skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Review on Wire-Laser Directed Energy Deposition: Parameter Control, Process Stability, and Future Research Paths
Wire-laser directed energy deposition has emerged as a transformative technology in metal additive manufacturing, offering high material deposition efficiency and promoting a cleaner process environment compared to powder processes. This technique has gained attention across diverse industries due to its ability to expedite production and facilitate the repair or replication of valuable components. This work reviews the state-of-the-art in wire-laser directed energy deposition to gain a clear understanding of key process variables and identify challenges affecting process stability. Furthermore, this paper explores modeling and monitoring methods utilized in the literature to enhance the final quality of fabricated parts, thereby minimizing the need for repeated experiments, and reducing material waste. By reviewing existing literature, this paper contributes to advancing the current understanding of wire-laser directed energy deposition technology. It highlights the gaps in the literature while underscoring research needs in wire-laser directed energy deposition.  more » « less
Award ID(s):
2338253
PAR ID:
10573001
Author(s) / Creator(s):
;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Journal of Manufacturing and Materials Processing
Volume:
8
Issue:
2
ISSN:
2504-4494
Page Range / eLocation ID:
84
Subject(s) / Keyword(s):
Wire LDED
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, American Society of Mechanical Engineers)
    The objective of this work is to detect process instabilities in laser wire directed energy deposition additive manufacturing process using real-time data from a high-speed imaging meltpool sensor. The laser wire directed energy deposition process combines the advantages of powder directed energy deposition and other wire-based additive manufacturing processes, such as wire arc additive manufacturing, as it provides both appreciable resolution and high deposition rates. However, the process tends to create sub-optimal quality parts with poor surface finish, geometric distortion, and delamination in extreme cases. This sub-optimal quality stems from poorly understood thermophysical phenomena and stochastic effects. Hence, flaw formation often occurs despite considerable effort to optimize the processing parameters. In order to overcome this limitation of laser wire directed energy deposition, real-time and accurate monitoring of the process quality state is the essential first step for future closed-loop quality control of the process. In this work we extracted low-level, physically intuitive, features from acquired meltpool images. Physically intuitive features such as meltpool shape, size, and brightness provide a fundamental understanding of the processing regimes that are understandable by human operators. These physically intuitive features were used as inputs to simple machine learning models, such as k-nearest neighbors, support vector machine, etc., trained to classify the process state into one of four possible regimes. Using simple machine learning models forgoes the need to use complex black box modeling such as convolutional neural networks to monitor the high speed meltpool images to determine process stability. The classified regimes identified in this work were stable, dripping, stubbing, and incomplete melting. Regimes such as dripping, stubbing, and incomplete melting regimes fall under the realm of unstable processing conditions that are liable to lead to flaw formation in the laser wire directed energy deposition process. The foregoing three process regimes are the primary source of sub-optimal quality parts due to the degradation of the single-track quality that are the fundamental building block of all manufactured samples. Through a series of single-track experiments conducted over 128 processing conditions, we show that the developed approach is capable of accurately classifying the process state with a statistical fidelity approaching 90% F-score. This level of statistical fidelity was achieved using eight physically intuitive meltpool morphology and intensity features extracted from 159,872 meltpool images across all 128 process conditions. These eight physically intuitive features were then used for the training and testing of a support vector machine learning model. This prediction fidelity achieved using physically intuitive features is at par with computationally intense deep learning methods such as convolutional neural networks. 
