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  • Open Access

    PROCEEDINGS

    Development of a High-Temperature Resistance SLS Sand Mold Process for Titanium Alloy Casting

    Shouyin Zhang1,*, Zhifeng Xu1, Qiangwei Xiao2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012141

    Abstract 3D printing sand mold has been widely used in casting production. However, there exist some problems hindering its application for titanium alloy casting, such as the large amount of gas evolution, cannot withstand high temperature impact, easy to react with titanium alloy melt, etc. This work develops a high-temperature resistance SLS (selective laser sintering) sand mold process by introducing inorganic binder in two different ways, i.e., bi-binder SLS process and SLS infiltration process. After sintering at 1100 ℃, SLS sand mold or core possesses high tensile strength and can be used for titanium alloy casting. More >

  • Open Access

    PROCEEDINGS

    Nonlocal Crystal Plasticity Modeling of Heterostructured Materials

    Jianfeng Zhao1,*, Xu Zhang2, Guozheng Kang2, Michael Ziaser3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012582

    Abstract A continuum model of dislocation transport incorporating grain boundary transmission was developed within a dislocation-based crystal plasticity framework, which was then adopted to study the deformation mechanisms of gradient-structured material and bimodal-grained material. The nonlocal nature of the model on the slip system level enables the direct investigation of strain gradient effects caused by internal deformation heterogeneities. Furthermore, the interaction between dislocations and grain boundaries leads to the formation of pileups near grain boundaries, which is key to studying the grain size effects in polycrystals. Finite element implementation of the model for polycrystals with different… More >

  • Open Access

    PROCEEDINGS

    Modelling and Simulation on Deformation Behaviour and Strengthening Mechanism of Multi-Principal Element Alloys

    Yang Chen1, Baobin Xie1, Weizheng Lu1, Jia Li1,*, Qihong Fang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011624

    Abstract In order to accurately predict and evaluate the mechanical properties of multi-principal element alloys (MPEAs), some new models and simulation methods need to be developed to solve the problems caused by its unique natural characteristics, such as severe lattice distortion. The existing models are based on the development of low concentration alloys, and cannot be well applied to MPEAs. Here, we develop i) the random field theory informed discrete dislocation dynamics simulations based on high-resolution transmission electron microscopy, to systematically clarify the role of heterogeneous lattice strain on the complex interactions between the dislocation loop… More >

  • Open Access

    PROCEEDINGS

    Strengthening Mechanical Performance with Robust and Efficient Machine Learning-Assisted Path Planning for Additive Manufacturing of Continuous Fiber Composites

    Xinmeng Zha1, Huilin Ren1,*, Yi Xiong1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011371

    Abstract Additive manufacturing of continuous fiber composites is an emerging field that enables the tunable mechanical performance of composite structure by flexibly controlling the spatial layout of continuous fibers. Transverse isotropic strengthening is advantageous property of continuous fiber, which is favorable to align with the principal stress orientation. However, the accuracy and efficiency of traditional methods for calculating principal stress field are unguaranteed due to the inherent complexity and variability of geometries, material properties, and operational conditions in additive manufacturing. Therefore, a machine learning-assisted path planning method is proposed to robustly and efficiently generate the continuous… More >

  • Open Access

    PROCEEDINGS

    Refined Microstructures and Enhanced Strength of In-Situ TiBw/Ti-6.5Al-2.5Zr-1Mo-1V Composites by Selective Laser Melting

    Qi An1,*, Lihua Cui1, Lujun Huang1, Lin Geng1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011973

    Abstract Ti-6.5Al-2.5Zr-1Mo-1V alloy is a near α titanium alloy, which has been widely used in aerospace fields due to its low density, high specific strength, good corrosion resistance and high-temperature durability. To further improve the strength and high-temperature durability of Ti-6.5Al-2.5Zr-1Mo-1V complex components, the spherical Ti-6.5Al-2.5Zr-1Mo-1V alloy powder with a particle size of 15~53 μm and TiB2 powder with a particle size of 0.5~1 μm were used to fabricate in-situ TiBw reinforced Ti-6.5Al-2.5Zr-1Mo-1V composites through low energy ball milling and selective laser melting (SLM). The results show that the TiB whiskers are uniformly distributed in the More >

  • Open Access

    EDITORIAL

    Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach

    Dominique Vervoort1,2,3,*, Amy Verstappen3, Sreehari Madhavankutty Nair4, Chong Chin Eu5, Bistra Zheleva3,6

    Congenital Heart Disease, Vol.19, No.2, pp. 131-138, 2024, DOI:10.32604/chd.2024.049814 - 16 May 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm

    Parth Khandelwal1, Harshit2, Indranil Manna1,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1727-1755, 2024, DOI:10.32604/cmc.2024.042752 - 25 April 2024

    Abstract Metallic alloys for a given application are usually designed to achieve the desired properties by devising experiments based on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises. However, the influence of process parameters and material properties is often non-linear and non-colligative. In recent years, machine learning (ML) has emerged as a promising tool to deal with the complex interrelation between composition, properties, and process parameters to facilitate accelerated discovery and development of new alloys and functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles, to design… More > Graphic Abstract

    Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm

  • Open Access

    ARTICLE

    Characterization and Selection of Microcrystalline Cellulose from Oil Palm Empty Fruit Bunches for Strengthening Hydrogel Films

    Susi Susi1,2,*, Makhmudun Ainuri3,*, Wagiman Wagiman3, Mohammad Affan Fajar Falah3

    Journal of Renewable Materials, Vol.12, No.3, pp. 513-537, 2024, DOI:10.32604/jrm.2024.045586 - 11 April 2024

    Abstract Microcrystalline cellulose (MCC) is one of the cellulose derivatives produced as a result of the depolymerization of a part of cellulose to achieve high crystallinity. When implemented in other polymers, high crystallinity correlates with greater strength and stiffnes, but it can reduce the water-holding capacity. The acid concentration and hydrolysis time will affect the acquisition of crystallinity and water absorption capacity, both of which have significance as properties of hydrogel filler. The study aimed to evaluate the properties and select the MCC generated from varying the proportion of hydrochloric acid (HCl) and the appropriate hydrolysis… More > Graphic Abstract

    Characterization and Selection of Microcrystalline Cellulose from Oil Palm Empty Fruit Bunches for Strengthening Hydrogel Films

  • Open Access

    ARTICLE

    Strengthening Network Security: Deep Learning Models for Intrusion Detection with Optimized Feature Subset and Effective Imbalance Handling

    Bayi Xu1, Lei Sun2,*, Xiuqing Mao2, Chengwei Liu3, Zhiyi Ding2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478 - 27 February 2024

    Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the More >

  • Open Access

    PROCEEDINGS

    Deformation Behaviour and Strengthening Mechanism of High-Entropy Alloys Using Model and Simulation

    Jia Li1, Yang Chen1, Baobin Xie1, Weizheng Lu1, Qihong Fang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010064

    Abstract The high-profile high-entropy alloy shows outstanding mechanical properties. However, the accurate and reasonable models for describing the mechanical behavior of HEAs are still scarce due to their distinctive characteristics such as serious lattice distortion, which limit the engineering application. We have developed a new general framework combining atomic simulation, discrete dislocation dynamics and crystal plasticity finite element method, to study the deformation behaviour and strengthening mechanism of HEAs, and realized the influence of complex cross-scale factors on material deformation [1-3]. Compared with the classic crystal plasticity finite element, the bottom-up hierarchical multiscale model could couple… More >

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