scholarly journals Optimization of Wire Electric Discharge Machining Parameters for Surface Roughness on 316 L Stainless Steel Using Full Factorial Experimental Design

2014 ◽  
Vol 5 ◽  
pp. 1670-1676 ◽  
Author(s):  
P. Raju ◽  
M.M.M. Sarcar ◽  
B. Satyanarayana
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Experimental Design ◽  
Machining Parameters ◽  
Factorial Experimental Design ◽  
Electric Discharge Machining ◽  
316 L Stainless Steel ◽  
Wire Electric Discharge Machining ◽  
Full Factorial Experimental Design ◽  
Full Factorial

scholarly journals The Design and Implementation of Adsorptive Removal of Cu(II) from Leachate Using ANFIS

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Nurdan Gamze Turan ◽  
Okan Ozgonenel
Keyword(s):  
Experimental Design ◽  
Contact Time ◽  
Fixed Bed ◽  
Experimental System ◽  
Factorial Experimental Design ◽  
Adsorbent Dosage ◽  
Initial Ph ◽  
Full Factorial Experimental Design ◽  
Full Factorial ◽  
Aqueous Leachate

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (23), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 23experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.

Analysis of the effects of fabric reinforcement parameters on the mechanical properties of textile-based hybrid composites by full factorial experimental design method

2017 ◽  
Vol 48 (3) ◽  
pp. 580-598 ◽  
Author(s):  
Hande Sezgin ◽  
Omer B Berkalp
Keyword(s):  
Experimental Design ◽  
Hybrid Composites ◽  
Design Method ◽  
High Strength ◽  
Factorial Experimental Design ◽  
Fabric Reinforcement ◽  
Experimental Design Method ◽  
Glass Carbon ◽  
Full Factorial Experimental Design ◽  
Full Factorial

In this study, the effect of some fabric reinforcement parameters (fabric direction, yarn type and stacking sequence) on the mechanical properties of textile based hybrid composites are analysed by using full factorial experimental design method. The analysis of the results is achieved by using Minitab 17 software program. One factor (fabric reinforcement direction) with two levels (warp direction and weft direction) and two factors (yarn type and stacking sequence) with three levels (jute/glass, jute/carbon, glass/carbon and consecutive, low strength inside, high strength inside) are selected as the reinforcement design. Full factorial experimental design analysis results indicate that, the highest tensile and impact strength values among the experimental design are realised when samples are taken from the warp direction and E-glass/carbon combination is chosen as the yarn (material) type. Moreover, it is verified that while higher tensile strength is achieved by placing higher strength fabrics to the inner layers, higher impact strength is achieved by placing high strength fabrics to the outer layers of hybrid composite structures. Analysis of variance tables also show that at 95% confidence level, the effects of the factors are statistically significant ( p < 0.05).

Modelling and multi-response optimization of wire electric discharge machining parameters using response surface methodology and grey–fuzzy algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1760-1774 ◽  
Author(s):  
Bikash Choudhuri ◽  
Ruma Sen ◽  
Subrata Kumar Ghosh ◽  
Subhash Chandra Saha
Keyword(s):  
Stainless Steel ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Wire Electric Discharge Machining ◽  
Grey Relational ◽  
Stainless Steel 316

Wire electric discharge machining is a non-conventional machining wherein the quality and cost of machining are influenced by the process parameters. This investigation focuses on finding the optimal level of process parameters, which is for better surface finish, material removal rate and lower wire consumption for machining stainless steel-316 using the grey–fuzzy algorithm. Grey relational technique is applied to find the grey coefficient of each performance, and fuzzy evaluates the multiple performance characteristics index according to the grey relational coefficient of each response. Response surface methodology and the analysis of variance were used for modelling and analysis of responses to predict and find the influence of machining parameters and their proportion of contribution on the individual and overall responses. The measured values from confirmation experiments were compared with the predicted values, which indicate that the proposed models can be effectively used to predict the responses in the wire electrical discharge machining of AISI stainless steel-316. It is found that servo gap set voltage is the most influential factor for this particular steel followed by pulse off time, pulse on time and wire feed rate.

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