Engineering, Multidisciplinary

Article Automation & Control Systems

LDD-Net: Lightweight printed circuit board defect detection network fusing multi-scale features

Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou

Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Automation & Control Systems

Progress and prospects of future urban health status prediction

Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li

Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Automation & Control Systems

A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization

Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello

Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Automation & Control Systems

Satellite constellation method for ground targeting optimized with K-means clustering and genetic algorithm

Soung Sub Lee

Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Automation & Control Systems

Robotic assembly control reconfiguration based on transfer reinforcement learning for objects with different geometric features

Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen

Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Automation & Control Systems

Adaptive stable backstepping controller based on support vector regression for nonlinear systems

Kemal Ucak, Gulay Oke Gunel

Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2024)

Article Engineering, Multidisciplinary

Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems

Prashant K. Jha

Summary: This work focuses on developing methods for approximating the solution operators of a class of parametric partial differential equations using neural operators. It addresses challenges such as generating appropriate training data, cost-accuracy trade-offs, and hyperparameter tuning. The study introduces a framework based on a linear variational problem to correct the predictions generated by neural operators, and analyzes the associated correction operator. Numerical results demonstrate a significant increase in approximation accuracy when neural operators are corrected using the proposed scheme. Additionally, the limitations of neural operators and the efficacy of the correction scheme are highlighted in a topology optimization problem.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation

Qiang Liu, Wei Zhu, Xiyu Jia, Feng Ma, Jun Wen, Yixiong Wu, Kuangqi Chen, Zhenhai Zhang, Shuang Wang

Summary: In this study, a multiscale and nonlinear turbulence characteristic extraction model using a graph neural network was designed. This model can directly compute turbulence data without resorting to simplified formulas. Experimental results demonstrate that the model has high computational performance in turbulence calculation.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Volume conservation issue within SPH models for long-time simulations of violent free-surface flows

C. Pilloton, P. N. Sun, X. Zhang, A. Colagrossi

Summary: This paper investigates the smoothed particle hydrodynamics (SPH) simulations of violent sloshing flows and discusses the impact of volume conservation errors on the simulation results. Different techniques are used to directly measure the particles' volumes and stabilization terms are introduced to control the errors. Experimental comparisons demonstrate the effectiveness of the numerical techniques.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing

Akshay J. Thomas, Mateusz Jaszczuk, Eduardo Barocio, Gourab Ghosh, Ilias Bilionis, R. Byron Pipes

Summary: We propose a physics-guided transfer learning approach to predict the thermal conductivity of additively manufactured short-fiber reinforced polymers using micro-structural characteristics obtained from tensile tests. A Bayesian framework is developed to transfer the thermal conductivity properties across different extrusion deposition additive manufacturing systems. The experimental results demonstrate the effectiveness and reliability of our method in accounting for epistemic and aleatory uncertainties.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Coupled total- and semi-Lagrangian peridynamics for modelling fluid-driven fracturing in solids

Changyi Yang, Fan Zhu, Jidong Zhao

Summary: This paper presents a novel computational approach for modelling fluid-driven fracturing in quasi-brittle solids using peridynamics. The approach combines total-Lagrangian formulation and semi-Lagrangian formulation to solve the Navier-Stokes equation and quantify the forces at the fluid-solid interface using a non-local differential operator. The proposed approach offers a unified peridynamics-based framework that enables simulations of a wide range of fluid-driven fracturing problems in solids and has been validated through various classic problems.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

A level set reliability-based topology optimization (LS-RBTO) method considering sensitivity mapping and multi-source interval uncertainties

Zeshang Li, Lei Wang, Geng Xinyu

Summary: With the diversification of engineering structure performance requirements and the continuous development of structural design refinement, structural design methods are facing more and more factors to be considered. This paper proposes a sensitivity mapping technique for topology optimization based on a gradient optimization algorithm and considers the influence of multi-source uncertainties.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Machine learning powered sketch aided design via topology optimization

