ANALYTICAL MODEL OF CALCULUS FOR INFLUENCE THE TRANSLATION GUIDE WEAR OVER THE MACHINING ACCURACY ON THE MACHINE TOOL by Ivona PETRE, Carmen POPA, Dumitru DUMITRU, Ciprian MANESCU

“…The wear of machine tools guides influences favorably to vibrations. …”
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Smart Manufacturing through Machine Learning: A Review, Perspective, and Future Directions to the Machining Industry by A. S. Rajesh, M. S. Prabhuswamy, Srinivasan Krishnasamy

“…One such subclass of artificial intelligence is machine learning, which uses a computer system for making predictions and performing definite tasks without any use of specific instructions to enhance the quality of the product, and rate of production, and to optimize the processes and parameters in machining operations. …”
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A Short Review: Tribology in Machining to Understand Conventional and Latest Modeling Methods with Machine Learning by Seisuke Kano

“…By integrating advanced monitoring technologies and machine learning, these methods enable real-time predictions within controllable parameters using live data. …”
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Investigation of machining performance of waspaloy using copper-graphite composite electrodes in electric discharge machining by Priyanka Putta, Jayakumar V, Giridharan PK

“…The parameters were optimized utilizing the PROMETHEE optimization technique; it was found that the CuGr-5 electrode with the assessment value 0.02458 was optimal for machining of Waspaloy.…”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

Subjects: “…deep extreme learning machine…”
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Comprehensive Evaluation Method for Complex Fluid in Low Porosity and Low Permeability Reservoirs by ZHENG Yang, LU Yunlong, SHI Xinlei, ZHU Meng

Subjects: “…fluid identification parameter…”
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Online Algebraic Estimation of Parameters and Disturbances in Brushless DC Motors by David Marcos-Andrade, Francisco Beltran-Carbajal, Alexis Castelan-Perez, Ivan Rivas-Cambero, Jesús C. Hernández

“…Parameter identification in dynamical systems is a well-known problem with many applications in control design, system monitoring, and fault detection. …”
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Analysis of Machined Depth and Hole Diameter on Soda-lime Glass Using Electrochemical Discharge Machining Process by Pawar P., Kumar A., Ballav R.

“…The observation results showed that voltage is the major parameter for machined depth and hole diameter followed by electrolyte concentration and rotation of tool electrode.…”
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An Improved Model Predictive Voltage Control for PMSM Drives without Motor Parameters by Xiaoguang Zhang, Ruifang Chen

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OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS by SUN YaoQin

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A Review of Insights and Research Progress on Hard Turning by S. Girisankar, S. Senthil Murugan, E. Shankar

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Milk characteristics and milking efficiency in Italian Mediterranean buffalo by Roberta Matera, Lorenzo Pascarella, Alessio Cotticelli, Giuseppe Conte, Alessia Tondo, Giuseppe Campanile, Gianluca Neglia

Subjects: “…milking machine parameters…”
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Drilling Parameter Control Based on Online Identification of Drillability and Multi-Objective Optimization by Jianbo Dai, Xilu Yin, Yan Zhang, Lei Si, Dong Wei, Zhongbin Wang, Longmei Zhao

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Research on the Dynamic Response of a Cracked-Spur Gear System with Parameter Uncertainty by Ping Fang, Yang Yang, Jin Zeng

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Combining Sensor Fusion and a Machine Learning Framework for Accurate Tool Wear Prediction During Machining by Swathi Kotha Amarnath, Vamsi Inturi, Sabareesh Geetha Rajasekharan, Amrita Priyadarshini

“…Three distinct input datasets are constructed: Dataset I comprises statistical parameters extracted exclusively from the force signals, Dataset II consists of statistical parameters derived from the vibration signals, and Dataset III integrates the individual statistical parameters from both force and vibration signals through feature-level fusion. …”
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A comprehensive review on powder mixed electrical discharge machining: advances in dielectric enhancement and machining efficiency by Dharmendra Kumar, Vimal Kumar Pathak, Ramanpreet Singh, Mithilesh K. Dikshit

“…The review also explores various Multi-Criteria Decision-Making (MCDM) techniques for optimizing machining parameters, identifying TOPSIS as the most popular approach, followed by AHP and VIKOR. …”
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Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations by Diego Valderrama, Olga Teplytska, Luca Marie Koltermann, Elena Trunz, Eduard Schmulenson, Achim Fritsch, Ulrich Jaehde, Holger Fröhlich

“…In this work we compare drug plasma concentraton predictions of well‐known population PK (PopPK) modeling with classical machine learning models and a newly proposed scientific machine learning (MMPK‐SciML) framework. …”
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Electrochemical micromachining of galvanized iron sheets: process optimization and performance evaluation by Nadanasabapathi Sivashankar, Kadhiresan Santhanam, Shikandar Prasad, Mamidala Jawahar, Rajasekaran Thanigaivelan

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Online-Identification of Electromagnetic Parameters of an Induction Motor by V. K. Tytiuk, M. L. Baranovskaya, O. P. Chorny, E. V. Burdilnaya, V. V. Kuznetsov, K. N. Bogatyriov

“…They are not identified in terms of the acceptance tests, and the values presented in catalogues and reference books are calculated ones that may differ considerably from the real values of a certain machine. Despite constant studies by the researchers, a task to identify electromagnetic parameters of the equivalent circuit of an induction motor is still important and topical. …”
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Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta,... by Ashrakat A. Lotfy, Mohamed E. Abuarab, Eslam Farag, Bilal Derardja, Roula Khadra, Ahmed A. Abdelmoneim, Ali Mokhtar

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