Rancang Bangun Perangkat Lunak Unit Kontrol Alat Ukur Sudu Cross Flow Water Turbine Berbasis Pengolahan Citra

Seiring dengan berkembangnya teknologi informasi dan komunikasi, alat ukur mengalami perkembangan yang cukup signifikan. Salah satu bentuk perkembangannya adalah dengan dibuatnya alat ukur sudu cross flow water turbine berbasis pengolahan citra oleh Rusweki dan Pradnyana pada tahun 2013. Namun, alat ukur ini masih dioperasikan secara manual. Tugas akhir ini bertujuan untuk melakukan pengembangan terhadap alat tersebut, khususnya dibidang rancang bangun perangkat lunak untuk unit kontrolnya. Metodologi yang diterapkan dalam tugas akhir ini yang pertama adalah mempelajari hal-hal yang berkaitan dengan topik bahasan dari berbagai literatur. Langkah kedua adalah menetukan perumusan masalah dan menentukan metode pembuatan perangkat lunak. Ketiga, pembuatan perangkat lunak dan verifikasi. Pada penelitian ini telah berhasil dirancang perangkat lunak untuk unit kontrol alat ukur sudu CFWT berbasis pengolahan citra. Berdasarkan hasil kalibrasi sensor inframerah Sharp GP2Y0A21 didapatkan bahwa nilai jarak adalah sama dengan 178924.57 dibagi dengan nilai output ADC desimal pangkat 1.08. Selisih maksimal antara jarak input dan jarak tempuh motor adalah 0.5mm, dan jarak kontrol antara 130-400mm. Dengn demikian, metode pengukuran menggunakan alat ini akan lebih mudah dan cermat, karena selain dapat mengukur benda dengan bentuk yang kompleks mngurangi resiko keausan benda an lebih teliti, juga bisa diakukan dengan mudah serta cepat karena adanya sistem kontro

Numerical Study of Blended Winglet Geometry Variations on Unmanned Aerial Vehicle Aerodynamic Performance

An unmanned aerial vehicle (UAV) is an unmanned aircraft that can be controlled remotely or flown automatically. Nowadays, the use of UAVs is extensive, not only limited to the military field but also in civilian tasks such as humanitarian search and rescue (SAR) tasks, railroad inspections, and environmental damage inspections. Therefore, study on UAV becomes essential to answer the challenges of its increasingly widespread use. This study explores the addition of a blended winglet on the swept-back wing of the UAV. It is to predict the effect of the aerodynamic performance. The backpropagation neural network (BPNN) method helps to predict the aerodynamic performance of the UAV in the form of a lift-drag coefficient ratio (CL/CD) and drag coefficient at 0O angle of attack (CD0). It is based on blended winglet parameters such as height, tip chord, and cant angle. The obtained BPNN modeling has a network architecture of 3 inputs, 2 hidden layers, and 1 output with a mean square error (MSE) of 4.9462e-08 and 4.4756e-06 for the relationships between blended winglet parameters with CL/CD and CD0, respectively

Perancangan Sistem Kontrol PID Untuk Pengendali Sumbu Elevasi Gun Pada Turret-Gun Kaliber 20 Milimeter

