Altab Hossain

A Fuzzy Logic-Based Prediction Model for Kerf Width in Laser Beam Machining

In laser beam machining, the main concern is the machining quality as kerf width of the end product. It is essential for industrial applications to cut the workpiece with minimum kerf width. However, it is difficult to develop a precise functional relationship between input and output variables in laser machining. Therefore, an effort has been conducted to build up an intelligent fuzzy expert system (FES) model to predict the kerf width in CO2 laser cutting. The employed input parameters were assisting gas pressure, laser power, cutting speed, and standoff distance. The fuzzy logic was performed on fuzzy toolbox in MATLAB R2009b by employing Mamdani technique. In total, 81 experiments were carried out and experimental results were used for training and testing of the developed fuzzy model. Relative error and goodness of fit were used to investigate the accuracy of the prediction ability and the values of 3.852% and 0.994, respectively, were found to be satisfactory. This paper will extend knowledge about the prediction of kerf width by using FES model.

Heat Transfer Performance of Different Nanofluids Flows in a Helically Coiled Heat Exchanger

Helically coiled heat exchangers are globally used in various industrial applications for their high heat transfer performance and compact size. Nanofluids can provide excellent thermal performance of this type of heat exchangers. In the present study, the effect of different nanofluids on the heat transfer performance in a helically coiled heat exchanger is examined. Four different types of nanofluids CuO/water, Al2O3/water, SiO2/water, and ZnO/water with volume fractions 1 vol.% to 4 vol.% was used throughout this analysis and volume flow rate was remained constant at 3 LPM. Results show that the heat transfer coefficient is high for higher particle volume concentration of CuO/water, Al2O3/water and ZnO/water nanofluids, while the values of the friction factor and pressure drop significantly increase with the increase of nanoparticle volume concentration. On the contrary, low heat transfer coefficient was found in higher concentration of SiO2/water nanofluids. The highest enhancement of heat transfer coefficient and lowest friction factor occurred for CuO/water nanofluids among the four nanofluids. However, highest friction factor and lowest heat transfer coefficient were found for SiO2/water nanofluids. The results reveal that, CuO/water nanofluids indicate significant heat transfer performance for helically coiled heat exchanger systems though this nanofluids exhibits higher pressure drop.

Load Distribution for an Intelligent Air-cushion Track Vehicle Based on Optimal Power Consumption

This study incorporates the computation of load distribution for an intelligent air-cushion track vehicle (IACTV) based on optimal power consumption (PC). A novel design auto adjusting air-cushion system has been designed and developed to minimise the PC. Focusing on optimising total PC, three main issues were studied in this paper. First, a theoretical model is developed for optimising total PC of the vehicle and the effects of load distribution on total PC. Second, a fuzzy expert system (FES) is used to control the swamp tracked vehicles intelligent air cushion system and thus minimise the PC. Third, an artificial neural network (ANN) model has been developed which has been trained to predict the total PC for IACTV and to provide illustration how FES might play an important role in the prediction of PC of the vehicles intelligent air-cushion system.

Integrated mechanics of hybrid electrical air-cushion tracked vehicle for swamp peat

This paper presents an integrated mechanics for the design of hybrid electrical air-cushion tracked vehicle. The air-cushion of HETAV is protected with a novel-design air-cushion supporting system which can adjust automatically. A propeller is installed on the vehicle to develop additional thrust for overcoming the dragging motion resistance of the air-cushion system. The mean values of traction for the vehicle with propeller compared with no propeller increased 10.21% and 6.47% for the vehicle weight of 2.45 kN and 3.43 kN, respectively. Similarly, it was found that the mean values of vehicle’s motion resistance decreases 12.63% and 25.81% for the vehicle weight of 2.45 kN and 3.43 kN, respectively.

Techno-economic analysis of a hybrid PV-wind-diesel energy system for sustainable development at coastal areas in Bangladesh

Power generation capacity of Bangladesh needs to be enhanced to support the rising electricity demand. Bangladesh has predominantly used fossil fuel generators for the past decades. Saint Martins Island and Kuakata are two significant areas that lie at or near the coast of Bangladesh with an average annual solar radiation of 4.81 and 4.65 kWh/m2/d respectively. The monthly average wind speeds at a height of 25 meters are 4.79 and 4.54 m/s, respectively. Considering this data and the benefits of the Optimized hybrid systems, HOMER (Hybrid Optimization Model for Electric Renewable) is used to optimize a system for each of these areas. The costs of energy found from the proposed optimized PV-wind-diesel hybrid Energy system for Saint Martins island and Kuakata are 30.768 and 30.759 Tk/kWh, respectively, the net present cost (NPC) also has been evaluated as 13219616 Tk. which are quite reasonable with respect to the present situation in Bangladesh. The major objective of this proposed optimized design is to supply the maximum load demand using renewable sources with the minimum cost of energy (COE) and reduce the burning of fuel and also reduce the emission of CO2. The proposed energy system is able to meet 67.3% and 62.3% load demand using renewable sources, which helps to reduce the GHG (Green Houses Gas) emission by 67% and 64% for Saint Martins island and Kuakata, respectively when compared to a diesel system. Total load served throughout the year is 33,611kWh, which is 16% higher than the previously designed system with approximately equivalent COE.

