Wire arc additive manufacturing (WAAM) presents a highly promising alternative to conventional subtractive manufacturing methods to produce metallic components, particularly in the aerospace industry, where there is a demand for 17–4 precipitation-hardened (PH) stainless steel structures. This study focuses on investigating the microstructural characteristics, showing microhardness evaluations, and analyzing the tensile properties of the as-printed parts during the 17–4 PH manufacturing process at different locations and directions. The fabrication is carried out using gas metal wire arc additive manufacturing (GM-WAAM). As a result, it was found that the microstructure of the as-deposited part showed a complex configuration consisting of both finely equiaxed and coarsely formed δ-ferrite phases with vermicular and lathy morphologies. These phases were dispersed inside the martensitic matrix, while a small amount of retained austenite was also present. It was observed that the volume fraction of retained austenite (20–5%) and δ-ferrite phases (15.5–2.5%) decreased gradually from the bottom to the top of the as-deposited wall. This reduction in the fractions of these phases resulted in a progressive increase in both hardness (∼37%) and ultimate tensile strength (UTS) along the building direction. This study successfully fabricates a high-strength and ductile 17–4 PH as-printed part using WAAM. The findings provide evidence supporting the feasibility of employing WAAM for producing defect-free, high-strength components on a large scale while maintaining mechanical properties similar or better than wrought alloy 17–4 PH.
more »
« less
This content will become publicly available on October 24, 2024
Integrating robotic wire arc additive manufacturing and machining: hybrid WAAM machining
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
- Award ID(s):
- 2219347
- NSF-PAR ID:
- 10472065
- Editor(s):
- Andrew Yeh-Ching Nee, editor-ion-chief
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- The International Journal of Advanced Manufacturing Technology
- ISSN:
- 0268-3768
- Subject(s) / Keyword(s):
- Hybrid manufacturing WAAM Machining Robots
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
S. Kapoor, editor-in-chief (Ed.)In this paper, a novel hybrid wire arc additive manufacturing (WAAM) and ultrasonic nanocrystal surface modification (UNSM) on porosity manipulation and surface properties of aluminum 5356 alloys was studied. The goal is to improve the quality of the WAAM-built part by eliminating bigger pores and reducing its size, reducing surface roughness, and increasing surface hardness. The as-built WAAM and WAAM-UNSM-treated samples were quantitatively studied for porosity using an X-ray micro-computed tomography (μ-CT). The surface roughness was measured on the surface profile of the same samples before and after UNSM treatment. Followed by the Vickers micro-hardness tests to evaluate the hardness modified by the influence of the UNSM treatment. It was found that the bigger pores in the as-built WAAM samples were eliminated and the medium-sized pores were shrunk to almost half the size after the UNSM treatment. Further, the UNSM treatment showed a significant improvement in both surface roughness and hardness on the WAAM Al5356 samples. This experimental work demonstrates the critical advantages of hybrid WAAM-UNSM in improving the qualities of the WAAM processed parts.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
-
more » « less
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
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.