Pertahanan negara pada hakikatnya adalah segala upaya pertahanan yang bersifat semesta yang didasarkan pada kesadaran atas hak dan kewajiban warga negara serta keyakinan pada kekuatan sendiri dengan tujuan untuk menjaga dan melindungi kedaulatan negara, keutuhan wilayah NKRI dan keselamatan segenap bangsa. Salah satu alat pendukung pertahanan yaitu senjata laras panjang, Turret-Gun.  Adapun langkah-langkah yang dilakukan dalam merancang pengendali PID Turret-Gun kaliber 20mm ini diawali dengan studi literatur serta studi lapangan mengenai mekanisme dan parameter-parameter yang terdapat pada Turret-Gun pada sumbu elevasi. Setelah itu dilakukan perancangan transmisi dan sistem kontrol Turret-Gun untuk dievaluasi grafik responnya yang akan digunakan sebagai acuan untuk merancang pengendali PID yang sesuai. Selanjutnya pengendali PID yang telah dirancang lalu disimulasikan, sehingga menghasilkan grafik respon yang sesuai dengan kriteria yang dibutuhkan. Hasil yang telah didapatkan dari penelitian ini adalah konstanta PID yang direkomendasikan untuk , , dan secara berturut-turut adalah sebesar 23061.024, 37820.07 dan 3515.4 yang menghasilkan transient response dengan nilai overshoot sebesar 19.9 % , steady state error sebesar 0 % serta settling time sebesar 0.935 detik. Hasil analisa kestabilan untuk sistem kontrol dengan konstanta PID tersebut menunjukkan bahwa sistem kontrol telah stabil, baik menggunakan metode Root Locus maupun metode Routh-Hurwitz

Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network

Steel is a material that is widely used in industry and construction. The tensile and compressive force of steel is relatively high compared to other materials. On the opposite, low corrosion resistance is the main weakness of steel, which can encourage steel deterioration and fatal accidents for the user. Furthermore, regular visual inspection by a human should be performed to prevent catastrophic incidents. However, human visual inspection increases the risk of work accidents and reduces work effectiveness. Therefore, a drone with a camera is one solution to increase efficiency, increase security levels, and minimize difficulties or risks during corrosion inspection. In this research, the drone has been used to capture corroded video of a construction structure. The convolutional neural network (CNN) method is then used to detect the location of the corroded images. This study has been conducted on Surabaya’s Petekan-bridge with the Mobilenet V1 SSD pre-training model. In this study, the distance between a drone and the detected object varied between 1 and 2 m. Next, the drone speed was varied into 0.6 m/s, 0.9m/s, and 1.3m/s. As a result, CNN can detect corrosion on the surface of steel materials with the best accuracy is 84.66% and minimum total loss value of 1.673 by applying 200 images, 200000 epochs, batch size at 4, learning rate at 0.001 and 0.1, the distance at 1 m, drone speed at 0.6 m/s.

The Effect of Rice Husk as Additive in Injection Molding Process

This study investigated the moldability and the mechanical properties of bio-composite with rice husk as natural reinforcement. Natural materials that are abundant in nature can be used as reinforcement for polymer materials. Natural materials as reinforcement in plastic materials were used to obtain alternative materials in an injection molding process. With rice husk, polypropylene, and MAPP, four compositions of bio-composite materials were made and used as raw material injection molding process. The moldability from this material was observed through visualization of the product. The mechanical properties of the materials were observed by the tensile strength and impact test on the injection molding product. The result showed that these materials could be injected to form ASTM D638-03 Type V tensile test and ASTM D256-04 impact test specimens. Visually, the more rice husk on the bio-composite material, the darker the product color. The differences in tensile strength values decreased along with increased rice husk content. All bio-composite materials had roughly the same tensile strength value and were lower than polypropylene, except RH-5%. The impact value of bio-composites was lower than polypropylene impact value and tended to decline along with the increase in the rice husk content. Scanning electron microscope (SEM) analyzes were done on the fracture side of the impact specimen. Microscale voids decreased and were rarely found by adding rice husk to the material bio-composite. On the other hand, rice husk breakage and pullout phenomenon on bio-composite material were found

Optimization of 3D Printing Parameter Process for Product Tensile Strength from PLA Materials Using the Taguchi Method

