# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials  
[PERSON]  
1 Faculty of Mechanical Engineering Technology, Pauh Putra Main Campus, Universit Malaysia Perlis, Perlis 02600, Malaysia; [EMAIL_ADDRESS] (N.R. ); [EMAIL_ADDRESS] (M.N.M.S. ); [EMAIL_ADDRESS] (M.F.G.) [PERSON]  
2 Center of Excellence Geopolymer and Green Technology (CEGeoGTech), Universit Malaysia Perlis, Perlis 01000, Malaysia; [PERSON] (M.F.G.) [PERSON]  
5 Department of Physics, Faculty of Processing Engineering and Materials Technology, Czerstochowa University of Technology, 42-200 Czestochowa University of Technology,  
demand. The quality of moulded parts does not depend solely on one factor; it is affected by several factors such as part design, input parameters, selection of material and mould design [1]. Therefore, the ability to produce a part of good quality is of the utmost importance. Finding the optimal setting of the processing parameter is the most vital step to improving the quality of the moulded parts [2]. The use of recycled materials affects the part properties, especially strength. The use of trial-and-error is one of the options available to optimize the associated parameters, but this approach is not suitable for current industries using complex manufacturing processes, as it is time-consuming and costly [3]. Therefore, applying optimisation methods such as the Response Surface Methodology (RSM), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Taguchi is a good option to optimise the setting of processing parameters to mould parts with better quality [4; 5; 6; 7; 8; 9; 10; 11]. RSM is among the optimisation methods that have been used by many researchers [6; 7; 8; 9; 10]. [PERSON] and [PERSON] [10] conducted research using the RSM optimisation approach to create optimal process settings to minimise warpage and volume shrinkage. General Purpose Polystyrene (GPPS) was used as the material. Eighty-four trials of central composite design (CCD) were developed and data simulation was performed using simulation tools. The Firefly Algorithm (FA) and Genetic Algorithm (GA) were used to find the optimal minimum shrinkage and warpage value of the part. The results that were acquired through these methods were compared with the experimental work. Seven parameters were studied, namely, the mould temperature, packing pressure, cooling time, melt temperature, packing time, packing pressure and flow rate profile. The analysis results showed that melt temperature and cooling time were most affected shrinkage and warpage. Besides that, FA was found to yield better shrinkage and warpage results than the GA, with differences of 4.05% (simulation) and 3.28% (experimental), compared to 7.4% (simulation) and 10.26% (experimental), respectively, with GA.  
[PERSON] et al. [6] used Taguchi and RSM methods to establish input parameters for the warp of plastic spur gears produced using polyoxymethylene (POM) content. The input parameters analysed were packing pressure, holding pressure, melt temperature, holding time and mould temperature. The optimal minimum warpage produced by Taguchi was compared with RSM. The Taguchi method showed that holding time was the most influential factor on part warpage. By using the best set of parameters, the error recorded between the experimental work and simulation study was only 2.07%. [PERSON] et al. [11] applied the PSO to optimise the injection mould parameters by obtaining the minimum warpage, whereby the parameters studied were cooling temperature, V/P switchover, injection time and mould temperature. The regression model that represents the relationship between the response and processing parameters was acquired from the previous researches [12]. As compared to the warpage value from the previous study, the warpage recorded a reduction of 2.21%, proving the ability of PSO to optimise the warpage problem. Variables such as the materials, processing parameter and design were seen to have an impact on the finished moulded part [13].