This study presents a bibliometric analysis of research on process parameter optimization of Polylactic Acid (PLA) in Fused Deposition Modelling (FDM). PLA is one of the most widely used thermoplastic materials in additive manufacturing due to its biodegradability, ease of processing, and environmental sustainability. However, the quality and performance of PLA-printed components are highly dependent on process parameters such as layer thickness, print speed, nozzle temperature, and infill density. Understanding research trends and identifying knowledge gaps in this area is essential for improving printing performance and guiding future investigations. The study employed Bibliometric, an R-based bibliometric analysis tool, to evaluate the scientific literature. The analysis included thematic mapping, keyword co-occurrence networks, and Bradford's Law to examine the intellectual structure of the field. The thematic map revealed that layer thickness, optimization, and Fused Deposition Modelling represent foundational themes that remain under continuous development. The co-occurrence network analysis indicated strong relationships among process parameters and mechanical performance, while also highlighting that parameter optimization is still an emerging research focus. Bradford's Law further demonstrated that the literature is concentrated within a limited number of core journals, with a wider dispersion across secondary and peripheral sources. The findings suggest that although research on PLA in FDM is growing, it is still evolving, with significant opportunities for further exploration in process optimization and performance enhancement. The study concludes that more interdisciplinary and advanced optimization approaches are required to fully exploit the potential of PLA in additive manufacturing applications. This bibliometric overview provides valuable insights for researchers aiming to identify trends, influential contributions, and future research directions in the field.

Keywords

Polylactic Acid (PLA); Fused Deposition Modelling (FDM); Process Parameter Optimization; Bibliometric Analysis; Additive Manufacturing