Article
Polymer Science
Bin Xu, Meng-Yang Wei, Xiao-Yu Wu, Jian-Guo Lei, Zhi-Wen Zhou, Lian-Yu Fu, Li-Kuan Zhu
Summary: This paper investigates the fabrication of composite parts using HFRP and PA6, and proposes an improvement method of creating micro-grooves on the surface of HFRP. The experimental results show that the micro-grooves can effectively improve the connecting strength between HFRP and PA6.
Article
Computer Science, Interdisciplinary Applications
Ping Lu, Shiyuan Guo, Yang Shu, Bin Liu, Peifeng Li, Wei Cao, Kaiyong Jiang
Summary: Natural element method (NEM) is a meshless method that simplifies the imposition of essential boundary conditions and has great potential to solve problems with large deformation. However, the high computational cost for searching natural neighbors is a main challenge in NEM. A local algorithm based on K-Nearest Neighbor is proposed in this paper to reduce the search scope and improve efficiency.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Review
Engineering, Manufacturing
B. X. Chai, B. Eisenbart, M. Nikzad, B. Fox, A. Blythe, P. Blanchard, J. Dahl
Summary: The advancements in process modelling and simulation for composites manufacturing have led to the importance of mould design optimization for improving manufacturing efficiency and product quality, particularly through the introduction of optimization algorithms in predictive numerical simulations. As simulation-based optimization becomes more prevalent, the discussion on search performance, solution optimality, and application suitability of different algorithms in various moulding scenarios is crucial for providing guidelines for future optimization studies.
COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
(2021)
Article
Engineering, Manufacturing
Rene Wick-Joliat, Maurice Tschamper, Roman Kontic, Dirk Penner
Summary: Researchers have developed a new strategy for using 3D printed sacrificial molds to injection mold complex geometries of ceramic parts. A comparison revealed that DLP printed molds are better suited for parts with very small structural features.
ADDITIVE MANUFACTURING
(2021)
Article
Polymer Science
Ivan V. Terekhov, Evgeniy M. Chistyakov
Summary: Binders, or tackifiers, are widely used in the production of new composite materials by liquid composite molding techniques. They stabilize preforms and improve fracture toughness of the composites. This article reviews the research from the 1960s to the present on the creation and properties of binders used in composite materials manufacturing. The influence of binders on preforming process, properties of preforms, and characteristics of the obtained composites are discussed.
Article
Mechanics
Jonas Nieschlag, Philipp Eisenhardt, Sven Coutandin, Juergen Fleischer
Summary: The study focuses on numerical analysis of rotational molded composite tie rods with optimized geometry to achieve higher mechanical tensile loads.
COMPOSITE STRUCTURES
(2021)
Article
Polymer Science
Sharlin Shahid, Eskil Andreasson, Viktor Petersson, Widaad Gukhool, Yuchi Kang, Sharon Kao-Walter
Summary: Injection-molded polyethylene plates exhibit highly anisotropic mechanical behavior. This article studies three different anisotropic yield criteria and compares their accuracy and computation time in finite element modeling. It is concluded that Barlat Yld91 and Barlat Yld2004-18P yield criteria can be calibrated with a few tensile tests and still capture anisotropy in deformation-stress-strain.
Article
Mechanics
Zebei Mao, Tong Li, Bo Wang
Summary: The random fiber distribution in short-fiber-reinforced polymer (sFRP), which depends on the manufacturing process, has a significant impact on its mechanical properties. In this study, an automatic analysis framework was developed to simulate the injection process and predict mechanical performance, enabling manufacturing process optimization and improved mechanical properties.
COMPOSITE STRUCTURES
(2023)
Article
Materials Science, Multidisciplinary
Gang Zhao, Kun Li
Summary: A multi-objective robust design method is proposed in this paper to improve the product safety and robustness of vehicle parts. By establishing a response surface model and applying an uncertain analysis method and optimization algorithm, robust optimization design is achieved.
MATERIALS CHEMISTRY AND PHYSICS
(2022)
Article
Materials Science, Multidisciplinary
Pengfei He, Wenbin Zhao, Bin Yang, Jihui Wang, Aiqing Ni, Shuxin Li
Summary: This study introduces a sensor-aided injection strategy to minimize void content in fiber-reinforced polymer composites by real-time monitoring and automatic adjustment of injection flow rate. Experimental results demonstrate a significant decrease in void content and improved part quality consistency with this strategy.
