Optimizing the parameters in durian skin fiber reinforced polypropylene composites by response surface methodology

Abstract

Awareness on the advantages of natural fibers stimulates the interest of researchers to use them as reinforcement in polymer composites for robust application. Therefore, investigation on optimizing the impact property of durian skin fiber (DSF) reinforced polypropylene (PP) composites was carried out to determine the effect of DSF content, fiber size and maleic anhydride polypropylene (MAPP) content. Response surface methodology (RSM) was used in the design of experiments and in the analysis of results. The optimized value for the maximum impact strength of PP-DSF composite was found at 50wt% DSF content and fiber size range of 250-500μm with 0wt% MAPP. The maximum impact property for PP-DSF composite was 10.66kJ/m . From the analysis of variance (ANOVA), fiber content significantly affects the strength of PP-DSF composites , followed by the fiber size and MAPP content. The

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

“…A study on optimization the parameters in durian skin fiber (DSF) reinforced polypropylene composites was done by using RSM [99]. The decomposition of the composites such as fiber content, fiber size and maleic anhydride polypropylene (MAPP) content are significantly affect the impact strength of PP-DSF composites.…”

“…In the current study, three different data-driven model, i.e. Multiple Linear Regression (MLR), Response Surface Methodology (RSM) and Taguchi Method (TM) [96][97][98][99][100][101][102] are used as a tools to optimize the mechanical properties in a natural fibre composites in automotive engineering industry. In fact, the industry can produce a green automotive component due to their degradability and recyclability features.…”

Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

“…RSM is one of the methods that widely used for prediction and optimisation [15][16][17]. In this study, before optimisation using the Genetic Algorithm (GA) method, the RSM used was to find out the mathematical model in parallel and normal direction.…”

Shrinkage Analysis on Thick Plate Part using Response Surface Methodology (RSM)

“…RSM method is widely used as a tool for modelling the prediction and optimisation in various areas [7][8][9]. It is an empirical model to define the relationship between the various processing of variable parameters and responses according to desire criteria and searches for the significance of these process parameters in the coupled responses [10].…”

“…The significant factors that contribute to the shrinkage defect were identified from DOE analysis. Furthermore, an optimal set of process conditions can be predicted from a polynomial regression equation that had been modelled from list of experiments [7]. Thick plate part was selected as a case study and Autodesk Moldflow Insight (AMI) 2012 was used to simulate and identify the shrinkage on the moulded parts.…”