Constructing a prediction model of machining performance is useful to improve its process efficiency. Artificial neural network (ANN) has been widely used in prediction works, capable of solving complex problems with numerous parameters. The present study aims to describe the application of the ANN technique in predicting the machining performance of a natural material. Bovine horns were the selected natural materials. Bovine horns are sustainable, recyclable, and abundant source for industrial applications. The outputs of the predictive model were surface roughness and energy consumption, whereas the input data were spindle speed, depth of cut and feed rate of a face milling. It was found that the ANN-based prediction model of bovine horns produced a high accuracy prediction (95.4%). The outcome of this study may be referred by similar studies on other natural materials, supporting the global efforts in improving the industrialization of natural materials.
Shrimp culture is carried out by ponds in open areas, especially near coastal areas. The ponds water condition or water quality has a significant impact on the shrimp culture. There are also frequent problems among these shrimp ponds, such as crop failure caused by bad water quality. The water quality monitoring in shrimp ponds is often done manually by the farmer in periodical times. The water quality monitoring that is done manually tends to be impractical, requires high worker wages, and has a high human error rate. With the advances in the field of Information Technology, data may be retrieved through sensors and collected into a server. Then the data may be processed and visualized in order to support precision aquaculture using the Internet of Things (IoT). Precision Fish Farming (PFF) or precision aquaculture is a concept that applies control-engineering principles to aquaculture industries. The PPF concepts allow farmers to have the ability to monitor, control, and document biological processes in aquaculture farms. This research aims were to design and build a multi node sensor and master board to monitor water quality in real time using the prototyping method. The system consists of several sensors for monitoring temperature, pH, and salinity in shrimp ponds that are installed at each node. Nodes are actively sending data to the master board. This model is done to reduce the need for direct data access to the internet. The monitoring system is tested in PB Tunas Baru shrimps pond in order to check if the system may work properly. The sensor is set to retrieve pond water quality data every 5 minutes in a total 100 minute period. The result shows that the model works properly, and the means value of the total error rate for the salinity sensors, pH, and temperature sensors consecutively is 1.65%, 1.25%, and 0%. This information allows the farmers to maintain the water quality precisely in aim to produce high quality shrimp crops toward the precision aquaculture concepts.
Till now titanium and its alloys used in different industrial sectors. Unique material characteristics make it as desirable raw material for the automotive, aerospace, petroleum, chemical, marine and biomedical industries. It requires deformation and fabrication process as difficult-to-cut material. There are several challenges hidden under the processes. Therefore, advanced machining process performance investigation in titanium and its titanium alloys machining has taken part of the research concern. A number of research works has been done in every year to show the research direction. However, most of them are specifically in one machining process. It’s important to have a clear picture of a research area for further research consideration. Therefore, this review aim to study recent articles of non-traditional machining process of titanium and its alloys. The focus of this review was on the contribution for solving existing problems by using non-traditional machining processes, most efficient process and general overview. At the end it also provided a summary of sustainable issue of non-traditional machining processes.
Strong crosswind gusts have been reported to seriously damage the running safety of modern road and rail vehicles all over the world. Sudden changes in rail vehicle aerodynamic forces due to transient wind conditions can have a negative impact on rail dynamics and stability. Furthermore, the majority of previous research on aerodynamic performance has been conducted in steady crosswind conditions. This highlights the significance of studying the effects of sudden changes in wind loads on high-speed rail vehicles. In this study, the unsteady aerodynamic performance of a next-generation high-speed train (NG-HST) subjected to transient crosswinds was investigated using CFD analysis. A stepwise velocity profile (25 m/s and 35 m/s) was chosen to generate transient wind with a train travelling at a maximum speed of 400 km/h. A Detached Eddy Simulation (DES) turbulent model was used to evaluate the unsteady characteristics of the complex flow structure. The results show that even low-velocity wind showed a relatively high impact on the aerodynamic loads of NG-HST. For instance, pitching moments (Cpitch) at crosswind angles of 13° and 18° were 192% and 194% higher than no crosswind conditions. In addition, changes in side force coefficient (Cs) values during transient loads were 183% and 190% higher compared to normal conditions for 13° and 18°, respectively. In terms of vortex structure, it was relatively complex and unsteady at a 13° yaw angle for transient conditions, compared to steady crosswind conditions. This result provides a strong justification for NG-HST operational safety under transient crosswind conditions.
In ultrasonic vibration-assisted turning (UVAT), vibration is one of the critical factors that causes noise during machining and affects cutting tool life, machining accuracy and workpiece surface quality. Vibration generated by piezoelectric actuators tends to transmit undesired vibration on the edge of the cutting tool and tool post. This situation hinders the maximization of vibration energy usage in the cutting tool. Thus, this paper investigated the vibration performance in the cutting tool by adding an isolator pad as damping element in the static zone of a tool holder to reduce the resonance generated during the machining process. The static, vibration and surface roughness analysis has been performed to determine the impact of damping on the machining performance. The results revealed a significant improvement in surface roughness where the best Ra for UVAT was 0.38 μm. In addition, vibration and static analysis showed the application of isolator pad capable of reducing 80% of energy loses and a supporter to increase the displacement, respectively. Ultimately this innovative solution can play an important role in improving UVAT performance.
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