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<p>Additive manufacturing (AM) is a non-conventional production technique that produces less waste and creates complex shapes with good quality and lower operating costs. Among different AM techniques, FDM (Fused Deposition Modelling) is a popular technique for manufacturing polylactic acid (PLA) components. PLA has a wide range of biomedical applications like bone scaffold &amp; Prosthetics manufacturing. This paper studies and analyzes polylactic acid reinforced with carbon fiber filament to select optimum process parameters that enhance the 3D printed part’s properties. The optimization process parameters considered in this study are Infill Density, Orientation, Layer Height, and Printing Speed to enhance properties such as flexural strength, ultimate tensile strength, and wear rate. Testing was conducted to evaluate FDM-printed PLA specimens’ tensile strength, flexural strength, and wear rate. Mechanical properties of PLA are predicted by adopting the Artificial Neural Network &amp; K-Nearest Neighbour algorithm to improve the quality and productivity of the 3D printing process.</p>

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Prediction of Mechanical Properties of Additively Manufactured Parts Using Machine Learning Techniques

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • M. Arunadevi,
  • V. N. Vivek Bhandarkar,
  • R. Keshavamurthy

摘要

Additive manufacturing (AM) is a non-conventional production technique that produces less waste and creates complex shapes with good quality and lower operating costs. Among different AM techniques, FDM (Fused Deposition Modelling) is a popular technique for manufacturing polylactic acid (PLA) components. PLA has a wide range of biomedical applications like bone scaffold & Prosthetics manufacturing. This paper studies and analyzes polylactic acid reinforced with carbon fiber filament to select optimum process parameters that enhance the 3D printed part’s properties. The optimization process parameters considered in this study are Infill Density, Orientation, Layer Height, and Printing Speed to enhance properties such as flexural strength, ultimate tensile strength, and wear rate. Testing was conducted to evaluate FDM-printed PLA specimens’ tensile strength, flexural strength, and wear rate. Mechanical properties of PLA are predicted by adopting the Artificial Neural Network & K-Nearest Neighbour algorithm to improve the quality and productivity of the 3D printing process.