Modeling of twist extrusion process parameters of AA6082-T6 alloy by response surface approach

2014 ◽  
Vol 228 (11) ◽  
pp. 1458-1468 ◽  
Author(s):  
U Mohammed Iqbal ◽  
VS Senthil Kumar
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Extrusion Process ◽  
Surface Approach ◽  
Twist Extrusion

Effect of Process Parameters on the Microstructure Homogeneity of AA6082 Aluminum Alloy Deformed by Twist Extrusion

2013 ◽  
Author(s):  
V. S. Senthil Kumar ◽  
U. Mohammed Iqbal
Keyword(s):  
Aluminum Alloy ◽  
Process Parameters ◽  
Effective Strain ◽  
Extrusion Process ◽  
Work Piece ◽  
Hardness Testing ◽  
Twist Extrusion ◽  
Experimental Values ◽  
3D Software ◽  
Deform 3D

Twist extrusion is a severe plastic deformation in which the rectangular shaped work piece is extruded through a die with a twist channel. In this work the twist extrusion process of AA6082 T6 aluminum samples were carried out to investigate the effects of process parameters like temperature and deformation passes on the microstructure homogeneity. The results indicate that the grain refinement of AA 6082 T6 aluminum alloy leads to inhomogeneous microstructure after one twist extrusion pass. On further extrusion passes the inhomogeneity in the microstructure is found to be disappeared. The homogeneity of the distribution of the deformation was confirmed by micro hardness testing. Finite element modeling has been performed in DEFORM 3D software for determining the homogeneity of the effective strain distribution which agreed well with the experimental values.

scholarly journals Optimization of Extrusion Process Using Response Surface Methodology for Producing Squid and Millet Based Extrudate

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Viswanth Ganesan ◽  
Balasundari Subbaiah ◽  
Nimish Mol Stephen ◽  
Manikandavelu Dhanushkodi ◽  
Rathnakumar Kolandaivadivel ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Extrusion Process ◽  
Expansion Ratio ◽  
Domestic Consumption ◽  
Screw Speed ◽  
Food Grain ◽  
Density Expansion ◽  
Product Weight

The squid is underutilized seafood than finfish and shellfishes in domestic consumption and millet is also an underutilized food grain. This study aimed to produce squid and millet- based extrudate under the optimized process parameters using Response Surface Methodology (RSM). The effects of process parameters such as heater 1 temperature (55°C, 60°C and 65°C), heater 2 temperature (120°C, 125°C and130°C), screw speed (250 rpm, 275 rpm and 300 rpm), and inclusion of squid powder (3%, 4%, 5%, 6% and 7%) were tested for responses like bulk density, expansion ratio and fifty product weight in RSM. All process parameters tested had significant (P<0.05) impact on responses. The optimized parameters were found to be 64.10°C of heater 1 temperature, 129.98°C of heater 2 temperature, screw speed of 317.87 rpm, and 6.56% of inclusion of squid powder.

Parametric Optimization in Drilling Operation Using Response Surface Approach

2016 ◽  
Vol 1137 ◽  
pp. 117-131
Author(s):  
Kamaljit Singh Boparai ◽  
Sandeep Singh ◽  
Amritpal Singh
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Metal Cutting ◽  
Parametric Optimization ◽  
Machining Parameters ◽  
Surface Approach ◽  
Contour Plots ◽  
Cutting Processes

Modeling and optimization of machining parameters are the indispensable elements in modern metal cutting processes. The present study realize the interaction of drilling input process parameters such as spindle speed, feed rate and number of holes and their influence on the surface roughness, diameter and position of hole obtained in drilling of mild steel. The contour plots were generated to highlights the interaction of process parameters as well as their effect on responses. An empirical model of surface roughness, diameter and position of hole was developed using response surface methodology (RSM). The model fitted and measured values were quite close, which indicates that the developed models can be effectively used to predict the respective response. The process parameters are optimized using desirability-based approach response surface methodology.

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