Abstract

Friction Stir Extrusion (FSE) was employed to convert cylindrical LM13 ingots into pipes, utilizing three distinct designs of rotating tool heads. This study examined the influence of process variables, consisting of tool rotational speed and plunging speed, on key properties of the resulting products. The properties investigated encompassed the size of Si precipitates, microhardness, wear resistance, and ultimate compressive strength (UCS). To effectively establish the relationships between the process input variables and the resulting mechanical and microstructural characteristics of the produced pipes, an artificial neural network (ANN) was used. This established correlation was integrated into a hybrid multi-objective optimization framework to identify the optimal process parameters. The investigation determined the ideal configuration: a plunging rate of 31 mm/min, a rotational rate of 653 rpm, and tool design number 3.

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