Deterministic-Stochastic Subspace Identification of the Modal Parameters of a Machine Tool During Milling by Willy Reichert, Martin Kolouch, Jan Berthold, Steffen Ihlenfeldt, Max Engelmann

“…The approach for identification of the modal parameters of a milling machine during machining, presented in the paper, is based on the deterministic-stochastic subspace identification, which can consider both the deterministic and the stochastic character of the excitation in machine tools better than the stochastic subspace identification being often used in operational modal analysis. …”
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Effect of Reinforcement Content and Cutting Parameters on Tool Wear in Machining of Aluminum Hybrid Composites by I. Sarpkaya, E. Özer, D. Zalaoğlu, M. Übeyli, Y. Fedai

“…The machinability of aluminum hybrid composites (AHCs) can be enhanced by utilization of optimum composition and cutting parameters. …”
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Selective Laser Sintering of Polymers: Process Parameters, Machine Learning Approaches, and Future Directions by Hossam M. Yehia, Atef Hamada, Tamer A. Sebaey, Walaa Abd-Elaziem

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Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters by Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães

“…In this study, we propose a machine learning-based approach to address the inverse design problem in nanostructures, using data generated by numerical simulations via the Finite Element Method (FEM). …”
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Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory by Tianyi Wang, Wenjun Yi, Youran Xia

“…The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. …”
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Machining characteristics and process parameter optimization of Near-dry electrical discharge milling of titanium alloy by Shaopeng Kang, Xianghua Zhuo, Linglei Kong, Yanxin Li, Yafeng He

“…Using Grey Relational Analysis (GRA), the multi-objective optimal machining parameters were identified as: 2 A current, 40% duty cycle, 0.2 MPa gas pressure, and 20 ml/min atomization rate. …”
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Structural Parameter Analysis and Optimization of Six-link Double-crank Slider Mechanism of Stamping Machine by Liu Shanmin, Wu Ziyue, Liu Shanzeng

Subjects: “…Stamping machine…”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

Subjects: “…rock parameters…”
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Advanced Multi-Parameter Prediction of Coal Quality Using LIBS and Ensemble Machine Learning Techniques by Qingsong Wang, Donglian Zhang, Youquan Dou, Qingzhao Wang, Yiyi Wang

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Intelligent Fault Severity Detection of Rotating Machines Based on VMD-WVD and Parameter-Optimized DBN by Ning Jia, Yao Cheng, Youyuan Tian, Feiyu Yang

“…The fitness function in the parameter optimization process is the network’s root mean square error (RMSE). …”
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Main Parameter Design of Disc Milling Cutter for Machining Equal-Base Circle Bevel Gear by Bin Wang, Kai Xu, Shuo Chen, Xinfeng Duan, Shutao Zhang

“…According to the theory and machining principle with disc milling cutter of the equal-base circle bevel gear, the mathematical model of disc milling cutter is established, the principle of machining equal-base circle bevel gear with disc milling cutter is analyzed, and the design methods of axial section profile and the parameters of disc milling cutter are studied, respectively. …”
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Parameter estimation of submarine power cables in offshore applications using machine learning-based methods by Felipe P. de Albuquerque, Rafael Nascimento, Gabriel de Castro Biage, Rooney R.A. Coelho, Ronaldo F. Ribeiro Pereira, Eduardo C. Marques da Costa, Mario L. Pereira Filho, Cassio G. Lopes, José R. Cardoso

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Machine learning for optimal parameter prediction in free space continuous-variable quantum key distribution by Dong Chen, Cheng Jin, Liu Ao, Chen Lanjian, Chen YuJie, Yin Peng, Wu TianYi

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Investigation of Effect of Machining Process Paramaeters on Surface Quality by Osman Hamdi Mete, Mehmet Şafak Baran

Subjects: “…machining…”
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Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

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Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka by W.M.S Bandara, W.A.R. De Mel

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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering by Ruirui Wang, Yaodong Ni, Lingli Zhang, Boyang Gao

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Prediction for Perioperative Stroke Using Intraoperative Parameters by Mi‐Young Oh, Young Mi Jung, Won‐Pyo Kim, Hyung‐Chul Lee, Tae Kyong Kim, Sang‐Bae Ko, Jaehyun Lim, Seung Mi Lee

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Optimization of machining parameters material removal rate and surface roughness by using reponse surface methodology and Grey Taguchi technique on Electric Discharge Machine by S. Hemalatha, K. Anusha

“…This study investigates the optimization of machining parameters in Electric Discharge Machining (EDM) for stainless steel (SS-304) using cast copper and pure copper electrodes. …”
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