Intelligent 3D Printing Algorithms: Accelerating Additive ‎Manufacturing with Precision and Defect-Free Fabrication

  • Authors

    • M. Kathiravan Professor, Dapartment of Computer Science, Saveetha Institute of Medical and Technical Sciences ( SIMATS),TamilNadu, India https://orcid.org/0000-0002-5377-7871
    • Kishore Kunal Professor of Business Analytics, Loyola Institute of Business Administration, Chennai, TamilNadu, India https://orcid.org/0000-0003-4154-690X
    • Vairavel Madeshwaren Department of Agriculture Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, TamilNadu, India
    • Pillalamarri Lavanya Assistant Professor, Department of Physics & Electronics, ‎Bhavan's Vivekananda College of Science, Humanities and Commerce, Telangana, India
    • Veeramani Ganesan Professor, Department of Management and Business Administration, Jeppiaar Institute of Technology, Sunguvarchatram, Sriperumbudur, ‎TamilNadu, India https://orcid.org/0009-0003-2242-2167
    • Sheifali Gupta Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India https://orcid.org/0000-0001-5692-418X
    https://doi.org/10.14419/fs83b471

    Received date: May 15, 2025

    Accepted date: June 20, 2025

    Published date: June 27, 2025

  • 3D Printing; Additive Manufacturing; Predictive Maintenance; Artificial Intelligence; Defect Detection; Adaptive Control

  • Abstract

    Additive Manufacturing (AM) has transformed modern production by enabling the fabrication of complex geometries with enhanced material ‎efficiency. However, traditional 3D printing techniques often face challenges such as incomplete fusion, material inconsistencies, and thermal ‎warping, which affect overall quality and productivity. This study introduces an intelligent 3D printing framework that integrates Artificial ‎Intelligence (AI) to enable real-time monitoring, defect detection, and adaptive process control, thereby addressing these limitations. The ‎proposed system utilizes Convolutional Neural Networks (CNNs) for computer vision-based quality inspection, enabling the detection of ‎structural anomalies during the printing process. Reinforcement Learning (RL) is employed for dynamic adjustment of parameters like nozzle ‎temperature, deposition speed, and material feed rate in response to real-time feedback, significantly reducing defect occurrence. Adaptive ‎machine learning algorithms like Random Forests and Gradient Boosting also facilitate process optimization and predictive maintenance. ‎Stereolithography (SLA), Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM) are among the AM platforms that use this AI-‎AI-enhanced closed-loop control approach. Material use, energy efficiency, production time, print quality and defect mitigation have all significantly ‎improved, as confirmed by experimental validation. With its ability to guarantee accuracy and dependability in contemporary 3D printing ‎processes, the framework shows great promise for developing industrial and biomedical applications‎.

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  • How to Cite

    Kathiravan, M., Kunal, K. ., Madeshwaren, V., Lavanya , P. ., Ganesan, V. ., & Gupta, S. . (2025). Intelligent 3D Printing Algorithms: Accelerating Additive ‎Manufacturing with Precision and Defect-Free Fabrication. International Journal of Basic and Applied Sciences, 14(2), 441-451. https://doi.org/10.14419/fs83b471