Yazar "Khanna, N." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning
    (Springer Science and Business Media Deutschland GmbH, 2023) Bagga, Prashant J.; Patel, K.M.; Makhesana, M.A.; Şirin, Şenol; Khanna, N.; Krolczyk, G.M.; Pala, A.D.
    One of the essential elements of automated and intelligent machining processes is accurately predicting tool life. It also helps in achieving the goal of producing quality products with reduced production costs. This work proposes a computer vision-based tool wear monitoring and tool life prediction system using machine learning methods. Gradient-boosted trees and support vector machine (SVM) techniques are used to predict tool life. The experimental investigation on the CNC machine is conducted to study the applicability of the proposed tool wear monitoring system. Experiments are performed using workpiece material made of alloy steel and PVD-coated cutting inserts, and flank wear is monitored. An imaging system consisting of an industrial camera, lens, and LED ring light is mounted on the machine to capture tool wear zone images. Images are then processed by algorithms developed in MATLAB®. Boosted tree methods and the SVM methodology have 96% and 97% prediction accuracy, respectively. Validation tests are carried out to determine the accuracy of proposed models. It is observed that the prediction accuracy of boosted three and SVM is good, with a maximum error of 5.89% and 7.56%, respectively. The outcome of the study established that the developed system can monitor the tool wear with good accuracy and can be adopted in industries to optimize the utilization of tool inserts. © 2023, The Author(s).
  • Küçük Resim
    Öğe
    A tribological performance of vegetable-based oil combined with GNPs and hBN nanoparticles on the friction-wear tests of titanium grade 2
    (Elsevier Ltd, 2023) Şirin, Şenol; Akıncıoğlu, Sıtkı; Gupta, M.K.; Kıvak, T.; Khanna, N.
    In this study, friction performance, surface textures, and wear mechanisms of Titanium Grade (Gr) 2 material were investigated with a pin-on-disc tester. The study was carried out under dry, basefluid (vegetable-based oil), graphene nanoplatelets (GNPs), hexagonal boron nitride (hBN) nanofluids, and GNPs+hBN hybrid nanofluid conditions. In addition, the fluid's viscosity and thermal conductivity coefficient measurements were carried out. As the performance criterion of the pin-on-disc test, coefficient of friction (COF), vibration peak values, surface roughness (Ra profile), and topography, wear and wear mechanisms were preferred. With the data obtained after pin-on-disc tests, it can be said that the GNPs+hBN hybrid nanofluid condition shows the best performance compared to other conditions. GNPs+hBN hybrid condition, compared to the dry condition, decreased the COF average, vibration average, and Ra values by 20%, 52%, and 62%, respectively. © 2023 The Authors