What question did this study set out to answer?

January 24, 2026Open Access

Machine Learning, Response Surface Method, Microscopic Analysis, and Optimization for Mechanical Properties of Electrically Conductive Polymer Composite Fabricated via Additive Manufacturing

IKImran KhanHamad bin Khalifa University ARAns Al Rashid MKMuammer KocHamad bin Khalifa University

Key Points

This study aims to optimize the mechanical properties of electrically conductive polymer composites made via additive manufacturing processes.
Experimental investigation of FFF process parameters including nozzle temperature, bed temperature, and cooling speed.
Statistical analysis to assess the impact of process parameters on mechanical properties.
Optimization of parameters using a composite desirability method.
Implementation of machine learning models for predictive accuracy.
Optimization yielded the best properties at NT = 220 °C, BT = 45 °C, and FS = 85%.
Achieved Young's modulus of 825.63 MPa and ultimate tensile strength of 26.37 MPa.
Fabrication of a functional conductive terminal demonstrated real-world application.
Gaussian process regression model achieved a validation error of less than 4% for property predictions.

Abstract

Abstract The benefits of electrically conductive polymer composites are recognized in various industries, including wearable electronics, medical, and sensors, where the combination of mechanical and electrical properties is required. In this study, we investigated the effects of fused filament fabrication (FFF) process parameters on the mechanical properties of conductive polylactic acid (CPLA) composite. The investigated process parameters included nozzle temperature (NT), bed temperature (BT), and cooling/fan speed (FS). After mechanical testing, it was found that the investigated parameters were significantly affecting the mechanical properties (statistically). The process parameters were optimized using a composite desirability based multi response optimization, to balance five mechanical properties simultaneously. The optimization suggested that NT = 220 °C, BT = 45 °C, and FS = 85% yielded the optimum values of Young’s modulus, ultimate tensile strength, break strain, and strain energy density of 825.63 MPa, 26.37 MPa, 9.24%, and 102.19 × 104 J/m3, respectively. To enhance prediction accuracy, machine learning (ML) models were applied to the same experimental dataset, and Gaussian process regression achieved the lowest validation error of less than 4% across all properties. A functional conductive contact terminal was then fabricated under the optimum parameters as qualitative proof of the concept, which could light the LEDs up. The combined statistical analysis and optimization, SEM, ML predictive approach, and the fabrication of the conductive terminal will serve as a guideline for researchers in additive manufacturing, ensuring the ever-growing use of FFF in various applications.

AIに質問

Bookmark

View Full Paper