A multi-objective optimization using response surface model coupled with particle swarm algorithm on FSW process parameters

Abstract

In this study, multi-objective optimization of mechanical properties in friction-stir-welding of AH12 1050 aluminum alloy is performed using a combination of the response surface method and multi-objective particle swarm optimization algorithm. The process parameters are considered as tool pin diameter, shoulder diameter, rotational speed, feed speed, and tool tilt angle. The heat-affected zone’s yield strength, fracture strain, impact toughness, and hardness on the advancing and retreating sides are selected as the objective functions. Threaded and simple conical pins are utilized to evaluate the effect of the pin geometry on the specimen mechanical properties. Optimization model outputs are in agree with the obtained experimental results. The effects of process parameters on the mechanical properties of the friction-stir-welded sheets are studied. Results reveal that the lower rotational speed and higher feed speed improve the material strength and hardness. Moreover, the microstructural analysis demonstrates that the proposed methodology can achieve a fine-grained structure with the minimum defects. Improvement in the material flow is observed for the threaded cylindrical pin compared with the conical pin due to the geometric shape of the tool pin leading to more functional mechanical properties. It is found that the combination of the response surface methodology and the multi-objective particle swarm algorithm led to the modeling and optimization of the process with outstanding accuracy and low experimental cost.

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

“…In a related development, Kahhal et al optimized and predicted the process parameters of friction stir welding of AH12 1050 using response surface algorithm coupled with PSO model. It was reported that that the hybrid model indicated superior prediction accuracy 53 . The prediction efficiency of HHO coupled with generalized neural network was reported in the literature 54 to predict the abrasion resistance of ultra-strength martensitic steel.…”

Parametric optimisation for the design of gravity energy storage system using Taguchi method

“…Furthermore, fewer experimental trials can yield more quantitative information. Taguchi's method has been used in various fields, including renewable energy generation and energy storage systems [38][39][40][41] .…”

New Benchmark Metal-Organic Framework for Ambient Hydrogen Storage: MOF-2087

“…Recent investigations on predicting the hydrogen storage in MOFs highlight seven significant crystallographic features of MOFs which include crystal density, gravimetric and volumetric surface areas (GSA and VSA) of MOFs, pore volume (PV), pore and cavity diameters (PLD and LCD), and void fraction (VF), proving sufficient for precise predictions 21,27,41,42 . Admitting the multi-objective particle swarm optimization's (MOPSO) [43][44][45] simplicity and moderate performance, we embedded the B-RFT with MOPSO to optimize the crucial crystallographic features of MOFs that enhance the hydrogen storage tradeoffs in MOFs. Subsequently, we identified 152 optimum combinations of features as a theoretical benchmark which is further matched with the 733,792 open-source MOF structures.…”