Reduced order modeling of selective laser melting: from calibration to parametric part distortion

Abstract

Additive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, and rich metallurgical transformations that this process entails, are at the origin of numerous process defects. Therefore, the numerical simulation of the process is gaining the interest of both the scientific and the industrial communities. However, simulating that process demands impressive computational resources, limiting high resolution simulations to the microscopic and mesoscopic scales. This paper proposes a thermo-mechanical modeling framework at the process scale as well as its associated reduced order simulation counterpart, enabling the parametric evaluation of the part distortion. It deeply addresses the process calibration using a high-resolution computational procedure based on the use of an in-plane-out-of-plane separated representation at the heart of the so-called Proper Generalized Decomposition (PGD), as well as the analysis of the transient thermal effects, defining the conditions in which the thermal and mechanical analyses can be decoupled.

Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy

“…The PGD approach for stochastic problems was applied to linear structural dynamics in [26]. The application of PGD models to reduce the computational effort of sampling-based approaches, can be found in the context of reliability analysis in [27,28] and for process calibration of additive manufacturing in [29]. Furthermore, Rubio et al [30] used a PGD forward model for real-time identification and model updating based on Monte Carlo sampling as well as based on a transport map sampling [31].…”