Changhong Linghu^1,*, Huajian Gao^1,2, K. Jimmy Hsia^1,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011590

Abstract Shape memory polymers (SMPs), with unique properties such as tunable elastic modulus, temporary shape-locking, and shape-recovery upon external stimulations, are emerging as a new class of smart materials with switchable adhesion capabilities. A prominent feature of the adhesion between SMP and a spherical indenter is the so-called R2G adhesion, defined as making contact in the rubbery state to a certain indentation depth followed by detachment in the glassy state. While it has been demonstrated that the R2G adhesion with SMPs can achieve orders of magnitude higher adhesive strength compared to conventional elastic adhesive systems, the… More >

Xudong Yu^1,*, Hai Shen¹, Jingyuan Lu¹, Shangqin Yuan², Ming Huang³

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011546

Abstract The continuous advancement of Additive Manufacturing (AM) technologies has revolutionized the production of intricate components and reinforced composites with tailored mechanical properties. However, the variability in AM techniques and processing parameters often leads to discrepancies in fibre volume fraction, porosity, and interfaces in AM composites, resulting in dispersed elastic moduli and mechanical responses, which necessitates robustness non-destructive evaluation (NDE) methods. Additionally, AM introduces new defect morphologies, dimensions, and locations, demanding new and more reliable non-destructive testing (NDT) techniques.
This research commences by quantifying orientation-dependent mechanical properties of laser-sintered nanocomposites of carbon nanotube (CNT) reinforced polyamine (PA).… More >

Jinlong Su^1,2, Chaolin Tan^2,*, Swee Leong Sing^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012733

Abstract Tailoring thermal history during additive manufacturing (AM) offers a viable approach to customising the microstructure and properties of materials without changing alloy compositions, which is generally overlooked as it is hard to achieve in commercial materials. In this work, a customised Fe-Ni-Ti-Al maraging steel with rapid precipitation kinetics offers the opportunity to leverage thermal history during AM for achieving large-range tunable strength-ductility combinations without post heat treatment or changing alloy chemistry. The Fe-Ni-Ti-Al maraging steel was processed by laser-directed energy deposition (LDED) with different deposition strategies to tailor the thermal history. As the phase transformation… More >

Ajay Dulichand Borkar¹, Dipjyoti Nath¹, Sachin Singh Gautam^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011404

Abstract In recent years, machine learning (ML) has emerged as a powerful tool for addressing complex problems in the realms of science and engineering. However, the effectiveness of many state-of-the-art ML techniques is hindered by the limited availability of adequate data, leading to issues of robustness and convergence. Consequently, inferences drawn from such models are often based on partial information. In a seminal contribution, Raissi et al. [1] introduced the concept of physics informed neural networks (PINNs), presenting a novel paradigm in the domain of function approximation by artificial neural networks (ANNs). This advancement marks a… More >

Bosong Li¹, Jamie J. Kruzic^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011331

Abstract Additive manufacturing has made the fabrication of large-dimensioned bulk metallic glasses (BMGs) achievable; however, questions remain regarding how to control the processing parameters to obtain dense and fully amorphous BMGs with desirable mechanical properties. Here, laser powder bed fusion (LPBF) was used to produce dense and fully amorphous Zr_59.3Cu_28.8Nb_1.5Al_10.4 BMG samples from two different starting powders within a large processing window of laser powers and scanning speeds. X-ray diffraction (XRD) revealed that fully amorphous materials with high relative densities (>99%) were obtained when the LPBF energy density ranged from ~20 J/mm³ up to ~33 J/mm³ for coarse… More >

Guan-Yan Yang^1,*, Yi-Heng Ko¹, Farn Wang¹, Kuo-Hui Yeh², Haw-Shiang Chang¹, Hsueh Yi Chen¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-4, 2024, DOI:10.32604/icces.2024.013297

Abstract 1 Introduction
Ensuring the absence of exploitable vulnerabilities within applications has always been a critical aspect of software development [1-3]. Traditional code security testing methods often rely on manual inspection or rule-based approaches, which can be time-consuming and prone to human errors. With the recent advancements in natural language processing, deep learning has emerged as a viable approach for code security testing. In this work, we investigated the application of deep learning techniques to code security testing to enhance the efficiency and effectiveness of security analysis in the software development process. In 2022, Wartschinski et al.… More >

Hideki Mori^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012935

Abstract To design the mechanical strength of iron, it is very important to clarify the detail of dislocation dynamics in Body-Centered Cubic (BCC) Iron. The dislocation core structures are typically confined to the nanometer scale.
This implies that the resistance force from discrete atomic columns has a direct bearing on dislocation mobility.
Recently, we've developed a high-fidelity inter-atomic potential leveraging neural networks built upon density functional theory (DFT) data. By conducting dislocation dynamics simulations, we've addressed shortcomings inherent in classical inter-atomic potential approaches. Nonetheless, a significant challenge persists: a three- to four-fold deviation exists between More >

Lu Liu¹, Bo Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012741

Abstract Powder-Bed Fusion (PBF) is a prominent metal additive manufacturing technology known for its adaptability and commercial viability. However, it is often hindered by defects such as voids, un-melted particles, microcracking, and columnar grains, which are generally more pronounced than those found in traditional manufacturing methods. Microcracking, in particular, poses a significant challenge, limiting the use of PBF materials in safety-critical applications across various industries. This study presents an advanced computational framework that effectively addresses the complex interactions of thermal, fluid dynamics, structural mechanics, crystallization, and fracture phenomena at meso and macroscopic levels. This framework has More >

Yoshitaka Umeno^1,*, Atsushi Kubo², Emi Kawai¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.012708

Abstract Fatigue fracture accounts for a substantial fraction of failure cases in industrial products, especially in metal materials. While the mechanism of fatigue crack propagation can be understood in the mechanical point of view considering the effect of microstructures and crystal orientations on crack growth, there is still much room for investigations of the mechanism of fatigue crack formation under cyclic loading. It is widely understood that the fatigue crack formation in macroscopic metal materials originates in the persistent slip band (PSB) formed as a result of self-organization of dislocation structures [1]. Nevertheless, the PSB formation… More >

Gwang Mok Kim^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012583

Abstract This study summarized a part of the research conducted by Kim et al. [1]. The utilization of low-lime calcium silicate cement presents a promising avenue for reducing CO2 emissions in construction fields. Ordinary Portland cement pastes with the type of calcium silicate cement powder were fabricated and solidified under carbonation curing conditions. The physicochemical characteristics of the pastes were examined via variable tests including initial setting and flow characteristics, compressive strength and so on. Limestone and silica fume were employed for the synthesis of the calcium silicate cement used here. The content of calcium silicate More >