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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

    FabriCast: Casting Silicone Structures via Direct Ink Writing on Textiles

    J. M. Tan1, A. Chooi2, C. Chen1, A. Castillo Ugalde2, T. Stalin2, T. Calais2, P. Valdivia y Alvarado1,2,3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011827

    Abstract In this study two novel forms of textile-assisted direct ink writing (DIW) of room temperature vulcanised (RTV) silicones were explored: Silicone DIW on spandex fabric, and Silicone DIW on dissolvable fabrics. These processes were evaluated by incorporating resulting components into 4 soft robotic devices: impact resistant elbow pads, a soft passive suction cup gripper, and two fiber embedded inflatable tendril-like soft grippers. More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

    Tao Yan1, Javed Rashid2,3, Muhammad Shoaib Saleem3,4, Sajjad Ahmad4, Muhammad Faheem5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

    Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

  • Open Access

    ARTICLE

    Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

    Nataliya Shakhovska1,2,*, Mykola Medykovskyi1, Oleksandr Gurbych1,3, Mykhailo Mamchur1,3, Mykhailo Melnyk1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

    Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

  • Open Access

    ARTICLE

    A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

    Shaoxiong Wu1, Ruoxin Li1, Xiaofeng Tao1, Hailong Wu1,*, Ping Miao1, Yang Lu1, Yanyan Lu1, Qi Liu2, Li Pan2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

    Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

  • Open Access

    ARTICLE

    Reliability Prediction of Wrought Carbon Steel Castings under Fatigue Loading Using Coupled Mold Optimization and Finite Element Simulation

    Muhammad Azhar Ali Khan1, Syed Sohail Akhtar2,3,*, Abba A. Abubakar2,4, Muhammad Asad1, Khaled S. Al-Athel2,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2325-2350, 2024, DOI:10.32604/cmes.2024.054741 - 31 October 2024

    Abstract The fatigue life and reliability of wrought carbon steel castings produced with an optimized mold design are predicted using a finite element method integrated with reliability calculations. The optimization of the mold is carried out using MAGMASoft mainly based on porosity reduction as a response. After validating the initial mold design with experimental data, a spring flap, a common component of an automotive suspension system is designed and optimized followed by fatigue life prediction based on simulation using Fe-safe. By taking into consideration the variation in both stress and strength, the stress-strength model is used… More >

  • Open Access

    ARTICLE

    Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

    Jiachang Liu1,*, Zhengwei Huang2, Junfeng Xiang1, Lu Liu1, Manlin Hu1

    Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

    Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

  • Open Access

    PROCEEDINGS

    Design and Fabrication of Porous Lithium-Containing Ceramic Tritium Breeders for Fusion Reactors

    Jili Cai1, Junyi Zhou1, Hangyu Chen1, Liang Huang1, Wenming Jiang1, Jie Liu1, Zhongwei Li1, Chao Cai1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011946

    Abstract Effectively obtaining tritium is one of the essential issues to realize commercial and controlled nuclear fusion [1]. Conventional lithium-containing ceramic tritium breeders with pebble bed configurations in fusion reactors have shown insurmountable structural drawbacks weakening tritium extraction, including inherently low packing fractions, extensive stress concentrations, and low thermal conductivity. Therefore, extensive efforts have been devoted to enhancing tritium extraction by improving the design of tritium breeders and addressing structural drawbacks [2-4]. In this study, porous block configurations were proposed to replace conventional pebble bed configurations for the ceramic tritium breeder. Utilizing fluid-solid coupled heat transfer… More >

  • Open Access

    PROCEEDINGS

    Topology Optimization of Mega-Casting Thin-Walled Structures of Vehicle Body with Stiffness Objective and Process Filling Constraints

    Jiayu Chen1, Yingchun Bai1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011393

    Abstract Mega-casting techniques are widely used to manufacture large piece of thin-walled structures for vehicle body in Automotive industries, especially with the rapid growing electric vehicle market. Topology optimization is effective design method to reach higher mechanical performance yet lightweight potential for casting structures [1-3]. Most of existing works is focused on geometric-type casting constraints such as drawn angle, partion line, undercut, and enclose holes. However, the challenges in mega-casting arise from the complexities in the casting process such as filling and solidification, and the corresponding defects have larger influences on the structural performances [4-6]. Partial… More >

  • Open Access

    ARTICLE

    A Complex Fuzzy LSTM Network for Temporal-Related Forecasting Problems

    Nguyen Tho Thong1, Nguyen Van Quyet1,2, Cu Nguyen Giap3,*, Nguyen Long Giang1, Luong Thi Hong Lan4

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4173-4196, 2024, DOI:10.32604/cmc.2024.054031 - 12 September 2024

    Abstract Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy… More >

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