Fused Deposition Modeling (FDM) is a common additive manufacturing technique known for its ability to quickly produce complex geometries and features according to customer requirements in a short timeframe. Parts produced by FDM technique with (Polylactic Acid) PLA can be used extensively as it is biocompatible in nature. The present investigation involves drilling the fabricated circular discs to assess the material removal rate (MRR) and hole profile. The inputs considered are spindle speed (SS), feed rate (fr), and drill diameter (dia), in relation to the output characteristics such as MRR, surface roughness (Ra and Rz), circularity, and cylindricity. This work employs the Proximity Indexed Value (PIV) tool to predict the near-optimal value for improving hole quality and enhancing the drilling process. In addition, Artificial neural network (ANN), Support vector machine (SVM), and Random Forest (RF) models were employed to predict the optimized results by PIV method. The tests show that the RF model, which has a R² value of 99.16%, does a better job of describing the desired outcome than the ANN model (89.45%) and the SVM model (89.08%). This also proves that the machine learning (ML) models offer better prediction to the optimization of drilling parameters with reference to the quality of the workpiece machined.

AU Sophisticated Testing and Instrumentation Centre Department of Mechanical Engineering Alliance School of Applied Engineering Alliance University Anekal Chandapura road 562106 Bangalore Karnataka India

Department of Artificial Intelligence Mepco Schlenk Engineering College 626005 Sivakasi Virudhunagar India

Department of Computer Science and Engineering Chennai Institute of Technology Chennai India

Department of Machining Assembly and Engineering Metrology Mechanical Engineering Faculty VŠB Technical University of Ostrava 17 listopadu 2172 15 708 00 Ostrava Czech Republic

Department of Mechanical Engineering Kalasalingam Academy of Research and Education 626126 Krishnankoil Tamil Nadu India

Department of Mechanical Engineering Mepco Schlenk Engineering College 626005 Sivakasi Tamilnadu India

Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

Department of Mechanical Engineering St Joseph'S College of Engineering Old Mahabalipuram Road Chennai 600 119 Tamil Nadu India

Department of Mechanical Engineering Viswajyothi College of Engineering and Technology Vazhakulam Kerala India

Faculty of Mechanical Engineering Opole University of Technology Proszkowska 76 45 758 Opole Poland

Graduate Institute of Manufacturing Technology National Taipei University of Technology 10608 Taipei Taiwan

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