skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Friday, November 15 until 2:00 AM ET on Saturday, November 16 due to maintenance. We apologize for the inconvenience.


Title: Detecting balling defects using multisource transfer learning in wire arc additive manufacturing
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
Award ID(s):
2015693
NSF-PAR ID:
10430599
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of Computational Design and Engineering
Volume:
10
Issue:
4
ISSN:
2288-5048
Page Range / eLocation ID:
p. 1423-1442
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Wire arc additive manufacturing is a promising additive manufacturing process because of its high deposition rate, and material diversity. However, the low quality of melted parts is a critical issue, owing to the difficulty in establishing design rules for process–structure–property–performance. Previous studies have resolved this challenge by deriving anomaly detection models for quality monitoring and have largely relied on machine learning by training melt pool image data. Acquiring sufficient data is a key to obtaining reliable models in machine learning; however, an issue arises from concerning the cost intensiveness in high-cost materials. We propose a material-adaptive anomaly detection method to detect balling defects in a target material using property-concatenated transfer learning. First, transfer learing is applied to derive convolutional neural network (CNN)-based models from a source material and transfer them to a target material, wherein data are insufficient and machine learning rarely achieves high performance. Second, material properties are concatenated on transfer learning as additional features onto image features, contrary to typical transfer learning where CNNs only extract image features. We perform experiments in a gas tungsten arc welding system with low-carbon steel (LCS), stainless steel (STS), and inconel (INC) materials. Our models achieve best classification accuracies of 82.95%, 89.47%, and 84.22% when transferring from LCS to STS, LCS to INC, and STS to INC, respectively, compared with 78.03%, 86.37%, and 73.63% obtained using typical transfer learning. The proposed method can effectively resolve the data scarcity by model transfer from sufficient datasets in low-cost materials to rare datasets in high-cost materials. Moreover, it outperforms typical transfer learning because material properties are learned as manufacturing-knowledge features, accounting for melting and hardening characteristics of materials. 
    more » « less
  2. 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
  3. 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
  4. Abstract The process uncertainty induced quality issue remains the major challenge that hinders the wider adoption of additive manufacturing (AM) technology. The defects occurred significantly compromise structural integrity and mechanical properties of fabricated parts. Therefore, there is an urgent need in fast, yet reliable AM component certification. Most finite element analysis related methods characterize defects based on the thermomechanical relationships, which are computationally inefficient and cannot capture process uncertainty. In addition, there is a growing trend in data-driven approaches on characterizing the empirical relationships between thermal history and anomaly occurrences, which focus on modeling an individual image basis to identify local defects. Despite their effectiveness in local anomaly detection, these methods are quite cumbersome when applied to layer-wise anomaly detection. This paper proposes a novel in situ layer-wise anomaly detection method by analyzing the layer-by-layer morphological dynamics of melt pools and heat affected zones (HAZs). Specifically, the thermal images are first preprocessed based on the g-code to assure unified orientation. Subsequently, the melt pool and HAZ are segmented, and the global and morphological transition metrics are developed to characterize the morphological dynamics. New layer-wise features are extracted, and supervised machine learning methods are applied for layer-wise anomaly detection. The proposed method is validated using the directed energy deposition (DED) process, which demonstrates superior performance comparing with the benchmark methods. The average computational time is significantly shorter than the average build time, enabling in situ layer-wise certification and real-time process control. 
    more » « less
  5. Andrew Yeh-Ching Nee, editor-ion-chief (Ed.)
    Wire arc additive manufacturing (WAAM) has received increasing use in 3D printing because of its high deposition rates suitable for components with large and complex geometries. However, the lower forming accuracy of WAAM than other metal additive manufacturing methods has imposed limitations on manufacturing components with high precision. To resolve this issue, we herein implemented the hybrid manufacturing (HM) technique, which integrated WAAM and subtractive manufacturing (via a milling process), to attain high forming accuracy while taking advantage of both WAAM and the milling process. We describe in this paper the design of a robot-based HM platform in which the WAAM and CNC milling are integrated using two robotic arms: one for WAAM and the other for milling immediately following WAAM. The HM was demonstrated with a thin-walled aluminum 5356 component, which was inspected by X-ray micro-computed tomography (μCT) for porosity visualization. The temperature and cutting forces in the component under milling were acquired for analysis. The surface roughness of the aluminum component was measured to assess the surface quality. In addition, tensile specimens were cut from the components using wire electrical discharge machining (WEDM) for mechanical testing. Both machining quality and mechanical properties were found satisfactory; thus the robot-based HM platform was shown to be suitable for manufacturing high-quality aluminum parts. 
    more » « less