Machinable brasses are a broad class of high strength copper-zinc alloys mainly containing lead to improve machinability. Conventional leaded brasses are widely used in several manufacturing sectors (i.e., fabrication of hydraulic components, fittings, valves, etc.) due to their superior workability in extrusion and drawing, together with their superior machinability for high efficiency production of final components in high speed/high precision machining centers. In addition to machinability, the mechanical behavior and general fracture mechanisms of these alloys are also important, due to their impact on the overall reliability and safety of brass components. In this study, the main fracture modes and mechanical characteristics of two industrial copper alloys, namely, CuZn39Pb3 and CuZn36Pb2As, are presented in relation to their microstructure. Optical metallography, macro-and microfractography, together with static and dynamic mechanical testing, were used as the principal analytical techniques for the present investigation.
The machinability in turning mode of three lead-free brass alloys, CuZn42 (CW510L), CuZn38As (CW511L) and CuZn36 (C27450) was evaluated in comparison with a reference free-cutting leaded brass CuZn39Pb3 (CW614N), as far as the quality characteristics, i.e., cutting force and surface roughness, were concerned. A design of experiments (DOE) technique, according to the Taguchi L 16 orthogonal array (OA) methodology, as well as analysis of variance (ANOVA) were employed in order to identify the critical-to-machinability parameters and to obtain their optimum values for high-performance machining. The experimental design consisted of four factors (cutting speed, depth of cut, feed rate and alloy) with four levels for each factor using the "smaller-the-better" criterion for quality characteristics' optimization. The data means and signal-to-noise (S/N) responses indicated that the depth of cut and the feed rate were the most influential factors for the cutting force and surface roughness, respectively. The optimized machining parameters for cutting force (34.59 N) and surface roughness (1.22 µm) minimization were determined. Confirmation experiments (cutting force: 39.37 N and surface roughness: 1.71 µm) seem to show that they are in close agreement to the main conclusions, thereby validating the findings of the statistical evaluation performed.
The aim of this work was the evaluation of the machinability of leaded brass namely CuZn39Pb3 (CW614N) in comparison to three lead-free brasses alloys namely CuZn42 (CW510L), CuZn38As (CW511L), and CuZn36 (C27450). The machinability of the studied alloys was investigated, based on chip morphology and power consumption, as quality characteristics. Microstructure examination and hardness testing was employed for the characterization of the selected alloys. Design of experiments (DOE) was employed in a multi-non-parametric study in order to identify the critical-to-machinability parameters and obtain their optimum values for high performance machining. The attempted joint screening using a four-level orthogonal array revealed that the depth of cut and the alloy type were the two statistically predominant effects. The chip morphology and the power consumption in a balanced concurrent optimization effort were optimal when the depth of cut was set at 0.5 mm for the alloy type CW614N chip morphology optimization. The optimal chip morphology response was split in equal chances to produce needle or arc chips. The predicted power consumption was confined in a range of values with an upper boundary at 65 W. The findings of the statistical evaluation were experimentally confirmed, validating the DOE approach. Keywords Machinable brass . Lead-free brasses . Machinability . Design of experiments (DOE) Nomenclature ANOVA Analysis of variance ER Error contribution HV final The chip average hardness (Vickers hardness) HV initial The initial surface hardness (Vickers hardness) MANOVA Multivariate analysis of variance M CM G Grand median value of CM data M P G Grand median value of P data mCM DC o Median value of CM for the optimal setting of DC effect mCM M o Median value of CM for the optimal setting of M effect mP DC o Median value of P for the optimal setting of DC effect mP M o Median value of P for the optimal setting of M effect MR Master response (ranked values of ssRS) OA Orthogonal array R 2 Coefficient of determination rCM i (i = 1,2) Ranked values of the response of the chip Morphology replicates rP i (i = 1,2) Ranked values of the response of the power consumption replicates srCM rSM 1 + rSM 2 srP rP 1 + rP 2 rCM Ranked values of srCM rP Ranked values of srP ssRS rCM 2 + rP 2 WH
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