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

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 02 | Page :
    By

    Harish Reddy Gantla,

  • Harish Chandra Mohanta,

  • Deepika Singh Singraur,

  • Sandeep Bansal,

  • Varinder Singh,

  • Pacha Supriya,

  1. Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, Telangana, India
  2. Professor, Department of Electronics and Communication Engineering, Centurion University of Technology and Management, Odisha, India
  3. Assistant Professor, Department of Mechanical Engineering, SGT University, Gurugram, Haryana, India
  4. Assistant Professor, Department of Mechanical Engineering, SGT University, Gurugram, Haryana, India
  5. Assistant Professor, School of Engineering and Technology, CGC University, Mohali, Punjab, India
  6. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

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 and thermal conductivity was developed as a supervised learning model using process-property data, which was obtained experimentally. Later, explainable parameter attribution methods were applied to attribute the effect of the parameters of individual fabrication to explain the performance of composite. Optimization of the parameter setting led to potential tensile strength increase of 14.6 % and thermal conductivity increase of 9.2 % relative to process settings at the baseline. The predictive model recorded coefficient of determination (R2) of 0.937 which meant high level of agreement between the predictive and experimentally measured composite performance. The explainability-based optimization strategy contributes to the process of parameter selection as well as promoting reliability in gradient polymer composite manufacturing processes. The framework suggested here is an incorporation of predictive modeling and interpretable analytics provided as a transparent yet performance-efficient solution to optimization of processes in gradient additive manufacturing of polymer composite systems.

 

Keywords: Gradient polymer composite, Explainable machine learning, Additive manufacturing optimization, Process parameter tuning, functionally graded materials.

How to cite this article:
Harish Reddy Gantla, Harish Chandra Mohanta, Deepika Singh Singraur, Sandeep Bansal, Varinder Singh, Pacha Supriya. Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
Harish Reddy Gantla, Harish Chandra Mohanta, Deepika Singh Singraur, Sandeep Bansal, Varinder Singh, Pacha Supriya. Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=240355


