Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning

Funding Information: AR, TGS and JPO acknowledge the Portuguese Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI). JPO acknowledges the funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020, UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication – i3N. AR acknowledges FCT - MCTES for funding the PhD grant UI/BD/151018/2021. This activity has received funding from the European Institute of Innovation and Technology (EIT) RawMaterials through the project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. This body of the European Union receives support from the European Union's Horizon 2020 research and innovation program. Prahalada Rao gratefully acknowledges funding from the following US federal government agencies for nurturing his scholastic research in metal additive manufacturing and smart manufacturing over the last decade through the following awards. National Science Foundation (NSF) via Grant Nos. CMMI-2428305, CMMI-2336449, CMMI-2309483/1752069, OIA-1929172, PFI-TT 2322322/2044710, CMMI-1920245, ECCS-2020246, CMMI-1739696, CMMI-2336449, and CMMI-2428305; US Department of Navy, Naval Surface Warfare Center (NAVAIR, N6833524C0215) and Office of Naval Research (ONR, N00014-21-1-2781); and the National Institute of Standards and Technology (NIST, 70NANB23H029T). Understanding the causal influence of process parameters on part quality and detection of defect formation using in-situ sensing was the major aspect of CMMI-2309483/1752069 (Program Officer: Pranav Soman). The use of machine learning and analytics for process diagnosis in additive manufacturing was funded via ECCS-2020246 (program officer: Richard Nash). Benjamin Bevans was funded through CMMI-2309483/1752069 and PFI-TT 2322322/2044710. Anis Assad and Fernando Deschamps were funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. The foregoing also funded a visiting student scholarship for Anis Assad to work at Virginia Tech under the supervision of Prahalada Rao. Publisher Copyright: © 2025 The AuthorsThis work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. Humping and humping-induced porosity are leading stochastic causes of poor WA-DED part quality that occur despite extensive optimization of processing conditions. It is therefore essential to understand, detect and control the causal meltpool phenomena linked to these instabilities. Accordingly, we used a high-speed camera to capture the meltpool dynamics of multi-layer depositions of ER90S-G steel parts and meltpool flow behavior related to process instabilities were demarcated and quantified. Next, physically intuitive meltpool morphology signatures were extracted from the imaging data. These signatures were used in a machine learning model trained to autonomously detect process instabilities. This novel process-aware machine learning approach classified onset of instabilities with ∼85 % accuracy (F1-score), outperforming black-box deep learning models (F1-score <66 %). These results pave the way for a physically intuitive process-aware machine learning strategy for monitoring and control of the WA-DED process.publishersversionpublishe

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

UID/00667/2020 (UNIDEMI). J. P. Oliveira acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which partially supported the doctoral graduate work of Mr. Benjamin Bevans at University of Nebraska-Lincoln Benjamin, Aniruddha, and Ziyad Smoqi were further supported by the NSF grants CMMI 1752069 (CAREER) and ECCS 2020246. Detecting flaw formation in metal AM using in-situ sensing and graph theory-based algorithms was a major component of CMMI 1752069 (program office: Kevin Chou). Developing machine learning alogirthms for advanced manufacturing applications was the goal of ECCS 2020246 (Program officer: Donald Wunsch). The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. The acquisition of the XCT scanner at University of Nebraska was funded through CMMI 1920245 (Program officer: Wendy Crone). Publisher Copyright: © 2022 The AuthorsThe goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.publishersversionpublishe

Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning

This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 % (F1-score) compared to F1-score 87 % for deep learning models

Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parameters

We developed and applied a model-driven feedforward control approach to mitigate thermal-induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process. The key idea was to avert heat buildup in a LPBF part before it is printed by adapting process parameters layer-by-layer based on insights from a physics-based thermal simulation model. The motivation being to replace cumbersome empirical build-and-test parameter optimization with a physics-guided strategy. The approach consisted of three steps: prediction, analysis, and correction. First, the temperature distribution of a part was predicted rapidly using a graph theory-based computational thermal model. Second, the model-derived thermal trends were analyzed to isolate layers of potential heat buildup. Third, heat buildup in affected layers was corrected before printing by adjusting process parameters optimized through iterative simulations. The effectiveness of the approach was demonstrated experimentally on two separate build plates. In the first build plate, termed fixed processing, ten different nickel alloy 718 parts were produced under constant processing conditions. On a second identical build plate, called controlled processing, the laser power and dwell time for each part was adjusted before printing based on thermal simulations to avoid heat buildup. To validate the thermal model predictions, the surface temperature of each part was tracked with a calibrated infrared thermal camera. Post-process the parts were examined with non-destructive and destructive materials characterization techniques. Compared to fixed processing, parts produced under controlled processing showed superior geometric accuracy and resolution, finer grain size, increased microhardness, and reduced surface roughness

Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning

This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 % (F1-score) compared to F1-score 87 % for deep learning models.This article is published as Assad, Anis, Benjamin D. Bevans, Willem Potter, Prahalada Rao, Denis Cormier, Fernando Deschamps, Jakob D. Hamilton, and Iris V. Rivero. "Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning." Materials & Design 245 (2024): 113281. doi: https://doi.org/10.1016/j.matdes.2024.113281

Born Qualified Additive Manufacturing: In-situ Part Quality Assurance in Metal Additive Manufacturing

Doctor of PhilosophyThe long-term goal of this dissertation is to develop quality assurance methodologies for parts made using metal additive manufacturing (AM). Additive manufacturing is becoming a prominent manufacturing process due to its ability to generate complex structures that would otherwise be impossible to produce using traditional machining. This freedom of complexity enables engineers to make more efficient components and reduce part counts in assemblies. However, the AM process tends to generate random flaws that require manufacturers to perform extensive testing on all manufactured samples to ensure part quality. Due to this extensive testing, manufacturers have been slow to adopt the AM process. Thus, the goal of this dissertation is to understand, monitor, and predict the quality of metal AM parts as they are being printed to remove the need for post-manufacturing testing – hence the phrase Born Qualified. To enable Born Qualified manufacturing with AM, the objective of this dissertation was to use sensors installed on AM machines to monitor part quality during the process. With this objective, this dissertation focused on: (1) using acoustic signal monitoring to determine the onset of process instabilities that would generate flaws; (2) monitoring the process with multiple sensors to determine the specific type of flaws formed; (3) developing novel methods to monitor the sub-surface effects; and (4) combining multiple streams of sensor data with thermal simulations to detect flaw formation along with mechanical and material properties of the manufactured parts

A Review of Modeling, Simulation, and Process Qualification of Additively Manufactured Metal Components via the Laser Powder Bed Fusion Method

Metal additive manufacturing (AM) has grown in recent years to supplement or even replace traditional fabrication methods. Specifically, the laser powder bed fusion (LPBF) process has been used to manufacture components in support of sustainment issues, where obsolete components are hard to procure. While LPBF can be used to solve these issues, much work is still required to fully understand the metal AM technology to determine its usefulness as a reliable manufacturing process. Due to the complex physical mechanisms involved in the multiscale problem of LPBF, repeatability is often difficult to achieve and consequently makes meeting qualification requirements challenging. The purpose of this work is to provide a review of the physics of metal AM at the melt pool and part scales, thermomechanical simulation methods, as well as the available commercial software used for finite element analysis and computational fluid dynamics modeling. In addition, metal AM process qualification frameworks are briefly discussed in the context of the computational basis established in this work

Predicting meltpool depth and primary dendritic arm spacing in laser powder bed fusion additive manufacturing using physics-based machine learning

The long-term goal of this work is to predict and control the microstructure evolution in metal additive manufacturing processes. In pursuit of this goal, we developed and applied an approach which combines physics-based thermal modeling with machine learning to predict two important microstructure-related characteristics, namely, the meltpool depth and primary dendritic arm spacing in Nickel Alloy 718 parts made using the laser powder bed fusion (LPBF) process. Microstructure characteristics are critical determinants of functional physical properties, e.g., yield strength and fatigue life. Currently, the microstructure of LPBF parts is optimized through a cumbersome build-and-characterize empirical approach. Rapid and accurate models for predicting microstructure evolution are therefore valuable to reduce process development time and achieve consistent properties. However, owing to their computational complexity, existing physics-based models for predicting the microstructure evolution are limited to a few layers, and are challenging to scale to practical parts. This paper addresses the aforementioned research gap via a novel physics and data integrated modeling approach. The approach consists of two steps. First, a rapid, part-level computational thermal model was used to predict the temperature distribution and cooling rate in the entire part before it was printed. Second, the foregoing physics-based thermal history quantifiers were used as inputs to a simple machine learning model (support vector machine) trained to predict the meltpool depth and primary dendritic arm spacing based on empirical materials characterization data. As an example of its efficacy, when tested on a separate set of samples from a different build, the approach predicted the primary dendritic arm spacing with root mean squared error ≈ 110 nm. This work thus presents an avenue for future physics-based optimization and control of microstructure evolution in LPBF

Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing

We developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (\u3c 100 μm), layer related inconsistencies at the meso-scale (100 μm to 1 mm) and geometry-related flaws at the macroscale (\u3e 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score)