilistic approaches in complex engineering systems;
• Highly efficient uncertainty propagation techniques in complex engineering systems;
• Applications of uncertainty quantification in design, manufacturing, or maintenance.

Dr. Chen Jiang
Dr. Zhenzhong Chen
Dr. Xiaoke Li
Dr. Xiwen Cai
Dr. Zan Yang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	3.4	2014	24 Days	CHF 2400
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Batteries batteries	4.6	4.0	2015	22 Days	CHF 2700
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.9 Days	CHF 2600
Machines machines	2.1	3.0	2013	15.6 Days	CHF 2400
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600

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Published Papers (20 papers)

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23 pages, 15297 KiB  

Open AccessArticle

Current-Based Analysis and Validation of a Wheel–Soil Interaction Model for the Trafficability of a Planetary Rover

by Yan Shen, Meng Zou, Hongtao Cao, Dong Pan, Baofeng Yuan and Lianbin He

Aerospace 2024, 11(11), 892; https://doi.org/10.3390/aerospace11110892 - 30 Oct 2024

Viewed by 414

Abstract

The assessment of trafficability for planetary rovers in relation to non-geometric hazards is a crucial issue in deep space exploration. This study relies on terramechanics theory and incorporates actual data from Mars soil and rover parameters to develop a model that accurately represents [...] Read more.

The assessment of trafficability for planetary rovers in relation to non-geometric hazards is a crucial issue in deep space exploration. This study relies on terramechanics theory and incorporates actual data from Mars soil and rover parameters to develop a model that accurately represents the interaction between the rover’s wheels and Martian soil. Through numerical simulations, this model specifically investigates the relationship between the current of the rover’s wheel drive motor and factors such as slip ratio, soil pressure parameters, and soil shear parameters. Terrestrial experiments are also conducted to verify the precision of certain numerical calculations. The proposed wheel–soil interaction model, based on wheel motor current, provides a foundation for assessing non-geometric trafficability and the inversion of planetary soil parameters. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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16 pages, 4927 KiB  

Open AccessArticle

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

by Xuedong Zhu, Jianhua Liu, Xiaohui Ao, Huanxiong Xia, Sihan Huang, Lijian Zhu, Xiaoqiang Li and Changlin Du

Sensors 2024, 24(19), 6460; https://doi.org/10.3390/s24196460 - 6 Oct 2024

Viewed by 1049

Abstract

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a [...] Read more.

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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15 pages, 8060 KiB  

Open AccessArticle

Influence of Check Gate Construction on Operation of Check Gate in Ship Lock

by Jozef Kulka, Martin Mantič, Melichar Kopas, Michal Fabian, Robert Grega, Peter Kaššay and Marián Siman

Machines 2024, 12(9), 641; https://doi.org/10.3390/machines12090641 - 13 Sep 2024

Viewed by 512

Abstract

The subject of investigation presented in this article is a filling and draining system of the ship lock installed in the Gabčíkovo Waterworks. This article describes the operation and construction of the special regulation segments, i.e., the check gates that are situated in [...] Read more.

The subject of investigation presented in this article is a filling and draining system of the ship lock installed in the Gabčíkovo Waterworks. This article describes the operation and construction of the special regulation segments, i.e., the check gates that are situated in the ship locks. After the failure and replacement of the original check gate with the new, improved one, the strain gauge sensors were applied to the new check gate in order to determine stress distribution on the segment surface as well as the loading of the actuating arms. The application method and application places of the strain gauge sensors are described in detail. The performed measurements detected the occurrence of additional motional resistances during the opening and closing of the check gate. These resistances caused a partial non-functionality of the original check gate actuating mechanism. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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20 pages, 871 KiB  

Open AccessArticle

A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic

by Marian Kampik, Jerzy Roj and Łukasz Dróżdż

Appl. Sci. 2024, 14(16), 7334; https://doi.org/10.3390/app14167334 - 20 Aug 2024

Viewed by 588

Abstract

The article describes a method for determining the resultant expanded uncertainty value in the case of analyzing an uncertainty budget with many components. It employs interval arithmetic, known for its low calculation complexity compared to other methods in the literature. The article includes [...] Read more.

