In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 %) on the response followed by interaction effect of the operating pressure and feed rate (23.54 %) as-well-as operating pressure and standoff distance (16.12 %). Whereas the contribution of operating pressure and standoff distance on response is 11.50% and 8.20 % respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.
|Number of pages||11|
|Publication status||Published - 01-01-2019|
|Event||2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa|
Duration: 08-03-2019 → 10-03-2019
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence