The effect of wire size on high deposition rate wire and plasma arc additive manufacture of Ti-6Al-4V

Chong, Wang; Suder, Wojciech; Ding, Jialuo; Williams, Stewart

doi:10.1016/j.jmatprotec.2020.116842

Cited by 67 publications

(23 citation statements)

References 13 publications

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“…Metal transfer can be done in two ways: free transfer and bridging transfer. The optimal transferring mode is droplet transfer to the molten pool via bridging transfer, which leads in layer formation and meets industry standards [26], [27].…”

Section: Methodsmentioning

confidence: 88%

Build Strategies Based on Substrate Utilization for 3-Axis Hybrid Wire Arc Additive Manufacturing Process

Sarma

Kapil

Joshi

2022

Advances in Materials Science and Engineering

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The hybrid wire arc additive manufacturing (H-WAAM) process is one of the prominent methods for realizing large near-net-shaped metallic objects. In this process, a CAD model of the component is sliced into a set of 2D contours followed by the generation of toolpaths. An arc welding torch then follows these toolpaths for adding material over a substrate to realize the near-net shape of the object. These near-net-shaped objects are then followed by a machining operation to convert them into a fully functional part. It is always anticipated that the near-net shape of an object is produced quickly and upholds a high geometrical accuracy. Conventionally, the deposition rate is increased to reduce the build time but with a compromisation in the geometrical accuracy and material integrity. Therefore, in this work, the authors have investigated three substrate utilization methods, viz., (i) reusable substrate, (ii) embedded substrate, and (iii) integrated substrate to achieve the same goal. The build strategies for these three substrate utilization methods are illustrated through several examples. Also, a case study was performed for fabricating an impeller-like structure through a 3-axis H-WAAM setup. It has been observed that the embedded substrate method exhibits superior geometrical accuracy and takes less time to build the part as compared to other methods. A maximum of 64.34% of the material and 89.17% of build time is saved by adopting proposed build strategies compared with the traditional subtractive process.

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Section: Methodsmentioning

confidence: 88%

Build Strategies Based on Substrate Utilization for 3-Axis Hybrid Wire Arc Additive Manufacturing Process

Sarma

Kapil

Joshi

2022

Advances in Materials Science and Engineering

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“…Two different sets of process parameters were used to test the control system with very different operating parameters which caused different processing conditions and thermal conditions. This meant that the optimal wire position was also different (Wang et al, 2021a(Wang et al, , 2021b. However, it was demonstrated that the proposed droplet transfer control system was able to find the optimum position even though these were different.…”

Section: Discussionmentioning

confidence: 98%

Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning

et al. 2022

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In the last decade, wire + arc additive manufacturing (WAAM), which is one of the most promising metal additive manufacturing technologies, has been attracting high interest from both academia and industry. WAAM systems are increasingly employed in the industry and academia, but there are still several challenges and barriers to process stability control. The process stability is highly dependent on how the molten feed wire is added into the melt pool, which is known as the droplet transfer mode. To ensure a stable WAAM deposition process, it is essential to maintain the transfer mode in a suitable stable status. Without an effective transfer mode control method, the operators need to determine and control the transfer mode based on their experience using manual adjustment, which is difficult to achieve in a long period of production process. In this paper, a deep learning-based technology was proposed for the control of the droplet transfer mode based on the data collected from the WAAM process. A long short term memory neural network was applied as the core transfer mode classification model. A time-series data, arc voltage, was collected and statistical and frequency features were extracted, which included 11 relevant features, as the inputs of the classification model. Then, the distance between the melted wire and the melt pool was adjusted based on the determined transfer mode to keep a suitable stability of the process. A case study was used to evaluate the proposed approach and to show its merit. The proposed approach was compared to three commonly used machine learning algorithms, k-nearest neighbours, support vector machine, and decision tree. The proposed method obtained the highest accuracy in determining the transfer mode, which was over 91%. The performance of the proposed approach was also evaluated by the single-pass and oscillated wall building. The proposed deep learning based approach improved the process stability in real-time, which resulted in better deposition qualities, in terms of geometry size and processing cleanliness compared to without control. Furthermore, this data-driven method could be applied to other WAAM processes and materials.

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“…The amount of material deposited in a layer is determined by the wire diameter and its feed speed, with the height and shape it adopts atop the previous WAAM depositions determined substantially by how quickly it cools from molten to solid. This is in turn determined by the thermal conductivity of the previously deposited layers, which is itself dependent on the height and shape of those layers [4,6,7,9]. In this way the height of future layers depends on the height of past layers allowing minor defects to grow into significant ones [4,5].…”

Section: Introductionmentioning

confidence: 99%

In-process range-resolved interferometric (RRI) 3D layer height measurements for wire + arc additive manufacturing (WAAM)

Hallam

Kissinger

Charrett

et al. 2022

Meas. Sci. Technol.

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In this work a range resolved interferometry (RRI) instrument for absolute distance measurements is integrated into a wire + arc additive manufacturing (WAAM) system to provide in-process monitoring of layer height, and prospects for volume and proﬁle monitoring are discussed. Interferometry as a coherent optical technique oﬀers a straightforward in-process measurement even in the harsh welding environment, as compared to non-coherent techniques based either on laser proﬁling or camera vision systems. RRI can be accomplished at signiﬁcantly lower cost, and with higher depth of ﬁeld (up to 10s of cm) than existing optical coherence tomography based weld monitoring. In this experiment titanium feedstock was used to create a 150mm long, 13.5mm high weld-wall comprised of 11 welded layers. The RRI in-process measurements are in very good agreement with both mid-process, on-machine micrometer measurements taken by hand after each weld, and post-process laser scanning measurements of the completed wall. The high depth of ﬁeld allows direct referencing of the layer height measurements to the build plate making the measurement independent of the motion system and build plate bending, considerably lowering uncertainties. This, together with the capability for cost-eﬀective in-process measurements in harsh environments, should make the proposed approach very interesting for routine use in WAAM systems.

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The effect of wire size on high deposition rate wire and plasma arc additive manufacture of Ti-6Al-4V

Cited by 67 publications

References 13 publications

Build Strategies Based on Substrate Utilization for 3-Axis Hybrid Wire Arc Additive Manufacturing Process

Build Strategies Based on Substrate Utilization for 3-Axis Hybrid Wire Arc Additive Manufacturing Process

Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning

In-process range-resolved interferometric (RRI) 3D layer height measurements for wire + arc additive manufacturing (WAAM)

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