Process parameter tuning

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• Published Subscription Original Research Special issue 20 Apr 2026

  Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites

  By Harish Reddy Gantla, Harish Chandra Mohanta, Deepika Singh Singraur, Sandeep Bansal, Varinder Singh, Pacha Supriya

  Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article →