Sensitivity and Principal Component Analysis-Driven Bayesian Optimization of Process Parameters for Enhancement of Mechanical Strengths in 3D-Printed Biocomposites
Year: 2025
Type: article
Abstract: This paper presents an in-depth investigation into the effects of process parameters affecting various mechanical properties of the 3D-printed biocomposite produced through the material extrusion (MEX) process. The biocomposite material under investigation is specially prepared polylactic acid reinforced with biochar. The process parameters such as biochar content (BC), raster angle (RA), infill pattern (IP), layer thickness (LT), and infill density (ID) are systematically varied. Test samples were prepared using MEX for tensile, flexural, and impact properties. The dataset gathered from the experimentation was used for analysis and optimization. Sensitivity analysis using SCILib revealed that the most tensile and flexural strength are highly sensitive to the ID and impact strength is sensitive to % of biochar added in the biocomposite. Principal component analysis was employed to reduce the complexity of the output features. A Bayesian approach was used for the optimization of the resultant principal strength component and further, the optimal MEX process parameters were extracted. The optimization results show that 2% BC, 0.25 mm LT, 0° RA, 99% ID, and octet IP are the optimum process parameters for obtaining the best possible mechanical strength of MEX printed biocomposite.
Source: Cureus Journal of Engineering.
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W4409344089