Title: On-the-Fly Annealing of Polyetherketoneketone PEKK via Fused Deposition Modeling Parameter Optimization Using Taguchi Method

Authors: Farshad Malekpour, Mehdi Hojjati

DOI: 10.33599/nasampe/s.25.0093

Abstract: Annealing is a key strengthening mechanism for enhancing the stiffness and strength of semicrystalline thermoplastics like Polyetherketoneketone (PEKK). It is particularly important in additive manufacturing when Fused Deposition Modeling (FDM) parts are in an amorphous state with low crystallization. However, annealing poses challenges such as material softening, shrinkage, and void deconsolidation before crystallization. To overcome these issues, this study introduces a novel method called on-the-fly annealing. Moreover, FDM additive manufacturing by its nature is a time-consuming process. This intuitive method minimizes the defect and reduces the manufacturing cost in terms of processing time and required facilities. The pathway is to deploy a comprehensive methodology through tuning thermal processing parameters, utilizing the Taguchi optimization method. Primarily, the limits of thermal degradation and crystallization zone temperature were determined through TGA and DSC analyses, respectively. Four thermal parameters including nozzle, bed, tuyere, and chamber temperature were investigated across three levels. Tensile tests and ANOVA analysis were used to assess the impact of these parameters on the mechanical properties of 3D-printed parts. Results indicate that bed temperature is key to on-the-fly annealing, as it controls heat flux and greatly enhances the strengthening and crystallization of the specimen. Nozzle and tuyere temperatures mainly influence the elastic modulus, while chamber temperature affects toughness and elongation. This study reveals that on-the-fly annealing is achievable, and the mechanical properties of 3Dprinted parts can also be adjusted by appropriately selecting thermal printing parameters.

References: [1] M. E. Tipping and C. M. Bishop, Probabilistic principal component analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1999, 61(3): 611-622. [2] S. Russo, G. Li, and K. Villez, Automated model selection in principal component analysis: A new approach based on the cross-validated ignorance score. Industrial & Engineering Chemistry Research, 2019, 58(30), 13448-13468. DOI: 10.1021/acs.iecr.9b00642 [3] T. Hastie, R. Tibshirani, and J Friedman, The Elements of Statistical Learning. Data Mining, Inference, and Prediction. New York: Springer, 2001. [4] T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 2007, 102, 359−378. DOI: 10.1198/016214506000001437 [5] M. S. McClain, D. Chowdhury, O. Eldaghar, K. Villez, Using the Optimal Combined Index Weight Ratio to Improve the Probability of Anomaly Detection in Big Area Additive Manufacturing. Virtual and Physical Prototyping, 2025, Accepted, In Press. [6] T. Fawcett, An introduction to ROC analysis. Pattern Recognition Letters, 2006, 27(8), 861-874. DOI: 10.1016/j.patrec.2005.10.010

Conference: SAMPE 2025

Publication Date: 2025/05/19

SKU: TP25-0000000093

Pages: 11

Price: $22.00