References

  • G. Kharate, P. Anerao, and A. P. Kulkarni, “Explainable AI Techniques for Comprehensive Analysis of the Relationship between Process Parameters and Material Properties in FDM-Based 3D-Printed Biocomposites,” Journal of manufacturing and materials processing, Aug. 2024, doi: 10.3390/jmmp8040171.
  • A. Shanto, “Leveraging Large Language Models for Process Parameter Optimization in 3D-Printed ABS Polymer Specimens,” Aug. 2025, doi: 10.21203/rs.3.rs-7437594/v1.
  • H. Dhage and N. Khedkar, “Predictive machine learning and printing parameter optimization for enhanced impact performance of 3D-printed Onyx-Kevlar composites,” Discover materials, vol. 5, no. 1, Aug. 2025, doi: 10.1007/s43939-025-00307-6.
  • Patil, S. Thanikodi, M. Nelson, R. Ghodhbani, B. Tarawneh, and N. A. Othman, “Optimization and Prediction of Tensile Strength in 3D‐Printed PLA/HAp Composites Using Response Surface Methodology and Integrated Machine Learning Technique,” Journal of Applied Polymer Science, Jun. 2025, doi: 10.1002/app.57505.
  • C. Abdullah, O. Ozarslan, S. S. Farshi, S. R. Dabbagh, and S. Tasoglu, “Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing,” Aggregate, Jan. 2024, doi: 10.1002/agt2.495.
  • Yossef, M. Noureldin, and A. Alqabbany, “Explainable artificial intelligence framework for FRP composites design,” Composite Structures, May 2024, doi: 10.1016/j.compstruct.2024.118190.
  • Shi, Y. Sun, H. Zhang, Z. Y. Yuan, T. Haiying, and H. Liu, “3D printed piezoelectric composite filament width and height prediction using individual and stacking ensemble of machine learning algorithms,” MATEC web of conferences, vol. 401, p. 02009, Jan. 2024, doi: 10.1051/matecconf/202440102009.
  • Ziadia, M. Habibi, and S. Kelouwani, “Machine Learning Study of the Effect of Process Parameters on Tensile Strength of FFF PLA and PLA-CF,” Engineer, Nov. 2023, doi: 10.3390/eng4040156.
  • Kamarulzaman et al., “The optimization of 3D printed PLA/EPO/Lignin Biocomposite’s tensile strength using three different supervised learning applications,” International Journal of Progressive Sciences and Technologies, vol. 51, no. 1, p. 16, Jun. 2025, doi: 10.52155/ijpsat.v51.1.7250.
  • Padhy, S. Simhambhatla, and D. Bhattacharjee, “Ensembled Surrogate Assisted Material Extrusion Based 3D Printing Process Parameter Optimization for Enhanced Mechanical Properties of PEEK,” Dec. 2023, doi: 10.21203/rs.3.rs-3427921/v1.
  • Kim and S.-H. Park, “Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique,” Advanced intelligent systems, p. 2200462, Mar. 2023, doi: 10.1002/aisy.202200462.
  • Lin, “In-mold condition-centered and explainable artificial intelligence-based (IMC-XAI) process optimization for injection molding,” Journal of Manufacturing Systems, Feb. 2024, doi: 10.1016/j.jmsy.2023.11.013.
  • Dwivedi, S. Joshi, R. Nair, M. Sapre, and V. S. Jatti, “Optimizing 3D Printed Diamond Lattice Structure and Investigating the Influence of Process Parameters on their Mechanical Integrity Using Nature-Inspired Machine Learning Algorithms,” Materials today communications, Jan. 2024, doi: 10.1016/j.mtcomm.2024.108233.
  • Schoenholz and N. Zobeiry, “An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning,” Composites Part A-applied Science and Manufacturing, Oct. 2023, doi: 10.1016/j.compositesa.2023.107842.
  • A Ghandehari, J. A. T. Negrete, J. Rajendran, Y. Qian, and R. Esfandyarpour, “Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network,” Discover Nano, vol. 19, no. 1, Dec. 2024, doi: 10.1186/s11671-024-04155-w.
  • M. Park, J. Jung, S. Lee, H.-K. Park, Y. W. Kim, and J.-H. Yu, “Data-driven Approach to Explore the Contribution of Process Parameters for Laser Powder Bed Fusion of a Ti-6Al-4V Alloy,” Journal of Korean Powder Metallurgy Institute, Apr. 2024, doi: 10.4150/jpm.2024.00038.
  • Kadri, B. Ali, M. A. Jaradat, M. Alkhader, and W. Abuzaid, “Hybrid Intelligent-Assisted Approach for Process Parameters Optimization to Enhance the Sustainable Manufacturing of Thermoplastic Laminates,” Key Engineering Materials, vol. 1002, pp. 13–23, Dec. 2024, doi: 10.4028/p-dar3uj.
