Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes.

ABSTRACT: This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root

SUBMITTER: Sarker B 

PROVIDER: S-EPMC11458828 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature