Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism

Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for...

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Veröffentlicht in:	Alexandria engineering journal Jg. 92; S. 39 - 49
1. Verfasser:	Denizhan, Onur
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.04.2024 Elsevier
Schlagworte:	Artificial neural networks Bayesian Regularization Levenberg-Marquardt Backpropagation Position analysis Scaled Conjugate Gradient Descent Slider-crank mechanism Scaled Conjugate Gradient Descent Bayesian Regularization Levenberg-Marquardt Backpropagation Position analysis Slider-crank mechanism Artificial neural networks
ISSN:	1110-0168
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance.
ISSN:	1110-0168
DOI:	10.1016/j.aej.2024.02.055