Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench by Luca Faller, Matthis Graßmann, Timo Lichtenstein

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Parameter Self-tuning of Machine Side PI Controller in Wind Power System Based on Particle Swarm Optimization by ZHENG Liang, ZHANG Xizheng, LIU Zhihua, SHU Conghui, LU Zhangyu

“…As this model has many PI controllers, it is difficult to determine the parameters of multiple PI controllers at the same time by the common PI controller parameter setting method. …”
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Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm by Premkumar Manoharan, Sowmya Ravichandran, Jagarapu S. V. Siva Kumar, Mustafa Abdullah, Tan Ching Sin, Tengku Juhana Tengku Hashim

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3D Part Machining Time Prediction With Parameter Extraction, Deep Learning, and 3D Data Augmentation by Umut Nazmi Aktan, Mehmet Dikmen

Subjects: “…3D model parameter extraction…”
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Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface by Nobuaki Kobayashi, Musashi Ino

“…Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. …”
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Process parameter optimization of laser beam machining for AISI -P20 mold steel using ANFIS method by Abdullah Eaysin, Sarower Kabir, Ebru Gunister, Nur Jahan, Amir Hamza, Muhammad Ali Zinnah, Adib Bin Rashid

“…This study employed a fiber laser beam for precise machining of AISI P20 mold steel. The experimental design, based on the Taguchi 27 model, was carried out using Minitab software to optimize machining parameters, including cutting speed, gas pressure, and laser power. …”
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Frequency Dynamic Characteristic Analysis of Ultra-Low Frequency Oscillation and Multi-Machine GPSS Parameter Tuning Method by ZENG Pijiang, XU Hao, YE Pengju, ZHENG Chao, SAI Xiangyu, CHEN Gang, ZHAO Yong, JIANG Chuyang, GAN Deqiang

“…The validity of the distribution vector and speed response function properties， as well as the overall model were validated using an IEEE 4-machine 11-node case study. The effectiveness of the GPSS parameter tuning method was validated via time-domain simulations and vector margin assessments for both a 10-machine 39-node system and an equivalent model of the Yunnan power grid. …”
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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms by Zhong-Han Zhuang, Hui-Ping Tsai, Chung-I Chen

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Predicting visual field global and local parameters from OCT measurements using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Erik Meijering, Michael Kalloniatis

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Machine learning investigation of wavy fin using penta type nanomaterial with multiple Parameters: Computational design by Badreddine Ayadi, Adnan, Sami Ullah Khan, Mutasem Z. Bani-Fwaz, Dana Mohammad Khidhir, Muhammad Mahmood Ali, Muhammad Nasir Bashir, Mohamed Bechir Ben Hamida

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Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy by İbrahim Turan, Barış Özlü, Hasan Basri Ulaş, Halil Demir

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Software Control of Machines Based on Typification of Transient Zone Parameters for Processing of Original Non-Circular Parts by Nikolay N. Popok, Nikolay V. Belyakov, Stanislav K. Seleznev

“…The paper provides an overview of trends in the preparation of control programs for CNC metal cutting machines. The direction in the development of programming of machining parts based on the transient zone parameter typification is described. …”
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Physics-based parameters selection and machine learning driven prediction of pool boiling bubble departure diameter by Uzair Sajjad, Yu-Hao Chu, Haseeb Yaqoob, Akash Sengupta, Hafiz Muhammad Ali, Khalid Hamid, Wei-Mon Yan

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TECHNOLOGICAL PROVISION OF ACCURACY AND QUALITY PARAMETERS OF INTRICATE PROFILE PARTS AT HIGH-SPEED MULTI-COORDINATE MACHINING by V. K. Sheleg, A. F. Prisevok, P. N. Klausuts

“…The paper considers requirements to CAM-systems for provision of high-speed multi-coordinate milling, principles of generation and recommendations on trajectory programming for high-speed machining, influence of vibration and balancing of the technological system on parameters of  the machining accuracy, characteristics of a cutting tool, types of tool coatings that is rather actual for improvement of accuracy and quality of intricate profile parts.…”
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Effect Analysis of Machining Errors of the Axle Wheel Hub Planet Carrier on Function and Durability-Related Parameters by Adam Fiausch

“…Due to its complexity, the planet carrier can be a potential source of functional parameter non-compliance (efficiency, noise, and vibration) in the short term; in the long term, there can be a reduction in the service life of the planetary gearset. …”
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Kalman filtering assimilated machine learning methods significantly improve the prediction performance of water quality parameters by Zhenyu Gao, Guoqiang Wang, Jinyue Chen, Lei Fang, Shilong Ren, A. Yinglan, Shuping Ji, Ruobing Liu, Qiao Wang

“…However, existing research on machine learning (ML)-based data assimilation methods remains limited, particularly in terms of addressing the combined impacts of climate change and anthropogenic activities. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…Abstract A new computational framework based on machine learning was developed for prediction of Hansen solubility parameters in preparation of pharmaceutical cocrystals with improved properties. …”
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Prediction of Tuber Damage from Harvesting and Processing Machine Working Units Based on the Recording of Impact Parameters by Michał Sypuła, Aleksander Lisowski, Jacek Klonowski, Tomasz Nowakowski, Jarosław Chlebowski, Magdalena Dąbrowska

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Innovative machine learning-based prediction of early airway hyperresponsiveness using baseline pulmonary function parameters by Hua Yang, Xingru Zhao, Zhuochang Chen, Lihong Yang, Guihua Zhao, Chenxiao Xu, Jinyi Xu

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Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis by Dongliang Hu, Manman Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wanliang Guo, Shenghong Ju, Guohua Fan, Wu Cai

“…Abstract Objective To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients. …”
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