Additive manufacturing (AM) has recently emerged as a promising method for processing lignin into high-value composites. Likewise, 3D-scanning has found its use in AM processes, implementing this technology for reverse engineering or quality control purposes. By comparing scanned digital models with the original 3D designs, dimensional accuracy and deviations can be precisely assessed. However, this dimensional accuracy evaluation method remains underutilised for biomass-derived materials, limiting the ability to quantify the impact of lignin content on composite performance. To address this gap, our research aims to develop a print-to-scan methodology for evaluating the dimensional accuracy and behaviour of 3D-printed lignin-based composites.
In this study, selective laser sintering (SLS), a laser powder bed fusion technology, is utilised for manufacturing lignin-based composites. Given the strict requirements for powder flowability and sintering performance, lignin is blended with polylactic acid (PLA) and processed into spherical microparticles via spray-drying. Optimised designs suitable for 3D-scanning are printed and assessed for dimensional accuracy. By correlating scanning data with shrinkage and deformation behaviours across varying lignin compositions, we aim to establish a robust framework for composite behaviour evaluation.
Preliminary findings indicate that our methodology provides precise accuracy assessments, more informative than the nominal resolution of 3D-printers, offering a more reliable approach to quantifying dimensional deviations. This study contributes novel insights into the role of lignin in SLS-processed bio-based composites, a field with limited prior research. To the best of our knowledge, only Ajdary et al. (2021) have reported the incorporation of lignin into composites manufactured by SLS, specifically with PA12. Our approach, leveraging lignin/PLA composites, represents a novel advancement with significant implications for sustainable additive manufacturing. By enhancing the precision of dimensional accuracy assessments, this work paves the way for broader adoption of lignin-based materials in AM applications.