Multi Response Optimization of WEDM Process on OHNS Die Steel Using ANN, SA and GA

2016 ◽  
Vol 3 (2) ◽  
pp. 16-49
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Material Removal ◽  
Removal Rate ◽  
Research Work ◽  
Control Parameters ◽  
Response Data ◽  
Relational Analysis ◽  
Significant Process ◽  
Response Optimization ◽  
Grey Relational ◽  
Wedm Process

In the present research work Oil Hardened Naturally Shrinking (OHNS) work-material which is commonly used in plastic industries is considered for machining by WEDM process. Four different control parameters are deliberated to study the effect on the responses like material removal rate, overcut and surface roughness. To reduce the total number of experiment, L27 orthogonal array is used. Analysis of Variance is applied to attain the significant process parameters affecting the responses. The effect of the responses with the control parameters is plotted through S/N ratio graphs. To find the effect of the parameters on the responses and thereby developing a mathematical model regression analysis is done. The response data are examined using artificial neural network. Single objective parametric combination for each response is obtained using simulated annealing. A multi response optimization for the responses is done initially by using genetic algorithm and finally by applying Grey relational analysis.

scholarly journals Multi-Response Optimization of Coagulation and Flocculation of Olive Mill Wastewater: Statistical Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 2344
Author(s):  
Srikanth Vuppala ◽  
Riyaaz Uddien Shaik ◽  
Marco Stoller
Keyword(s):  
Grey Relational Analysis ◽  
Research Work ◽  
Olive Mill Wastewater ◽  
Relational Analysis ◽  
Coagulation And Flocculation ◽  
Coagulant Dosage ◽  
Response Optimization ◽  
Grey Relational ◽  
Flocculation Process ◽  
Olive Mill

Olive oil production is one of the important industrial sectors within the agro-food framework of the Mediterranean region, economically important to the people working in this sector, although there is also a threat to the environment due to residues. The main wastes of the olive oil extraction process are olive mill wastewater (OMW) and olive husks which also require proper treatment before dismissal. In this research work, the main goal is to introduce grey relational analysis, a technique for multi-response optimization, to the coagulation and flocculation process of OMW to select the optimum coagulant dosage. The coagulation and flocculation process was carried out by adding aluminum sulfate (Alum) to the waste stream in different dosages, starting from 100 to 2000 mg/L. In previous research work, optimization of this process on OMW was briefly discussed, but there is no literature available that reports the optimal coagulant dosage verified through the grey relational analysis method; therefore, this method was applied for selecting the best operating conditions for lowering a combination of multi-responses such as chemical oxygen demand (COD), total organic carbon (TOC), total phenols and turbidity. From the analysis, the 600 mg/L coagulant dosage appears to be top ranked, which obtained a higher grey relational grade. The implementation of statistical techniques in OMW treatment can enhance the efficiency of this process, which in turn supports the preparation of waste streams for further purification processes in a sustainable way.

Optimization of process parameters of powder mixed EDM for high carbon high chromium alloy steel (D2 steel) through GRA approach

2018 ◽  
Vol 8 (4) ◽  
pp. 388-398 ◽  
Author(s):  
Kanwal Jit Singh
Keyword(s):  
Material Removal ◽  
Removal Rate ◽  
Chromium Alloy ◽  
High Carbon ◽  
Content Type ◽  
Practical Applications ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational ◽  
D2 Steel

Purpose The purpose of this paper is to investigate the process parameters and optimise the machining input parameter of powder mixed electric discharge machining for high carbon high chromium alloy steel (D2 steel) for the industrial application. Grey relational analysis approach has been used to obtain the multiple performance output response. Design/methodology/approach In this experimental work, input parameters, namely, pulse on-time, discharge current, tool material and grit size, are selected. The design of the experiment has been constructed with the help of MINITAB 7 Software, in which L16 orthogonal array has been preferred for the experimentation. The effect of input parameters, namely, material removal rate, tool wear rate and surface roughness, is investigated. Grey relational analysis and analysis of variance are performed to optimise the input parameters and better output results. Findings In this experimentation, there is an increment of tool wear rate by 64.49 per cent, material removal rate by 47.14 per cent and surface roughness by 35.82 per cent. Practical implications A lot of practical applications have been found in many different material processing industries like metallurgy, machinery, electronics, transportation, military science, agricultural machinery, etc. These practical applications have brought forward definite and noticeable economic benefits. Originality/value The reader is given a general overview on the machining investigation and optimisation of processes parameters through the grey theory approach. It gives a new framework to investigate the problems where multiple input machining variable and various output responses are obtained in single optimised parameters.

