Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms
Journal article, Peer reviewed
Accepted version
![Thumbnail](/ntnu-xmlui/bitstream/handle/11250/2629464/AAM.pdf.jpg?sequence=6&isAllowed=y)
View/ Open
Date
2019Metadata
Show full item recordCollections
Original version
10.1016/j.jestch.2019.09.003Abstract
Contemporary manufacturing processes are substantially complex due to the involvement of a sizable number of correlated process variables. Uncovering the
correlations among these variables would be the most demanding task in this scenario, which require exclusive tools and techniques. Data-driven surrogateassisted optimization is an ideal modeling approach, which eliminates the necessity of resource driven mathematical or simulation paradigms for the manufacturing
process optimization. In this paper, a data-driven evolutionary algorithm is introduced, which is based on the improved Non-dominated Sorting Genetic Algorithm
(NSGA-III). For objective approximation, the Gaussian Kernel Regression is selected. The multi-response manufacturing process data are employed to train this
model. The proposed data-driven approach is generic, which could be evaluated for any type of manufacturing process. In order to verify the proposed methodology,
a comprehensive number of cases are considered from the past literature. The proposed data-driven NSGA-III is compared with the Multi-Objective Evolutionary
Algorithm based on Decomposition (MOEA/D) and shown to attain improved solutions within the imposed boundary conditions. Both the algorithms are shown to
perform well using statistical analysis. The obtained results could be utilized to improve the machining conditions and performances. The novelty of this research
is twofold, first, the surrogate-assisted NSGA III is implemented and second, the proposed approach is adopted for the multi-response manufacturing process
optimization.