An Efficient Multi-Scale Simulation Architecture for the Prediction of Performance Metrics of Parts Fabricated Using Additive Manufacturing

Abstract

In this study, an overview of the computational tools developed in the area of metal-based additively manufactured (AM) to simulate the performance metrics along with their experimental validations will be presented. The performance metrics of the AM fabricated parts such as the inter-and intra-layer strengths could be characterized in terms of the melt pool dimensions, solidification times, cooling rates, granular microstructure, and phase morphologies along with defect distributions which are a function of the energy source, scan pattern(s), and the material(s). The four major areas of AM simulation included in this study are thermomechanical constitutive relationships during fabrication and in-service, the use of Euler angles for gaging static and dynamic strengths, the use of algorithms involving intelligent use of matrix algebra and homogenization extracting the spatiotemporal nature of these processes, a fast GPU architecture, and specific challenges targeted toward attaining a faster than real-time simulation efficiency and accuracy.

On efficiency and effectiveness of finite volume method for thermal analysis of selective laser melting

“…3.2 Temperature field prediction of finite volume method compared with finite element method To provide benchmark results of the proposed FVM method in Section 2.5, threedimensional FEM models based on the element birth and death method are built using ABAQUS (Fu et al, 2014). Note that in literature, numerous researchers such as Michaleris (2014) and Pal et al (2014Pal et al ( , 2015 have adopted the FEM method to conduct a thermal analysis of AM processes. It has been regarded as a mature approach for this purpose.…”

“…To decrease the number of the elements, a cut-and-paste stiffness matrix methodology was used to create the finer meshing only around regions, which are sensitive to the energy input. Also, to realize decimal truncation and data compression, they introduced an intelligent Cholesky algorithm (Pal et al, 2015) that neglects the decimal part of the temperature at each node, and cuts down the FLOP number (high-precision floating-point). Konstantinou and Vosniakos (2016) designed a macroscopic crude numerical model that only focuses on the heat transfer to attain a roughcut fast numerical investigation of temperature fields, and neglects the part of non-major heat loss (heat convection and radiation) in the SLM process.…”