Multi Response Optimization by Using the Hybrid Technique in Electro Discharge Machining of AISI 304

2016 ◽  
Vol 26 ◽  
pp. 68-75 ◽  
Author(s):  
Munmun Bhaumik ◽  
Kali Pada Maity
Keyword(s):  
Wear Rate ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Tool Wear Rate ◽  
Aisi 304 ◽  
Relational Analysis ◽  
Electro Discharge Machining ◽  
Grey Relational

Electro discharge machining (EDM) is most popular non-conventional electro-thermal machining process where electrical energy is used to generate a spark and thermal energy used to remove material from the workpiece. The primary goal of EDM is getting more material removal rate (MRR) with lower tool wear rate (TWR). For this investigation, machining parameters like peak current, pulse on time, gap voltage and duty cycle are considered as process parameter, and material removal rate (MRR) and tool wear rate (TWR) are considered as response. AISI 304 stainless steel and tungsten carbide are used as work material and tool material respectively. Taguchi L27 orthogonal array has been applied for designing the experiment. A hybrid optimization technique like desirability in combination with grey relational analysis (GRA) has been performed to get the optimum level of the control parameter for getting higher MRR and lower TWR. Analysis of variance (ANOVA) is performed for the statistical analysis. These results show that peak current is the most significant parameter for MRR and TWR. The optimal parameter setting for maximum MRR and minimum TWR has obtained by desirability coupled with Grey relational analysis.

Performance analysis of process parameters on machining α–β titanium alloy in electrochemical micromachining process

2016 ◽  
Vol 232 (9) ◽  
pp. 1577-1589 ◽  
Author(s):  
T Geethapriyan ◽  
K Kalaichelvan ◽  
T Muthuramalingam ◽  
A Rajadurai
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Micromachining ◽  
Relational Analysis ◽  
Micro Tool ◽  
Grey Relational

Due to inherent properties of Ti-6Al-4V alloy, it is being used in the application of fuel injector nozzle for diesel engine, aerospace and marine industries. Since the electrochemical micromachining process involves the no heat-affected zone, no tool wear, stress- and burr-free process compared to other micromachining processes, it is widely used in the manufacturing field to fabricate complex shape and die. Hence, it is highly important to compute the optimum input parameters for enhancing the machining characteristics in such machining process. In this study, an attempt has been made to find the influence of the process parameters and optimize the parameters on machining α–β titanium alloy using Taguchi-grey relational analysis. Since applied voltage, micro-tool feed rate, electrolyte concentration and duty cycle have vital role in the process, these parameters have been chosen as the input parameters to evaluate the performance measures such as material removal rate, surface roughness and overcut in this study. From the experimental results, it has been found that micro-tool feed rate has more influence due to its importance in maintaining inter electrode gap to avoid micro-spark generation. It has also been found that lower electrolyte concentration with lower duty cycle produces lower surface roughness with better circularity on machining α–β titanium alloy. The optimum combination has been found using Taguchi-grey relational analysis and verified from confirmation test. It has also been inferred that the multi-response characteristics such as material removal rate, surface roughness and overcut can be effectively improved through the grey relational analysis.

