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Metals 2018, 8(6), 394; https://doi.org/10.3390/met8060394

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

1
Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
2
Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia
3
Foundry Department, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia
4
Department of Mechanical Design and Production, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
*
Author to whom correspondence should be addressed.
Received: 27 April 2018 / Revised: 21 May 2018 / Accepted: 21 May 2018 / Published: 29 May 2018
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Abstract

Aluminum Alloy 6061 components are frequently manufactured for various industries—aeronautics, yachting, and optical instruments—due to their excellent physical and mechanical properties, including corrosion resistance. There is little research on the mechanical tooling of AA6061 and none on its structure and properties and their effects on surface roughness after finish turning. The objective of this comprehensive study is, therefore, to ascertain the effects of both the modern method of hardening AA6061 shafts and the finish turning conditions on surface roughness, Ra, and the minimum machining time for unit-volume removal, Tm, while also establishing the cost price of processing one part, C. The hardening methods improved both the physical and the mechanical material properties processed with 2, 4, and 6 passes of equal channel angular pressing (ECAP) at room temperature, using an ECAP-matrix with a channel angle of 90°. The reference workpiece sample was a hot extruded chip under an extrusion ratio (ER) of 5.2 at an extrusion temperature of 500 °С (ET = 500 °C). The following results were obtained: grain size in ECAP-6 decreased from 15.9 to 2.46 μm, increasing both microhardness from 41 Vickers hardness value (HV) to 110 HV and ultimate tensile strength from 132.4 to 403 MPa. The largest decrease in surface roughness, Ra—70%, was obtained turning a workpiece treated with ECAP-6. The multicriteria optimization was computed in a multilayer perceptron-based artificial neural network that yielded the following optimum values: the minimal length of the three-dimensional estimates vector with the coordinates Ra = 0.800 μm, Tm = 0.341 min/cm3, and С = 6.955 $ corresponded to the optimal finish turning conditions: cutting speed vc = 200 m/min, depth of cut ap = 0.2 mm, and feed per revolution fr = 0.103 mm/rev (ET-500 extrusion without hardening). View Full-Text
Keywords: artificial neural network; equal channel angular pressing; extrusion; recycled aluminum alloy 6061 chips; turning operation; optimization; surface roughness artificial neural network; equal channel angular pressing; extrusion; recycled aluminum alloy 6061 chips; turning operation; optimization; surface roughness
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Abbas, A.T.; Pimenov, D.Y.; Erdakov, I.N.; Taha, M.A.; El Rayes, M.M.; Soliman, M.S. 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, 8, 394.

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