Objective Optimization Technique Research Articles

Structural Analysis & Effect of Impingement Cooling Flow in the Internal Surface on Temperature Distribution of a Vane in Gas Turbine Using Taguchi Technique

Abstract: Gas turbine always consider one of the most important systems in the modern engineering applications, because it has continuous ability to generate electric power. In gas turbines the major portion of performance dependency lies upon turbine blade design ,the blades are considered one of the important and expensive parts in the gas turbines , where the blades of first stage from failure .The blades of the gas turbine suffer from tensile stresses due to centrifugal forces resulting from the high rotational speed and because of the loading of densegasses at a high temperature and speed ,as the centrifugal force is one of the problems that thedesigner of the turbine blades faces , as the designer aims to reduce stresses within the permissible limit. CFD study was carried out for evaluating the performance of a utility Steam Turbine. The flow in a turbine blade passage is complex and involves understanding of energy conversion in three dimensional geometries. The performance of turbine depends on efficient energy conversion and analyzing the flow path behavior in the various componentsof Steam Turbine. This study seeks the optimization of an axial turbine from a small gas turbine engine developed by ITA using Computational Fluid Dynamics (CFD) and Multi- Objective Optimization techniques focused on geometry changes to maximize the turbine performance. The simulation process will be done through the use of the commercial software SolidWorks.

A review on multi objective optimization problems and advancement on its solutions

A plenty of literatures are available for finding solution of optimization problem of single objective. Very few literatures are available for finding solution of optimization problem with multi objective optimization. Many researchers analysing multi objective problems by treating as single objective, keeping rest of variables as constant. With the tremendous rise of innovations in evolutionary computation, that has added feathers to Multi Objective Optimization techniques.

Multiple objective optimal design for an electrically assisted bicycle under dynamic finite element analysis

ABSTRACT Electrically assisted bicycles have become increasingly popular in modern cities. This article presents an optimization design process to enhance the strength of an electrically assisted bicycle frame (EABF) model. This process includes several techniques such as uniform design (UD), Kriging interpolation (KGI), entropy weighting analysis (EWA), gray relational analysis (GRA), and genetic algorithm (GA). The EN 15,194 test standard is used to calculate the maximum deformation of the fork in an EABF, through falling mass (FM) and falling frame (FF) impact simulations with ANSYS/LS-DYNA software. Six geometry characteristics of an EABF are selected as control factors. The UD generates multiple simulation tests, as each control factor in the design space is continuous. Dynamic finite element analysis (FEA) is conducted to determine the maximum impact deformation (ID) of each experiment in the UD. Applying a multiple objective optimization process, the best design for an EABF is obtained. The newly designed model offers a maximum improvement of 4.20% over the original design for the largest distortion. In conclusion, the multiple objective optimization technique improves the maximum ID of an EABF.

Multi-Objective Optimization in WEDM Process of Titanium Alloy (Ti-3Al-2.5V-2.0WC) using Desirability Approach

Recently, Titanium alloy (Ti-3Al-2.5V) reinforced with Tungsten Carbide (Ti-3Al-2.5V-2.0WC) developed using the microwave sintering process has plays a major role in mining industry. It has very good mechanical property and corrosion resistance. Due to its better strength and high melting point, it is difficult to machine in conventional machining process. Therefore, WEDM process give the solution to cut this high strength material in an effective manner. In this present work, the WEDM input variables such as Servo Voltage (SV), Pulse Time - On (PT-ON), Pulse Time – Off (PT-OFF) and Cutting Speed Percentage (CSP) are considered. L09 Orthogonal Array Matrix (OAM) is considered for experimental work. The output variables like Material Removal Rate (MRR) and Surface Roughness (SR) are considered to obtained the quality machining process. Multi objective optimization technique like Taguchi-Desirability Approach method is implemented to find the optimum input variables. The Improvement of MRR from 0.05978 g/min to 0.06420 g/min and SR from 3.812 μm to 3.452 μm are obtained using Desirability Approach.

