A multi-criterion optimization of mechanical properties and sustainability performance in friction stir welding of 6061-T6 AA

Nowadays, the transportation industry is curious about fabricating lightweight vehicles under a sustainable manufacturing process to minimize fuel consumption costs and environmental footprints. Assessing the sustainability performance metrics of the friction stir welding (FSW) process reduces the sawing costs and environmental impacts. However, the evaluation and assessment of the sustainability performance metrics integrated with the mechanical properties of the weld joint have not been sufficiently studied. Thus, this study investigates the multi-criterion process parameter optimization of friction stir welding (FSW) to attain an optimum parameter to enhance the mechanical properties of the weld joint and decrease energy consumption and carbon dioxide emission. Experimentations are conducted using a vertical CNC milling machine on a 5 mm 6061-T6 AA material with a butt joint orientation. Tool rotational speed, traverse speed, and tool profile were used as processing parameters. Response of the study, such as transient temperature and energy consumption, was measured during the joining process, whereas hardness, tensile strength, and CO2 emission were examined post-processing. The multi-criterion optimization was done using the Taguchi-based grey relational analysis method. In addition, the transient temperature was predicted by an auto-regressive moving average (ARIMA) and Ansys simulation. The finding showed that higher rotational and minimum welding speeds had imparted a sound weld. Besides, the analysis of variance results showed that welding speed has a significant parameter at a 95% confidence interval. The proposed ARIMA model's result showed that the transient temperature prediction accuracy reached 99.922%, and the error percentage between the FEA simulation and experimental work reached 2.067%.