Effect of equal-channel angular pressing on the surface roughness of commercial purity aluminum during turning operation

Advances in Mechanical Engineering

et al. 2017

Self Cite

Nowadays solid state recycling of aluminum chips provides a competitive market for the aluminum foundry. Chip recycling requires an efficient pretreatment system in terms of optimizing consolidating technique to achieve a high metal recovery with rapid return of investment and hence drive down fixed cost and increase productivity without compromising on metal quality. Solid state recycling through hot extrusion is a promising technique to recycle machining chips without re-melting. In this article, aluminum chips were cold compacted followed by hot extrusion under extrusion ratio of 5.2 and at three different extrusion temperatures of 350°C, 425°C, and 500°C. The effect of extrusion temperature on the surface roughness (Ra and Rz) and machining time needed to remove a unit volume of aluminum alloy 6061 machined by turning were investigated. The effect of extrusion temperature was investigated using the Box-Behnken design; three machining parameters (depth of cut, feed rate, and cutting speed) on the surface roughness of the machined specimens were also examined. Obtained results were in favor of the presence of significant effect of those four parameters on specimen surface roughness with dominating effect of feed rate. Moreover, the optimum cutting conditions for minimum machining time per unit volume (T m) while Ra is kept below an industrially accepted value of 0.8 mm was found for the original alloy for the following cutting conditions: cutting speed of 116 m/min, depth of cut of 0.33 mm, and feed rate of 0.11 mm/rev.

Section: Resultsmentioning

confidence: 99%

“…The added term is kept in the model if it has a significant effect (p-value \ 0.05), otherwise, it is removed. The process is repeated until the model includes only all of the significant terms 19…”

Section: Resultsmentioning

confidence: 99%

Effect of extrusion temperature on the surface roughness of solid state recycled aluminum alloy 6061 chips during turning operation

Ragab

Taha

Advances in Mechanical Engineering

et al. 2017

Self Cite

“…It is worth mentioning that surface roughness also influenced the mechanical properties such as corrosion resistance, creep life, and fatigue behavior. Extensive previous work [1][2][3][4] has been done on investigating the effect of the following parameters: cutting depth, feed rate, cutting speed, tool nose radius, lubrication condition, and cutting tool material, on the following response variables: tool wear, surface roughness, cutting forces, production time, and cost. These studies were conducted using different routes; analysis of variance, neural networking coupled with genetic algorithm, and neural networking coupled with electromagnetism optimization [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

Alharthi

Bingöl

Advances in Materials Science and Engineering

et al. 2017

In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.

“…Abbas et al [16] investigated the surface roughness for five starting material conditions (annealed and ECAP processed to 1, 2, 3, and 4 passes) for 1050 aluminum alloy. Quadratic models that related the four surface roughness ( , , and ) values to the three cutting parameters (cutting speed, feed rate, and depth of cut) were developed, while the fourth parameter (number of ECAP passes) was considered as categorical factor.…”

Section: Introductionmentioning

confidence: 99%

Effect of Equal Channel Angular Pressing on the Surface Roughness of Solid State Recycled Aluminum Alloy 6061 Chips

Advances in Materials Science and Engineering

Taha

Ragab

et al. 2017

Self Cite

Solid state recycling through hot extrusion is a promising technique to recycle machining chips without remelting. Furthermore, equal channel angular pressing (ECAP) technique coupled with the extruded recycled billet is introduced to enhance the mechanical properties of recycled samples. In this paper, the surface roughness of solid state recycled aluminum alloy 6061 turning chips was investigated. Aluminum chips were cold compacted and hot extruded under an extrusion ratio (ER) of 5.2 at an extrusion temperature (ET) of 425°C. In order to improve the properties of the extruded samples, they were subjected to ECAP up to three passes at room temperature using an ECAP die with a channel die angle(Φ)of 90°. Surface roughness (RaandRz) of the processed recycled billets machined by turning was investigated. Box-Behnken experimental design was used to investigate the effect of three machining parameters (cutting speed, feed rate, and depth of cut) on the surface roughness of the machined specimens for four materials conditions, namely, extruded billet and postextrusion ECAP processed billets to one, two, and three passes. Quadratic models were developed to relate the machining parameters to surface roughness, and a multiobjective optimization scheme was conducted to maximize material removal rate while maintaining the roughness below a preset practical value.