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  • Open Access

    ARTICLE

    A Digital Twin Approach for Agile Additive Manufacturing of Automotive Components

    Chinmai Bhat1,2, Mayur Jiyalal Prajapati2, Yulius Shan Romario3, Wojciech Macek4, Maziar Ramezani5, Cho-Pei Jiang1,2,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075197 - 09 April 2026

    Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion… More >

  • Open Access

    ARTICLE

    An Isothermal Surface Imaging and Transfer Learning Framework for Fast Isothermal Surface Prediction and 3D Temperature Field Reconstruction in Metal Additive Manufacturing

    Zhidong Wang, Yanping Lian*, Mingjian Li, Jiawei Chen, Ruxin Gao

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026

    Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >

  • Open Access

    ARTICLE

    Prediction of Wall Thickness Parameters in TPMS Models Based on CNN-SVM and MLR

    Qian Zhang1, Lei Fu1,2, Renzhou Chen3, Xu Zhan4,*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074939 - 12 March 2026

    Abstract Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated,… More >

  • Open Access

    ARTICLE

    Experimental Investigation on Fatigue Life of Carbon Fiber-Reinforced Nylon (Onyx) Based on Extrusion Printing

    Moises Jimenez-Martinez1,*, Gael Ramirez2, Giancarlo Marchetta-Cruz3, Manuel Coca-Gonzalez1

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074260 - 12 March 2026

    Abstract Most failures in component operation occur due to cyclic loads. Validation has been performed under quasistatic loads, but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life. Fatigue is a damage accumulation process where loads degrade the material, depending on the characteristics and number of repetitions of the load. Studies on the mechanical fatigue of 3D-printed Onyx are limited. In this paper, the strength of 3D-printed Onyx components under dynamic conditions (repetitive loads) is estimated. Fatigue life prediction is influenced by manufacturing processes, material properties, and… More >

  • Open Access

    ARTICLE

    A novel Angle-Constrained Optimization method of Conformal Lattice Structures

    Jun Yan1,2, Weibin Xu1, Fuhao Wang1, Sixu Huo3, Kun Yan4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076948 - 26 February 2026

    Abstract Conformal truss-like lattice structures face significant manufacturability challenges in additive manufacturing due to overhang angle limitations. To address this problem, we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates. First, a build direction is selected to minimize the number of violating struts. Then, an angular-constraint matrix is assembled from strut direction vectors, and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints. Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor More >

  • Open Access

    ARTICLE

    Development of AI-Based Monitoring System for Stratified Quality Assessment of 3D Printed Parts

    Yewon Choi1,2, Song Hyeon Ju2, Jungsoo Nam2,*, Min Ku Kim1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071817 - 29 January 2026

    Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

  • Open Access

    ARTICLE

    Porosity-Impact Strength Relationship in Material Extrusion: Insights from MicroCT, and Computational Image Analysis

    Jia Yan Lim1,2, Siti Madiha Muhammad Amir3, Roslan Yahya3, Marta Peña Fernández2, Tze Chuen Yap1,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

    Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >

  • Open Access

    PROCEEDINGS

    Crashworthiness Design of Composite Thin-Walled Structures Manufactured by Additive Manufacturing

    Kui Wang*, Qianbing Tan, Yisen Liu

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012404

    Abstract To address the increasing demands for lightweight and passive safety in transportation equipment, a series of studies on the crashworthiness design of composite thin-walled structures were conducted. These investigations leveraged the high specific strength/stiffness advantages of carbon fiber-reinforced polyamide composites and the high-formability benefits of fused deposition modeling (FDM) additive manufacturing technology. Compared with traditional composite manufacturing processes, lattice-filled thin-walled structures, integrally fabricated via additive manufacturing, exhibited significant synergistic interactions between their internal lattice and outer walls during compression. This synergy effectively enhanced the energy absorption capacity of the structures and achieved a "1+1>2" synergistic… More >

  • Open Access

    PROCEEDINGS

    Vat Photopolymerization 3D Printing of NiO-YSZ Anode for Solid Oxide Fuel Cells

    Jinsi Yuan, Haijiang Wang*, Jiaming Bai*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.011073

    Abstract Solid oxide fuel cells (SOFCs) have attracted considerable attention for their high efficiency, environmental advantages, and versatility in fuel sources. Research has shown that optimizing the structure of SOFCs can lead to significant performance improvements. Additive manufacturing (AM) has emerged as a promising technology for geometrical optimization of SOFCs, owing to its capability to create complex and programmable structures. However, fabricating three-dimensional electrode structures with fine, highly resolved features remains a significant challenge. Herein, a vat photopolymerization (VPP) 3D printing process was developed for fabricating the Nickel Oxide-Yttria Stabilized Zirconia (NiO-YSZ) anode structure of SOFC.… More >

  • Open Access

    PROCEEDINGS

    Internal Connection Between the Microstructures and the Mechanical Properties in Additive Manufacturing

    Yifei Wang, Zhao Zhang*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011121

    Abstract Additive manufacturing (AM) reveals high anisotropy in mechanical properties due to the thermal accumulation induced microstructures. How to reveal the internal connection between the microstructures and the mechanical properties in additive manufacturing is a challenge. There are many methods to predict the mechanical properties based on the microstructural evolutions in additive manufacturing [1–3]. Here we summarized the main methods for the prediction of the mechanical properties in additive manufacturing, including crystal plasticity finite element method (CPFEM), dislocation dynamics (DD), and molecular dynamics (MD). We systematically examine these primary approaches for mechanical property predictions in AM,… More >

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