Surface Science; Reports summarize surface science research from Multimedia University

Wear and friction characteristics of oil palm fiber reinforced polyester (OPRP) composite and neat polyester were tested at different sliding distances (0-5 km), sliding velocities (1.7-3.9m/s), and applied loads (30-70 N) under dry contact condition, scientists in Melaka, Malaysia report

Friction, Lubrification and Wear; Scientists at Multimedia University Publish New Data on Friction, Lubrification and Wear

According to the authors of a study from Melaka, Malaysia, The aim of the present work is to investigate the effect of aging process on the wear and frictional characteristics of polyester composites based on oil palm fibres

Mechanical Engineering; Scientists at Multimedia University report research in mechanical engineering

The results revealed that all test parameters have a very significant influence on the frictional and wear characteristics of the materials. [...] a higher coefficient of friction was exhibited in the four-layer composite

A review on the degradability of polymeric composites based on natural fibres

The applications of natural fibre/polymer composites in civil engineering are mostly concentrated on non-load bearing indoor components due to its vulnerability to environmental attack. This paper evaluates the characteristics of several natural fibre composites exposed to moisture, thermal, fire, and ultraviolet degradation through an extensive literature review. The effects of chemical additives such as fibre treatments, fire retardants and Ultraviolet (UV) stabilizers are also addressed. Based on the evaluation conducted, optimum fibre content provides strength in a polymer composite but it also becomes an entry point for moisture attack. Several fibre treatments are also being used to improve fibre/matrix interface, thereby increasing moisture durability. However, the treated fibres were found to behave poorly when exposed to weather. The addition of UV stabilizers and fire retardants are suggested to enhance outdoor and fire performance of natural fibre/polymer composite but compromises its strength. Therefore, from the collected data and various experimental results, it was concluded that an optimum blend ratio of chemical additives must be employed to achieve a balance between strength and durability requirements for natural fibre composites

CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NOx and exhaust temperature, respectively.CNG fuel ANN Engine performance Engine emission

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