Viscosity , Relaxation and Stability of Natural Rubber
Mooney viscosity and Mooney relaxation of unaged and oven aged SMR L natural rubber samples were studied. The viscosity was characterized by ML (1+4) 100°C and the rate of stress relaxation by the exponent a in a power law M = k (t) a . With the increasing time of oven aging at 140°C the decrease of both viscosity and stress relaxation rate exponent were observed. Aging of raw natural rubber can therefore be studied by Mooney viscosity and Mooney relaxation tests and obtained results are sensitive enough to be used for characterization of raw natural rubber stability.
Classification of RRIM clone series using artificial neural network
This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured.  RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.
Dimensional Errors of Fixtures, Locating and Measurement Datum Features in the Stream of Variation Modeling in Machining
Machining processes are usually multi-station processes involving a large number of operations and several locating datum changes. Machining errors are thus introduced, transformed and accumulated as the workpiece is being machined. This paper introduces procedures for expressing the influence of errors in fixtures, locating datum features and measurement datum features on dimensional errors in machining. These procedures are essential in the derivation of the Stream of Variation model of dimensional machining errors using the CAD/CAPP parameters of the machining process. The linear state space form of the Stream of Variation model allows for advanced control theory achievements to be employed in formal solutions to problems in multi-station machining. Modeling procedures presented in this paper were experimentally verified in machining of an automotive cylinder head.