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 layerbylayer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theorybased 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 illconsidered part design leads to defects, such as warping and thermal stressinduced cracking. Existing nonproprietary approaches to predict the heat flux in AM at the partlevel predominantly use meshbased 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 more »
 Award ID(s):
 1752069
 Publication Date:
 NSFPAR ID:
 10140522
 Journal Name:
 ASME Manufacturing Science and Engineering Conference
 Sponsoring Org:
 National Science Foundation
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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 layerbylayer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theorybased 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 stressinduced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use meshbased 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 insteadmore »

Purpose The purpose of this paper is to develop, apply and validate a meshfree graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach In this study, the authors develop a novel meshfree graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti6Al4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications This study uses data from fixed thermocouples when printing thinwall DED parts. In the future, the authors will incorporate infrared thermal cameramore »

Despite its potential to overcome the design and processing barriers of traditional subtractive and formative manufacturing techniques, the use of laser powder bed fusion (LPBF) metal additive manufacturing is currently limited due to its tendency to create flaws. A multitude of LPBFrelated flaws, such as partlevel deformation, cracking, and porosity are linked to the spatiotemporal temperature distribution in the part during the process. The temperature distribution, also called the thermal history, is a function of several factors encompassing material properties, part geometry and orientation, processing parameters, placement of supports, among others. These broad range of factors are difficult and expensive to optimize through empirical testing alone. Consequently, fast and accurate models to predict the thermal history are valuable for mitigating flaw formation in LPBFprocessed parts. In our prior works, we developed a graph theorybased approach for predicting the temperature distribution in LPBF parts. This meshfree approach was compared with both nonproprietary and commercial finite element packages, and the thermal history predictions were experimentally validated with in situ infrared thermal imaging data. It was found that the graph theoryderived thermal history predictions converged within 30–50% of the time of nonproprietary finite element analysis for a similar level of prediction error. However,more »

Abstract The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history in additively manufactured parts that was recently published in these transactions. In the present paper the graph theory approach is validated with insitu infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize this objective through the following three tasks. First, two types of test parts (stainless steel) are made in two corresponding build cycles on a Renishaw AM250 LPBF machine. The intent of both builds is to influence the thermal history of the part by changing the cooling time between melting of successive layers, called interlayer cooling time. Second, layerwise thermal images of the top surface of the part are acquired using an insitu a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds were used to validate the graph theorypredicted surface temperature trends. Furthermore, the surface temperature trends predicted using graph theory are compared with results from finite element analysis. As an example, for one the builds, the graph theory approach accurately predicted the surface temperature trends to within 6% mean absolute percentage error,more »

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Abstract The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history in additively manufactured parts that was recently published in the ASME transactions. In the present paper the graph theory approach is validated with insitu infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize this objective through the following three tasks. First, two types of test parts (stainless steel) are made in two corresponding build cycles on a Renishaw AM250 LPBF machine. The intent of both builds is to influence the thermal history of the part by changing the cooling time between melting of successive layers, called interlayer cooling time. Second, layerwise thermal images of the top surface of the part are acquired using an insitu a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds were used to validate the graph theorypredicted surface temperature trends. Furthermore, the surface temperature trends predicted using graph theory are compared with results from finite element analysis. As an example, for one the builds, the graph theory approach accurately predicted the surface temperature trends to within 6% mean absolute percentagemore »