Advanced process parameter optimization for dimensional accuracy in SS 316L 3D printing using response surface methodology by Venkata Phani Babu Vemuri, Veeresh Kumar Gonal Basavaraja, Ravikiran Chinthalapudi, Bridjesh Pappula

“…This study investigates the enhancement of process parameters to produce SS 316L components employing the EOS M 290, 3D printing machine adapting Direct Metal Laser Sintering (DMLS) technique. …”
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The Effect of Hearing Aid Amplification on Gait Parameters: A Pilot Study Using Ear-Worn Motion Sensors by Ann-Kristin Seifer, Arne Küderle, Kaja Strobel, Ronny Hannemann, Björn M. Eskofier

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Optimization method for main parameters of vibration protection system in motor grader seat with quasi-zero static characteristic by M. S. Korytov, I. E. Kashapova, V. S. Shcherbakov

“…The need to reduce the number of independent varying parameters was due to the relatively long simulation time of the individual process of moving the machine along the microrelief of the supporting surface using the developed complex simulation mathematical model of a motor grader with vibration-proof supports of the operator’s cabin and with a vibration-proof mechanism of the operator’s seat. …”
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Statistical methodologies for determining critical parameters in a pick-and-place process on a surface mount device line by S. Lima, L. Silva, A. Manuela Gonçalves, M. T. Malheiro, M. Pinhão, J. Machado, J. Meireles, A. J. Pontes

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Research on Optimizing Spray Parameters for Cr3C2 - NiCr Coating Created on Alloy Steel by Plasma Spraying Technique by Thao Xuan Dang, Nguyen Hong Son, Pham Duc Cuong

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The Changes in Color and Image Parameters and Sensory Attributes of Freeze-Dried Clones and a Cultivar of Red-Fleshed Apples by Ewa Ropelewska, Mariusz Lewandowski

“…Furthermore, freeze-drying caused statistically significant changes in analyzed image textures named XHMean, RHMean, SHMean, VHMean, LHMean, and UHMean. Machine learning models developed based on the color parameters <i>L*</i>, <i>a*</i>, and <i>b*</i> distinguished raw and freeze-dried red-fleshed apples with an average accuracy of 84% for clone 90 up to 99.0% for clone 156, and models based on twenty selected image textures exhibited an accuracy of 98.5% for clone 156 to 100% for clones 90 and 158 and the cultivar ‘Trinity’. …”
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Machine Learning Methods in the Comparative Evaluation of Various Approaches to the Surgical Treatment of Primary Angle Closure by N. I. Kurysheva, A. L. Pomerantsev, O. Ye. Rodionova, G. A. Sharova

“…Since all these parameters correlate with each other, machine learning methods were used: PCA and the DD-SIMCA one-class classification method. …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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CONTROL OF METAL SURFACES MACHINED IN ACCORDANCE WITH THE DIAMOND NANOMACHINING TECHNOLOGY BASED ON THE ELECTRON WORK FUNCTION by G. V. Sharonov, A. L. Zharin, N. I. Muhurov, K. U. Pantsialeyeu

“…Dimensional machining technology is based on the use of integrated geometric parameters of machined surfaces. …”
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Some Studies on the Mechanism of Chip Formation during Machining of Chromium Manganese Austenitic Stainless Steel by Swarup De, Kalyan Chakraborty

“…The mechanism of chip formation during machining depends on the type of (i) work material, (ii) tool material, (iii) machining parameters, (iv) tool geometry, etc. …”
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Research on Correction Method of pin-by-pin Homogenization Environmental Effect of PWR Based on Machine Learning by LI Tianya1, 2, , LUO Qi3, , YAO Dong2, HE Caiyun1, CHAI Xiaoming2, CAI Yun2, ZHANG Bin2, ZHANG Hongbo2, LIAO Hongkuan1, DUAN Yongqiang1

“…Finally, a method for predicting pin-by-pin homogenized parameters was proposed: the correction method of pin-by-pin homogenization environmental effect of PWR based on machine learning. …”

Machine learning techniques in monitoring and controlling friction stir welding process: a critical review by Bhardwaj Kulkarni, Saurabh Tayde, Yashwant Chapke, Swapnil Vyavahare, Avinash Badadhe

“…By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. …”
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Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization by Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu

“…To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. …”
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Structuring a textile knitting dataset for machine learning and data mining applicationsMendeley Data by Toufique Ahmed, Abu Saleh Muhammad Junayed

“…Knitting is a vital sector of the fabric manufacturing industry. Concurrently, machine learning is emerging as a highly regarded technique for predicting patterns and classifying various parameters derived from datasets. …”
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The predictive value of thyroid hormone sensitivity parameters for cervical lymph node metastasis in patients with differentiated thyroid cancer by Jingcheng Bi, Tianqi Yao, Yu Yao, Zhengcai Zhu, Qiucheng Lei, Lianghe Jiao, Tao Li

“…Statistical analyses including descriptive statistics, comparative analysis, Pearson’s correlation analysis, logistic regression analysis, receiver operating characteristic (ROC) analysis and construction of a multivariable prediction model based on machine learning using the xgbTree method were employed to evaluate the associations and predictive value of thyroid hormone sensitivity parameters for cervical lymph node metastasis.Results The study revealed significant associations between several thyroid hormone sensitivity parameters and cervical lymph node metastasis in patients with DTC. …”
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Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism by Hong Yin, Shuqiang Yang, Xiaoqian Zhu, Songchang Jin, Xiang Wang

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Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms by Chao Li, Jinhui Li, Zhongqi Shi, Li Li, Mingxiong Li, Dianqi Jin, Guo Dong

“…Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. …”
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COMBINED COUNTERSINK-TAP TOOL FOR THE PROCESSING OF PARTS OF SHIP MACHINES, MECHANISMS AND FITTINGS: DESIGN AND CALCULATIONS by N. M. Vagabov, A. Z. Kurbanov, M. A. Magomedovа

“…Conclusion The developed flowchart of the calculation algorithm with the preset programme allows the process of calculating the basic parameters of the combined countersink-tap tools, used for threading in the parts of ship machines, mechanisms and ship fittings from hard-to-process materials, to be automated. …”
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Ensemble machine learning prediction model for clinical refraction using partial interferometry measurements in childhood. by Sa Ra Kim, Dong Hyun Kang, Gon Soo Choe, Dae Hee Kim

“…<h4>Purpose</h4>To develop an ensemble machine learning prediction model for clinical refraction in childhood using partial interferometry measurements.…”
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Design and Simulation Research of Reciprocating Traction Drive Test Machine based on Virtual Prototype Technology by Chen Huitao, Gao Haichao, Chai Shanshan, Yan Fan, Sun Guangya

“…The results validated the correctness of the model,and the motion parameters and trajectory changing with the time of the reciprocating traction drive test machine is obtained. …”
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