Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity

Yulaikha Maratullatifah; Dwi Utari Iswavigra; Very Dwi Setiawan; Mursalim Mursalim; Budi Wibowo

doi:10.61132/ijmicse.v2i1.400

Authors

Yulaikha Maratullatifah Universitas Sugeng Hartono
Dwi Utari Iswavigra Universitas Sugeng Hartono
Very Dwi Setiawan Universitas Pignatelli Triputra
Mursalim Mursalim Universitas Sugeng Hartono
Budi Wibowo Institut Teknologi Budi Utomo

DOI:

https://doi.org/10.61132/ijmicse.v2i1.400

Keywords:

Additive Manufacturing, Evolutionary Algorithms, Mechanical Properties, Process Optimization, Surface Quality

Abstract

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

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