Iterative hybrid manufacture of a titanium alloy component

Honeycutt, Andrew; Mhatre, Paritosh; Gibson, Brian T.; Smith, Scott; Richardson, Brad

doi:10.1016/j.mfglet.2021.07.003

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…Unfortunately, owing to its low carbon and high alloy content, the inclination of this austenitic grade is to work harden at a very rapid rate, which poses considerable difficulties for the finish machining of additive parts to net-shape [17]. Undertaken separately and sequentially after AM, out-of-envelope machining poses additional challenges for aligning, workpiece-holding, and referencing, especially due to the lack of precise geometric datum in the as-built part [18].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of 316L Stainless Steel Manufactured by a Hybrid Additive/Subtractive Technology

Sarafan

Wanjara

Gholipour

et al. 2022

JMMP

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This research study investigated the hybrid processing of 316L stainless steel using laser powder bed (LPB) processing with high-speed machining in the same build envelope. Benchmarking at four laser powers (160 W, 240 W, 320 W, and 380 W) was undertaken by building additively with machining passes integrated sequentially after every ten deposited layers, followed by the final finishing of select surfaces. The final geometry was inspected against the computer-aided design (CAD) model and showed deviations smaller than 280 µm for the as-built and machined surfaces, which demonstrate the good efficacy of hybrid processing for the net-shape manufacturing of stainless steel products. The arithmetic average roughness values for the printed surfaces, Ra (linear) and Sa (surface), were 11.4 um and 14.9 um, respectively. On the other hand, the vertical and horizontal machined surfaces had considerably lower roughness, with Ra and Sa values ranging between 0.33 µm and 0.70 µm. The 160 W coupon contained layered, interconnected lack of fusion defects which affected the density (7.84 g·cm−3), yield strength (494 MPa), ultimate tensile strength (604 MPa), Young’s modulus (175 GPa), and elongation at break (17.3%). By contrast, at higher laser powers, near-full density was obtained for the 240 W (7.96 g·cm−3), 320 W (7.94 g·cm−3), and 380 W (7.92 g·cm−3) conditions. This, combined with the isolated nature of the small pores, led to the tensile properties surpassing the requirements stipulated in ASTM F3184—16 for 316L stainless steel.

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Section: Introductionmentioning

confidence: 99%

Benchmarking of 316L Stainless Steel Manufactured by a Hybrid Additive/Subtractive Technology

Sarafan

Wanjara

Gholipour

et al. 2022

JMMP

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“…Renowned for its superior dimensional stability and mechanical prowess in cryogenic environments, Invar plays a pivotal role in applications demanding precision and reliability, as seen in metrology and aerospace components. The formidable challenge posed by its malleability and low heat conductivity in machining scenarios drives the interest in additive manufacturing for Invar, offering preforms aligning closely with final part geometries [12][13][14]. Invar's application through PBF-LB/M has revealed optimal energy density ranges for mechanical properties [15], along with characteristic microstructures [16].…”

Section: Introductionmentioning

confidence: 99%

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Fernández-Zabalza,

Veiga,

Suárez

et al. 2024

Applied Sciences

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Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

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Controls and process planning strategies for 5-axis laser directed energy deposition of Ti-6Al-4V using an 8-axis industrial robot and rotary motion

Gibson

Mhatre²,

Borish³

et al. 2022

Additive Manufacturing

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Iterative hybrid manufacture of a titanium alloy component

Cited by 8 publications

References 8 publications

Benchmarking of 316L Stainless Steel Manufactured by a Hybrid Additive/Subtractive Technology

Benchmarking of 316L Stainless Steel Manufactured by a Hybrid Additive/Subtractive Technology

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Controls and process planning strategies for 5-axis laser directed energy deposition of Ti-6Al-4V using an 8-axis industrial robot and rotary motion

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