    more » « less
  2. ; ; (, Metals)
    Directed energy deposition (DED) additive manufacturing (AM) enables the production of components at a high deposition rate. For certain alloys, interpass temperature requirements are imposed to control heat accumulation and microstructure transformation, as well as to minimize distortion under varying thermal conditions. A typical strategy to comply with interpass temperature constraints is to increase the interpass dwell time, which can lead to an increase in the total deposition time. This study aims to develop an optimized tool path that ensures interpass temperature compliance and reduces overall deposition time relative to the conventional sequential deposition path during the DED process. To evaluate this, a compact analytic thermal model is used to predict the thermal history during laser-based directed energy deposition (DED-LB/M) hot wire (lateral feeding) of ER100S-G, a welding wire equivalent to high yield steel. A greedy algorithm, integrated with the thermal model, identifies a tool path order that ensures compliance with the interpass requirement of the material while minimizing interpass dwell time and, thus, the total deposition time. The proposed path planning algorithm is validated experimentally with in situ temperature measurements comparing parts fabricated with the baseline (sequential) deposition path to the modified path (resulting from the greedy algorithm). The experimental results of this study demonstrate that the proposed path planning algorithm can reduce the deposition time by 9.2% for parts of dimensions 66 mm × 73 mm × 16.5 mm, comprising 15 layers and a total of 300 beads. Predictions based on the proposed path planning algorithm indicate that additional reductions in deposition time can be achieved for larger parts. Specifically, increasing the (experimentally validated) part dimension perpendicular to the deposition direction by five-times is expected to result in a 40% reduction in deposition time. 
    more » « less
  3. ; ; (, Metals)
    Laser Hot Wire (LHW) Directed Energy Deposition (DED) Additive Manufacturing (AM) processes are capable of manufacturing parts with a high deposition rate. There is a growing research interest in replacing large cast Nickel Aluminum Bronze (NAB) components using LHW DED processes for maritime applications. Understanding thermomechanical behavior during LHW DED of NAB is a critical step towards the production of high-quality NAB parts with desired performance and properties. In this paper, finite element simulations are first used to predict the thermomechanical time histories during LHW DED of NAB test coupons with an increasing geometric complexity, including single-layer and multilayer depositions. Simulation results are experimentally validated through in situ measurements of temperatures at multiple locations in the substrate as well as displacement at the free end of the substrate during and immediately following the deposition process. The results in this paper demonstrate that the finite element predictions have good agreement with the experimental measurements of both temperature and distortion history. The maximum prediction error for temperature is 5% for single-layer samples and 6% for multilayer samples, while the distortion prediction error is about 12% for single-layer samples and less than 4% for multilayer samples. In addition, this study shows the effectiveness of including a stress relaxation temperature at 500 °C during FE modeling to allow for better prediction of the low cross-layer accumulation of distortion in multilayer deposition of NAB. 
    more » « less
  4. ; ; ; ; (, American Society of Mechanical Engineers)
    Abstract Directed Energy Deposition (DED) is one of the main additive manufacturing (AM) families, enabling the fabrication of multi-material parts with high material addition rates. However, the incremental nature of DED fabrication makes it prone to local defect formation due to process condition fluctuations. Known for its rapid and precise 3D surface measurement capabilities, digital fringe projection (DFP) was previously demonstrated in process monitoring for powder bed AM. This study brings DFP to the DED process through development of a custom motor stage system and validates its effectiveness in assessing surface topography and build height measurement. Measurements were taken on both correctly deposited builds and builds with off-nominal deposition conditions, where the system was able to detect pitting as small as 0.425 mm in the lateral size and 0.154 mm in depth in the case of reduced laser energy. This work paves the way for future machine learning-enabled interlayer defect identification, classification, and healing via altering subsequent processing settings. 
    more » « less
  5. ; (, Frontiers in Materials)
    As efforts associated with the exploration of multi-principal element alloys (MPEAs) using computational and data-intensive methods continue to rise, experimental realization and validation of the predicted material properties require high-throughput and combinatorial synthesis of these alloys. While additive manufacturing (AM) has emerged as the leading pathway to address these challenges and for rapid prototyping through part fabrication, extensive research on developing and understanding the process-structure-property correlations is imminent. In particular, directed energy deposition (DED) based AM of MPEAs holds great promise because of the boundless compositional variations possible for functionally graded component manufacturing as well as surface cladding. We analyze the recent efforts in DED of MPEAs, the microstructural evolution during the laser metal deposition of various transition and refractory elements, and assess the effects of various processing parameters on the material phase and properties. Our efforts suggest that the development of robust predictive approaches for process parameter selection and modifying the synthesis mechanisms are essential to enable DED platforms to repeatedly produce defect free, stable and designer MPEAs. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email