Study of working processes of reciprocating compressors of road-building machines by Sergey S. Busarov, Marina A. Rashchupkina, Dmitry R. Marchenko

“…Reciprocating compressors of road construction machines have been investigated, for which, as for any mobile stations, the issue of reducing the weight and size parameters of process equipment is acute. …”
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MOUNTABILITY PARTS OF MACHINE WITH ROTATING SURFACE, FITTED WITH POSITIVE CLEARANCE by Zbigniew BUDNIAK

“…In this paper demonstrates the conditions of automatic assembly the parts of machines with rotating surfaces, fitted with positive clearance. …”
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Classification of grapevine cultivars using Kirlian camera and machine learning by Danijel SKOČAJ, Igor KONONENKO, Irma TOMAŽIČ, Zora KOROŠEC-KORUZA

“…To complete the measurements we described acquired coronas of the berries with numerical parameters and used machine learning algorithms to classify grapevine cultivars. …”
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Analysis and prediction of atmospheric ozone concentrations using machine learning by Stephan Räss, Stephan Räss, Markus C. Leuenberger, Markus C. Leuenberger

“…As a first step, we used techniques like best subset selection to determine the measurement parameters that might be relevant for the prediction of ozone concentrations; in general, the parameters identified by these methods agree with atmospheric ozone chemistry. …”
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Cutting ceramics for turning of specialised stainless hard-to-machine steel by Boris Ya. Mokritskiy, Pavel A. Sablin, Aleksandr V. Kosmynin

“…The transition from these parameters to the predictive design of cutting ceramics was performed by measuring the cutting force during natural cutting. …”
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Multi-Spectral Optimization for Tissue Probing Using Machine Learning by Yarden Tzabari Kelman, Hadas Lupa Yitzhak, Nadav Shabairou, Shahaf Finder, Zeev Zalevsky

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Research on Forced Cooling of Machine Tools and its Operational Effects by Jerzy JEDRZEJEWSKI, Zdzislaw WINIARSKI, Wojciech KWASNY

“…The aim of this paper was to analyse in depth the existing research on the effectiveness of forced cooling and the directions in its improvement and development against the background of the increasing needs of machine tools and machining processes. The forced cooling methods used and their importance from the point of view of the development of machine tools are discussed. …”
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Machine-learning synergy in high-entropy alloys: A review by Sally Elkatatny, Walaa Abd-Elaziem, Tamer A. Sebaey, Moustafa A. Darwish, Atef Hamada

“…These include optimisation of the alloy composition, processing parameters, and microstructural characteristics to enhance the mechanical properties. …”
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Fuzzy-Sliding Mode Force Control Research on Robotic Machining by Shou-yan Chen, Tie Zhang, Yan-biao Zou

“…The robotic machining dynamics is first analyzed to identify the parameters with focus on the system stiffness and the behavior during machining process. …”
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A Method of Word Sense Disambiguation with Restricted Boltzmann Machine by ZHANG Chun-xiang, LI Hai-rui, GAO Xue-yao

“…For polysemy phenomenon in Chinese, Restricted Boltzmann Machine (RBM) is adopted to determine the true meaning of ambiguous vocabulary where linguistic knowledge in context is used Word form, part of speech and semantic categories in four left and right lexical units adjacent to an ambiguous word are selected as disambiguation features At the same time, RBM is used to construct word sense disambiguation (WSD) model Training corpus in SemEval-2007: Task#5 and semantic annotation corpus in Harbin Institute of Technology are used to optimize parameters of RBM Test corpus in SemEval-2007: Task#5 is used to evaluate WSD model Experimental results show that compared with Bayesian word sense disambiguation classifier, disambiguation accuracy of WSD method with RBM is improved…”
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Impact of surface micro-dimples on machinability of Inconel 718 alloy by Kanishk Jain, Mukesh Tak, Rakesh G. Mote

“…How this micro-dimple patterning influences the machinability of Inconel 718 is analyzed via orthogonal cutting experiments, and with the optimal parameters, the cutting temperature is lowered by 45.5% and the cutting forces are reduced significantly, i.e., the tangential cutting force and the thrust force are reduced by 61.1% and 47.1%, respectively. …”
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Recent advances in ultra-precision machining of lithium niobate crystals by Yebing TIAN, Chengwei WEI, Xiaomei SONG, Cheng QIAN

“…New technologies, such as high-shear and low-pressure grinding and magnetorheological shear thickening polishing, are the most promising methods for achieving ultra-precision machining of LiNbO3 crystals. Considering the complex interplay between material properties, processing parameters, and underlying mechanisms, the ongoing exploration of new ultra-precision machining techniques and process optimizations for LiNbO3 crystals is critical. …”
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INFLUENCE OF THE ELECTRODE MATERIAL ON ELECTRICAL DISCHARGE MACHINING PROCESS PERFORMANCE by OANA GHIORGHE, CAROL SCHNAKOVSZKY, EUGEN HERGHELEGIU, MARIA CRINA RADU, BOGDAN ALEXANDRU CHIRITA, NICOLAE CATALIN TAMPU, BOGDAN NITA, PETRICA RADU

“…Electrical discharge machining is a non-conventional technology widely used to meet the rigors of industrial requirements imposed by the processing of emerging and advanced materials (e.g., geometrical complexity, high dimensional accuracy, and high surface quality). …”
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Harness AI and machine learning in de-emulsifier chemical selection by Sai Ravindra Panuganti, Nor Hadhirah Halim, Tan Nian Wei, Wasan Saphanuchart, Emad Elsebakhi

“…This work presents a faster alternative for choosing de-emulsifier chemicals by using machine learning. For data to train and test machine learning models, several bottle tests are analyzed at different combination of essential parameters. …”
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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. …”
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Machine Learning Approach on Time Series for PV-Solar Energy by S. Sivakumar, B. Neeraja, M. Jamuna Rani, Harishchander Anandaram, S. Ramya, Girish Padhan, Saravanakumar Gurusamy

“…Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. …”
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Machine Learning-based Water Quality Forecasting for Shenzhen Bay by XIONG Jianzhi, XIONG Rui, LU Haiyan, ZHENG Yi

“…Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). …”
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Intelligent Assessment of Personal Credit Risk Based on Machine Learning by Chuansheng Wang, Hang Yu

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Automated generation of structure datasets for machine learning potentials and alloys by Marvin Poul, Liam Huber, Jörg Neugebauer

“…Abstract We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials (MLIP) for multicomponent alloys, called Automated Small SYmmetric Structure Training or ASSYST. …”
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CONCEPTUAL PRINCIPLES OF INTELLIGENT AGRICULTURAL MACHINES IN THE CASE OF COMBINE HARVESTER by E. V. Zhalnin, Z. A. Godzhaev, S. N. Florentsev

“…An operator cannot quickly react to constantly  changing agricultural background parameters while the machine is in motion. The authors offered to automate the management of the  majority of all technological operations using devices that the machinery is supplied with. …”
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