Development Of An Automated Book Casing For Ic Industry

Today, most of the IC industries are using fully automatic machines to perform the process of placing the casing onto the IC With high technology machines, daily production can be increased. However, the cost of those machines are extremely high. Therefore, the small and medium scale industries are unable to purchase these machines. In order to over come this problem, an inexpensive automatic book casing system is proposed and developed. The main objective of this project is to design a low cost machine with safety features, fully automatic and a user-friendly. This machine should be able to perform the application such punching and stamping the casing of the Integrated Circuit (IC) as well as pick and place application.The structure design of this project is separated into two parts. They are the hardware construction and the software development. The hardware part involves the design and construction of an input station, an output station, a conveyer station, a pick and place robotic arm, a punching station, a pressure faulty detection system, a power distribution module, a pneumatic control system and data logger module. The software part involves the design and development of the system control software. The system control software is created by using FPSOFT PLC programming software. FPSOFT can create the PLC programming more effectively because it uses the graphic symbol to create the PLC ladder diagram. FPSOFT is also efficient in terms of trouble shooting and programming modification. This project implements the Matsushita NAIS FPO Programmable Logic Control (PLC) to control the overall system of the machine. FPO is a simple and user friendly controller. FPO can extend its 1/O port to 128 units for large number of input and output devices control. The proposed project was successfully designed, constructed and tested. It is also working and functioning accurately as planned in its design stage. This can be shown from the experimental results conducted on the system

Optimization of Digital Electronic Circuit Structure Design Using Genetic Algorithm

The complexity of the digital electronic circuit is due to the number of gates used per system as well as the interconnection of the gates. Diminution of the total number of gates used and interconnection in the system would reduce the cost in the design, as well as increasing the efficiency of the overall system. As a result, the higher integration level, the better and the cheaper final product produced. The conventional digital circuit design method is based on Boolean algebra. There are no specific procedure to choose the right theorem or postulate for the Boolean expression simplification and it is very impractical to design the digital circuits that have more than four variable. Karnaugh map can provide the simple minimization process for Boolean expression, but it encounters difficulties when the variable is more than four In this research, Genetic Algorithm (GA) technique is used as a tool to search for the optimal solution for the digital circuit structure. The GA process (Inter Loop GA), crossover operator (Fix Multiple Point Crossover), mutation operator (Random Discrete Mask Mutation) and fitness function (Constraint Fitness and Gate Optimization Fitness) were developed in this research. The simulator called Optimal Digital Circuit Structure Designer (ODCSD) is also developed in this work. ODCSD is a digital circuit structure design simulation program. Further more, a prototype hardware has been designed and constructed to test the success chromosome string, which called as GA based Logic Implementer (GALI). GALI is programmed by the success chromosome bits obtained from the simulation phase. This chromosome bits are used to set up the gates arrangement in the hardware. A number of experiments are implemented to design 3-bit, 4-bit, 5-bit and 6-bit circuits. The results show that the proposed method is able to produce the optimized circuit with lesser number of gates compared to the conventional methods. In the future development, the proposed system can be used as the discrete controller when it implemented in the process control application

Double Helix Structure and Finite Persisting Sphere Genetic Algorithm in Designing Digital Circuit Structure

This paper proposes a new approach of chromosome representation in digital circuit design which is Double Helix Structure (DHS). The idea of DHS in chromosome representation is inspired from the nature of the DNA\u27s structure that built up the formation of the chromosomes. DHS is an uncomplicated design method. It uses short chromosome string to represent the circuit structure. This new structure representation is flexible in size where it is not restricted by the conventional matrix structure representation. There are some advantages of the proposed method such as convenience to apply due to the simple formation and flexible structure, less requirement of memory allocation and faster processing time due to the short chromosomes representation. In this paper, DHS is combined with Finite Persisting Sphere Genetic Algorithm (FPSGA) to optimal the digital circuit structure design. The experimental results prove that DHS uses short chromosome string to produce the flexible digital circuit structure and FPSGA further optimal the number of gates used in the structure. The proposed method has better performance compared to other methods

Double Helix Structure and Finite Persisting Sphere Genetic Algorithm in Designing Digital Circuit Structure

This paper proposes a new approach of chromosome representation in digital circuit design which is Double Helix Structure (DHS). The idea of DHS in chromosome representation is inspired from the nature of the DNA\u27s structure that built up the formation of the chromosomes. DHS is an uncomplicated design method. It uses short chromosome string to represent the circuit structure. This new structure representation is flexible in size where it is not restricted by the conventional matrix structure representation. There are some advantages of the proposed method such as convenience to apply due to the simple formation and flexible structure, less requirement of memory allocation and faster processing time due to the short chromosomes representation. In this paper, DHS is combined with Finite Persisting Sphere Genetic Algorithm (FPSGA) to optimal the digital circuit structure design. The experimental results prove that DHS uses short chromosome string to produce the flexible digital circuit structure and FPSGA further optimal the number of gates used in the structure. The proposed method has better performance compared to other methods

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R2) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R2) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection

Analysis on the voltage stability on transmission network with PV interconnection

Voltage stability means the ability of the power system network to maintain steady-state voltage value at all buses in the system under normal condition and after being subjected to a disturbance. This research highlights the effect of solar photovoltaic (PV) as the subject of disturbance to the network system as this kind of energy source has emerged towards higher level of integration into the national grid. High penetration of solar PV into the grid may cause several issues of stability and security to the system particularly effecting the normal voltage and line overloading. This research is focused on the simulation of power flow to study the transmission network behavior with and without the solar PV interconnection. To accomplish the research objectives, the network system will be modelled in a software known as Power System Simulator for Engineering (PSSE). The simulation result will be discussed and analyzed using Voltage Stability Indices (VSI) to prove and strengthen the theory behind the literature review

Design of digital circuit structure based on evolutionary algorithm method

Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, abstractions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an m * n matrix form in which m is the number of input variables

An Approach for the optimization of thermal conductivity and viscosity of hybrid (Graphene Nanoplatelets, GNPs : Cellulose Nanocrystal, CNC) nanofluids using Response Surface Methodology (RSM)

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions