Machining of tough polyethylene pipe material: surface roughness and cutting temperature optimization

Machining of tough polyethylene pipe material: surface roughness and cutting temperature... Manufacturing of high-density polyethylene (HDPE) pipes is usually achieved by extrusion processes. However, various joining or fitted parts and mechanical testing samples are prepared by material removal methods. This study focuses on the machinability of the HDPE tough resin used for piping and fittings to make standard test specimens. The experimental plan has been conducted using a Taguchi (L27) orthogonal array. Input machining parameters are cutting speed (V c), feed rate (f), and depth of cut (a p) while output effects are represented by conventional surface roughness criteria (Ra, Rt, and Rz) and the measured cutting temperature (t°). As a result, a second-order model was established between input and output parameters via the response surface methodology (RSM). The development of predictive models is essential for machining of extruded HDPE since few experimental trends and data are available in literature as compared to metals. Optimum cutting conditions are determined using the desirability function approach. It is revealed that the feed rate (f) is the main contributing factor when minimizing surface roughness of HDPE material. The analysis of variance (ANOVA) results showed that the contributions for surface roughness criteria (Ra, Rt, and Rz) are 96.11, 86.92, and 92.22% respectively. On the other hand, cutting temperature is mainly influenced by cutting speed (V c), depth of cut (a p), and the three interactions (V c × f), (V c × a p), and (f × a p). Finally, optimized cutting temperature is discussed for the three main cases, which are lower, targeted, and upper values, i.e., t° minimum, t° targeted = 30 °C, and t° maximum allowable = 32 °C. The International Journal of Advanced Manufacturing Technology Springer Journals

Machining of tough polyethylene pipe material: surface roughness and cutting temperature optimization

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Springer London
Copyright © 2017 by Springer-Verlag London
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
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