4.5 Article

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 Chips

Journal

METALS
Volume 8, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/met8060394

Keywords

artificial neural network; equal channel angular pressing; extrusion; recycled aluminum alloy 6061 chips; turning operation; optimization; surface roughness

Funding

  1. King Saud University [RG-1439-020]
  2. Act 211 of the Government of the Russian Federation [02.A03.21.0011]

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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, T-m, 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 degrees. The reference workpiece sample was a hot extruded chip under an extrusion ratio (ER) of 5.2 at an extrusion temperature of 500 degrees C (ET = 500 degrees C). The following results were obtained: grain size in ECAP-6 decreased from 15.9 to 2.46 mu m, 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 mu m, Tm = 0.341 min/cm(3), and C = 6.955 $ corresponded to the optimal finish turning conditions: cutting speed v(c) = 200 m/min, depth of cut a(p) = 0.2 mm, and feed per revolution f(r) = 0.103 mm/rev (ET-500 extrusion without hardening).

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