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<p>In extrusion-based additive manufacturing, a key challenge is translating material formulation into predictable process outcomes without reliance on iterative trial-and-error. This study proposes a process-oriented framework in which fresh-state rheology is treated as a process-state variable linking formulation to manufacturing performance. A range of biobased wood–CNC–clay slurries containing sawdust, cellulose nanocrystals (CNC), xanthan gum, glycerol, and varying water content was systematically investigated using a Taguchi design of experiments. Key rheological descriptors, including storage modulus, yield stress, flow stress, and structural recovery, were extracted and used as inputs to multivariate linear regression models to predict buildability, volumetric shrinkage, and compressive strength. The results revealed a clear hierarchy in linear predictability. Volumetric shrinkage exhibited strong dependence on fresh-state rheology (R²_pred ≈ 66%), indicating that dimensional stability is governed by resistance to deformation and structural rebuildability established during deposition. Buildability showed moderate predictability (R²_pred ≈ 45%), reflecting the competing effects of stiffness, extrusion flow, and post-deposition relaxation. In contrast, compressive strength could not be reliably predicted using linear combinations of rheological descriptors, highlighting the dominant role of post-printing processes such as densification and particle rearrangement. Cross-metric ANOVA and microstructural analysis further reveal that sawdust, xanthan gum, and CNC each operate through distinct, concentration-dependent mechanisms under shear, thus governing the rheology–performance relationships identified in this study. From a manufacturing perspective, the proposed rheology-based framework enables formulation screening and process optimization using measurable process-state variables rather than composition-specific inputs. This approach reduces dependency on material-specific calibration and supports scalable, data-efficient decision-making in extrusion-based additive manufacturing of complex, multi-component materials.</p>

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A process-oriented rheology framework for predicting printability and performance in extrusion-based additive manufacturing of wood–CNC–clay composites

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Biva Gyawali,
  • Aviral Arora,
  • Pavan Akula,
  • Kiana Shirzad,
  • Abhisek Shetty,
  • Kamran Alba,
  • Vahid Nasir

摘要

In extrusion-based additive manufacturing, a key challenge is translating material formulation into predictable process outcomes without reliance on iterative trial-and-error. This study proposes a process-oriented framework in which fresh-state rheology is treated as a process-state variable linking formulation to manufacturing performance. A range of biobased wood–CNC–clay slurries containing sawdust, cellulose nanocrystals (CNC), xanthan gum, glycerol, and varying water content was systematically investigated using a Taguchi design of experiments. Key rheological descriptors, including storage modulus, yield stress, flow stress, and structural recovery, were extracted and used as inputs to multivariate linear regression models to predict buildability, volumetric shrinkage, and compressive strength. The results revealed a clear hierarchy in linear predictability. Volumetric shrinkage exhibited strong dependence on fresh-state rheology (R²_pred ≈ 66%), indicating that dimensional stability is governed by resistance to deformation and structural rebuildability established during deposition. Buildability showed moderate predictability (R²_pred ≈ 45%), reflecting the competing effects of stiffness, extrusion flow, and post-deposition relaxation. In contrast, compressive strength could not be reliably predicted using linear combinations of rheological descriptors, highlighting the dominant role of post-printing processes such as densification and particle rearrangement. Cross-metric ANOVA and microstructural analysis further reveal that sawdust, xanthan gum, and CNC each operate through distinct, concentration-dependent mechanisms under shear, thus governing the rheology–performance relationships identified in this study. From a manufacturing perspective, the proposed rheology-based framework enables formulation screening and process optimization using measurable process-state variables rather than composition-specific inputs. This approach reduces dependency on material-specific calibration and supports scalable, data-efficient decision-making in extrusion-based additive manufacturing of complex, multi-component materials.