A new fine-tuned random vector functional link model using Hunger games search optimizer for modeling friction stir welding process of polymeric materials

Modeling of manufacturing processes using artificial intelligence-based techniques has recently received considerable attention. The current investigation introduces a new artificial intelligence-based predictive model for friction stir welding of dissimilar polymeric materials. The welded joint is made of acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) sheets. The proposed model is used to correlate the joint characteristics (tensile strength, joint efficiency and extensibility) with the welding variables (rotational tool speed, welding speed and tool tilt angle). The model consists of a random vector functional link (RVFL) model optimized by Hunger games search (HGS) optimizer. HGS optimizer is utilized to find out the optimal internal parameters of RVFL that boost the model accuracy. The proposed model is compared with two other optimized models in which RVFL is integrated with the sine cosine algorithm (SCA) or ecosystem-based optimizer (AEO). RVFL-HGS outperformed RVFL-SCA and RVFL-AEO based on different statistical measures. For all investigated joint characteristics, the determination coefficient ranges between 0.907 and 0.993 for RVFL-HGS, 0.869 and 0.972 for RVFL-AEO, 0.829 and 0.983 for RVFL-SCA. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The study introduced a new AI-based predictive model for friction stir welding of dissimilar polymeric materials. The model improved accuracy by optimizing internal parameters and outperformed other optimized models.