MATERIALS RESEARCH EXPRESS
(2021)
Article
Computer Science, Artificial Intelligence
Edwin Lughofer, Kurt Pichler
Summary: This paper proposes an approach for the automated prediction of possible quality deteriorations at injection molding machines using data-driven models. The approach relies on data solely recorded during the regular production phase and does not require collecting data from anomalous phases or knowing specific fault modes in advance. The approach embeds two main concepts: establishing causal relations between process variables and directly predicting quality criteria. The approach was successfully evaluated during real injection molding processes and achieved early detection of quality deteriorations.
APPLIED SOFT COMPUTING
(2024)
Article
Polymer Science
Jinsu Gim, Byungohk Rhee
Summary: The study proposed an analysis methodology to examine the effect of cavity pressure profile on part quality. Using a neural network for interpretation and process state points as input features, it clarified the influence of cavity pressure profile on part weight. The methodology can be used to set target points and bounds for monitoring, and optimize the injection-molding process through feature contributions.
Article
Materials Science, Multidisciplinary
Seung In Kang, Jung Jae Yoo, Min Gyoung Kim, Dong Gi Seong
Summary: We proposed a new liquid composite molding process called multi-drop filling (MDF) that improves the impregnability and filling time of resin by using an automatic resin dropping system and a pressing system. The carbon fiber composites fabricated using the MDF process showed similar strength and void content to those fabricated using the conventional VARTM process, but with remarkable improvement in void content at corner regions and decreased cycle time.
MATERIALS TODAY COMMUNICATIONS
(2022)
Article
Chemistry, Physical
Yilei Wang, Can Weng, Zijian Deng, Huijie Sun, Bingyan Jiang
Summary: Reducing interfacial interactions is crucial in achieving non-destructive molding of polymer micro/nano structures during demolding in micro-injection molding. This study fabricated Ni-based composite mold inserts with low surface energy and low friction coefficients using polytetrafluoroethylene (PTFE), molybdenum disulfide (MoS2), and tungsten disulfide (WS2) via electroforming. The addition of nanoparticles effectively reduced the surface energy and adhesion of the inserts. Molecular dynamics simulation showed that the water contact angle could be more accurate by considering surface roughness. The use of composite mold inserts with low surface energy and low friction coefficient can prevent microstructure deformation in demolding stage of injection molded polymer microfluidic chips.
APPLIED SURFACE SCIENCE
(2023)
Article
Materials Science, Multidisciplinary
Aimin Zhang, Jiachang Wang, Guilong Wang, Lei Jiang, Xiangwei Meng, Guoqun Zhao
Summary: In this study, a method for fabricating ultra-lightweight, high strength, surface quality, and thermal insulation polytetrafluoroethylene (PTFE) microfibrils reinforced polypropylene (PP) foams using mold opening microcellular injection molding (MOMIM) was reported. Different morphology PP/PTFE composites were prepared by melt blending, and their crystallization behavior and rheological properties were investigated. The results showed that fibrillated PTFE accelerated crystallization, refined crystals, and enhanced viscoelasticity of PP. The PP/fibrillated-PTFE foams exhibited improved mechanical properties and thermal insulation compared to pristine PP foams, and MOMIM-fabricated parts had better surface quality than MIM-fabricated ones.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2023)
Article
Engineering, Multidisciplinary
Beatriz Moya, Alberto Badias, Iciar Alfaro, Francisco Chinesta, Elias Cueto
Summary: Digital twins are digital representations of physical entities that use real-time data to understand the operating conditions. This article presents a unique type of digital twin that combines computer vision, scientific machine learning, and augmented reality. This digital twin can observe and interpret what it sees, make adjustments to its model when necessary, and present the information in augmented reality.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Quercus Hernandez, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
Summary: The method developed uses feedforward neural networks to learn physical systems from data while ensuring compliance with the first and second principles of thermodynamics. By enforcing the metriplectic structure of dissipative Hamiltonian systems, it minimizes the amount of data required and naturally achieves conservation of energy and dissipation of entropy in its predictions. No prior knowledge of the system is necessary, and the method can handle both conservative and dissipative, discrete and continuous systems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Mathematics
Tarek Frahi, Francisco Chinesta, Antonio Falco, Alberto Badias, Elias Cueto, Hyung Yun Choi, Manyong Han, Jean-Louis Duval
Summary: The study utilizes motion sensor data and incorporates topological data analysis to design a model for accurately predicting the state of drivers, with experiments demonstrating the effectiveness of the model.