The article describes a method for determining the resultant expanded uncertainty value in the case of analyzing an uncertainty budget with many components. It employs interval arithmetic, known for its low calculation complexity compared to other methods in the literature. The article includes a detailed explanation of the proposed method and showcases its applications. Results are compared with those from the Monte Carlo method and assumptions based on the central limit theorem. The relative error in estimating the resulting expanded uncertainty value typically stays within ±5% of the Monte Carlo method’s result. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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28 pages, 10848 KiB  

Open AccessArticle

Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model

by Dario Domingo, Mohammad Royapoor, Hailiang Du, Aaron Boranian, Sara Walker and Michael Goldstein

Energies 2024, 17(16), 4014; https://doi.org/10.3390/en17164014 - 13 Aug 2024

Viewed by 971

Abstract

Energy models require accurate calibration to deliver reliable predictions. This study offers statistical guidance for a systematic treatment of uncertainty before and during model calibration. Statistical emulation and history matching are introduced. An energy model of a domestic property and a full year [...] Read more.

Energy models require accurate calibration to deliver reliable predictions. This study offers statistical guidance for a systematic treatment of uncertainty before and during model calibration. Statistical emulation and history matching are introduced. An energy model of a domestic property and a full year of observed data are used as a case study. Emulators, Bayesian surrogates of the energy model, are employed to provide statistical approximations of the energy model outputs and explore the input parameter space efficiently. The emulator’s predictions, alongside quantified uncertainties, are then used to rule out parameter configurations that cannot lead to a match with the observed data. The process is automated within an iterative procedure known as history matching (HM), in which simulated gas consumption and temperature data are simultaneously matched with observed values. The results show that only a small percentage of parameter configurations (0.3% when only gas consumption is matched, and 0.01% when both gas and temperature are matched) yielded outputs matching the observed data. This demonstrates HM’s effectiveness in pinpointing the precise region where model outputs align with observations. The proposed method is intended to offer analysts a robust solution to rapidly explore a model’s response across the entire input space, rule out regions where a match with observed data cannot be achieved, and account for uncertainty, enhancing the confidence in energy models and their viability as a decision support tool. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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20 pages, 4526 KiB  

Open AccessArticle

Calibrating the Discrete Boundary Conditions of a Dynamic Simulation: A Combinatorial Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) Approach

by Jah Shamas, Tim Rogers, Anton Krynkin, Jevgenija Prisutova, Paul Gardner, Kirill V. Horoshenkov, Samuel R. Shelley and Paul Dickenson

Sensors 2024, 24(15), 4883; https://doi.org/10.3390/s24154883 - 27 Jul 2024

Viewed by 796

Abstract

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a [...] Read more.

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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15 pages, 6721 KiB  

Open AccessArticle

Twist Angle Error Statistical Analysis and Uncertain Influence on Aerodynamic Performance of Three-Dimensional Compressor Rotor

by Yue Dan, Ruiyu Li, Limin Gao, Huawei Yu and Yuyang Hao

Aerospace 2024, 11(8), 614; https://doi.org/10.3390/aerospace11080614 - 26 Jul 2024

Viewed by 797

Abstract

Twist angle errors along the blade radial direction are uncertain and affected by cutting force, tool wear, and other factors. In this paper, the measured twist angle errors of 13 sections of 72 rotor blades were innovatively analyzed to obtain the rational statistical [...] Read more.

Twist angle errors along the blade radial direction are uncertain and affected by cutting force, tool wear, and other factors. In this paper, the measured twist angle errors of 13 sections of 72 rotor blades were innovatively analyzed to obtain the rational statistical distribution. It is surprisingly found that the under-deflection systematic deviation of twist angle errors shows a gradually increasing W-shaped distribution along the radial direction, while the scatter is nearly linear. Logically, the statistical model is established based on the linear correlation of the scatter by regression analysis to reduce variable dimension from 13 to 1. The influence of the radial non-uniform twist angle errors’ uncertainty on the aerodynamic performance of the three-dimensional compressor rotor is efficiently quantified combining the non-intrusive polynomial chaos method. The results show that the mean values of mass flow rate, total pressure ratio, and isentropic efficiency at the typical operating conditions are lower than the nominal values due to the systematic deviation, indicating that the under-deflection twist angle errors lead to the decrease in compressor thrust. The compressor’s stable operating range is more sensitive to the scatter of twist angle errors, which is up to an order of magnitude greater than that of the total pressure ratio and isentropic efficiency, indicating the compressor’s safe and stable operation risk increases. Additionally, the flow field at the tip region is significantly affected by twist angle errors, especially at the shock wave position of the near-stall condition. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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14 pages, 2634 KiB  