Multi-Objective Optimization of ECG Process Applying Soft Computing Techniques

2019 ◽  
pp. 68-98
Author(s):  
Pritam Pain ◽  
Goutam Kumar Bose
Keyword(s):  
Soft Computing ◽  
Removal Rate ◽  
Research Work ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Soft Computing Techniques ◽  
Response Optimization ◽  
Grey Relational ◽  
Electrochemical Grinding

The present research work focuses on the selection of significant machining parameters depending on the nature-inspired algorithm while machining alumina-aluminum interpenetrating phase composites during electrochemical grinding. Control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) have been considered for experimentation. The response data are initially trained and tested by using Artificial Neural Network. The contradictory responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are ensured individually by employing Firefly Algorithm. A multi-response optimization for all the responses is done initially by using the Genetic algorithm. Finally, in order to obtain a single set of parametric combination for all the output simultaneously fuzzy based Grey Relational Analysis technique is adopted. These natures driven soft computing techniques corroborates well during the parametric optimization of the electrochemical grinding process.

Multi-Response Optimization of CNC WEDM Process Parameters for Machining Titanium Alloy Ti 6AI-4V Using Response Surface Methodology (RSM)

2014 ◽  
Vol 541-542 ◽  
pp. 354-358 ◽  
Author(s):  
C. Nandakumar ◽  
B. Mohan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Titanium Alloy ◽  
Response Surface ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Response Optimization ◽  
Wedm Process

This research deals with the multi-response optimization of CNC WEDM process parameters for machining titanium alloy Ti 6AI-4V using Response Surface Methodology (RSM) to achieve higher Material Removal Rate (MRR) and lower surface roughness (Ra). The process parameters of CNC WEDM namely pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were optimized to study the responses in terms of material removal rate and surface roughness. The surface plot and the contour plots were generated between the process parameters and the responses using MINITAB software. The results show that the Response surface methodology (RSM) is a powerful tool for providing experimental diagrams and statistical-mathematical models to perform the experiments appropriately and economically.

Multi-Response Optimization of Dissimilar Al-Ti Alloy FSW Using Taguchi - Grey Relational Analysis

2020 ◽  
Vol 833 ◽  
pp. 35-39 ◽  
Author(s):  
Shival Patel ◽  
Kishan Fuse ◽  
Khushboo Gangvekar ◽  
Vishvesh Badheka
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Traverse Speed ◽  
Anova Analysis ◽  
Friction Stir ◽  
Relational Analysis ◽  
Significant Process ◽  
Grey Relational Grade ◽  
Response Optimization ◽  
Grey Relational

This article presents multi-response optimization of friction stir welding of dissimilar Al 6061-Titanium alloy using Taguchi based grey relational analysis. Taguchi’s L9 orthogonal array was used for designing the experiments. Process parameters considered for the experiments were rotational speed, traverse speed and tilt angle. Ultimate tensile strength, yield strength, and % elongation were the responses measured which all are larger-the-better characteristics. Based on grey relational grade, optimum levels of process parameters were identified and further ANOVA analysis was carried out to find most significant process parameter.

scholarly journals Multi-objective optimization of drilling parameters for orthopaedic implants

2020 ◽  
pp. 002029402094712
Author(s):  
Parvesh Antil ◽  
Sundeep Kumar Antil ◽  
Chander Prakash ◽  
Grzegorz Królczyk ◽  
Catalin Pruncu
Keyword(s):  
Experimental Study ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

Titanium (Ti) and its alloys have gained immense popularity as biomaterials in recent years. Their excellent specific strength makes them outstanding materials for orthopaedic applications. However, in the orthopaedic application, precise micro-drilling (i.e. implants inserts) is required, which is very challenging for these materials. To overcome this issue, the present research proposes an experimental study corroborated with a multi-objective optimization by simulating the drilling under electric discharge machining of Ti-6Al-4V. Taguchi’s methodology–based L9 orthogonal array was used for the experimental study. Voltage, current, pulse on and pulse off were used as the input parameters for the experimental investigation. In order to achieve suitable precise drilling, the material removal rate and surface finish were used as response parameters. Here, by optimizing parameters of the precise drilling, it is possible to obtain high material removal rate and better surface finish simultaneously. The Grey relational analysis was adopted to analyse the output quality characteristics. The optimized results generated through the Grey relational analysis are highly accurate with respect to the experimental outcomes.

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