Multi objective optimization of EDM process parameters for HCHCr steel

Electric discharge machining (EDM) or spark machining involves controlled erosion of electrically conducting materials by an interrupted, repetitive electric spark creating discharge between the cathode- tool and anode- work-piece, separated by a dielectric fluid medium. The objective of this work is to study the influence of machining characteristics during EDM of high carbon and high chromium steel (HCHCr) with copper electrode. EDM of HCHCr steel is conducted according to Taguchi orthogonal array (OA) design of experiment to evaluate the material removal rate (MRR), tool wear rate (TWR) and surface finish (Ra).The experiments are conducted on die-sinking EDM with varying current, voltage, and pulse-on-time to measure the above responses. Finally, the multi objective optimization technique namely principal component analysis (PCA) is used for optimizing the process parameters. The experimental results indicate the optimum parameters as: current 9 Amps, pulse-on time 15 µs and pulse-off time 10 µs.

Experimental Investigation into the Influence of the Process Parameters of Wire Electric Discharge Machining Using Nimonic-263 Superalloy.

Nimonic alloy is difficult to machine using traditional metal cutting techniques because of the high cutting forces required, poor surface integrity, and tool wear. Wire electrical discharge machining (WEDM) is used in a number of sectors to precisely machine complex forms of nickel-based alloy in order to attempt to overcome these challenges and provide high-quality products. The Taguchi-based design of experiments is utilized in this study to conduct the tests and analyses. The gap voltage (GV), pulse-on time (Ton), pulse-off time (Toff), and wire feed (WF), are considered as the variable process factors. GRA is used for the WEDM process optimization for the Nimonic-263 superalloy, which has multiple performance qualities including the material removal rate (MRR), surface roughness (SR), and kerf width (KW). ANOVA analysis was conducted to determine the factors' importance and influence on the output variables. Multi objective optimization techniques were employed for assessing the machining performances of WEDM using GRA. The ideal input parameter combinations were determined to be a gap voltage (GV) of 40 V, a pulse-on time (Ton) of 8 µs, a pulse-off time (Toff) of 16 µs, and a wire feed (WF) of 4 m/min. A material removal rate of 8.238 mm3/min, surface roughness of 2.83 µm, and kerf width of 0.343 mm were obtained. The validation experiments conducted also demonstrated that the predicted and experimental values could accurately forecast the responses.

Kerf width analysis of wire electrical discharge machining of titanium alloy

Wire Electrical Discharge Machining (WEDM), is a machining technique that utilizes electro-thermal principles to remove material from workpieces. It achieves material melting and vaporization by generating controlled and discrete sparks between a conductive work surface and a wire electrode. The process parameters involved in WEDM can significantly impact the machining results. This study focuses on four input process parameters: A: Gap Voltage (GV) ranging from 40 to 70 V, B: Pulse on time (Ton) ranging from 2 to 8 microseconds, C: Pulse off time (Toff) ranging from 10 to 16 microseconds, and D: Wire Feed (WF) ranging from 1 to 4 m per minute. These parameters are considered variables that can be adjusted to achieve desired machining outcomes. To conduct the experiments and analyses, the researchers employed a Taguchi-based Design. The Taguchi method is a statistical approach for designing experiments and optimizing processes by considering various factors and levels. By utilizing this method, the researchers conducted tests to investigate the effects of the process parameters on the output, which in this case is the kerf width. An analysis of variance (ANOVA) was performed to determine the significance of each factor and assess their influence on the kerf width. ANOVA helps identify the key factors that contribute most significantly to the observed variation in the output. Finally, the study employed single objective optimization techniques to achieve the minimum kerf width for titanium alloy. The optimization methods are used to find the optimal combination of process parameter settings that would result in the desired outcome of minimizing the kerf width. By utilizing this approach, the study aims to understand the impact of process parameters on the machining of titanium alloy using WEDM and optimize the parameters to achieve the desired outcome of minimum kerf width.

Parametric optimization of friction stir welding process parameters of dissimilar welded joints using grey relational analysis and desirability function approach

Purpose The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA). Design/methodology/approach The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined. Findings The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2° are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters. Research limitations/implications In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels. Practical implications Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property. Originality/value Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

Multi-objective optimization for joint actuator and sensor placement for guided waves based structural health monitoring using fibre Bragg grating sensors

