Effect of Drilling Parameters on Surface Roughness and Delamination of Ramie–Bamboo-Reinforced Natural Hybrid Composites by Krishna Kumar P, Gaddam Lokeshwar, Chamakura Uday Kiran Reddy, Arun Jyotis, Surendra Shetty, Subash Acharya, Nagaraja Shetty

“…Making holes helps in part assembly, which is a crucial activity in the machining of composite constructions. As a result, choosing the right drill bit and cutting parameters is crucial to creating a precise and high-quality hole in composite materials. …”
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Preparation of Ag/Cu/Ti Nanofluids by Spark Discharge System and Its Control Parameters Study by Kuo-Hsiung Tseng, Juei-Long Chiu, Heng-Lin Lee, Chih-Yu Liao, Hong-Shiou Lin, Yi-Syuan Kao

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Optimization of grinding process parameters for slender tubes through orthogonal experiments and grey relational analysis by Lianzhi Zhang

“…This study delved into the grinding mechanism of composite magnetorheological fluid and developed a material removal rate (MRR) model to identify key grinding process parameters. The influence of these parameters and their interactions on MRR and surface roughness (Sq) was studied, subsequently, the regression model constructed accordingly can predict machining performance. …”
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Evaluation of the Dynamic Parameters Under Seismic Conditions for a Maxwell Rheological Base Isolation System by Polidor Bratu, Patricia Murzea, Oana Tonciu, Nicusor Dragan, Cornelia Florentina Dobrescu

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Influence of Drilling Parameters on the Delamination and Surface Roughness of Insulative-Coated Glass/Carbon-Hybrid Composite by Sarower Kabir, Faiz Ahmad, Chowdhury Ahmed Shahed, Ebru Gunister

“…However, 6000 RPM and 0.02 mm/rev were found optimum parameters for drilling HFRP composite with 1.5 mm coating thickness.…”
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Theoretical backgrounds for the bionic substantiation of the parameters of the ring-cutting soil-cultivating roller working bodies by L. F. Babitsky, I. V. Sobolevsky, V. A. Kuklin, Y. N. Ismailov

“…To improve the efficiency of this process in the development of new forms and justification of parameters and modes of tillage devices mechanical-bionic approach should be used, which proved effective in the development of design schemes and justification of the parameters of various agricultural machines. …”
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Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm by V. Sathiyamoorthy, T. Sekar, N. Elango

“…The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. …”
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Study of the effect of cutting modes on output parameters under high-speed steel turn-milling by D. A. Matlygin, A. V. Savilov, A. S. Pyatykh, S. A. Timofeev

“…The experiment was carried out on a turning machining center with a driving tool. Powdered highspeed steel BÖHLER S390 MICROCLEAN was used as sample material for the experiment. …”
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Fault Diagnosis of Bearing Based on Cauchy Kernel Relevance Vector Machine Classifier with SIWPSO by Sheng-wei Fei, Yong He

“…As the selection of the Cauchy kernel parameter has a certain influence on the diagnosis result of relevance vector machine, stochastic inertia weight PSO is used to select the Cauchy kernel parameter. …”
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Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to... by Yuan Zhang, Yanting Li, Yang Li, Lin Zhao, Yongkui Yang

“…Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. …”
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Introducing MLOps to Facilitate the Development of Machine Learning Models in Agronomy: A Case Study by Dario Ruggeri, Gabriele Tazza, Laszlo Vidacs

“…While machine learning (ML) and deep learning (DL) are increasingly being adopted in agronomy, the literature shows that the use of ML Operations (MLOps) frameworks remains scarce during the research stage. …”
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Accuracy in Actuator Positioning of Numerically-Controlled Machine Tools while Using Variable Feed by I. A. Kashtaiyan

“…Results of the experimental research of variable feed parameter influence on accuracy in actuator positioning of numerically-controlled machine tools are presented in the paper. …”
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Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample by Kupidura Przemysław, Kępa Agnieszka, Krawczyk Piotr

“…The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. …”
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A study for method-level code smells detection using machine learning algorithms by Rajwant Singh Rao, Seema Dewangan, Alok Mishra, Manjari Gupta

“…Methodology: This study employs a rigorous methodology to investigate the detection of four method-level code smells—Long Parameter List (LPL), Switch Statement (SS), Feature Envy (FE), and Long Method (LM) using twenty machine learning algorithms. …”
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Detection of Chatter in Machining Processes by the Multiscale Maximum Approximate Entropy and Continuous Wavelet Transform by Daniel Pérez-Canales, Juan Carlos Jáuregui-Correa, José Álvarez-Ramírez, Luciano Vela-Martínez

“…Chatter is a complex dynamic instability in machining processes and presents nonlinear and nonstationary behavior. …”
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Reactor physics fast calculation method based on model order reduction and machine learning by Chen Zhao, Qinyi Zhang, Bin Zhang, Jiangyu Wang, Jiayi Liu, Lianjie Wang, Bangyang Xia, Xiaoming Chai, Xingjie Peng

“…Based on AI technology, a fast calculation method for reactor physics has been established, which combines model order reduction and machine learning to address the challenges of excessive parameter quantities in machine learning-based parameter prediction. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…In this work, multiple machine learning models, including linear regression (LR), support vector machine (SVM), decision tree regressor (DTR), random forest regressor (RFR), and multilayer perceptron (MLP), are used to predict the IFT of the CO2 and aqueous solution of NaCl. …”
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Analisis Sistem Pendeteksi Penipuan Transaksi Kartu Kredit dengan Algoritma Machine Learning by Putu Tirta Sari Ningsih, Muhammad Gusvarizon, Rudi Hermawan

“…Dalam machine learning terdapat banyak algoritma yang pada dasarnya memiliki tingkat akurasi dan efisiensi berbeda-beda. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. …”
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Analysis of Influence Factors on Torque Control for Asynchronous Machine in Rail Transit Traction System by YANG Liangchong

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