TY - JOUR
T1 - AWJ machining of epoxy/crumb rubber/rice-straw powder composite
T2 - optimisation of MRR using Taguchi approach
AU - Doreswamy, Deepak
AU - Jain, Krrish
AU - Agarwal, Navya
AU - Madival, Abhishek Sadananda
AU - Bhat, Subraya Krishna
N1 - Publisher Copyright:
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2025
Y1 - 2025
N2 - Growing global concern over agricultural and tire waste has spurred research into sustainable recycling solutions. This study examines the abrasive water jet machining (AWJM) of a novel epoxy composite reinforced with rice straw powder and crumb rubber. The material removal rate (MRR) was evaluated using a Taguchi L9 array, varying stand-off distance (SOD), traverse speed (TS), and number of passes across three material compositions. Results showed MRR increased with higher SOD but decreased with faster TS and more passes. A higher rice straw content and lower crumb rubber percentage also reduced MRR. Atomic force microscopy revealed that a low TS produces a smooth surface, even at high SOD and a single pass. A regression model (R² = 90.12%) was developed to accurately predict MRR. This work provides crucial guidelines for manufacturers on machining this sustainable composite, balancing material composition and process parameters for optimal results.
AB - Growing global concern over agricultural and tire waste has spurred research into sustainable recycling solutions. This study examines the abrasive water jet machining (AWJM) of a novel epoxy composite reinforced with rice straw powder and crumb rubber. The material removal rate (MRR) was evaluated using a Taguchi L9 array, varying stand-off distance (SOD), traverse speed (TS), and number of passes across three material compositions. Results showed MRR increased with higher SOD but decreased with faster TS and more passes. A higher rice straw content and lower crumb rubber percentage also reduced MRR. Atomic force microscopy revealed that a low TS produces a smooth surface, even at high SOD and a single pass. A regression model (R² = 90.12%) was developed to accurately predict MRR. This work provides crucial guidelines for manufacturers on machining this sustainable composite, balancing material composition and process parameters for optimal results.
UR - https://www.scopus.com/pages/publications/105016733070
UR - https://www.scopus.com/pages/publications/105016733070#tab=citedBy
U2 - 10.1080/14484846.2025.2559219
DO - 10.1080/14484846.2025.2559219
M3 - Article
AN - SCOPUS:105016733070
SN - 1448-4846
JO - Australian Journal of Mechanical Engineering
JF - Australian Journal of Mechanical Engineering
ER -