Wind energy is the most abundantly available clean form of renewable energy in the earth crust. Wind turbines produce electricity by using the power of wind to drive an electric generator. There are two kinds of wind turbines according to the axis of rotation to the ground, horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). VAWTs include both a drag type configuration like Savonius wind turbine and a lift-type configuration like Darrieus wind turbine. Savonius wind rotor has many advantages over others in that its construction is simpler and cheaper. It is independent of the wind direction and has a good starting torque at lower wind speeds. The experimental study conducted in this paper aims to investigate the effect of number of blades on the performance of the model of Savonius type wind turbine. The experiments used to compare 2, 3, and 4 blades wind turbines to show tip speed ratio, torque and power coefficient related with wind speed. A simulation using ANSYS 13.0 software will show pressure distribution of wind turbine. The results of study showed that number of blades influence the performance of wind turbine. Savonius model with three blades has the best performance at high tip speed ratio. The highest tip speed ratio is 0.555 for wind speed of 7m/s

Maintenance is an activity that cannot be separated from the context of product manufacturing. It is carried out to maintain the components’ or machines’ function so that no failure can reduce the machine’s productivity. One type of maintenance that can mitigate total machine failure is predictive maintenance. Predictive maintenance, along with the times, no longer relies on visuals or other senses but can be combined into automated observations using machine learning methods. It can be applied to a toothpaste factory with a tube filling machine by combining the results of sensor observations with machine learning methods. This research aims to increase the Overall equipment effectiveness (OEE) to 10% by predicting the components that will be damaged. The machine learning methods tested in this study are random forest regression and linear regression. This study indicates that the prediction accuracy of machine learning with the random forest regression method for PHM predictive is 88%of the actual data, and linear regression has an accuracy of 59% of the actual data. After implementing the system on the machine for three months, the OEE value increased by 13.10%, and unplanned machine failure decreased by 62.38% in the observed part. Implementation of the system can significantly reduce the failure factor of unplanned machines.

Wind turbines are widely regarded as an alternative source for electrical power generation. Modern wind turbines are classified into horizontal axis wind turbine (HAWT) and vertical axis wind turbine (VAWT) categories. Vertical axis wind turbines (VAWTs) are attractive for applications in the built environment due to their ability to capture wind from different directions. One type of VAWTs is Darrieus turbine or H-type turbine. Experiments conducted in this paper aims to investigate the effect of number of blades in the performance of H-Darrieus type wind turbine. The experiments used 2, 3 and 4 blades to show rotation, torque and TSR (tip speed ratio) of wind turbine. A simulation using ANSYS 13.0 software will show the coefficient of power of wind turbine. The results of experiments showed that the number of blades rotation and torque influence the performance of wind turbine.

Metal machining processes require primarily cutting tool materials with high hardness, high resistance to the abrasive wear and thermal stability. The development of cutting tool materials results in advanced tool materials such are primarily ceramics, cubic boron nitride and sintered carbides which are considered to have the ability to cut hard materials. As a finishing process, the machined surface of the final product needs to control the surface quality. The quality of machined surface can be determined by properties such as surface roughness, hardness variations, micro-structural changes, residual stresses, etc. These properties belong to the surface integrity of the work piece material. The surface integrity affects significantly the mechanical properties of the parts such as fatigue limit, stress-corrosion resistance, dimensional stability, etc. This paper presents an experimental study to analyze the evolution of residual stresses in relation to the different parameter of machining. For precision milling of hardened steel, parameters are cutting speed and feed rate with a constant depth of cut. Two different cutting tool materials were used in this study, ceramic and cubic boron nitride. The results show that residual stresses near the machined surface of hardened steel are suitable for compressive stress.

Effects of immersion in the NaCl and H2SO4 solutions on the corrosion rate, microstructure, and hardness of stainless steel 316L