An experimental study on material removal rate and surface roughness of Cu-Al-Mn ternary shape memory alloys using CNC end milling

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Date

2024

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Institute of Physics

Abstract

This study investigates the impact of Computer Numerical Control (CNC) milling parameters on Cu-Al-Mn SMAs (Shape memory alloys) to evaluate the effects on Surface Roughness (SR) and Material Removal Rate (MRR). The primary variables examined comprise of cutting speed, feed rate, and depth of cut. Results indicate that the Shape Memory Effect (SME) is higher in Copper Aluminium Manganese (CAM 3) compared to CAM 1 and CAM 2, with SME improving from 3.5% to 5.5% as Manganese (Mn) content increases, reflecting an increase in dislocations within the metal’s crystal structure. Surface roughness increases with higher feed rates and depths of cut but decreases with increased cutting speed. MRR shows a positive correlation with feed rate, depth of cut, and cutting speed, though it decreases with higher Mn content. Notably, CAM 3 exhibits lower MRR compared to CAM 1 and CAM 2. Scanning Electron Microscopy (SEM) reveals that at lower feed rates (0.10 mm rev−1), the surface is smooth and free of ridges or feed marks, while at higher feed rates (0.18 mm rev−1), noticeable surface imperfections and plastic deformation occur. The addition of Mn improves surface smoothness and machinability, it also affects MRR. Further suggesting that Mn content and milling parameters significantly influence both the mechanical properties and machinability of Cu-Al-Mn SMAs respectively. © 2024 The Author(s). Published by IOP Publishing Ltd.

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Keywords

Aluminum alloys, Cams, Copper alloys, Crystal cutting, Manganese alloys, Manganese metallurgy, Metal castings, Metal cutting, Milling (machining), Samarium alloys, Ternary alloys, Computer numerical control end milling, Cu-al-manganese shape memory alloy, Cutting speed, End milling, Feedrate, Material removal rate, Memory alloy, Numerical control, Shape-memory, Shape-memory effect, Surface roughness

Citation

Materials Research Express, 2024, 11, 9, pp. -

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