MULTI-OBJECTIVE OPTIMIZATION OF FDM: INTEGRATING ANN AND MOSOS-DS FOR IMPROVED MATERIAL CONSUMPTION, TENSILE STRENGTH, AND DIMENSIONAL ACCURACY
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.24205Keywords:
Multi-objective optimisation (MOO), symbiotic organism search (SOS), fused deposition modelling (FDM), artificial neural network (ANN), diversification strategyAbstract
Data-driven modelling and metaheuristic algorithms offer powerful tools for navigating the complexity of fused deposition modelling (FDM) process optimization. This study presents an integrated approach that addresses the nonlinear relationships among key input parameters, including layer height, infill density, printing temperature, and printing speed. Conventional methods often struggle to optimize conflicting objectives such as reducing material consumption while improving tensile strength and dimensional accuracy. To overcome these limitations, Artificial Neural Networks (ANN) were used to model each response based on data from 78 PLA+ specimens fabricated using a Face-Centered Central Composite Design (FCCCD). Rather than applying a fixed model structure, the best-performing ANN architecture was identified separately for each output. The selected models demonstrated high accuracy, with R² values of 0.9919 for material consumption, 0.9360 for tensile strength, and 0.9558 for dimensional accuracy. These models were embedded into an enhanced version of the Symbiotic Organism Search (SOS) algorithm, which incorporated a Diversification Strategy (DS) to form MOSOS-DS. Pareto-based optimization and Euclidean distance analysis yielded the best compromise solution with 4.41 g material consumption, 37.76 Pa tensile strength, and 1.05 mm dimensional accuracy. Although the individual techniques are established, their tailored combination and response-specific implementation provide a scalable framework for precise, data-driven process control in additive manufacturing (AM).
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