journal article·2025-07Open access

Mitigating machining damage: Advanced modelling of pumice loading effects in Kevlar fibre-pumice sandwich polymer composites at the AWJM zone

Venkatesh Chenrayan, A. Saravanakumar, Kiran Shahapurkar, S. Vaishnavi, S. Selvam, Chandru Manivannan, Vineet Tirth, Ali Algahtani, Jana Petrů, Muhammad Nasir Bashir, Manzoore Elahi M. Soudagar, Krishna Prakash Arunachalam, H.M. Vidhya

Abstract

The development of varieties of fibre-reinforced polymer composite (FRP) has become a part of day-to-day life and is the core agenda of the manufacturing industries in new product development. Despite the wider applications, the machining of FRPs is encountering a greater challenge in overcoming the machining damage. It is the right proposal to deplete the prominent machining damages like kerf taper, delamination and surface roughness by introducing novel hybrid statistical and soft computing models. The novel Kevlar fiber woven mat-pumice particles reinforced sandwich is fabricated using open moulding and machined with Abrasive Water Jet Machining by following L9 orthogonal guidelines. The three different pumice compositions, standoff distance, abrasive flow rate and hydraulic pressure were considered the prime variables to influence the damages. The hybrid analysis results declare that trial No.3 is an optimal parameter, which comprises 100 (MPa) of hydraulic pressure, 2 mm of standoff distance, 300 (gm/min) of abrasive flow rate and 10% of pumice particles to scale down the damages. A noticeable 1.69%, 25.12% and 40.71% decrement in machining damages, along with the finding of a higher contribution of pumice content in minimising the damages, would benefit the FRP industries to march towards the implementation of lean manufacturing and new product development (NPD).
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced machining processes and optimization · Erosion and Abrasive Machining · Advanced Surface Polishing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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