Convection Coefficient Estimation of Still Air Using an Infrared Thermometer and Curve-Fitting

Yener, Tuba; Yener, Şuayb Çağrı; Mutlu, Reşat

doi:10.30931/jetas.598862

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Calculate the thermal distribution using the state equation [33] 22.5 Diffusivity, α (m 2 /s) [33] 5.632 × 10 −6 Melting temperature, Tm (K) [33] 1658 Convection coefficient, h (W/(m 2 K)) [31] 25 Initial temperature, T(x,y,z,0) (K) 293 Ambient temperature, Ta (K) 293…”

Section: Scan Sequence Optimization Using Control Theorymentioning

confidence: 99%

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SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing

Ramani

Tsai

et al. 2022

Additive Manufacturing

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Section: Scan Sequence Optimization Using Control Theorymentioning

confidence: 99%

“…table for AISI 316L stainless steel (under solidus conditions, where relevant). The convection coefficient for a surface under still air is obtained from Ref [31]…”

mentioning

confidence: 99%

SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing

Ramani

Tsai

et al. 2022

Additive Manufacturing

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“…For the traditional furnace-based heating method, fundamental heat transfer mechanics of heat at the surface from the atmosphere is slow. Heat transfer coefficients for metal-to-air interfaces are low [13], whereas induction-heating methods cause Eddy-current losses producing a Joule heating effect [14]. It has traditionally been considered that any process-induced variation in the AA2014 microstructure and precipitate distribution is an unwanted effect, as it adds uncertainty regarding the starting condition of the material.…”

Section: Resultsmentioning

confidence: 99%

On the Pre-Forging Heating Methods for AA2014 Alloy

Turner,

2023

WJET

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The aluminium alloy AA2014 is one of the most widely used of the 2xxx series alloys, owing to its superior strength-to-weight ratio and stiffness. It is commonly forged to shape for use in aerospace parts. Three different small pilot-scale AA2014 billets were subjected to different heating operations, to physically simulate a pre-forge heating operation. The unheated sample and heated samples were then analysed for micro structural evolution and mechanical properties, to understand how the pre-forge heat treatments may vary the starting condition of the alloy before being forged. It was shown that an induction heating process has the greatest impact upon the precipitation distribution. Whilst this variation is commonly considered a negative impact, the opportunity to control the induction heating to promote preferential microstructure at specific locations within the billet may be possible.

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Section: Scan Sequence Optimization Using Control Theorymentioning

confidence: 99%

SmartScan: An Intelligent Online Scan Sequence Optimization Approach for Uniform Thermal Distribution, Reduced Residual Stresses and Deformations in PBF Additive Manufacturing

Ramani¹,

He²,

Tsai³

et al. 2021

Preprint

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Parts produced by laser or electron-beam powder bed fusion (PBF) additive manufacturing are prone to residual stresses, deformations, and other defects linked to non-uniform temperature distribution during the manufacturing process. Several researchers have highlighted the important role scan sequence plays in achieving uniform temperature distribution in PBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we have articulated a vision for an intelligent online scan sequence optimization approach to achieve uniform temperature distribution, hence reduced residual stresses and deformations, in PBF using physics-based and data-driven thermal models. This paper proposes SmartScan, our first attempt towards achieving our vision using a simplified physics-based thermal model. The conduction and convection dynamics of a single layer of the PBF process are modeled using the finite difference method and radial basis functions. Using the model, the next best feature (e.g., stripe or island) that minimizes a thermal uniformity metric is found using control theory. Simulations and experiments involving laser marking of a stainless steel plate are used to demonstrate the effectiveness of SmartScan in comparison to existing heuristic scan sequences for stripe and island scan patterns. In experiments, SmartScan yields up to 43% improvement in average thermal uniformity and 47% reduction in deformations (i.e., warpage) compared to existing heuristic approaches. It is also shown to be robust, and computationally efficient enough for online implementation.

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Convection Coefficient Estimation of Still Air Using an Infrared Thermometer and Curve-Fitting

Cited by 9 publications

References 23 publications

SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing

SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing

On the Pre-Forging Heating Methods for AA2014 Alloy

SmartScan: An Intelligent Online Scan Sequence Optimization Approach for Uniform Thermal Distribution, Reduced Residual Stresses and Deformations in PBF Additive Manufacturing

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