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Thermodynamics
Saad M. Alshahrani et al.
Summary: This study investigated the solubility data of ANA (Anastrozole) drug in a supercritical solvent and developed models to predict the solubility values. The aim was to provide a predictive methodology for determining drug solubility in various operational parameters for green pharmaceutical manufacturing. The models used temperature and pressure as inputs and employed three different models based on support vector regression. After optimization, all three models showed a coefficient of determination (R2) higher than 0.98. Additionally, considering RMSE, the error rates for Ada-Boosted SVR, Bagging SVR, and SVR were 2.31E-01, 4.31E-01, and 5.01E-01, respectively.
CASE STUDIES IN THERMAL ENGINEERING
(2023)
Article
Thermodynamics
Li Zhaolong et al.
Summary: The primary transmission mechanism of high-end CNC machine tools is a high-speed electric spindle. Thermal displacement occurs in the spindle due to heat generated during transmission, affecting the machining precision. Establishing a high-precision motorized spindle simulation model is crucial for optimizing and testing the spindle's construction and material, adjusting for thermal errors, and estimating its life.
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Article
Engineering, Multidisciplinary
J. Rajevenceltha et al.
Summary: This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model based on texture and structural information. The proposed model extracts important texture features using local binary patterns (LBP) and computes image quality based on statistical feature measures. Experimental results demonstrate a high correlation with human visual perceptions and competitiveness with other state-of-the-art models.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Thermodynamics
Zhicheng Zhou et al.
Summary: This paper optimizes the Support Vector Regression model through the particle swarm algorithm to establish a thermal displacement model of the motorized spindle. The model predicts the thermal elongation change and compensates for the thermal error, improving the machining accuracy of the motorized spindle. The SA-PSO-SVR model successfully predicts the thermal displacement change and compensates for the thermal error of the motorized spindle.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Bin Chen et al.
Summary: In this paper, a three-dimensional finite element analysis model is established to simulate the temperature and thermal displacement fields of the spindle under thermal load. It is found that the axial thermal displacement has the greatest impact on machining accuracy. Two innovative methods are proposed to optimize the spindle temperature distribution and reduce thermal deformation.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Majid Ashouri et al.
Summary: This paper utilizes transfer learning technique to predict the Nusselt number for natural convection flows in enclosures. By training artificial neural networks with a multi-grid dataset and performing transfer learning using deep neural networks, the model is able to account for additional input features.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Zhaolong Li et al.
Summary: The thermal error of electric spindles is a crucial factor affecting machining accuracy in machine tools. This study simulated and analyzed the thermal characteristics of the A02 electric spindle using ANSYS to establish a thermal error prediction model. The best temperature measuring point was determined through this analysis. Experimental results obtained the temperature and thermal error at different rotational speeds. Systematic clustering and grey relational analysis were applied to select the four best temperature measuring points out of ten. The Aquila Optimizer (AO) was used to optimize a least squares support vector machine (LSSVM) prediction model, which outperformed the Particle Swarm Optimization (PSO) model in prediction accuracy, goodness of fit, root mean square error, and mean absolute error. The experimental data-based AO-LSSVM model achieved a prediction accuracy of over 94% for the thermal error of the motorized spindle, demonstrating good stability and generalization ability.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Zhaolong Li et al.
Summary: In this study, the thermal displacement of high-speed electric spindles was predicted using simulation and experimental methods. An extreme learning machine model based on the marine predator algorithm was proposed, which showed better prediction accuracy.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Halime Hizarci et al.
Summary: The study proposes a new acceleration coefficient to improve the balance between local and global search ability in the particle swarm optimization algorithm. The coefficient is applied in the distribution network reconfiguration problem to reduce power loss. Simulation studies validate the effectiveness of the proposed method and compare the computational times and solution speeds of different methods.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Engineering, Multidisciplinary
Qazi Salman Khalid et al.
Summary: Integrating intelligent manufacturing planning with agricultural operations can increase efficiency in yield, but traditional agricultural product manufacturing planning and scheduling techniques need to be revised. Cellular Manufacturing Systems face three major problems: cell formation, product family selection, and product scheduling. This study focuses on solving the issue of product scheduling in the CMS environment.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Computer Science, Artificial Intelligence
Jialan Liu et al.
Summary: This study focuses on error compensation to increase the thermal stability of machine tools by analyzing the error mechanism of spindle systems and demonstrating the long-term memory characteristics of thermal errors. Utilizing VMD technology for data decomposition and optimizing neural network parameters have proven to enhance the robustness and generalization capability of the error model. Additionally, the VMD-GW-LSTM network model exhibits better predictive performance and compensation performance compared to other models.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Yu-Chi Liu et al.
Summary: A key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool were proposed, utilizing clustering and modeling techniques to achieve high accuracy in thermal displacement prediction. The combined methodology demonstrated excellent accuracy and robustness in experiments.
APPLIED SCIENCES-BASEL
(2021)
Article
Thermodynamics
Gang Zhou et al.
CASE STUDIES IN THERMAL ENGINEERING
(2020)
Article
Engineering, Multidisciplinary
Aybuke Kececi et al.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2020)
Article
Engineering, Manufacturing
Jialan Liu et al.
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
(2019)
Article
Green & Sustainable Science & Technology
Hanieh Borhanazad et al.
Article
Engineering, Multidisciplinary
Abdalhossein Rezai et al.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2014)
Article
Computer Science, Artificial Intelligence
O Chapelle et al.