Article
Engineering, Multidisciplinary
Quercus Hernandez, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
Summary: The algorithm presented in this study uses sparse autoencoders to reduce the dimensionality of a physical system and predict its time evolution using deep neural networks. It ensures the conservation of total energy and entropy inequality, making it suitable for both conservative and dissipative systems. The method has been tested in examples from fluid and solid mechanics.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Polymer Science
Joaquin Lluch-Cerezo, Maria Desamparados Meseguer, Juan Antonio Garcia-Manrique, Rut Benavente
Summary: The study focused on using a ceramic powder mould to treat FDM-printed PLA and ABS parts, analyzing the influence of the mould on part length and mechanical properties. Results showed that effectiveness of the mould increases with annealing temperature, but is lower for PLA parts.
Article
Chemistry, Multidisciplinary
Daniele Di Lorenzo, Victor Champaney, Claudia Germoso, Elias Cueto, Francisco Chinesta
Summary: In this study, a methodology is proposed to locally correct or globally enrich models using collected data. The technique achieved satisfactory results in correcting localized damage and improving accuracy of structural performance predictions, with correction rates of up to 90% in the local problem and 60% in the global problem.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Alberto Badias, Iciar Alfaro, David Gonzalez, Francisco Chinesta, Elias Cueto
Summary: This article proposes a new methodology to estimate the 3D displacement field of deformable objects. By solving the hyperelasticity problem and not imposing any prior conditions, the article achieves real-time and accurate estimation of object deformation, including occluded areas.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Review
Computer Science, Interdisciplinary Applications
Elias Cueto, Francisco Chinesta
Summary: Thermodynamics, as a higher level of physics, has the potential to aid accurate and credible predictions in machine learning. This review explores how thermodynamics provides insights in the learning process, considering factors such as scale, choice of variables, and learning techniques.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Mathematics, Interdisciplinary Applications
Beatriz Moya, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
Summary: Learning and reasoning about physical phenomena is a challenge in robotics, and computational sciences play a key role in finding accurate methods for explaining past events and predicting future situations. This paper proposes a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. The method demonstrates the importance of physics and knowledge in data-driven modeling and adaptation to low-data regimes.
COMPUTATIONAL MECHANICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Quercus Hernandez, Alberto Badias, Francisco Chinesta, Elias Cueto
Summary: We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. We modify the port-Hamiltonian formalism to achieve a port-metriplectic one in order to satisfy the principles of thermodynamics in the learned physics. Our constructed networks are able to learn the physics of complex systems by parts and make predictions at the scale of the complete system. Examples are provided to demonstrate the performance of the proposed technique.
COMPUTATIONAL MECHANICS
(2023)
Article
Engineering, Multidisciplinary
Quercus Hernandez, Alberto Badias, Francisco Chinesta, Elias Cueto
Summary: This research presents a method for computing the dynamic response of deformable objects induced by user interactions in mixed reality using deep learning. The graph-based architecture ensures thermodynamic consistency, while the visualization pipeline provides a realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to demonstrate the method's performance.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Mechanics
Abel Sancarlos, Victor Champaney, Elias Cueto, Francisco Chinesta
Summary: Regressions created from experimental or simulated data are widely used in engineering applications to construct metamodels. These metamodels are used for optimization, inverse analysis, uncertainty propagation, and simulation-based control. The challenge lies in solving high-dimensional problems while ensuring accuracy and avoiding overfitting. This paper proposes and discusses advanced regressions based on the proper generalized decomposition (PGD) with different regularization techniques and the ANOVA-based PGD method to address these challenges.
ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES
(2023)
Article
Remote Sensing
Cesar Garcia-Gascon, Pablo Castello-Pedrero, Juan Antonio Garcia-Manrique
Summary: This paper describes the methodology used in the design and manufacture of a fixed-wing aircraft using additive techniques and solar panel technology. The objective is to increase the autonomy and range of the UAV's missions, improve the capabilities of the aeronautical industry towards sustainable aircrafts, and acquire better mechanical properties through additive technologies and new printing materials. The paper proposes the use of minimal surfaces for reinforcing the UAV aircraft wings, and explores the potential of additive manufacturing for small aircrafts in the future.
