Covariance matrix adapted grey wolf optimizer tuned eXtreme gradient boost for bi-directional modelling of direct metal deposition process

Abstract

The process of direct metal deposition for cladding has become extremely popular. Online condition monitoring of the process is necessary to obtain a desired clad geometry. The aforementioned fact has necessitated the development of accurate predictive models for establishing relationships between the input and the output parameters of the process in a bi-directional fashion. The present study attempts to build both forward and backward models of a direct deposition process of austenitic steel utilizing the extreme gradient boost technique. Inspired by the successful application of grey wolf optimization algorithm in solving real life problems, the authors further propose a novel modification of the same and use it to tune the parameters of the model with an aim to improve its performance. The improvement has been accomplished by changing the initial perspective prey location guided by the covariance matrix adaptation and recombination. The new optimization algorithm has been tested and compared with the original algorithm along with some of its variants and several other state-of-the-art meta-heuristics on 23 benchmark objective functions from literature and 5 from CEC 2017. The new optimizer has been further validated on some real engineering case studies. The developed hybrid models for bi-directional mappings using different optimizer turn-by-turn have been trained and tested using statistically augmented experimental data-set. The overall mean absolute percentage errors in testing forward mapping and backward mapping using the model with new optimizer have been found within 10%, which is quite satisfactory. The adequacy of the developed models has also been found acceptable.