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Prediction of friction coefficient of treated betelnut fibre reinforced polyester (T-BFRP) composite using artificial neural networks

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Abstract

The current work is an attempt of using artificial neural network configuration to predict frictional performance of treated betelnut fibre reinforced polyester (T-BFRP) composite. Experimental dataset at different applied loads (5–30 N) and sliding distances (0–6.72 km) was used to train the ANN configuration with a large volume of experimental data (492 sets) where three different fibre mat orientations were considered (anti parallel, parallel and normal orientations). Results obtained from the developed ANN model were compared with experimental results. It is found that the experimental and numerical results showed good accuracy when the developed ANN model was trained with Levenberg–Marqurdt training function.

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Temperature sensitivity of friction materials has always been a critical aspect while ensuring their smooth and reliable functioning, and that sensitivity need to be constantly optimized. The performance of friction materials at elevated temperatures is defined by their fading performance. In this paper, possibilities for predicting the fading performance of the friction materials, regarding their formulation and manufacturing conditions, have been investigated by means of artificial neural networks. The neural modelling of the friction materials behaviour at elevated temperatures has been based on the two different training data sets regarding the number, type, and distribution of the stored data. The first training data set is consisted by 360 data related to cold, fading, and recovery performance. These data have been used for developing of the neural model for predicting not only the fading performance but also cold and recovery performance. The second training data set, consisted by 120 data, has been used for developing the neural model that is going to be only used for predicting the fading performance of the friction materials. In this paper, 18 neural networks have been trained by the 5 training algorithms. These networks have been tested by the testing data set formed using the parameters of formulating, manufacturing, and testing of the two friction materials which input parameters were completely unknown for the networks.
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Notes that the real test of maintenance stratagem success (or failure in financial terms) can only be resolved when a comparison of machine maintenance costs can be made to some benchmark standard. Presents a comparative study between two models developed to predict the average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. The models use the conventional statistical technique multiple regression, and artificial neural networks. Performance analysis using mean percentage error, mean absolute percentage error and percentage cost accuracy intervals was conducted. Results reveal that both models performed well, having low mean absolute percentage error values (less than 5 percent) indicating that predictor variables were reliable inputs for modelling average hourly maintenance cost. Overall, the neural network model performed slightly better as it was able to predict up to 95 percent of cost observations to within =q £5. Moreover, summary statistical analysis of residual values highlighted that predicted values using the neural network model are less subject to variance than the multiple regression model.
Article
An artificial neural network (ANN) technique is applied to predict the wear properties of polymer-matrix composites. Based on an experimental database for short fiber reinforced polyamide 4.6 composites, the specific wear rate, frictional coefficient and furthermore some mechanical properties, such as compressive strength and modulus, were successfully calculated by a well-trained ANN. 3-D plots for the predicted wear and mechanical characteristics as a function of material compositions and testing conditions were established. The results are in good agreement with measured data. It shows that the prediction accuracy is reasonable, and the network has potential to be improved if the experimental database for network training could be expanded.
Article
There is increasing interest in the interactions of microabrasion, involving small particles of less than 10 mm in size, with corrosion. This is because such interactions occur in many environments ranging from the offshore to health care sectors. In particular, microabrasion–corrosion can occur in oral processing, where the abrasive components of food interacting with the acidic environment, can lead to degradation of the surface dentine of teeth. Artificial neural networks (ANNs) are computing mechanisms based on the biological brain. They are very effective in various areas such as modelling, classification and pattern recognition. They have been successfully applied in almost all areas of engineering and many practical industrial applications. Hence, in this paper an attempt has been made to model the data obtained in microabrasion–corrosion experiments on polymer/steel couple and a ceramic/lasercarb coating couple using ANN. A multilayer perceptron (MLP) neural network is applied and the results obtained from modelling the tribocorrosion processes will be compared with those obtained from a relatively new class of neural networks namely resource allocation network.
