An official website of the United States government
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
Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis
The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.
more »
« less
- PAR ID:
- 10497534
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8723-3
- Subject(s) / Keyword(s):
- Rapid Prototyping and Solid Freeform Fabrication, Welding and Joining, Control and Automation.
- Format(s):
- Medium: X
- Location:
- New Brunswick, New Jersey, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Wire arc additive manufacturing (WAAM) has gained attention as a feasible process in large-scale metal additive manufacturing due to its high deposition rate, cost efficiency, and material diversity. However, WAAM induces a degree of uncertainty in the process stability and the part quality owing to its non-equilibrium thermal cycles and layer-by-layer stacking mechanism. Anomaly detection is therefore necessary for the quality monitoring of the parts. Most relevant studies have applied machine learning to derive data-driven models that detect defects through feature and pattern learning. However, acquiring sufficient data is time- and/or resource-intensive, which introduces a challenge to applying machine learning-based anomaly detection. This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality monitoring in WAAM. The proposed method uses convolutional neural network models to extract sufficient image features from multisource materials, then transfers and fine-tunes the models for anomaly detection in the target material. Stepwise learning is applied to extract image features sequentially from individual source materials, and composite learning is employed to assign the optimal frozen ratio for converging transferred and present features. Experiments were performed using a gas tungsten arc welding-based WAAM process to validate the classification accuracy of the models using low-carbon steel, stainless steel, and Inconel.more » « less
-
The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs.more » « less
-
Abstract Wire arc additive manufacturing (WAAM) is an efficient technique for producing medium to large‐size components, due to its accessibility and sustainability in fabricating large‐scale parts with high deposition rates, employing low‐cost and simple equipment, and achieving high material efficiency. Consequently, WAAM has garnered attention across various industrial sectors and experienced significant growth, particularly over the last decade, as it addresses and mitigates challenges within production markets. One of the primary limitations of WAAM is its thermal history during the process, which directly influences grain formation and microstructure heterogeneity in the resulting part. Understanding the thermal cycle of the WAAM process is thus crucial for process improvement. Typically, fabricating a part using WAAM results in a microstructure with three distinct zones along the build direction: an upper zone (thin surface layer) with fine grains, a middle zone dominated by undesirably long and large columnar grains covering more than 90% of the produced part, and a lower zone with smaller to intermediate columnar grains closer to the substrate material. These zones arise from variations in cooling rates, with the middle zone exhibiting the lowest cooling rate due to 2D conduction heat transfer. Consequently, producing a component with a microstructure comprising three different zones, with a high fraction of large and long columnar grains, significantly impacts the final mechanical properties. Therefore, controlling the size and formation of these grain zones plays a key role in improving WAAM. The aim of this work is to investigate the formation of undesired columnar grains in austenitic stainless steel 316L during WAAM and propose a simple hybrid technique by combining WAAM with a hot forging process (with or without interlayer cooling time). This approach targets the disruption of the solidification pattern of columnar grain growth during deposition progression and aims to enhance the microstructure of WAAM components.more » « less
-
Convolutional neural network (CNN), a type of deep learning algorithm, is a powerful tool for analyzing visual images. It has been actively investigated to monitor metal additive manufacturing (AM) processes for quality control and has been proven effective. However, typical CNN algorithms inherently have two issues when used in metal AM processes. First, in many cases, acquiring datasets with sufficient quantity and quality, as well as necessary information, is challenging because of technical difficulties and/or cost intensiveness. Second, determining a near-optimal CNN model takes considerable effort and is time-consuming. This is because the types and quality of datasets can be significantly different with respect to different AM processes and materials. The study proposes a novel concatenated ensemble learning method to obtain a flexible and robust algorithm for in-situ anomaly detection in wire + arc additive manufacturing (WAAM), a type of wire-based direct energy deposition (DED) process. For this, data, as well as machine learning models, were seamlessly integrated to overcome the limitations and difficulties in acquiring sufficient data and finding a near-optimal machine learning model. Using inexpensively obtainable and comprehensive datasets from the WAAM process, the proposed method was investigated and validated. In contrast to the one-dimensional and two-dimensional CNN models’ accuracies of 81.6 % and 88.6 %, respectively, the proposed concatenated ensemble model achieved an accuracy of 98 %.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2023-101622