Structural Health Monitoring (SHM) systems have a potential to reduce lifecycle costs of structures. As a result, there is a lot of active research in the area for SHM of civil and mechanical structures. Guided waves (GW) based SHM techniques allow monitoring of large plate-like structures with a few sensors and have been identified as the most promising of techniques for SHM. Fibre Bragg grating (FBG) sensors due to their low weight, and ability to be multiplexed have been long thought to be ideal sensors for SHM. The recent development of the edge filtering approach has increased their sensitivity to GW sensing and made them ideal sensors. Unfortunately the FBG sensors are passive sensors and show directional sensitivity. These operational constraints make extension of the earlier developed GW based SHM techniques for FBG sensors difficult. Recently the authors developed a technique for damage detection specifically designed for a network with FBG sensors. This paper builds on the past work by the authors and develops a methodology for a design of an actuator–sensor (AS) network for improving the damage assessment capability of the previously proposed method. The paper develops a multi objective optimization technique for the joint optimization of actuator and sensor placement for a network with FBG sensors. The joint optimization of the actuators and sensors is necessary due to the passive nature of the FBG sensors and also incorporates the directional nature of the FBG sensors. The paper develops an integer encoded NSGA-II for the optimization of the AS network. The objectives for the optimization are derived from the specific damage detection technique tailored for the use of FBG sensors. The objective are: coverage with at least 2 AS pairs, coverage with at least 1 edge reflected path and the cost of the deployed network. The results indicate that the encoding of the objectives of the optimization is valid and indeed the damage detection capabilities of the AS network are as predicted analytically. The paper for the first time develops a joint optimization of network for FBG sensors. It is also the first attempt at a truly multi-objective optimization of the AS network and promises to have applications on real structures.

An Enhanced Cluster based Energy Efficient Optimal Routing in WSN

In recent years, Wireless sensor networks (WSNs) have been emerged as an important research area due to its wide spread application in various domains such as military sensing and tracking, environment monitoring, patient monitoring, etc. WSN also have various advantages in gathering the data also with data transmission as well. Even though WSN has such advantages, there is certain drawback related to the energy consumption for data transmission over the network. Wireless sensor networks basically depend on the availability of nodes for transmission and if some dead nodes are available on the designated path of transmission, there will be delay in communication and also will affect the energy consumptions. Also when a particular node is transmitting any data packet with high power, it may lead to interference which will affect the proper transmission of data and wastage of power as well. For power level reduction proper methodology has to be followed starting with the clustering and designing of routing protocols. We intend to develop an enhanced clustering algorithm for initial clustering of sensor nodes for data transmission. Nodes will be clustered based on working attributes. Once nodes are clustered into different groups, transmission path will be assigned. An energy efficient optimal protocol will be designed in our approach for routing to improve the energy utilization by optimal power utilization. For optimization, we can employ multi objective optimization techniques which can enhance the optimal selection of power utilization. The proposed scheme will be then compared with some existing techniques to show the efficiency of the proposed approach.

Design of centralized controller for multivariable process using MOPSO algorithm

Objective: To estimate centralized PID controller parameters for 4 outputs and 5 inputs crude distillation non-square system with RHP zeros process. Methods/Analysis: The Multi- Objective Particle Swam Optimization (MOPSO) algorithm is applied to determine the PID controller parameters for the considered distillation column process. Findings: The performance of the proposed controller is compared with two centralized controller schemes, Davison’s and Tanttu and Lieslehto methods. The Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral of Time Absolute Error (ITAE) are chosen as performance indices. The simulation results prove that MOPSO tuned centralized controller gives the best performance when compared to other analytical techniques for both set point tracking and in disturbance rejection environment. Novelty: In practice, conventional PID controllers are tuned using classical methods, which require complex numerical calculations. In this paper, an attempt is made to fine tune the PID controller for a MIMO process using Multi Objective optimization technique and obtained challenging results as compared to conventional methods. Keywords: Nonsquare system; Centralized control; Multi Objective Particle Swam Optimization; PID controller

Friction stir welding parameter optimization using novel multi objective dragonfly algorithm

Friction stir welding (FSW) is a type of welding in which the joint is made in the solid phase using the heat generated through friction. FSW is used to produce welds with superior strength with low distortion. At present, the FSW technique is enormously applied in aerospace-related applications. In the present study, a multi objective optimization technique based on Dragonfly Algorithm (DFA) is proposed for optimizing the weld strength of FSW of aluminum alloy 6082-T6. The prime purpose of this work is to determine the optimal parameter combination of the input process parameters namely the tool rotational speed, the welding speed, and the tool tilt angle to achieve the optimal tensile strength and the impact strength of the weld. The optimal process parameters are validated and simulated using MATLAB software.

Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review.

Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.

Modelling and Optimization of Phase Locked Loop under Constrained Channel Length and Width of MOSFETs

CMOS integrated circuits consisting of MOSFETs have tradeoffs among their performance parameters. Hence they need minimization in those tradeoffs calling for multi objective optimization to yield a circuit with enhanced characteristics. To perform simultaneous optimization of the Phase locked loop (PLL) performances using an effective multi objective optimization technique saving the designer’s time and causing the near best performance is the motivation of this work. Though the designer can optimize the circuit in the netlist level, it is less effective and a time consuming iterative process and sometimes it is next to impossible for complex and nanoscale circuits with large number of MOSFET devices and interconnects. Performance parameters like phase noise, lock time and power consumption are optimized subject to the practical design constraints using an efficient multi-objective optimization technique, infeasibility driven evolutionary algorithm (IDEA) in a real time environment. Using design parameters like the channel length and width of the MOSFETs for optimal performance, the PLL is simulated for model validation. Significantly superior performance achieved by the designed PLL is demonstrated. The phase noise, average power consumption and lock time achieved here are −126.3 dBc/Hz at 1 MHz offset frequency, 1.523 mW and 50 nS respectively.

Multi objective taguchi–grey relational analysis and krill herd algorithm approaches to investigate the parametric optimization in abrasive water jet drilling of stainless steel

This novel study deals with the investigation of abrasive mixtures on abrasive water jet (AWJ) drilling of stainless steel 304 using multi objective soft computing techniques. In this study, the drilling parameters such as abrasive mixture, stand-off distance and feed rate were varied. The abrasive mixture was prepared with the composition of different types of abrasives such as silicon carbide & garnet, and aluminium oxide & garnet, and different mixture ratios. This mixing ratio was done based on the total mass of the abrasive mixture. The effect of abrasive water jet drilling parameters was examined on the hole features such as the hole diameter, circularity, cylindricity, and surface roughness. Multi objective optimization and algorithm techniques were employed in this study, namely Taguchi–Grey Relational Analysis (TGRA) and Krill Herd Algorithm (KHA). In this research work, the performance of KHA method was also compared with another recent metaheuristic technique i.e. grey wolf optimization (GWO) based on the quality measurement tools such as Spacing and Inverted generational distance. For this approach, the main parameters of metaheuristics algorithms were tuned using a robust design approach to acquire the best feasible solution. Besides, different multiple linear regression model equations were established to determine the best model for the KHA method based on the similarity between experimental and calculated attributes. With the assistance of these approaches, it is found that the abrasive mixtures have improved the performance of the AWJ drilling process in SS 304 rather than the use of a single type abrasive such as 100% Garnet. The results of this study proved that the KHA optimization technique is successfully utilized to find the best configuration parameter setting for AWJ drilling process, and that results are found to be efficient than the TGRA. To validate the predicted results of KHA, confirmation test was conducted. The results of the confirmation test showed that the predicted hole features of KHA were acceptable as that the error deviation was found as less than 2% with the experimental results. It is also noticed that the computational time and the selected quality metrics of KHA are found to be lower than the GWO method. Hence, it is confirmed that a new metaheuristic algorithm namely, KHA was found suitable for AWJ drilling process. The outcome of the present work explores a new paradigm to the AWJ machining to improve performance features in various operations.

Multi objective optimization in machining of Inconel 718 using taguchi method

Nickel based super alloys find wide application in industries like aerospace engine, nuclear reactors and other high temperature applications etc. Inconel 718 alloy is well suited for high temperature applications with excellent mechanical strength. Due to high strength it is difficult to machine. Many researchers were concentrated on optimization of various machining process for single response criteria. It is very difficult to select an optimal parameter for multi response criteria. To overcome this problem multi objective optimization technique is used to find optimal setting of machining parameter in milling of Inconel 718 alloy. In this paper, the effects of various machining parameters like cutting speed, feed, and depth on tool wear, MRR and surface roughness are studied. Three different coated carbide inserts are used for machining process and to perform multi objective optimization which would provide minimum tool wear and surface roughness and maximum MRR.

