IIUM Repository

Optimizing tensile strength of PLA-Lignin Bio-composites using machine learning approaches

Manshor, Mohd Romainor and Kamarulzaman, Amjad Fakhri and Anuar, Hazleen and Toha @ Tohara, Siti Fauziah and Ali, Fathilah and Sukindar, Nor Aiman and Suhr, Jonghwan and Haris, Nursyam Dzuha (2023) Optimizing tensile strength of PLA-Lignin Bio-composites using machine learning approaches. In: 5th International Conference on Advances in Manufacturing and Materials Engineering, 9th - 10th August 2022, Kuala Lumpur, Malaysia.

[img] PDF (Full Paper) - Published Version
Restricted to Registered users only

Download (7MB) | Request a copy
[img]
Preview
PDF (Scopus) - Supplemental Material
Download (321kB) | Preview

Abstract

It is imperative to accurately estimate the final performance of composite parts during the initial design phase of the manufacturing process. In generating sustainable bio composites with superior mechanical properties such as tensile strength, the combination of fillers and plasticizers, as well as their concentration in the mixture, are always deemed crucial. In order to reduce the number of experimental runs and their associated costs and timescales, statistical optimization of the core design elements has become increasingly important. The filler and plasticizer concentrations of extruded bio composites were adjusted in this study utilizing both statistical (analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning (Multilayer Perceptron (MLP)) approaches. Initial ANOVA results indicated that lignin, epoxidized palm oil (EPO), and their respective combinations were the most influential factors in enhancing the durability of lignin/polylactic acid (PLA) bio composites. In this work, RSM and MLP were used to model and predict the data in order to maximize the various solutions and establish the nonlinear relationship between the concentration of lignin and EPO.

Item Type: Conference or Workshop Item (Slide Presentation)
Uncontrolled Keywords: Multilayer Perceptron (MLP) Machine learning Analysis of Variance (ANOVA) Response Surface Methodology (RSM) Lignin bio composites
Subjects: T Technology > TS Manufactures > TS195 Packaging
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Biotechnology Engineering
Kulliyyah of Engineering > Department of Manufacturing and Materials Engineering
Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr Hazleen Anuar
Date Deposited: 01 Jun 2023 16:49
Last Modified: 17 Jul 2023 10:04
URI: http://irep.iium.edu.my/id/eprint/104852

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year