Open AccessArticle

Research on the Movement Speed of Situational Map Symbols Based on User Dynamic Preference Perception

by Mu Tong, Shanguang Chen, Xinyue Wang and Chengqi Xue

Aerospace 2024, 11(6), 478; https://doi.org/10.3390/aerospace11060478 - 17 Jun 2024

Viewed by 764

Abstract

When designing situational maps, selecting distinct and visually comfortable movement speeds for dynamic elements is an ongoing challenge for designers. This study addresses this issue by conducting two experiments to measure the human eye’s ability to discern moving speeds on a screen and [...] Read more.

When designing situational maps, selecting distinct and visually comfortable movement speeds for dynamic elements is an ongoing challenge for designers. This study addresses this issue by conducting two experiments to measure the human eye’s ability to discern moving speeds on a screen and examines how symbol movement speeds within situational maps affect users’ subjective experiences, task performance, and visual comfort. The first experiment measured participants’ speed discrimination capabilities for Landolt Ring of varying sizes moving at 0–256°/s, yielding speed discrimination thresholds of 7–23% and a sensitive velocity range of 1–64°/s. The second experiment evaluated observers’ visual perceptions of moving elements within a cognitive task across the same range of 1–64°/s, identifying three significant benchmarks—8°/s, 16°/s, and 32°/s. These can be utilized to categorize slow-, moderate-, and fast-moving symbols in situational maps. The findings can aid in designing human–machine interface environments with improved viewer experience and visual comfort for both Air Traffic Control interfaces and situational maps. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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17 pages, 2668 KiB  

Open AccessArticle

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes

by Xiaolei Pan, Hongxiao Chen, Ao Shen, Dongdong Zhao and Xiaoyan Su

Sensors 2024, 24(11), 3446; https://doi.org/10.3390/s24113446 - 27 May 2024

Viewed by 826

Abstract

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the “curse of dimensionality”, which refers to the exponential growth of [...] Read more.

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the “curse of dimensionality”, which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding $2\times 2$ dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system’s non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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18 pages, 10107 KiB  

Open AccessArticle

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

by Zhiming Rong, Yuxiong Li, Li Wu, Chong Zhang and Jialin Li

Sensors 2024, 24(11), 3394; https://doi.org/10.3390/s24113394 - 24 May 2024

Viewed by 856

Abstract

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To [...] Read more.

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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21 pages, 4838 KiB  

Open AccessArticle

Rigid–Flexible Coupling Dynamics Analysis of Coordination Arm and Application of a New Directional Subinterval Uncertainty Analysis Method

by Xuan Gao, Longmiao Chen and Jingsong Tang

Aerospace 2024, 11(6), 419; https://doi.org/10.3390/aerospace11060419 - 22 May 2024

Viewed by 711

Abstract

Cartridge delivery systems are commonly employed in aerospace engineering for the transportation of cylindrical projectiles. The coordination mechanism plays a pivotal role in ensuring reliable cartridge conveying, with its positioning accuracy being of utmost importance. However, accurately depicting the nonlinear relationship between input [...] Read more.

