Artificial neural network-based prediction technique for wear loss quantities in Mo coatings

Çetinel, Hakan; Öztürk, Hasan; Çelik, Erdal; Karlık, Bekir

doi:10.1016/j.wear.2006.01.040

Cited by 70 publications

(37 citation statements)

References 17 publications

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“…Artificial neural network is a technique that involves database training to predict property-parameter (output) evolutions, more quickly [23,24]. This section presents the database construction, implementation protocol and a set of predicted results for coating deposition efficiency [25].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

Behera¹,

Mishra²

2012

OJCM

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Modern industrial technologies call for the development of novel materials with improved surface properties, lower costs and environmentally suitable processes. Plasma spray coating process has become a subject of intense research which attempts to create functional layers on the surface is obviously the most economical way to provide high performance to machinery and industrial equipments. The present work aims at developing and studying the industrial wastes (Flay-ash, Quartz and illmenite composite mixture) as the coating material, which is to be deposited on Mild Steel and Copper substrates. To study and evaluate Coating deposition efficiency, artificial neural network analysis (ANN) technique is used. By this quality control technique, it is sufficient to describe approximation complex of inter-relationships of operating parameters in atmospheric plasma spray process. ANN technique helps in saving time and resources for experimental trials. The aim of this work is to outline a procedure for selecting an appropriate input vectors in ANN coating efficiency models, based on statistical pre-processing of the experimental data set. This methodology can provide deep understanding of various co-relationships across multiple scales of length and time, which could be essential for improvement of product and process performance. The deposition efficiency of coatings has a strong dependence on input power level, particle size of the feed material, powder feed rate and torch to substrate distance. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency, when an appropriate size of training set and network is utilized.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

Behera¹,

Mishra²

2012

OJCM

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“…However, there are a few elements that control the ANN performance, i.e. training function [4][5][6][7][8][9][10][11][12][13][14][15][16], input data [4, 5, 10, 12, 13, 16], and the number of hidden layers [4,5,9,12,13]. For instance, in reference [12] and [13], it has been found that a larger training database provides higher accuracy of ANN.…”

Section: Introductionmentioning

confidence: 99%

“…In reference [4] the input parameters were 9, therefore the hidden layer was 3. While in reference [14], the input parameters were only 2, and the hidden layer was only 1. In other words, it was found in the previous works that the number of hidden layers and their volume depend on the complexity of the system, i.e.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network for prediction of the friction coefficient of multi-layer polymeric composites in three different orientations

Nasir

Yousif

McWilliam

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part C:

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Abstract:In the present work, an artificial neural network (ANN) model was developed to predict frictional performance of a polymeric composite. The experimental dataset at different applied loads (30-100 N), sliding speeds (300-700 r/min), and up to 10 min of sliding duration was used to train the model. The ANN model was trained with a large volume of experimental data (7389 sets). In addition to that, fibre mat orientation was considered in ANN development. Various configurations with different functions of training were used to find the optimal model. As a result of this work, single-layered models with large number of neurons showed high accuracy, up to 90 per cent in prediction, when trained with the Levenberg-Marqurdt function.

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“…Takođe su detaljno objašnjeni efekat fedinga (pada performansi usled dejstva visoke temperature), kao i složena zavisnost koeficijenta trenja od brzine klizanja. Pokušaji da se opiše mehanizam habanja frikcionog materijala, kao i kako on može da utiče na performanse kočnice je takođe proučavan u [14,15,16].…”

Section: P Pr Re Eg Gl Le Ed D Sunclassified

“…U tom slučaju se moment kočenja može dobiti kao linearna funkcija pritiska aktiviranja kočnice, u skladu sa jednačinom 5. 16.…”

Section: Slika 54 Položaj Težišta Vozila I Sile U Kontaktu Pneumatiunclassified

The potentional application of artificial intelligence in motor vehicles braking system performance

Ćirović¹

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Artificial neural network-based prediction technique for wear loss quantities in Mo coatings

Cited by 70 publications

References 17 publications

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

An artificial neural network for prediction of the friction coefficient of multi-layer polymeric composites in three different orientations

The potentional application of artificial intelligence in motor vehicles braking system performance

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