Article
Computer Science, Artificial Intelligence
Abel Sancarlos, Morgan Cameron, Jean-Marc Le Peuvedic, Juliette Groulier, Jean-Louis Duval, Elias Cueto, Francisco Chinesta
Summary: The concept of hybrid twin (HT) combines physics-based models and data science to correct deviations between measurements and predictions in real-time. This paper focuses on computing stable, fast, and accurate corrections in the HT framework, introducing a new approach to ensure stability.
DATA-CENTRIC ENGINEERING
(2021)
Article
Engineering, Mechanical
Agathe Reille, Victor Champaney, Fatima Daim, Yves Tourbier, Nicolas Hascoet, David Gonzalez, Elias Cueto, Jean Louis Duval, Francisco Chinesta
Summary: This study proposes a data-driven technique for learning the rich behavior of local patches in large structures with localized behaviors, and integrating it into a standard coarser description at the structure level to solve mechanical problems. This approach allows localized behaviors to impact the global structural response without needing an explicit description of fine scale behaviors.
MECHANICS & INDUSTRY
(2021)
Article
Engineering, Multidisciplinary
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
Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis
Summary: In this study, deep learning and artificial intelligence were used to discover a mathematical model for the progression of Alzheimer's disease. By analyzing longitudinal tau positron emission tomography data, a reaction-diffusion type partial differential equation for tau protein misfolding and spreading was discovered. The results showed different misfolding models for Alzheimer's and healthy control groups, indicating faster misfolding in Alzheimer's group. The study provides a foundation for early diagnosis and treatment of Alzheimer's disease and other misfolding-protein based neurodegenerative disorders using image-based technologies.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
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
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
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
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)
Article
Engineering, Multidisciplinary
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
Leilei Chen, Zhongwang Wang, Haojie Lian, Yujing Ma, Zhuxuan Meng, Pei Li, Chensen Ding, Stephane P. A. Bordas
Summary: This paper presents a model order reduction method for electromagnetic boundary element analysis and extends it to computer-aided design integrated shape optimization of multi-frequency electromagnetic scattering problems. The proposed method utilizes a series expansion technique and the second-order Arnoldi procedure to reduce the order of original systems. It also employs the isogeometric boundary element method to ensure geometric exactness and avoid re-meshing during shape optimization. The Grey Wolf Optimization-Artificial Neural Network is used as a surrogate model for shape optimization, with radar cross section as the objective function.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
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
Ye Lu, Weidong Zhu
Summary: This work presents a novel global digital image correlation (DIC) method based on a convolution finite element (C-FE) approximation. The C-FE based DIC provides highly smooth and accurate displacement and strain results with the same element size as the usual finite element (FE) based DIC. The proposed method's formulation and implementation, as well as the controlling parameters, have been discussed in detail. The C-FE method outperformed the FE method in all tested examples, demonstrating its potential for highly smooth, accurate, and robust DIC analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Mojtaba Ghasemi, Mohsen Zare, Amir Zahedi, Pavel Trojovsky, Laith Abualigah, Eva Trojovska
Summary: This paper introduces Lung performance-based optimization (LPO), a novel algorithm that draws inspiration from the efficient oxygen exchange in the lungs. Through experiments and comparisons with contemporary algorithms, LPO demonstrates its effectiveness in solving complex optimization problems and shows potential for a wide range of applications.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Jingyu Hu, Yang Liu, Huixin Huang, Shutian Liu
Summary: In this study, a new topology optimization method is proposed for structures with embedded components, considering the tension/compression asymmetric interface stress constraint. The method optimizes the topology of the host structure and the layout of embedded components simultaneously, and a new interpolation model is developed to determine interface layers between the host structure and embedded components.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
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
Jacinto Ulloa, Geert Degrande, Jose E. Andrade, Stijn Francois
Summary: This paper presents a multi-temporal formulation for simulating elastoplastic solids under cyclic loading. The proper generalized decomposition (PGD) is leveraged to decompose the displacements into multiple time scales, separating the spatial and intra-cyclic dependence from the inter-cyclic variation, thereby reducing computational burden.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
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)
