Parth Khandelwal¹, Harshit², Indranil Manna^1,3,*

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

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 novel copper alloys for achieving… More > Graphic Abstract

Xue-Qin Li¹, Lu-Kai Song^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 665-684, 2024, DOI:10.32604/cmes.2024.048445

Abstract Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of… More >

Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >

Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded… More > Graphic Abstract

Yanmei Shi¹, Wei Yu^2,*, Yanxia Zhao^3,*, Yungang Jia⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140

Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core of the methodology lies in… More >

YAO FANG^1,2,#, FEI XIA^1,#, FEIFEI TIAN³, LEI QU¹, FANG YANG¹, JUAN FANG^1,2, ZHENHONG HU^1,*, HAICHAO LIU^1,*

BIOCELL, Vol.48, No.4, pp. 613-621, 2024, DOI:10.32604/biocell.2024.031539

Abstract Objective: In the realm of Class I pathogens, Burkholderia pseudomallei (BP) stands out for its propensity to induce severe pathogenicity. Investigating the intricate interactions between BP and host cells is imperative for comprehending the dynamics of BP infection and discerning biomarkers indicative of the host cell response process. Methods: mRNA extraction from BP-infected mouse macrophages constituted the initial step of our study. Employing gene expression arrays, the extracted RNA underwent conversion into digital signals. The percentile shift method facilitated data processing, with the identification of genes manifesting significant differences accomplished through the application of the t-test. Subsequently, a comprehensive analysis… More >

B. MOULIPRASANTH^A, P. HARIHARAN^B

Journal of Polymer Materials, Vol.36, No.4, pp. 351-369, 2019, DOI:10.32381/JPM.2019.36.04.5

Abstract Electrical discharge machining is a non-traditional machining processes in which it is based upon thermal and electrical energy source as an interval energy pulse discharge in-between the work piece and tool electrode so as to remove the material. A systematical investigation of melting and vaporising of aluminium to find the output responses such as Material removal rate (MRR), Electrode wear rate (R_a), and Surface finish (EWR) in EDM using two different dielectrics was conducted as experimental work. The working fluids are Polyethylene glycol (PEG 600) and kerosene. It is the hour of need to get the maximum MRR and surface… More >

LAVANYA, R¹, NATCHIMUTHU, N^2,*

Journal of Polymer Materials, Vol.38, No.1-2, pp. 89-100, 2021, DOI:10.32381/JPM.2021.38.1-2.8

Abstract Rubber composites of nitrile (NBR) and Ethylene-Propylene-Diene (EPDM) containing unmodified and modified microcrystalline cellulose(MCC) are evaluated for their processing behaviour. The used modified MCC (T-MCC) was treated by N,N-dimethylacetamide/lithium chloride (DMAc/ LiCl).ATR-FTIR spectra of NBR-MCC composites have indicated N-H stretching and bending vibrations and confirmed interactions between nitrile rubber and MCC. AFM studies have indicated that the average roughness of NBR-T-MCC was significantly reduced when compared to that of NBR-untreated MCC. Important processing parameters such as scorch time and cure time are found to decrease significantly for both NBR and EPDM composites withT-MCC. Mechanical properties of these composites are found… More >

NIKHIL R. MENE¹, SARITA KALLA^1,*, Z.V.P. MURTHY^1,*

Journal of Polymer Materials, Vol.39, No.3-4, pp. 307-323, 2022, DOI:10.32381/JPM.2022.39.3-4.9

Abstract Membrane distillation-crystallization (MDC) is presented as a novel technique in the treatment of waste concentrated water which produces valuable crystals along with pure water. In the present study, multi-walled carbon nanotubes (MWCNT)/polyvinylidene fluoride (PVDF) flat sheet membranes were prepared via the wet phase inversion method and applied in MDC for the treatment of pharmaceutical waste. The pure and modified membrane surface properties are characterized with the help of SEM, FTIR, and contact angle measurement. The present work reported the effect of MWCNT content and feed temperature on the MDCperformance and measured pure water flux and pharmaceutical compounds recovery. The observed… More >

ABHIJEET SURYAWANSHI¹, NIRANJANA BEHERA^2,*

Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest… More >