Applied Research and Smart Technology (ARSTech)
Aims International Journal of Applied Research and Smart Technology (ARSTech) is a peer-reviewed, biannual journal that promotes the development and application of smart technologies in various sectors, such as mechanical & materials engineering, automotive & manufacturing process, energy conversion & renewable energy, robotics, mechatronic & artificial intelligent, chemical & biomedical engineering, marine & aerospace technologies, transportations, infrastructures and environment. Smart technologies offer practical and sustainable solutions in the modern life of humankind by employing the latest technological advancements. Scope The journal presents and disseminates new developments and the latest findings in all fields of engineering and technology, especially those that contribute to the implementation of smart technologies. The topics covered by the journal include but are not limited to: autonomous systems, mechatronics and robotics, control systems in automobiles and intelligent transport systems, smart structures, materials, and metallurgy nanotechnologies and advanced materials in engineering application, sustainable and green buildings, green technology and industry 4.0, IoT-based systems, sensor network, artificial intelligence and smart grids, biomedical engineering, bioenergy technologies, design and development of automotive technologies and manufacturing process, vehicle modelling and safety, modelling and simulation (CFD) in engineering application, vehicle design and aerodynamics, applied mechanics, structure and manufacturing technology, material processing and technology for vehicles and other mechanical use, coatings technologies in engineering application, engine technologies and development for vehicles and other engineering application, hybrid and electric vehicle technologies, vehicle braking and suspension systems, thermodynamics application in engineering application, combustion and reacting flows in automotive and other engineering application, applied heat and mass transfer, fluid and thermal engineering, heating and cooling systems (HVAC) in vehicles and engineering application, fuels and lubricants in automotive engineering, development of energy conversion and conservation, new-and-renewable energy, and alternative energy in engineering application, fuel cell and solar energy, the engine technology and emission control, automotive pollution and control, vehicle motion and control systems, noise and vibrations control, pneumatic and hydraulic systems, tribology in engineering application.
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<![CDATA[Redesign of water-diesel emulsion fuel mixer ]]>
<![CDATA[Mahadi, Abram Anggit]]>;
<![CDATA[Santoso, Budi]]>;
<![CDATA[Ubaidillah, Ubaidillah]]>;
<![CDATA[Lenggana, Bhre Wangsa]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v3i1.406
<![CDATA[Emissions and fuel consumption are essential parameters to indicate the working of a combustion engine. This paper discusses the methods to achieve lower emissions and efficient fuel consumption. One of the methods is by making emulsion fuel. The emulsion-making methods are surfactant emulsion, micro-emulsion, ultrasonic emulsion, and real-time emulsion non-surfactant (RTES). In the research, the emulsion fuel is a mixture of B30 Biodiesel and water without surfactant that is supplied in real-time after being mixed in a mixer. The ratio is 85% biodiesel and 15% water. The RTES mixer in the old model has a big size, and high-power consumption of about 150-433,5 W. This research's purpose is to develop a simple design and low power consumption of the RTES Mixer. The new RTES mixer design only needs 150 W motor power. Its dimension is more superficial and produces tiny droplets with a main diameter range between 0.1 to 0.5 ?m, with good-mixed visually. ]]>
<![CDATA[Investigation of electrical tree stress using colour techniques ]]>
<![CDATA[Abderrazzaq, Mohammad]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v3i1.456
<![CDATA[Treeing is one of the severe problems which cause the deterioration and breakdown of electrical insulation materials. Different approaches have been used to characterise this phenomenon, from experiments to analysis. The results of both methods were criticised for their dependence on the assumptions and applied conditions. In this work, the role of colours in understanding the characteristics of electrical treeing in composite insulation was employed. The relationship between the induced strain, associated with the electrical tree, and the change of colour parameters, represented by hue, saturation, and value indicators, was presented. The images were created by relative retardation orthogonal components of the polarised white light used to illuminate the specimens in the microscope. An image-editing software was used to analyse the tree colours, whereas the MATLAB program was written to determine the colour mapping of examined image. The variation of each colour parameter was linked with the tree distribution. It was then introduced as an indicator of stress at each examined point. Therefore, the contribution of the present paper is summarised as an introduction of a new tool to characterise stress in insulation materials by converting a treed image into a numerical array of data without the need to follow a complex mathematical procedure. Finally, this paper can better assess the treeing phenomenon by correlating the direction of tree growth to the rate of change for each colour parameter in that direction.]]>
<![CDATA[IoT-based system for monitoring the drying time of date seeds in the manufacturing of date coffee ]]>
<![CDATA[Jasmine, Arviena]]>;
<![CDATA[Triawati, Erma]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v3i1.480
<![CDATA[Date palm (Phoenix dactylifera) is a plant of the Phoenix palms whose fruit tastes sweet and can be consumed by humans. These fruit seeds usually only become a waste and never be utilised by people. The research tries to reduce the waste of date palm by using the date palm fruit seeds to replace the coffee beans. One of the processes of changing date palm fruit seeds to coffee beans is drying the seeds. Instead of using a traditional drying method, this study has designed a prototype of an Internet of Things (IoT) based monitoring system tools inside the drying room that allow humidity and temperature sensors, heater, fan, and mixer to be monitored through a smartphone in real-time. Hence, the monitoring tools inside the drying room could be controlled easily, and the data could be saved as databases in smartphone applications.]]>
<![CDATA[Effects of heat treatment on microstructure and hardness of D2 tools ]]>
<![CDATA[Hariningsih, Hariningsih]]>;
<![CDATA[Lutiyatmi, Lutiyatmi]]>;
<![CDATA[Daryanto, Tri]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v3i1.761
<![CDATA[D2 high chromium tool steel is widely used to produce tools and components that work at significant dynamic loads, such as dies, punches and rollers. The steel must have a good combination of strength and toughness, which heat treatment can obtain. Therefore, this study discusses the effect of normalising, hardening, and tempering on the microstructure and hardness of D2 tools. Normalising and hardening were carried out respectively at 1020°C for 30 minutes, followed by rapid cooling using oil. Tempering was realised by reheating the quenched sample at 250°C and 400°C with variations in holding time of 15 minutes, 30 minutes, and 45 minutes. The hardness of the specimens was measured using a Rockwell hardness tester, whereas the microstructure was observed with an optical microscope. The results indicated that the microstructure changes to martensite and carbide after quenching, while the microstructure becomes tempered martensite and carbide after tempering. Normalising and hardening have almost no impact on hardness, and the increase in temperature and holding time causes a decrease in hardness. The reduction in hardness is noticeable for steels tempered to 400 °C and held for 45 minutes.]]>
<![CDATA[Image-based disease detection and classification in Indian apple plant species by using deep learning ]]>
<![CDATA[Wani, Sidrah Fayaz]]>;
<![CDATA[Ashraf, Arselan]]>;
<![CDATA[Sophian, Ali]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v3i1.1021
<![CDATA[Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural productivity growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing an intelligent automated technique for plant disease detection is explored in this research. Deep learning is used to create a smart system for image-based disease detection in Indian apple plant species. Specifically, this study uses a convolution neural network architecture, ResNet-34, to identify diseases in apple plants. Based on 70-30% and 80-20% dataset partition, the proposed model obtained an accuracy of 97.5% and 98.4%, respectively. The results obtained from this study illustrate the productive exploration and utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that provide cure and preventative measures for the detected diseases. ]]>
