Mechanical Properties and Depth Penetration Optimization Using NSGA-III in Hybrid Laser Arc Welding

Abstract

Hybrid welding is a process used in aeronautical materials to obtain benefits such as complete penetration, narrow heat affected zones, reduce filler material used, among others, mainly due the ability of the process to control filler-metal additions and heat input in materials such as steels, titanium and aluminum alloys. Recently a new industrial revolution is taken place called manufacturing 4.0, the aeronautical and automotive industry have a great interest in all the pillars which making it up, one of the principal pillars is the big data analysis such as welding parameters applied in advances welding processes. The present work describes a welding optimization applying non-dominated sorting genetic algorithm-III (NSGA-III) to find and predict depth penetration (DP) and mechanical properties, specifically ultimate tensile strength (UTS) in ASTM 1520 steel welded by a hybrid laser arc welding with the objective to improve the weld quality. A diverse experiment were used in order to obtain a suitable model, considering welding speed (WS), wire feed rate (WF), voltage (V), current (A) and laser power (P). The experimental results demonstrated that the pareto front values obtained by NSGA-III improve the DP and UTS. Microstructural phases and mechanical properties were discuss to complement the values obtained and chosen.

Análisis cuantitativo sobre la participación de investigadoras en México en temáticas de soldadura

“…Incluso sobre la importancia de los trabajos multidisciplinarios se ha visto un aumento en técnicas mediante optimización de algoritmos genéticos o tablas de control multi-atributo. Estos trabajos están enfocados principalmente en características de calidad de la soldadura, que resultan ser importantes en aplicaciones industriales; por ejemplo, en optimización de parámetros para predecir la profundidad de penetración relacionada con la resistencia máxima en procesos híbridos de soldadura, la autora Pérez Medina (2019), propuso un método mediante algoritmo genético de clasificación no dominado-III, que le permitió obtener valores máximos de resistencia (Pérez-Medina, Carrum-Siller, Miranda-Pérez, y Garcés, 2019). Por otro lado, en los procesos de producción existen pérdidas de calidad por diversos factores, lo cual puede ser evaluado mediante los atributos, inspección visual o ensayos destructivos, por lo que la autora Ramírez Méndez (2018) propuso una carta de control que puede identificar varios atributos con el fin de identificar la pérdida de calidad en las soldaduras (Ramírez-Méndez, et al, 2018).…”

Weld optimisation

“…The literature related to the use of MaOO in welding optimisation is very limited. NSGA-III was employed to predict various weld attributes for a hybrid laser arc welding of ASTM 1520 steel [146]. To the best of the authors' knowledge, detailed input-output modelling of any welding process using MaOO has not yet been reported.…”

Depth Penetration Optimization Using Metaheuristic in Hybrid Laser Arc Welding

“…There are two different ways to optimize parameters in the welding process that the researchers are using calling mathematical methods and metaheuristic methods [2]. The metaheuristic methods can use a population of individuals in the search space or can use a multi-objective function like the non-dominated sorting genetic algorithm-III (NSGAIII) where the output is multivariate model [3]. There are several studies based on Design of Experiment (DOE) applied to optimize welding process.…”