Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms
| Autor: | Kristian Martinsen, Tamal Ghosh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Computer Networks and Communications Process (engineering) Computer science 020209 energy NSGA-III Evolutionary algorithm MOEA/D 02 engineering and technology Biomaterials symbols.namesake Resource (project management) Machining Genetic algorithm 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) Gaussian function Machining process optimization Civil and Structural Engineering Fluid Flow and Transfer Processes Mechanical Engineering 020208 electrical & electronic engineering Metals and Alloys Sorting Many-response parametric design Data-driven surrogate model Electronic Optical and Magnetic Materials lcsh:TA1-2040 Hardware and Architecture symbols lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | Engineering Science and Technology, an International Journal (JESTECH) Engineering Science and Technology, an International Journal, Vol 23, Iss 3, Pp 650-663 (2020) |
| ISSN: | 2215-0986 |
| Popis: | 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. © 2019. This is the authors’ accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Databáze: | OpenAIRE |
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