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Polypropylene based composites are generally used for interior applications in automotive designs, food containers, reusable products etc. Particularly those reinforced with E-glass have good durability combined with the ease of fabrication and temperature tolerance. High production rates of these polymer composites with close tolerances are made possible by plastic injection moulding (PIM). It is a common manufacturing process for reinforced plastics with precision and hence efficient usage of the PIM process becomes essential as these equipment and moulds are costly. PIM can be used to form complex profiles with good surface finish and the process involves four stages viz. melting of raw material, injecting through a nozzle, applying packing pressure and cooling the mould (Chiang & Chang, 2006). From the existing literature it was found that process parameters like melting temperature, injection pressure, injection speed, injection time, packing pressure, packing time along with the cooling temperature and cooling time play a vital role in affecting the quality of the injection moulded parts (Wu & Liang, 2005; Kuo-Ming Tsai et al., 2009). Selecting the correct injection moulding conditions was always a major concern in plastics industries. The fibre orientation in these composites was also found to play a vital role in affecting the mechanical strength of the moulded components (Sadabadi & Ghasemi, 2007). PIM was generally characterized by the presence of shrinkage phenomena but selection of proper mould temperature and packing pressure could be a solution for avoiding shrinkage and warping (Sanchez et al., 2012). It was also found that the flexural modulus of the parts was affected greatly by the mould temperature (Ozcelik et al., 2010). The quality characteristics of PIM products could be studied through the dimensional properties, the surface properties and the mechanical or optical properties (Yang & Gao, 2006). Accelerated injection rate was observed to be an utmost essential parameter while moulding ultra-thin components. Enhanced injection rates were found to bring a great increase in filling ratio (Song et al., 2007). The cooling time was also found to affect the mechanical properties of injection moulded plastics and its interaction with the packing pressure was observed to play a vital role in reducing the shrinkage (Karasu et al., 2014).
Taguchi’s optimization approach involving signal to noise ratio and orthogonal arrays was an accepted methodology for improving productivity. Taguchi method was used to identify the optimal setting of input parameters for good mechanical properties, surface finish and reduced shrinkage in polymers processed by PIM (Li et al., 2007; Ozcelik, 2011). Taguchi orthogonal design was used to optimize the roughness of machined surface effectively (Das & Sahoo, 2011). However most researches based on this technique were concerned with single response optimization while a practical situation demands simultaneous optimization of multiple responses. Techniques for handling the multi response optimization problems include the grey relational analysis (GRA), principal component analysis (PCA), artificial neural networks (ANN) and data envelopment analysis (Krishnaiah & Shahabudeen, 2012). A combined approach based on Taguchi design and back propagation network was observed to create an effective dynamic quality predictor for warpage but ANN requires tedious training of the network to perform effectively (Chen et al., 2008). An integrated technique based on BPN, genetic algorithm (GA) and Taguchi parameter design was employed to optimize the process parameters by treating the PIM as a multi input multi output process but it was found that the GA cannot scale well with complexity (Chen et al., 2009). The methodology using Taguchi design and PCA was found to be effective in optimizing the PIM parameters for fibre-reinforced composites. The technique involves conversion of correlated properties into a set of uncorrelated components (Fung & Kang, 2005).