Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to nonfederal websites. Their policies may differ from this site.

Free, publiclyaccessible full text available December 1, 2023

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 »Free, publiclyaccessible full text available August 12, 2023

The objective of this work is to predict a type of thermalinduced process failure called recoater crash that occurs frequently during laser powder bed fusion (LPBF) additive manufacturing. Rapid and accurate thermomechanical simulations are valuable for LPBF practitioners to identify and correct potential issues in the part design and processing conditions that may cause recoater crashes. In this work, to predict the likelihood of a recoater crash (recoater contact or impact) we develop and apply a computationally efficient thermomechanical modeling approach based on graph theory. The accuracy and computational efficiency of the approach is demonstrated by comparison with both nonproprietary finite element analysis (Abaqus), and a proprietary LPBF simulation software (Autodesk Netfabb). Based on both numerical (verification) and experimental (validation) studies, the proposed approach is found to be 5 to 6 times faster than the nonproprietary finite element modeling and has the same order of computational time as a commercial simulation software (Netfabb) without sacrificing prediction accuracy.Free, publiclyaccessible full text available August 5, 2023

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 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 »

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 »

Abstract Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step toward this goal, the objectives of this work are twofold. First, to develop and apply a finite elementbased framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts. Second, validate the modelderived thermal profiles with experimental inprocess measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical firststep toward understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. Frommore »

The goal of this work to mitigate flaws in metal parts produced from laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step towards this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of insitu layerwise images obtained from an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thinwall features (fins) from Titanium alloy (Ti6Al4V) material with varying lengthtothickness ratio. These thinwall test parts were printed under three different build orientations and insitu images of their top surface were acquired during the process. The parts were examined offline using Xray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCTderived statistical quality features using the layerwise optical images of the thinwall part as inputs. The statistical correlation between CNNbased predictions and XCTobserved quality measurements exceeds 85%. This work has two outcomes consequential to the sustainability of additive manufacturing: (1) It provides practitioners with a guideline for building thinwallmore »