Article
The current work is an attempt to use betelnut fibres as reinforcement for tribopolyester composite. The composite was fabricated using hand lay-up technique. It consists of 13 layers of randomly distributed betelnut fibre mats and 15 layers of polyester. Wear and frictional behaviours of the composite were studied against a polished stainless steel counterface using a newly developed block-on-disc machine. Tests were conducted at 2.8 m/s sliding velocity, different applied loads (5-30 N), and sliding distances (0-6.72 km). In addition, the orientation of the fibre mats, with respect to the sliding direction of the counterface, was considered, i.e. antiparallel (AP-O), parallel (P-O), and normal (N-O). The worn surface morphology was studied using a scanning electron microscope. Optical microscopy was used to observe the wear track surface on the counterface. In addition, the modifications on the counterface roughness were studied. This work revealed that the presence of betelnut fibre in the matrix, namely P-O, enhanced the wear and frictional performance of the polyester by about 98 and 73 per cent. Applied load has less effect on the specific wear rate and friction coefficient of the composite, especially in P-O and AP-O. The composite behaved differently in N-O in which the wear and friction increased when the applied load and sliding distance increased. The composite exhibited higher wear performance in P-O compared with AP-O followed by N-O. In N-O, poor support of the fibres to the resinous was observed, Le delamination, pullout, and breakage in the fibres. In AP-O, the wear mechanism was predominated by plastic deformation, micro- and macro-cracks in the resinous regions associated with pullout, and breakage of the fibre. In P-O, debonding of fibres was the main wear mechanism.
Article
In the present article artificial neural networks (ANN) were used to study the effects of pv factor and contact temperature on the dry sliding tribological behaviour of 30 wt.% carbon-fibre-reinforced polyetheretherketone composite (PEEK-CF30). An experimental plan was performed on a pin-on-disc machine for obtained experimental results. By the use of back propagation (BP) network, the non-linear relationship models of friction coefficient and weight loss of PEEK-CF30 versus pv factor and contact temperature were built. The test results show that the well-trained BP neural network models can precisely predict friction coefficient and wear weight loss according to pv factor and contact temperature. The obtained results show that friction coefficient was mainly influenced by the pv factor (mechanical factor), and the weight loss was mainly influenced by the contact temperature (thermal factor).
Article
Graphite modified polyester–cotton composites were developed and studied for friction and sliding wear behaviour at different applied loads and graphite concentrations. Sliding wear tests were conducted using pin-on-disc apparatus. The composite pins were tested against EN-31 steel disc. The specific wear rate of polyester reduced on reinforcement of cotton and on addition of graphite. The coefficient of friction of polyester resin increased on cotton reinforcement and reduced significantly on addition of graphite in cotton–polyester composite. The temperature of contact surface reduced on addition of graphite in cotton–polyester composite. The reduction in wear rate of graphite modified polyester–cotton composite has been discussed with the help of scanning electron microscope (SEM) observations of worn surfaces, coefficient of friction and the temperature of contact surface.
Article
Adequate estimation of construction costs is a key factor in construction projects. This paper examines the performance of three cost estimation models. The examinations are based on multiple regression analysis (MRA), neural networks (NNs), and case-based reasoning (CBR) of the data of 530 historical costs. Although the best NN estimating model gave more accurate estimating results than either the MRA or the CBR estimating models, the CBR estimating model performed better than the NN estimating model with respect to long-term use, available information from result, and time versus accuracy tradeoffs.
Article
The potential of using neural network in prediction of wear loss quantities of some aluminum–copper–silicon carbide composite materials has been studied in the present work. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al–4 wt.%Mg metal matrix have been investigated. Different Al–Cu alloys and composites were subjected to dry sliding wear test using pin-on-disk apparatus under 40 N normal load with rotational speed of counter face disk of 150 rpm at room conditions (∼20 °C and ∼50% relative humidity). The experimental results were firstly coded prior to training in a feed forward back propagation artificial neural network (ANN) and the results were compared with experimental results. The average value of absolute relative error of un-coded values reaches 2.40%.
Article
In the present investigation, artificial neural network (ANN) approach was used to predict the wear behaviour of A356/SiC metal matrix composites (MMCs) prepared using rheocasting route. The ANN model was obtained to aid in prediction and optimization of the wear rates of the composites. The effect of the SiC particles size, SiC weight percent, applied pressure and test temperature on the wear resistance was evaluated using the ANN model. The results have shown that ANN is an effective tool in the prediction of the properties of MMCs, and quite useful instead of time-consuming experimental processes.
Article
Using a multiple-layer feed-forward artificial neural network (ANN), the specific wear rate and frictional coefficient have been predicted based on a measured database for short fibre reinforced polyamide 4.6 (PA4.6) composites. The results show that the predicted data are well acceptable when comparing them to the real test values. The predictive quality of the ANN can be further improved by enlarging the training datasets and by optimising the network construction. A well-trained ANN is expected to be very helpful for an optimum design of composite materials, for a particular tribological application and for systematic parameter studies.
