Optimization of Process Parameters in Injection Moulding of FR Lever Using GRA and DFA and Validated by Ann

M.S. Vinmathi,P. Venkataramaiah,S. Selvaraj

doi:10.19026/rjaset.11.2090

Abstract

This study deals with the optimization of the injection moulding process parameters in the production of the FR (Forward Reverse) Lever, using the Grey Relational Analysis (GRA) and Desirability Function Approach (DFA) and the results are validated by ANN. The FR lever is used to control the direction of the rotation of spindles in conventional machines. Parameters such as injection pressure, injection speed and injection temperature, which influence the quality of the final product of the injection moulding process, are called the input parameters. Parameters such as Shrinkage and Surface Roughness, which are considered as the quality characteristics of this product, are called the output parameters. FR levers are produced using a fabricated Injection moulding tool, according to Taguchi’s experimental design and the response data are recorded. The recorded experimental data are analyzed and the optimum process parameters combinations have been found, by using the GRA and DFA. An ANN has been developed using the experimental data and the responses (output data) are predicted for the corresponding optimal parameters combination. Finally, the obtained optimum parameter combinations are tested by both ANN and the experiment and the results are found to be satisfactory.

Highlights

Many Engineers and researchers have done research on the optimization of the process parameters of Plastic Injection Moulding (PIM) for various thermoplastic materials and attempted to reduce the shrinkage, surface roughness and Warpage of plastic moulded products
Irene et al (2010) presented a framework on a Multidisciplinary Design Optimization methodology, which tackles the design of an injection mold to achieve significant improvements
The Analysis of variance (ANOVA) is performed on the overall grey relational grade, the composite desirability obtained from the Grey Relational Analysis (GRA) and DRA and it is revealed that injection pressure is the most influencing parameter

Summary

Introduction

Many Engineers and researchers have done research on the optimization of the process parameters of Plastic Injection Moulding (PIM) for various thermoplastic materials and attempted to reduce the shrinkage, surface roughness and Warpage of plastic moulded products. Some authors have presented a few case studies on improving the Quality characteristics of surface roughness, shrinkage and Warpage, by applying the Taguchi technique, Artificial Neural Network (ANN), Fuzzy logic and combination methods. Huizhuo et al (2010) presented a combination of artificial neural network and Design of Experiment (DOE) method is used to build an approximate function relationship between Warpage and the process parameters. Irene et al (2010) presented a framework on a Multidisciplinary Design Optimization methodology, which tackles the design of an injection mold to achieve significant improvements Pirc et al (2008) introduced a practical methodology to optimize the position and the shape of the cooling channels in injection moulding processes and Pirc et al (2009) developed an iterative dual reciprocity boundary element method (DRBEM) to solve optimization of 3D cooling channels in injection molding. Gang et al (2012) developed an experimentbased optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process using particle swarm optimization algorithm. Huizhuo et al (2010) presented a combination of artificial neural network and Design of Experiment (DOE) method is used to build an approximate function relationship between Warpage and the process parameters. Irene et al (2010) presented a framework on a Multidisciplinary Design Optimization methodology, which tackles the design of an injection mold to achieve significant improvements

Methods

Results

Conclusion