Three-dimensional printing or 3D Printing is one of the revolutionary machines in addictive manufacturing techniques to create three-dimensional objects with complex structures. Until now there are many techniques in 3D printing, one of which is Fused Deposition Modeling (FDM), which is currently widely used because of its ease and low operational costs. However, in the printing process, there are important things that must receive attention, namely the process parameters. Because this is what really determines the quality of the printout. In this research, an analysis of the effect of process parameters such as: infill rate, infill pattern, extrusion temperature and layer thickness were carried out on the tensile strength of the printed product. The method used is the Taguchi method with the Orthogonal Array L 9 (3 4) experimental design. Three tensile test specimens were printed for each variation using a Cubic Chiron 3D printer, so a total of 27 specimens were printed. All specimens were tensile tested according to ASTM D638 standard, the results were analysed based on the average value and signal to ratio (SNR) value and their significance by analysis of variance (ANOVA). The results of the analysis show that the infill rate, infill pattern and layer thickness have a significant effect on the tensile strength of the printing results. The optimal value of the tensile strength is 56,876 MPa, occurs in the concentric pattern with an infill rate of 90%, and a layer thickness of 0.2 mm. From the confirmation test, the confidence interval values were obtained from 55,477 MPa to 58,275 MPa, meaning that the optimal predictive value was not significantly different from the confirmation test value

Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

The polymer matrix composite (PMC) in use today is generally made of synthetic fibers which are expensive and not environmentally friendly. The use of synthetic fibers can be replaced with natural fibers, which are more environmentally friendly at a lower price. The natural fiber material used in this study is made from husks, with a particle size of 500 µm (mesh 35). In the PMC manufacturing process, rice husks are mixed with polypropylene (PP) and maleic anhydride polypropylene (MAPP) with a composition of 10 wt% RH, 85 wt% PP and 5 wt% MAPP. PMC materials using natural fibers are called biocomposite materials. The result of mixing PMC with natural fibers in the form of pellets is then carried out by the injection process using an injection molding machine. The printed results are in the form of tensile test specimens based on ASTM D 638-03 type V testing standards and impact test specimens based on ASTM D 256-04 testing standards. The research was conducted by optimizing the responses i.e. tensile strength and impact strength of the biocomposite material in the injection molding machine process, whereas varied process parameters, namely barrel temperature, injection pressure, holding pressure, injection velocity were selected as process parameters. The backpropagation neural network (BPNN) training method is used to recognize the pattern of the relationship between process parameters and response parameters based on the previous experiment, while the genetic algorithm (GA) optimization method is to determine the variation settings for process parameters that can optimize tensile and impact strength. The results of the BPNN training have a 4-9-9-2 network architecture consisting of 4 input layers, 2 hidden layers with 9 neurons, and 2 neurons in the output layer. Optimization with GA produces a combination of variable process parameters barrel temperature 217◦C, injection pressure 55 Bar, holding pressure 41 Bar and injection velocity 65 mm/sec. The results of statistical validation using one sample T test show that the average value of tensile strength and impact strength from the results of the confirmation experiment is the same as the value of the tensile strength and impact strength of the optimization prediction

Optimization of Interval Between Overhaul on Steam Power Plant with Risk Based On Human Error and Profi

Power plant companies have many different standard interval between overhauls. The difference may be due to the different methods used by the company. However, these methods do not take into account aspects of risk, aspects of human error and financial aspects simultaneously. The purpose of this study is to determine the optimal interval overhaul by considering aspects of risk, human error aspects and financial aspects simultaneously. The propsed method to calculate reliability plant model using the Criticality Risk Matrix tool and elimination of equipment that can be overhauled at the time the plant under operating conditions, succeeds in reducing the number of equipment that needs to be analyzed, from 210 to 30 equipment. Parameter reliability plant obtained β: 0.9755, η: 602.0508, γ: 7.5942. The reliability plant model is combined with reliability constant affected by human error resulting in a combined reliability model. The combination of combined unreliability model, multiply to Economical Consequences so that the Total Cost model can be obtained. Genetic algorithm is an effective method to be used in the optimization process of a non linear function. The difference between net income model and total cost will produce profit model, so the optimal overhaul interval can be known by doing optimization on the model so that obtained top1 = 7698 hours. Optimization of total cost model can be done to find out the latest time the plant must be shutdown to do overhaul in order to avoid cost inefficiency. Optimal time total cost obtained top2 = 17645 hour

