Milling of additively manufactured AlSi10Mg with microstructural porosity defects, finite element modeling and experimental analysis

Abstract

Metal additive manufacturing finds its applications in various sectors, especially the automotive and aerospace industries, wherein weight reduction of components is of paramount importance with respect to performance and fuel consumption. However, additively manufactured metallic components usually undergo post-processing steps to make them ready for end use application. One of these steps is finish machining that is employed to overcome the surface irregularities and poor tolerances produced as a result of additive manufacturing (AM). These finish machining processes involve turning, milling, drilling, reaming, or grinding. This paper presents a three-dimensional (3D) finite element (FE) model to simulate the chip formation during the milling of an AM aluminum alloy AlSi10Mg, while taking microstructural porosity defects into consideration. The material was chosen for its significance in the aerospace and automotive industries. The proposed numerical model was developed using ABAQUS™ and is capable of predicting the cutting forces during the milling of AM AlSi10Mg. The Johnson-Cook strength and fracture models were utilized to simulate the deformation mechanics of the workpiece material subjected to high strains, high strain rates, and high temperatures caused by machining. Two algorithms have been proposed and discussed to model the microstructural defects in the AM metal. Machining tests were performed on an AM AlSi10Mg specimen block to validate the proposed finite element model. To further demonstrate the capability of the proposed model in capturing the influence of porosity on cutting forces, a study has been conducted revolving around the impact of different pore sizes. The chips produced during the machining tests were also collected to compare the morphologies between the numerically simulated chips and the ones obtained experimentally, to further strengthen the validity of the proposed model.