Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

The development of process parameters and scanning strategies for bulk metallic glass formation during additive manufacturing is time-consuming and costly. It typically involves trials with varying settings and destructive testing to evaluate the final phase structure of the experimental samples. In this study, we present an alternative method by modelling to predict the influence of the process parameters on the crystalline phase evolution during laser-based powder bed fusion (PBF-LB). The methodology is demonstrated by performing simulations, varying the following parameters: laser power, hatch spacing and hatch length. The results are compared in terms of crystalline volume fraction, crystal number density and mean crystal radius after scanning five consecutive layers. The result from the simulation shows an identical trend for the predicted crystalline phase fraction compared to the experimental estimates. It is shown that a low laser power, large hatch spacing and long hatch lengths are beneficial for glass formation during PBF-LB. The absolute values show an offset though, over-predicted by the numerical model. The method can indicate favourable parameter settings and be a complementary tool in the development of scanning strategies and processing parameters for additive manufacturing of bulk metallic glass.

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Numerical Simulation of Riveting Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.641 ◽

2010 ◽

Vol 34-35 ◽

pp. 641-645

Author(s):

Hong Shuang Zhang

Keyword(s):

Numerical Simulation ◽

Finite Element Method ◽

Residual Stress ◽

Finite Element ◽

Process Parameters ◽

Static Method ◽

Riveting Process ◽

Relationship Of ◽

The Relationship ◽

Element Method

In order to fully understanding the distribution of residual stress after riveting and the relationship between residual stress and riveting process parameters during riveting, Finite Element Method was used to establish a riveting model. Quasi-static method to solve the convergence difficulties was adopted in riveting process. The riveting process was divided into six stages according to the stress versus time curves. The relationship of residual stress with rivet length and rivet hole clearance were established. The results show numerical simulation is effective for riveting process and can make a construction for the practical riveting.

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New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

ASME Letters in Dynamic Systems and Control ◽

10.1115/1.4050079 ◽

2021 ◽

pp. 1-7

Author(s):

Dan Wang ◽

Xinyu Zhao ◽

Xu Chen

Keyword(s):

Finite Element ◽

Additive Manufacturing ◽

Process Variations ◽

Element Model ◽

Hammerstein Model ◽

Powder Bed Fusion ◽

Modeling And Analysis ◽

Powder Bed ◽

Melt Pool ◽

Linearized Model

Abstract Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been identified fundamental for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linearized model with a memoryless nonlinear submodel, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.

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