Cartridge delivery systems are commonly employed in aerospace engineering for the transportation of cylindrical projectiles. The coordination mechanism plays a pivotal role in ensuring reliable cartridge conveying, with its positioning accuracy being of utmost importance. However, accurately depicting the nonlinear relationship between input parameters and output response is challenging due to the involvement of numerous complex, uncertain factors during the movement process of the coordination mechanism. To address this issue, this study proposes a dynamics model that incorporates hinged gaps to represent rigid–flexible coupling within the coordination mechanism. Experimental validation confirms its effectiveness, while computational efficiency is enhanced through the utilization of a deep learning neural network surrogate model. Furthermore, an improved method for the uncertainty analysis of directional subintervals is introduced and applied to analyze uncertainty in coordination mechanisms, yielding results that demonstrate superior efficiency compared to other approaches. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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17 pages, 332 KiB  

Open AccessArticle

Estimation of the Resultant Expanded Uncertainty of the Output Quantities of the Measurement Chain Using the Discrete Wavelet Transform Algorithm

by Marian Kampik, Jerzy Roj and Łukasz Dróżdż

Appl. Sci. 2024, 14(9), 3691; https://doi.org/10.3390/app14093691 - 26 Apr 2024

Cited by 1 | Viewed by 652

Abstract

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of [...] Read more.

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of the wavelet transform algorithm are estimated. Subsequently, in accounting for the algorithm’s properties, the parameters of its output values are determined, and the resulting uncertainty values of the output quantities of the measurement chain are estimated. The interval reduction arithmetic method is employed in the calculations for estimating the expanded uncertainty. All findings were validated through measurements conducted using the implemented measurement chain. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

20 pages, 457 KiB  

Open AccessArticle

Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm

by Marian Kampik, Jerzy Roj and Łukasz Dróżdż

Appl. Sci. 2024, 14(8), 3461; https://doi.org/10.3390/app14083461 - 19 Apr 2024

Cited by 2 | Viewed by 736

Abstract

This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities [...] Read more.

This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both the deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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35 pages, 7701 KiB  

Open AccessArticle

Parameterized Reduced-Order Models for Probabilistic Analysis of Thermal Protection System Based on Proper Orthogonal Decomposition

by Kun Zhang, Jianyao Yao, Wenxiang Zhu, Zhifu Cao, Teng Li and Jianqiang Xin

Aerospace 2024, 11(4), 269; https://doi.org/10.3390/aerospace11040269 - 29 Mar 2024

Viewed by 909

Abstract

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and [...] Read more.

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 °C and 6 °C when the maximum reaches 1200 °C and 150 °C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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19 pages, 8123 KiB  

Open AccessArticle

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

by Xing Shui, Zhijun Rong, Binbin Dan, Qiangjian He and Xin Yang

Machines 2024, 12(3), 184; https://doi.org/10.3390/machines12030184 - 12 Mar 2024

Cited by 1 | Viewed by 1417

Abstract

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making [...] Read more.

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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19 pages, 3222 KiB  

Open AccessArticle

Bayesian Averaging Evaluation Method of Accelerated Degradation Testing Considering Model Uncertainty Based on Relative Entropy

by Tianji Zou, Wenbo Wu, Kai Liu, Ke Wang and Congmin Lv

Sensors 2024, 24(5), 1426; https://doi.org/10.3390/s24051426 - 22 Feb 2024

Viewed by 943

Abstract

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, [...] Read more.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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22 pages, 4668 KiB  

Open AccessArticle

Cooperative Game-Based Digital Twin Drives Decision Making: Overall Framework, Basic Formalization and Application Case

by Fuwen Hu, Song Bi and Yuanzhi Zhu

Mathematics 2024, 12(2), 355; https://doi.org/10.3390/math12020355 - 22 Jan 2024

Viewed by 1842

Abstract

The emerging progress brought about by Industry 4.0 generates great opportunities for better decision making to cope with increasingly uncertain and complex industrial production. From the perspective of game theory, methods based on computational simulations and methods based on physical entities have their [...] Read more.

