Abstract

Metal injection moulding has gain much attention due to flexibility and high productivity of the plastics injection moulding with the powder metallurgy method of sintering. In order to gain better shape retention, optimum density of green part is required. This paper deals with the application of Taguchi optimisation technique on getting the optimum density for Metal Injection Moulding (MIM) components base on certain parameters in process injection. For this purposes only 3 process parameters were considered here including its interactions which are injection pressure, injection temperature and mould temperature. Since its more close to the final products these parameters were selected and other parameters will be kept constant. An orthogonal array of L16 experimental base design was conducted. Confirmation test will be done base on Signal-to-Noise (S/N) ratio and it Means.

Highlights

  • Sustainability development must not compromises the standards and health of future generations [1]

  • Taguchi method (L16) is used in this work to generate a single response which is green density of injection moulded sample which is important before undergo debinding process for optimum density

  • Taguchi (L16) design of experiments is used in order to improve the green density of the injected samples

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Summary

INTRODUCTION

Sustainability development must not compromises the standards and health of future generations [1]. Numerous researchers like Zu and Lin [7] in their work of debinding of injection moulded for optimum mechanical properties have shown that principal factors that affecting it, is solvent debinding temperature and thermal debinding atmosphere. They used Taguchi Method in implemented the Design of Experiments (DOE) for finding the optimum process variables. Taguchi method (L16) is used in this work to generate a single response which is green density of injection moulded sample which is important before undergo debinding process for optimum density

Materials
Design of Experiments
Optimise and Predict
SUMMARY
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