Multi-objective optimization in abrasive water jet peening on AA6063 alloy

Abrasive Water Jet Peening (AWJP) is a surface betterment process which is generally used to enhance the mechanical properties of the material in the surface and subsurface regions. In this research work, Box Bhenken Experimental design was used to carryout peening experiments on AA 6063-T6 alloy by varying Water Pressure, Standoff Distance and Traverse rate. The response parameters like surface roughness and Hardness were considered for the study. Since more than one response parameters are involved in the experiment it is essential to perform a multi objective optimization technique. Genetic algorithm is a popular method for multi objective optimization which uses the mathematical relation between the input and responses. The experimental results are compared with the predicted results and essential recommendations are presented.

Multi objective optimization of wire-electrical discharge machining of stellite using Taguchi – Grey approach

Wire Electrical Discharge Machining (WEDM) is one of advanced method of machining conceived from the concept of Electrical Discharge Machining. Unconventional method of material removal processes have been developed to overcome the difficulties in making of complex shapes in any electrically conductive work material by conventional machining methods. Stellite is a difficult to machine superalloy and extensively used in wear environments where its low friction coefficient minimizes the risk of seizing and galling. It possess good strength and lower thermal diffusion results in decreased tool life and poor machinability by conventional methods of machining. The present work details the multi objective optimization of WEDM process parameters by Taguchi-Grey approach. The experimental runs are planned by Taguchi’s design approach and Taguchi’s approach single objective optimization technique was employed for attaining the best possible process parameters combination for obtaining better machining performance. The experiments have been performed by considering the process parameters such as pulse on time, pulse off time and wire tension at three various levels. The ascendency of process variables are determined by ANOVA analysis. Taguchi - Grey methodology has been adopted for the WEDM process and optimum process parameters are determined for attaining improved multi performance characteristics.

Machinability studies on wire electrical discharge machining of Nickel alloys using multiple regression analysis

Wire Electrical Discharge Machining (WEDM) is most advanced method of machining conceived from the concept of spark erosion machining. Unconventional method of material removal processes have been developed to evade the difficulties in making of complex shapes in any electrically conductive work material with the help of traditional machining methods. Decision-making is progressively important in manufacturing domain due to its need for manufacturing components with quality and high precision. Stellite6 is difficult to machine and expansively used in wear surroundings where its low friction coefficient minimizes the danger of seizing and galling. The present work details the development of multiple regression equations for predicting the desired performance features in Wire EDM of Stellite material. Pulse On Time (μs), Pulse Off Time (μs) and peak current are selected as input variables and the material removal rate (MRR) is opted as performance characteristics in this present experimentation. The experimental runs are planned by Taguchi’s design approach and Taguchi’s single objective optimization technique was employed for attaining the best possible process parameters combination for obtaining better machining performance. Multiple regression analysis has been applied for developing empirical relationship among the process parameters. The developed regression equations are further used for prediction and comparison with the actual experimental results. The comparative results shows that the values predicted are closer with the experimental outcomes. The outcome attained from this exploration will be a wide-ranging support to the manufacturers for enhancing the rate of production and quality of products made with an assistance of WEDM process.

Navigating the Best Path to Optimality in a University Grants Administration Workload Assignment Problem

ABSTRACTThis research addresses a preaward grant administration workload‐to‐agent assignment problem that occurs in the office of research and sponsored programs (ORSP) at many universities. We first identify the optimal (utopian) workload assignment plan using a mixed‐integer linear programming problem. This optimal assignment of academic department workload to ORSP administrators may differ considerably from the status quo, requiring multiple reassignments from the current state to reach optimality. The number of reassignments raises concerns related to loss of administrator–department relationship, loss of department‐related knowledge, and increase in managerial inconvenience. To achieve the best workload reassignment with the fewest changes from the current status quo, while still placing a greater emphasis on the effective use of limited resources, we propose and illustrate a multiple objective optimization technique to identify the N best departmental reassignments from the current state that provide the greatest progress toward the utopian balanced workload solution. Solving this problem over several values of N and plotting the results allow the decision maker to visualize the trade‐off between the number of reassignments and the resulting progress achieved toward the utopian solution. This system could support the manager of pre‐award administrators in making an informed decision about the best number of reassignments to choose based on an objective assessment of the relevant trade‐offs. The technique proposed in this article is widely relevant with slight modifications to the mathematical model. Additional applications include the management of employee‐to‐task assignments in home healthcare, youth sports, nurse scheduling, blood bank facilities, and early education.