The emerging progress brought about by Industry 4.0 generates great opportunities for better decision making to cope with increasingly uncertain and complex industrial production. From the perspective of game theory, methods based on computational simulations and methods based on physical entities have their intrinsic drawbacks, such as partially accessible information, uncontrollable uncertainty and limitations of sample data. However, an insight that inspired us was that the digital twin modeling method induced interactive environments to allow decision makers to cooperatively learn from the immediate feedback from both cyberspace and physical spaces. To this end, a new decision-making method was put forward using game theory to autonomously ally the digital twin models in cyberspace with their physical counterparts in the real world. Firstly, the overall framework and basic formalization of the cooperative game-based decision making are presented, which used the negotiation objectives, alliance rules and negotiation strategy to ally the planning agents from the physical entities with the planning agents from the virtual simulations. Secondly, taking the assembly planning of large-scale composite skins as a proof of concept, a cooperative game prototype system was developed to marry the physical assembly-commissioning system with the virtual assembly-commissioning system. Finally, the experimental work clearly indicated that the coalitional game-based twinning method could make the decision making of composite assembly not only predictable but reliable and help to avoid stress concentration and secondary damage and achieve high-precision assembly. Obviously, this decision-making methodology that integrates the physical players and their digital twins into the game space can help them take full advantage of each other and make up for their intrinsic drawbacks, and it preliminarily demonstrates great potential to revolutionize the traditional decision-making methodology. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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27 pages, 15861 KiB  

Open AccessArticle

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

by Shancheng Tang, Ying Zhang, Zicheng Jin, Jianhui Lu, Heng Li and Jiqing Yang

Appl. Sci. 2024, 14(1), 386; https://doi.org/10.3390/app14010386 - 31 Dec 2023

Cited by 2 | Viewed by 1349

Abstract

The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the [...] Read more.

The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the aluminum profile surface presents non-uniform and non-periodic features, and this irregular distribution makes it difficult for classical reconstruction networks to accurately reconstruct the normal features, resulting in low performance of related unsupervised detection methods. Aiming at such problems, a feature-oriented reconstruction method of unsupervised surface-defect detection method for aluminum profiles is proposed. The aluminum profile image preprocessing stage uses techniques such as boundary extraction, background removal, and data normalization to process the original image and extract the image of the main part of the aluminum profile, which reduces the influence of irrelevant data features on the algorithm. The essential features learning stage precedes the feature-optimization module to eliminate the texture interference of the irregular distribution of the aluminum profile surface, and image blocks of the area images are reconstructed one by one to extract the features through the mask. The defect-detection stage compares the structural similarity of the feature images before and after the reconstruction, and comprehensively determines the detection results. The experimental results improve detection precision by 1.4% and the F1 value by 1.2% over the existing unsupervised methods, proving the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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38 pages, 1728 KiB  

Open AccessArticle

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability

by Xuemiao Cui, Jiping Lu and Yafeng Han

Sensors 2024, 24(1), 165; https://doi.org/10.3390/s24010165 - 27 Dec 2023

Viewed by 1123

Abstract

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, [...] Read more.

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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17 pages, 6198 KiB  

Open AccessArticle

Mechanical Performance of a Node-Reinforced Body-Centered Cubic Lattice Structure: An Equal-Strength Concept Design

by Zeliang Liu, Rui Zhao, Chenglin Tao, Yuan Wang and Xi Liang

Aerospace 2024, 11(1), 4; https://doi.org/10.3390/aerospace11010004 - 19 Dec 2023

Cited by 5 | Viewed by 1678

Abstract

Lattice structures are characterized by a light weight, high strength, and high stiffness, and have a wide range of applications in the aerospace field. Node stress concentration is a key factor affecting the mechanical performance of lattice structures. In this paper, a new [...] Read more.

Lattice structures are characterized by a light weight, high strength, and high stiffness, and have a wide range of applications in the aerospace field. Node stress concentration is a key factor affecting the mechanical performance of lattice structures. In this paper, a new equal-strength body-centered cubic (ES-BCC) lattice structure was additively manufactured using 316L stainless steel via selective laser melting (SLM). The results of a mechanical compression test and finite element analysis revealed that the failure location of the ES-BCC structure changed from the nodes to the center of the struts. At the same density, the energy absorption, elastic modulus, and yield strength of the ES-BCC structure increased by 11.89%, 61.80%, and 53.72% compared to the BCC structure, respectively. Furthermore, the change in angle of the ES-BCC structure achieves significant changes in strength, stiffness, and energy absorption to meet different design requirements and engineering applications. The equal-strength concept design can be applied as a general design method to the design of other lightweight energy-absorbing lattice structures. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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