Prediction and analysis of surface roughness in selective inhibition sintered high-density polyethylene parts: A parametric approach using response surface methodology–grey relational analysis

Author:

Rajamani D1ORCID,Ziout Aiman2,Balasubramanian E1,Velu R1,Sachin Salunkhe1,Mohamed Hussein3

Affiliation:

1. Centre for Autonomous System Research (CASR), Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

2. Department of Mechanical Engineering, College of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates

3. Department of Mechanical Engineering, Helwan University, Cairo, Egypt

Abstract

Selective inhibition sintering (SIS) process intends to produce near-net-shape components through sintering of specific region of powder particles. The prediction of surface quality in SIS parts is a challenging task due to its complex part building mechanism and influence of abundant process parameters. Therefore, this study investigates the key contributing parameters such as layer thickness, heater energy, heater feedrate and printer feedrate on the surface quality characteristics ( Ra, Rz and Rq) of high-density polyethylene specimens fabricated through selective inhibition sintering process. The SIS system is custom built and experiments are conducted based on four-factor, three-level Box–Behnken design. The empirical models have been developed for predicting the influence of selected parameters on surface quality. The optimal process parameters such as the layer thickness of 0.1 mm, heater energy of 28.48 J/mm2, heater feedrate of 3.25 mm/s and printer feedrate of 110 mm/min are attained using grey relational multi-criteria decision-making approach. Furthermore, response surface analysis revealed that surface quality of sintered components is influenced significantly with heater energy and heater feedrate, followed by layer thickness. The confirmation experiments based on optimal process variables validate the developed grey relational analysis strategy.

Funder

Defence Research and Development Organization, Government of India

Publisher

SAGE Publications

Subject

Mechanical Engineering

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