Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Bhaumik, Shubrajit; Kamaraj, M.

doi:10.1177/1350650120965754

Cited by 14 publications

(15 citation statements)

References 41 publications

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“…A pin-on-disc tribometer (make: Magnum, model: POD1) was used to measure the tribological properties of the textured steel disc surface ( Figure 4 ). The usage of a rotary tribometer was chosen in order to understand the efficiency of the textured surfaces in the presence of centrifugal force due to the rotation of the disc and the starved lubrication regime [ 37 , 38 ]. Cylindrical flat-faced steel pins (Φ10 mm and 30 mm length) were taken as the counterpart to the disc.…”

Section: Methodsmentioning

confidence: 99%

“…A typical ANN model contains an input layer, hidden layer and output layer ( Figure 5 ). A supervised multi-layered feed-forward network with a scale conjugate back-propagation algorithm was used in this work [ 38 , 39 ]. Several ANN models were generated using the data, and the model with highest regression coefficient was chosen for the present work.…”

Section: Methodsmentioning

confidence: 99%

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Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

et al. 2022

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The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

et al. 2022

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“…Jaringan saraf tiruan (JST) dapat mengolah atau memproses informasi yang terinspirasi oleh struktur otak manusia [1][2] [3]. Jaringan saraf tiruan terdiri dari berbagai macam algoritma salah satunya Algoritma Backpropagation.…”

Section: Pendahuluanunclassified

Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

Falah

Rini

Pahendra

2021

mib

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Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.

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“…Estas modificaciones se han centrado principalmente en la simulación de nuevas concentraciones determinadas por un sistema de toma de decisiones multicriterio, basadas en valores objetivo asignados por expertos. Por ejemplo, se ha determinado que una alta concentración de glicerol en lubricante vegetal es beneficiosa para las propiedades tribológicas del lubricante como la disminución del COF [17]. En la misma línea se han implementado algoritmos de retro propagación para investigar el efecto de un lubricante con aditivo tipo polytetrafluorethylene sobre el comportamiento de fricción en un cojinete a presión constante.…”

Section: Introductionunclassified

“…Tal y como se ha introducido anteriormente, las herramientas de IA han sido aplicadas exitosamente en el estudio de COF [8] y la tasa de desgaste [10] en los contactos tribológicos, así como en el desarrollo de lubricantes [12] y la optimización de sus aditivos [17]. Continuando con estas investigaciones, este trabajo presenta una metodología que permite correlacionar mediante las herramientas de IA la concentración de aditivos FM, EP y AW de un lubricante con el comportamiento de su fricción en un ensayo de gripado.…”

Section: Introductionunclassified

Aplicación de herramientas de Inteligencia Artificial para la optimización frente al gripado del contenido de aditivos FM, EP y AW en un lubricante

2022

Congreso Iberoamericano De Ingeniería Mecánica-Cibim 2022

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Este trabajo presenta una metodología para correlacionar la concentración de aditivos modificadores de fricción (FM), extrema presión (EP) y anti desgaste (AW) en un lubricante con su comportamiento de fricción en un ensayo de scuffing mediante el uso de herramientas de Inteligencia Artificial (IA). Para ello, se ha integrado una gran cantidad de información procedente de estudios experimentales previos sobre scuffing y valores propios experimentales de fricción, obtenidos en una Mini-Máquina de Tracción (MTM), con aceites que contienen diferentes concentraciones de aditivos. Con la información seleccionada se ha contrastado la eficacia y fiabilidad de la predicción con dos herramientas de IA: RNA (Red Neuronal Artificial) y SVM (Máquina de Soporte Vectorial). Los parámetros de creación y entrenamiento de las herramientas se han seleccionado de forma que se obtenga un valor de correlación que supere el 98 % para ambos casos. Posteriormente, en una segunda fase, se aplica la predicción de la concentración óptima de aditivo del lubricante que mejora la resistencia del lubricante al scuffing.

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Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Cited by 14 publications

References 41 publications

Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

Aplicación de herramientas de Inteligencia Artificial para la optimización frente al gripado del contenido de aditivos FM, EP y AW en un lubricante

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