Article
This paper aims at illustrating the compared results of the application of two different approaches—respectively parametric and artificial neural network techniques—for the estimation of the unitary manufacturing costs of a new type of brake disks produced by an Italian manufacturing firm. The results seem to confirm the validity of the neural network theory in this application field, but not a clear superiority with respect to the more “traditional” parametric approach: in particular, the ANN seems to be characterised by a better trade-off between precision and cost of development, while a critical point—especially in the specific application context—is represented by the reduced possibility of interpreting output data (which is critical for the “optimisation” of design solutions during the new product development process).
Article
Composites of an aliphatic polyester (Bionolle) with natural flax fibres are prepared by batch mixing. The effect of processing conditions on fibre length distribution and the dependence of the composite mechanical properties on fibre content are investigated. The tensile modulus changes with fibre content according to the modified rule-of-mixture equation, with a fibre orientation efficiency factor η0=0.194. The strength of Bionolle/flax composites tends to decrease with fibre loading, showing that there is no adhesion between matrix and fibres. With the aim to improve fibre–matrix adhesion, surface chemically modified flax fibres are also tested as reinforcing agents. A 30% strength increase is observed when natural fibres (25 vol%) are substituted by fibres containing acetate groups. No significant strength changes are observed in composites containing fibres with valerate groups or polyethylene glycol chains grafted at the surface.
Article
In this work, tribological investigations on the neat polyester (NP) and woven (600 g/m2)-glass fabric reinforced polyester (WGRP) composite were carried out. Friction and wear characteristics of the WGRP composite were measured in three principal orientations, i.e., sliding directions relative to the woven glass fabric (WGF) orientations in the composites. These are longitudinal (L), transverse (T), and parallel (P) orientations. The experiments were conducted using a pin-on-disc (POD) machine under dry sliding conditions against a smooth stainless steel counterface. Results of friction coefficient and wear resistance of the composites were presented as function of normal loads (30–100 N) and sliding distances (0.5–7 km) at different sliding velocities, 1.7, 2.8, and 3.9 m/s. Scanning electron microscopy (SEM) was used to study the mechanisms of worn surfaces. Experimental results revealed that woven glass fabric improved the tribological performance of neat polyester in all three tested orientations. In L-orientation, at a low velocity of 1.7 m/s, WGRP exhibited significant improvements to wear resistance of the polyester composite compared to other orientations. Meanwhile, at high velocities (2.8 and 3.9 m/s), T-orientation gave higher wear resistance. SEM microphotographs showed different damage features on the worn surfaces, i.e., deformation, cracks, debonding of fiber, and microcracks.
Article
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.
Article
This paper focuses on the machine-tool selection problem, which consists of choosing the most suitable machine to satisfy the needs of a manufacturing company. The final decision affects the performance of the production system. Selecting an inadequate machine can negatively affect the company's results. For this reason, this is an important process that may imply some difficulties for the decision-maker.The objective of this work was to develop a cost model for vertical high-speed machining (HSM) centres based on machine characteristics. It is important to determine the cost of the machine tool, which is based on the tool's characteristics and needs to satisfy both the buyer and the manufacturer.In order to determine the main machine specifications associated with machine cost, a preliminary analysis was conducted with entry-level vertical HSM centres. As a result, two models were developed: one from the buyer's point of view and the other from the manufacturer's point of view. The cost estimation models were developed using two different techniques: multiple regression analysis (MRA) and artificial neural networks (ANN). The paper then examines the performance of the models, and compares the models’ outputs to determine which model offers the best results. Cost estimation is important to determine the machine costs that adapt best to the characteristics of manufacturing factories.The correlation obtained by the multilayer ANN models is better than the one obtained by MRA. Applying the proposed cost models will help the user (engineers or machine manufacturers) to determine the approximate machine cost based on its characteristics when they select a vertical HSM centre.
Article
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Engineering mechanic of composite materials
  • Daniel Im Ishai
Daniel IM, Ishai O. Engineering mechanic of composite materials, 2nd ed.2006.
Results of parameter study by the use of the neural network concept: friction coefficient as a function of applied load and sliding distance for T-BFRP composite in AP, P and N orientations. (dots represents experiment data whereas the rest of the 3D plane was predicted by an ANN approach)
  • Article In
  • Fig
ARTICLE IN PRESS Fig. 14. Results of parameter study by the use of the neural network concept: friction coefficient as a function of applied load and sliding distance for T-BFRP composite in AP, P and N orientations. (dots represents experiment data whereas the rest of the 3D plane was predicted by an ANN approach).
Biodegradable polymers
  • Oksman