Dyeing process parameters optimisation and colour strength prediction for viscose/lycra blended knitted fabrics using Taguchi method

The main purpose of this study was to find the optimal dyeing conditions as well as predict the colour strength (CS) of viscose/lycra blended knitted fabrics using Taguchi method. The controllable factors such as dye concentration, temperature, time, alkali concentration, salt concentration and liquor ratio have been used as input variables and CS of the fabric as response variable for the construction of Taguchi model. An L25 orthogonal array design has been chosen and conducted 25 experiments with three runs for each experiment. The optimal parameters in the dyeing process have been identified as dye concentration 9%, time 60 min, temperature 75 °C, salt concentration 50 g/l, alkali concentration 14 g/l and liquor ratio 1:8. Taguchi mathematical model built in this study has been confirmed by confirmation experiment as well as unseen experimental data. The mean absolute error and coefficient of determination (R2) were found to be 3.48% and 0.88, respectively, from the actual and predicted CS. It is concluded that Taguchi method is efficient on the optimisation and prediction of fabric CS in non-linear complex dyeing.

Nonlinear controller of an air-cushion system for a swamp terrain vehicle: fuzzy logic approach

This paper presents the fuzzy logic controller (FLC) of an air-cushion system for a swamp peat terrain vehicle and describes the process by which it functions. Cushion pressure is controlled by an electronic proportional control valve and FLC using the output signal of the distance (height) measuring sensor that was attached to the vehicle. The main purpose of this study was to develop a control scheme for an air-cushion system and to investigate the relationship between vehicle vertical position and the air-cushion system, and to illustrate the important role of the fuzzy logic control system. Experimental values were recorded in the laboratory for control system testing, and in the swamp peat terrain field for vehicle performance investigation. In this paper, a fuzzy logic expert system (FLES) model, based on the Mamdani approach, was developed to predict the changes in flowrate. The mean relative error of actual and predicted values from the FLES model of flowrate was found to be slightly above the acceptable limit. The goodness of fit of the prediction values from the FLES model was found to be close to 1.0 as expected, and hence demonstrated the good performance of the developed system.

Experimental and Theoretical Investigation of Powder–Binder Mixing Mechanism for Metal Injection Molding

Metallic powder and binder mixing mechanism plays a vital role in the quality of molded parts in metal injection molding. The present study is intended for experimental and theoretical investigation of powder–binder mixing mechanism to investigate the functional correlation among mixing parameters and performance characteristics for different composition of feedstocks. Powder loading and shear rate are considered as input parameter. Fuzzy expert system is adopted to test the validity of the experimental results by analyzing different numerical error criteria using the viscosity as output with respect to input parameters. The mean relative error and correlation coefficient for type A and type B were found to be 6.09% and 8.51% (<10%) and 0.990 and 0.998, respectively. Hence the result indicates a reliable acceptability of the proposed amount of powder loading for feedstock preparation.

Fuzzy logic-based Model for Prediction of Building Wall Humidity Level

This paper presents the viability of using fuzzy logic expert system for the prediction of building wall’s relative humidity. In comparison with traditional logic model, fuzzy logic is more efficient in linking the multiple units to a single output and is invaluable supplements to classical hard computing techniques. The purpose of this work was to investigate the relationship between wall material composition and climate condition, as well as to evaluate the viability of using fuzzy logic expert system in predicting the building wall’s humidity level. The experimental data was taken at different time of the day for different material composition. A fuzzy logic expert system model based on Mamdani approach was subsequently developed to predict the building wall’s humidity level. Verification of the developed fuzzy logic model was carried out through various numerical error criteria. The study revealed that the developed fuzzy system was able to predict the humidity level in the wall material within high accuracy with relative error of less than the acceptable limits of 10% and a goodness of fit very close to unity.

Predicting the colour properties of viscose knitted fabrics using soft computing approaches

Abstract The aim of this paper was to predict the colour strength of viscose knitted fabrics by using fuzzy logic (FL) model based on dye concentration, salt concentration and alkali concentration as input variables. Moreover, the performance of fuzzy logic (FL) model is compared with that of artificial neural network (ANN) model. In addition, same parameters and data have been used in ANN model. From the experimental study, it was found that dye concentration has the main and greatest effects on the colour strength of viscose knitted fabrics. The coefficient of determination (R2), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by FL model are found to be 0.977, 1.025 and 4.61%, respectively. Further, the coefficient of determination (R2), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by ANN model are found to be 0.992, 0.726 and 3.28%, respectively. It was found that both ANN and FL models have ability and accuracy to predict the fabric colour strength effectively in non-linear domain. However, ANN prediction model shows higher prediction accuracy than that of Fuzzy model.