Department of Mechanical Engineering

This is a placeholder tab content. It is important to have the necessary information in the block, but at this stage, it is just a placeholder to help you visualise how the content is displayed. Feel free to edit this with your actual content.

1. Selvarajan, L., Arun, C., & Borah, J. (2026). Enhancing machining performance of AlSi10Mg composites using EDM: a Taguchi–RSM–random forest approach. Metallurgical Research & Technology, 123(3), 302.
2. Borah, J., Chandrasekaran, M. & Selvarajan, L. Optimization of Fused Deposition Modeling Process Parameters for Enhanced Mechanical Properties of PEEK: An Experimental Investigation Using a Genetic Algorithm-Integrated Grey-ANFIS Hybrid Statistical Approach. J. of Materi Eng and Perform 34, 28461–28474 (2025). https://doi.org/10.1007/s11665-025-11365-5
3. Sarma, D., Ozah, R., Borah, J. et al. Intelligent Machine Learning Strategies for Minimizing Machining Power in Minimum Quantity Lubrication Machining of Ti6Al4V Alloy. J. of Materi Eng and Perform 34, 24415–24428 (2025). https://doi.org/10.1007/s11665-025-10989-x
4. Borah, J., & Chandrasekaran, M. (2025). Application of Machine Learning-Based approach to predict and optimize mechanical properties of additively manufactured polyether ether ketone biopolymer using fused deposition modeling. Journal of Materials Engineering and Performance. https://doi.org/10.1007/s11665-024-10629-w
5. Borah, J., & Chandrasekaran, M. (2024e). Development of ANN model for predicting mechanical properties of 3d printed peek polymer using FDM and optimization of process parameters for better mechanical properties. Physica Scripta. https://doi.org/10.1088/1402-4896/ad7f0f
6. Borah, J., & Chandrasekaran, M. (2024d). Predictive modeling and optimization of Rockwell hardness of additively manufactured peek using RSM, ANFIS and RNN integrated with PSO. Physica Scripta. https://doi.org/10.1088/1402-4896/ad6514
7. Borah, J., Chandrasekaran, M. Prediction and optimization of tensile strength of additively manufactured peek biopolymer using machine learning techniques. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00505-4
8. Borah, J., Chandrasekaran, M., & Selvarajan, L. (2023b). Taguchi-Based Experimental Investigation and Modeling of 3D-Printed PEEK Parts as Biomedical Implants using Fused Deposition Modeling for Improving Mechanical Strength and Surface Quality. Journal of Materials Engineering and Performance. https://doi.org/10.1007/s11665-023-09036-4
9. Borah, J., Chandrasekaran, M. Evaluation of tensile strength of 3D printed PLA component and Taguchi optimization, in Neuro Quantology, vol. 20, 2022, pp. 1714-1726.
10. Sarma, D., Borah, J., & Chandrasekaran, M. (2021). Multi optimization of nano fluid based machining of titanium alloy: A green manufacturing approach. Materials Today: Proceedings, 46, 8921–8926. https://doi.org/10.1016/j.matpr.2021.05.362