Currently there is greater interest and an industrial need to create a bond between cast steel and aluminum alloys. The bond quality between these two metals must be considered, since it is affected by several casting techniques and parameters. This research aims to find the right combination of techniques and parameters to make a good bond between cast steel and aluminum alloys. This research systematically used the response surface methodology (RSM). Three important casting techniques and parameters are selected as independent variables, which are preheating temperature of cast steel, pouring temperature of aluminum alloy molten, and surface cleaning of cast steel. The gap between cast steel and aluminum alloy is used as dependent variable, which is defined as the quality measurement of the bond between two metals. The experiments were conducted on 48 samples, in which destructive test was performed in order to measure the gap. From the methodology, it is found that the recommended preheating temperature of cast steel is 491 °C, the recommended pouring temperature of aluminum alloy is 696 °C, and the recommended technique is cleaning the cast steel insert using degreasing. For practical purpose, the preheating temperature of cast steel can be set at 490 ± 10 °C and the pouring temperature of aluminum alloy can be set at 695 ± 10°C. This research limits on bimetal casting between cast steel and aluminum alloys, and the casting process is gravity die casting process. This paper is able to find the best casting techniques and parameters for cast steel and aluminum alloy bond using RSM. This paper also proposes gap bond between two metals as bond quality measurement.
The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together.
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