scholarly journals Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Metals ◽  
2018 ◽  
Vol 8 (6) ◽  
pp. 394 ◽  
Author(s):  
Adel Abbas ◽  
Danil Pimenov ◽  
Ivan Erdakov ◽  
Mohamed Taha ◽  
Magdy El Rayes ◽  
...  
2017 ◽  
Vol 9 (10) ◽  
pp. 168781401773415 ◽  
Author(s):  
Adham Ezzat Ragab ◽  
Mohamed Adel Taha ◽  
Adel Taha Abbas ◽  
Essam Ali Al Bahkali ◽  
Ehab Adel El-Danaf ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adel Taha Abbas ◽  
Mohamed Adel Taha ◽  
Adham Ezzat Ragab ◽  
Ehab Adel El-Danaf ◽  
Mohamed Ibrahim Abd El Aal

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.


2012 ◽  
Vol 152-154 ◽  
pp. 74-79
Author(s):  
Tadahiro Wada ◽  
Koji Iwamoto ◽  
Hiroaki Sugita

In cutting aluminum alloy 6061, continuous chips have a negative influence on the machining operation. Usually, Pb is added in order to break continuous chips. However, from the standpoint of environmental protection, it is necessary to improve chip breakability without adding Pb. One effective measure to improve chip breakability is by adding Si to aluminum alloy 6061. However, the influence of Si content on tool wear has not been fully examined. In this study, in order to clarify the influence of a diamond-like carbon (DLC) coating layer with a Cr-based interlayer, namely (Al,Cr)N, on cutting performance, aluminum alloys having different Si contents were turned. The substrate of the tool material was high-speed steel (1.4%C). The tool wear and the surface roughness were experimentally investigated. The following results were obtained: (1) In cutting two kinds of Al-Si alloys, namely the Al-2%Si alloy and Al-4%Si alloy, the progress of wear of the DLC/(Al,Cr)N-coated tool was slower than that of the DLC-coated tool. Therefore, the (Al,Cr)N interlayer was effective for decreasing the tool wear of the DLC-coated tool. (2) The wear progress of the two kinds of DLC-coated tools in cutting of Al-4%Si alloy was faster than that in cutting of Al-2%Si alloy. (3) In cutting of Al-2%Si alloy with the (Al,Cr)N/DLC-coated tool, the surface roughness was almost constant in the range of a cutting distance from 0.1 km to 9.5 km.


2011 ◽  
Vol 63-64 ◽  
pp. 412-415 ◽  
Author(s):  
Yu Mei Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li

The surface roughness is difficult to estimate in machining, especially for weak stiffness workpiece. So, prediction model of surface roughness using artificial neural network (ANN) is developed. This model investigates the effects of cutting parameters during milling Aluminum alloy 6061. The experiments are planned with four factors and four levels for developing the knowledge base for ANN training. Three-dimensional surface plots are generated using ANN model to study the effects of cutting parameters on surface roughness. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the axial and radial depth of cut has less effect. And the variations of surface roughness are highly non-linear with the cutting parameters.


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