Thermal Modeling in Metal Additive Manufacturing Using Graph Theory: Experimental Validation With Laser Powder Bed Fusion Using In Situ Infrared Thermography Data

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
M. Reza Yavari ◽  
Richard J. Williams ◽  
Kevin D. Cole ◽  
Paul A. Hooper ◽  
Prahalada Rao
Keyword(s):  
Finite Element ◽  
Graph Theory ◽  
Additive Manufacturing ◽  
Surface Temperature ◽  
Theory Approach ◽  
Powder Bed ◽  
Temperature Trends ◽  
History Of ◽  
Made In

Abstract The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history of additively manufactured parts. The graph theory approach for thermal modeling in additive manufacturing (AM) was recently published in these transactions. In the present paper, the graph theory approach is validated with in situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize the foregoing objective through the following four tasks. First, two kinds of test shapes, namely, a cylinder and cone, are made in two separate builds on a production-type LPBF machine (Renishaw AM250); the material used for these tests is stainless steel (SAE 316L). The intent of both builds is to influence the thermal history of the part by controlling the cooling time between the melting of successive layers, called the interlayer cooling time (ILCT). Second, layer-wise thermal images of the top surface of the part are acquired using an in situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds is used to validate the graph theory-predicted surface temperature trends. Fourth, the surface temperature trends predicted using graph theory are compared with results from finite element (FE) analysis. The results substantiate the computational advantages of the graph theory approach over finite element analysis. As an example, for the cylinder-shaped test part, the graph theory approach predicts the surface temperature trends to within 10% mean absolute percentage error (MAPE) and approximately 16 K root mean squared error (RMSE) relative to the surface temperature trends measured by the thermal camera. Furthermore, the graph theory-based temperature predictions are made in less than 65 min, which is substantially faster than the actual build time of 171 min. In comparison, for an identical level of resolution and prediction error, the finite element approach requires 175 min.

Thermal Modeling in Metal Additive Manufacturing Using Graph Theory

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
M. Reza Yavari ◽  
Kevin D. Cole ◽  
Prahalada Rao
Keyword(s):  
Finite Element ◽  
Graph Theory ◽  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Process Parameters ◽  
Green’S Functions ◽  
Theory Approach ◽  
Finite Element Analyses ◽  
Part Quality ◽  
Am Processes

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

A Graph Theoretic Approach for Near Real-Time Prediction of Part-Level Thermal History in Metal Additive Manufacturing Processes

2019 ◽  
Author(s):  
Reza Yavari ◽  
Kevin D. Cole ◽  
Prahalad Rao
Keyword(s):  
Finite Element Analysis ◽  
Heat Flux ◽  
Finite Element ◽  
Graph Theory ◽  
Additive Manufacturing ◽  
Layer By Layer ◽  
Theory Approach ◽  
Element Analysis ◽  
Part Quality ◽  
Am Processes

Abstract The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the heat flux in the part. For instance, constrained heat flux because of ill-considered part design leads to defects, such as warping and thermal stress-induced cracking. Existing non-proprietary approaches to predict the heat flux in AM at the part-level predominantly use mesh-based finite element analyses that are computationally tortuous — the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational thermal models to predict the heat flux, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared to finite element analysis techniques, the proposed mesh-free graph theory-based approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer. To explore these assertions we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach, with finite element analysis and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume, and (2) simulating the layer-by-layer deposition of three part geometries in a laser powder bed fusion metal AM process with: (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the heat flux predictions from the last two approaches with a commercial solution. From the first study we report that the heat flux trend approximated by the graph theory approach is found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the heat flux trends predicted for the AM parts using graph theory approach agrees with finite element analysis with error less than 15%. More pertinently, the computational time for predicting the heat flux was significantly reduced with graph theory, for instance, in one of the AM case studies the time taken to predict the heat flux in a part was less than 3 minutes using the graph theory approach compared to over 3 hours with finite element analysis. While this paper is restricted to theoretical development and verification of the graph theory approach for heat flux prediction, our forthcoming research will focus on experimental validation through in-process sensor-based heat flux measurements.

Towards process consistency and in-situ evaluation of porosity during laser powder bed additive manufacturing

2020 ◽  
Vol 25 (8) ◽  
pp. 679-689
Author(s):  
J. Raplee ◽  
J. Gockel ◽  
F. List ◽  
K. Carver ◽  
S. Foster ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed

scholarly journals Correlation between surface texture and internal defects in laser powder-bed fusion additive manufacturing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makiko Yonehara ◽  
Chika Kato ◽  
Toshi-Taka Ikeshoji ◽  
Koki Takeshita ◽  
Hideki Kyogoku
Keyword(s):  
Additive Manufacturing ◽  
Surface Texture ◽  
In Situ Monitoring ◽  
Mean Square ◽  
Powder Bed Fusion ◽  
Internal Defects ◽  
Powder Bed ◽  
Texture Parameters ◽  
Areal Surface

AbstractThe availability of an in-situ monitoring and feedback control system during the implementation of metal additive manufacturing technology ensures that high-quality finished parts are manufactured. This study aims to investigate the correlation between the surface texture and internal defects or density of laser-beam powder-bed fusion (LB-PBF) parts. In this study, 120 cubic specimens were fabricated via application of the LB-PBF process to the IN 718 Ni alloy powder. The density and 35 areal surface-texture parameters of manufactured specimens were determined based on the ISO 25,178–2 standard. Using a statistical method, a strong correlation was observed between the areal surface-texture parameters and density or internal defects within specimens. In particular, the areal surface-texture parameters of reduced dale height, core height, root-mean-square height, and root-mean-square gradient demonstrate a strong correlation with specimen density. Therefore, in-situ monitoring of these areal surface-texture parameters can facilitate their use as control variables in the feedback system.

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

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.

Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review

2016 ◽  
Vol 231 (1) ◽  
pp. 96-117 ◽  
Author(s):  
Babis Schoinochoritis ◽  
Dimitrios Chantzis ◽  
Konstantinos Salonitis
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Experimental Validation ◽  
Metallic Powder ◽  
Manufacturing Processes ◽  
Powder Bed ◽  
Modeling Methodology ◽  
Melt Pool ◽  
Element Simulation ◽  
Melt Pool Characteristics

This article provides a literature review of finite element simulation studies for metallic powder bed additive manufacturing processes. The various approaches in the numerical modeling of the processes and the selection of materials properties are presented in detail. Simulation results are categorized according to three major findings’ groups (i.e. temperature field, residual stresses and melt pool characteristics). Moreover, the means used for the experimental validation of the simulation findings are described. Looking deeper into the studies reviewed, a number of future directions are identified in the context of transforming simulation into a powerful tool for the industrial application of additive manufacturing. Smart modeling approaches should be developed, materials and their properties should be further characterized and standardized, commercial packages specialized in additive manufacturing simulation have to be developed and simulation needs to become part of the modern digital production chains. Finally, the reviewed studies are organized in a table and characterized according to the process and material studied, the modeling methodology and the experimental validation method used in each of them. The key findings of the reviewed studies are also summarized.

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