Experimental investigation of efficiency of worm gears and modeling of power loss through artificial neural networks

Karabacak, Yunus Emre; Baş, Hasan

doi:10.1016/j.measurement.2022.111756

Cited by 14 publications

(10 citation statements)

References 54 publications

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“…The reducer prototype was assembled and the transmission accuracy was tested, as shown in Figure 15. 29–31…”

Section: Measuring Of Toroidal Helix Tooth Surfacementioning

confidence: 99%

“…The reducer prototype was assembled and the transmission accuracy was tested, as shown in Figure 15. [29][30][31] The rotation angle of worm was collected though angle encoder I, while the rotation angle of worm wheel was collected though angle encoder II. Based on the angle encoder, the input angle u 1 and the output angle u 2 can be obtained, and the transmission error is e = u 2 2 u 1 /i 21 , where i 21 is the transmission ratio.…”

Section: Transmission Errors Of Hourglass Worm Drivementioning

confidence: 99%

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Study on the toroidal helix and manufacturing method of enveloping hourglass worm tooth surface

Chen

Wang

Ma³

2023

Advances in Mechanical Engineering

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As a complex space enveloped surface, the tooth surface of dual-lead helical gear enveloping hourglass worm is characteristic with difference tooth profile, unequal pitch and variable tooth thickness. To grasp the geometry characteristics and precision manufacturing method of it, the analytical method for toroidal helix is presented. The mathematical model of toroidal helix is developed, and the geometry characteristics of helix angle, axial tooth thickness, and tooth surface undercutting are analyzed. The helix angle of toroidal helix presents the convex shape, and the undercutting characteristic is affected by the pressure angle, helix angle, transmission ratio, and normal modulus. Based on the toroidal helix of tooth surface, the precision manufacturing method is proposed, and the measuring method is developed. The hourglass worm sample was manufactured and measured, and the transmission accuracy of hourglass worm drive is tested. The results show that the manufacturing and measuring method of toroidal helix is effective and feasible.

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“…The reducer prototype was assembled and the transmission accuracy was tested, as shown in Figure 15. 29–31…”

Section: Measuring Of Toroidal Helix Tooth Surfacementioning

confidence: 99%

Section: Transmission Errors Of Hourglass Worm Drivementioning

confidence: 99%

Study on the toroidal helix and manufacturing method of enveloping hourglass worm tooth surface

Chen

Wang

Ma³

2023

Advances in Mechanical Engineering

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“…It should also be noted that the results obtained may require experimental validation. [51][52][53] In recent years, there has been a growing utilization of ML techniques in the field of engineering tribology, as evidenced by the research conducted in the literature. Thankachan et al 54 conducted a study where they utilized statistical and ML techniques to optimize the tribological behavior of hybrid copper surface composites.…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing historical tribological data, these models can make predictions about the future behavior of the system. 51–53 Diverse approaches are employed by ML algorithms to examine tribological data and identify relevant features. These techniques encompass linear regression, decision trees, support vector machines, neural networks, and deep learning models.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

Baş,

Karabacak

2023

Proceedings of the Institution of Mechanical Engineers, Part J:

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Additive manufacturing is a rapidly developing technology that enables the production of complex parts with intricate geometries. Self-lubricating radial bearings are one of the machine elements that can be produced using additive manufacturing. In this research, we present a machine learning-based approach to model the friction coefficient and friction torque in self-lubricating radial bearings manufactured by additive manufacturing using polyether ether ketone (PEEK), polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and nylon. The proposed approach includes different machine learning models (artificial neural networks, support vector machines, regression trees, linear regression models) that utilize experimental data to predict the coefficient of friction and friction torque of different types of radial bearings. Experimental data were obtained by performing tribological tests on self-lubricating radial bearings under various operating conditions. The results reveal that the machine learning models are successful in predicting the friction coefficient and friction torque in self-lubricating radial bearings with high accuracy. The approach can be utilized to optimize the design and performance of self-lubricating radial bearings manufactured using additive manufacturing.

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Intelligent milling tool wear estimation based on machine learning algorithms

Karabacak

2024

J Mech Sci Technol

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Experimental investigation of efficiency of worm gears and modeling of power loss through artificial neural networks

Cited by 14 publications

References 54 publications

Study on the toroidal helix and manufacturing method of enveloping hourglass worm tooth surface

Study on the toroidal helix and manufacturing method of enveloping hourglass worm tooth surface

Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

Intelligent milling tool wear estimation based on machine learning algorithms

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