Rancang Bangun Perangkat Lunak Unit Kontrol Alat Ukur Sudu Cross Flow Water Turbine Berbasis Pengolahan Citra

Seiring dengan berkembangnya teknologi informasi dan komunikasi, alat ukur mengalami perkembangan yang cukup signifikan. Salah satu bentuk perkembangannya adalah dengan dibuatnya alat ukur sudu cross flow water turbine berbasis pengolahan citra oleh Rusweki dan Pradnyana pada tahun 2013. Namun, alat ukur ini masih dioperasikan secara manual. Tugas akhir ini bertujuan untuk melakukan pengembangan terhadap alat tersebut, khususnya dibidang rancang bangun perangkat lunak untuk unit kontrolnya. Metodologi yang diterapkan dalam tugas akhir ini yang pertama adalah mempelajari hal-hal yang berkaitan dengan topik bahasan dari berbagai literatur. Langkah kedua adalah menetukan Perumusan masalah dan menentukan metode pembuatan perangkat lunak. Ketiga, pembuatan perangkat lunak dan verifikasi. Pada penelitian ini telah berhasil dirancang perangkat lunak untuk unit kontrol alat ukur sudu CFWT berbasis pengolahan citra. Berdasarkan hasil kalibrasi sensor inframerah Sharp GP2Y0A21 didapatkan bahwa nilai jarak adalah sama dengan 178924.57 dibagi dengan nilai output ADC desimal pangkat 1.08. Selisih maksimal antara jarak input dan jarak tempuh motor adalah 0.5mm, dan jarak kontrol antara 130-400mm. Dengn demikian, metode pengukuran menggunakan alat ini akan lebih mudah dan cermat, karena selain dapat mengukur benda dengan bentuk yang kompleks mngurangi resiko keausan benda an lebih teliti, juga bisa diakukan dengan mudah serta cepat karena adanya sistem kontro

Optimizing the Tuning of Fuzzy-PID Controllers for Motion Control of Friction Stir Welding Robots

Friction stir welding (FSW) is defined as a solid-state welding method that is required to be accurate, especially for its motion. This requirement can be satisfied by implementing an accurate controller. The aim of this research was to develop an accurate control system based on a fuzzy-proportional integral derivative (PID) controller for parallel manipulator FSW robots. In order to achieve a higher accuracy in motion control, the tuning optimisation process for a fuzzy-PID controller was conducted using a genetic algorithm (GA) and particle swarm optimisation (PSO). The optimisation algorithms were applied to simultane-ously tune the fuzzy rules and output of the membership function from the fuzzy inference system (FIS). The PID controller was designed and tuned using a MATLAB® PID Tuner to obtain the desired response. It was then developed into a fuzzy-PID controller with Sugeno type-1 FIS with 2 inputs and 1 output. The tuning optimisation of the fuzzy-PID controller using GA and PSO was performed to achieve the global minimum integral absolute error (IAE) of the angular velocity. MATLAB® Simulink® was employed to test and simulate the controllers for three motors in the FSW robot model. The IAE values of the PID controller implemented for each motor were 0.03644, 0.04893, and 0.04893. The IAEs of the implemented fuzzy-PID-GA (output and rules) controller were 2.061, 2.048, and 2.048; of the implemented fuzzy-PID-GA (output) controller were 0.03768, 0.05059, and 0.05059; of the fuzzy-PID-PSO (output and rules) controller were 0.01886, 0.0253, and 0.02533; and of the fuzzy-PID-PSO (output) controller were 0.03767, 0.05059, and 0.05059. Therefore, the fuzzy-PID-PSO (output and rules) controller gave the most accurate results and outperformed the others. Keywords—Angular velocity, control system, friction stir welding, fuzzy-PID, genetic algorithm, motion, motor, parallel manipulator, particle swarm optimisation