Weisheng Zhang, Yue Wang, Sung-Kie Youn, Xu Guo

Summary: This study proposes a sketch-guided topology optimization approach based on machine learning, which incorporates computer sketches as constraint functions to improve the efficiency of computer-aided structural design models and meet the design intention and requirements of designers.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture

Jonghyuk Baek, Jiun-Shyan Chen

Summary: This paper introduces an improved neural network-enhanced reproducing kernel particle method for modeling the localization of brittle fractures. By adding a neural network approximation to the background reproducing kernel approximation, the method allows for the automatic location and insertion of discontinuities in the function space, enhancing the modeling effectiveness. The proposed method uses an energy-based loss function for optimization and regularizes the approximation results through constraints on the spatial gradient of the parametric coordinates, ensuring convergence.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Stabilized mixed material point method for incompressible fluid flow

Bodhinanda Chandra, Ryota Hashimoto, Shinnosuke Matsumi, Ken Kamrin, Kenichi Soga

Summary: This paper proposes new and robust stabilization strategies for accurately modeling incompressible fluid flow problems in the material point method (MPM). The proposed approach adopts a monolithic displacement-pressure formulation and integrates two stabilization strategies to ensure stability. The effectiveness of the proposed method is validated through benchmark cases and real-world scenarios involving violent free-surface fluid motion.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Numerical analysis of non-proportional biaxial reverse experiments with a two-surface anisotropic cyclic plasticity-damage approach

Zhichao Wei, Steffen Gerke, Michael Bruenig

Summary: This paper explores the numerical analysis of ductile damage and fracture behavior under non-proportional biaxial reverse loading conditions. A two-surface anisotropic cyclic elastic-plastic-damage continuum model is presented, taking into account various effects. An efficient Euler explicit numerical integration algorithm is used, and discussions are provided on achieving convergence within the global Newton-Raphson scheme.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

A stochastic LATIN method for stochastic and parameterized elastoplastic analysis

Zhibao Zheng, David Neron, Udo Nackenhorst

Summary: This paper presents a stochastic LATIN method to solve stochastic and/or parameterized elastoplastic problems. The method approximates the stochastic solution by decomposing it into spatial, temporal, and stochastic spaces and using a set of triplets of spatial functions, temporal functions, and random variables. The method efficiently handles nonlinearity and randomness and/or parameters.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

A unified analytical expression of the tangent stiffness matrix of holonomic constraints

Chao Peng, Alessandro Tasora, Dario Fusai, Dario Mangoni

Summary: This article discusses the importance of the tangent stiffness matrix of constraints in multibody systems and provides a general formulation based on quaternion parametrization. The article also presents the analytical expression of the tangent stiffness matrix derived through linearization. Examples demonstrate the positive effect of this additional stiffness term on static and eigenvalue analyses.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

Automated translation and accelerated solving of differential equations on multiple GPU platforms

Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma, Tim Besard, Prakitr Srisuma, Tim Gymnich, Adam R. Gerlach, Alan Edelman, George Barbastathis, Richard D. Braatz, Christopher Rackauckas

Summary: This article presents a high-performance vendor-agnostic method for massively parallel solving of ordinary and stochastic differential equations on GPUs. The method integrates with a popular differential equation solver library and achieves state-of-the-art performance compared to hand-optimized kernels.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)

Article Engineering, Multidisciplinary

On the detection of nonlinear normal mode-related isolated branches of periodic solutions for high-dimensional nonlinear mechanical systems with frictionless contact interfaces

Thibaut Vadcard, Fabrice Thouverez, Alain Batailly

Summary: This contribution presents a methodology for detecting isolated branches of periodic solutions to nonlinear mechanical equations. The method combines harmonic balance method-based solving procedure with the Melnikov energy principle. It is able to predict the location of isolated branches of solutions near families of autonomous periodic solutions. The relevance and accuracy of this methodology are demonstrated through academic and industrial applications.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024)