English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The welding to be performed must be defect free, offer lower residual stress and strain, be compact in size and withstand different load conditions. However, the existing investigations in this scenario are still not modernized. Therefore, in this study, a specific welding method called laser beam welding (LBW) is performed and different weld parameters have been inspected and analysed. Advanced instruments based on the non-destructive (ND) are implemented to find the variable LBW responses such as weld bead defects, residuals and strain. The experimentation has been designed using Design Expert software, response surface methodology (RSM) and Box Behnken design (BBD) and verified by analysis of variance (ANOVA) analysis and FIT statistics. Moreover, a hybrid deep neural network-based Krill Herd optimization (DNN-KHO) is implemented to predict the output parameters like, undercut (µm), overlap (µm), total strain (mm/mm) and residual stress (MPa) during welding. The proposed DNN-KHO was also used to optimize LBW input parameters such as, peak power (W), weld speed (mm/s), gas flow rate (l/min) and beam diameter (µm) simultaneously. Predictions show that the proposed DNN-KHO algorithm outperformed by 21.53%, 45.428% and 41.31% higher in accuracy compared to respective hybrid random forest based grey wolf method (RF-GWO), RF and DNN predictions.

Design performance optimization of laser beam welded joints made for vehicle chassis application using deep neural network-based Krill Herd method