  • Pelzer, T. Schulze, D. Buschmann, C. Enslin, R. Schmitt, and C. Hopmann, “Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning,” Polymers, vol. 15, no. 17, p. 3509, Aug. 2023, doi: 10.3390/polym15173509.
  • Samadiani, A. S. Barnard, D. R. Gunasegaram, and N. Fayyazifar, “Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing,” Journal of Intelligent Manufacturing, Oct. 2024, doi: 10.1007/s10845-024-02490-4.
  • Yeh, M. Bayat, A. Arzani, and J. H. Hattel, “Accelerated Process Parameter Selection of Polymer-based Selective Laser Sintering via Hybrid Physics-informed Neural Network and Finite Element Surrogate Modelling,” Applied Mathematical Modelling, Mar. 2024, doi: 10.1016/j.apm.2024.03.030.
  • Saleh, Y. Guo, and W. Guo, “Enhanced Counterfactual Explanations for Optimizing 3D Printing Parameters Using SHAP and Nearest-Neighbor Constraints with Physics-Based Validation,” Journal of Manufacturing Science and Engineering-transactions of The Asme, pp. 1–53, Sep. 2025, doi: 10.1115/1.4069715.
  • Zhang, Y. Wang, and W. Wang, “Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization,” Gels, vol. 11, no. 8, p. 582, Jul. 2025, doi: 10.3390/gels11080582.
  • Okolo, “Investigation of the influence of AI-controlled process parameter adjustment on the mechanical properties of LBPF-manufactured parts,” Materials research proceedings, vol. 54, pp. 218–227, Jan. 2025, doi: 10.21741/9781644903599-24.
  • Heistracher, A. Wachsenegger, A. Weißenfeld, and P. Casas, “MOXAI – Manufacturing Optimization through Model-Agnostic Explainable AI and Data-Driven Process Tuning,” Proceedings of the European Conference of the Prognostics and Health Management Society (PHME), vol. 8, no. 1, p. 7, Jun. 2024, doi: 10.36001/phme.2024.v8i1.4111.
  • Kanaga Suba Raja et al., “Sustainable manufacturing of FDM-manufactured composite impellers using hybrid machine learning and simulation-based optimization,” Materials science in additive manufacturing, vol. 4, no. 3, p. 025200033, Jul. 2025, doi: 10.36922/msam025200033.
  • B. Toprak and C. U. Dogruer, “Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Oct. 2024, doi: 10.1177/09544062241288948.
  • Ramasubbu, Rajkumar & Kayambu, Arumugam & Palanisamy, Sivasubramanian. (2024). Mechanical properties of epoxy composites reinforced with Areca catechu fibers containing silicon carbide. BioResources. 19. 2353-2370. 10.15376/biores.19.2.2353-2370. J. Borah and M. Chandrasekaran, “Predictive modeling and optimization of Rockwell hardness of additively manufactured PEEK using RSM, ANFIS and RNN integrated with PSO.,” Physica Scripta, Jul. 2024, doi: 10.1088/1402-4896/ad6514.
  • Pelzer, A. F. Posada-Moreno, K. O. Müller, C. Grab, and C. Hopmann, “Process Parameter Prediction for Fused Deposition Modeling Using Invertible Neural Networks,” Polymers, vol. 15, no. 8, p. 1884, Apr. 2023, doi: 10.3390/polym15081884.
  • Gawade, Y. B. Guo, and W. Guo, “Layer-wise emission prediction for laser powder bed fusion with an attribution-based explainable deep learning model,” Journal of Manufacturing Science and Engineering-transactions of The Asme, pp. 1–47, Jun. 2025, doi: 10.1115/1.4068937.
  • A Kemp, “A reinforcement learning approach for process parameter optimization in additive manufacturing,” Additive manufacturing, vol. 71, p. 103556, Jun. 2023, doi: 10.1016/j.addma.2023.103556.
  • Guo et al., “Optimizing Parameters for Microcellular Foaming and In‐Mold Decoration of Talc/PP Composites Using PSOBP Neural Network,” Journal of Applied Polymer Science, Aug. 2025, doi: 10.1002/app.57817.
  • R. Hoffmann, S. M. Pai, R. Q. Albuquerque, S. Kastl, and H. Ruckdäschel, “Active Learning‐Driven Inverse Design of Polyurethane Foams for EV Battery Applications,” Journal of polymer science, Jul. 2025, doi: 10.1002/pol.20250250.
  • L. Meinhold, T. Tankersley, R. Darlington, and J. L. Robinson, “Mandrel Diameter Is a Dominating Parameter for Fiber Alignment Control in Rotating Mandrel Electrospinning Systems,” Journal of Applied Polymer Science, May 2025, doi: 10.1002/app.57364.
  • Sivasubramanian P, Palanisamy S, et al. Mater Res Express. 2023;10:08550.
Ahead of Print Subscription Original Research
Volume 14
02
Received 24/02/2026
Accepted 09/03/2026
Published 20/04/2026
Publication Time 55 Days
2

Login


My IP

PlumX Metrics