It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data.
Indonesia has a large variety of natural fibers in abundance. Some of natural fibers become organic waste if not used for something needed by humans. One of the potential uses of natural fiber composite materials is to be used in automotive components. But before natural fiber composites are used in automotive components, it is necessary to examine first what are the requirements for mechanical properties or other properties required by the automotive components. Especially the automotive components which have been made from Polymers, like dash board, Car interior walls, front and rear bumper and Car body, etc. Each of these automotive components has different function and condition, and that caused different mechanical properties needed. The purpose of this study is collecting the data from the literature, related to the properties needed for these automotive components. This study was conducted by studying the literature of research journals in the last 10 years. From the research journals, data on the requirements of mechanical properties for automotive components will be collected. Furthermore, the data of mechanical properties required for automotive components can be used as a reference to determine the reliability of automotive components made from composite
The use of a spray-dryer is very popular in the drying process in the food and beverage industry. However, due to the properties of the sensitive product that the quality will degrade in drying at high temperature, the innovative design of spray-dryer is developed which can increase the heat transfer rate at moderate temperature. This research was conducted to develop a spray-dryer design to improve thermal-hydraulic performance, with a high transfer rate and low-pressure drop at such a temperature. The design varies by several inlets categorized as design A with one inlet, design B with two inlets, and design C with three inlets. This simulation uses ANSYS FLUENT17, and the independence of the mesh was evaluated to improve the result of the simulation. The efficient mesh number is obtained from the independence of the mesh at around one million. The result shows that design C has the lowest pressure loss and the highest transfer rate due to high vortex and swirl flow generation, improving the mixture quality and direct contact between droplet and dry-air.
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