High resolution schemes for hyperbolic conservation laws

Geometry: shape, size, aspect ratio and orientation Flow Type: forced, natural, laminar, turbulent, internal, external Boundary: isothermal (Tw = constant) or isoflux (q̇w = constant) Fluid Type: viscous oil, water, gases or liquid metals Properties: all properties determined at film temperature Tf = (Tw + T∞)/2 Note: ρ and ν ∝ 1/Patm ⇒ see Q12-40 density: ρ ((kg/m) specific heat: Cp (J/kg ·K) dynamic viscosity: μ, (N · s/m) kinematic viscosity: ν ≡ μ/ρ (m/s) thermal conductivity: k, (W/m ·K) thermal diffusivity: α, ≡ k/(ρ · Cp) (m/s) Prandtl number: Pr, ≡ ν/α (−−) volumetric compressibility: β, (1/K)

Convection Heat Transfer

Hydrogen storage and delivery: Review of the state of the art technologies and risk and reliability analysis

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

https://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/pdf

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

https://link.springer.com/content/pdf/10.1186%2Fs40192-016-0045-4.pdf

In-process sensing in selective laser melting (SLM) additive manufacturing

The effect of oxygen on transitional Marangoni flow in laser spot welding

Extending the weldability of novel materials, and improving the weld quality by tailoring weld microstructures are key factors to obtain the welding techniques demanded in the modern manufacturing industries. This can be done, for example, by feeding chemical elements from a consumable wire into the weld pool during welding. The mixing of chemical components in the weld pool and the resulting post-solidification weld microstructures are influenced by weld pool hydrodynamics. Weld pool hydrodynamics is known to be primarily driven by Marangoni forces acting at the free liquid surface, i.e by tangential gradients in surface tension along the liquid surface due to pronounced lateral gradients in temperature and surface active element concentration. In this research, we develop a Computational Fluid Dynamics model to study steel weld pool hydrodynamics during conduction mode laser spot welding. It is concluded that free surface deformations and instabilities have a strong impact on the fluid flow and heat transfer in weld pools, and should therefore be accounted for in weld pool simulations. With increasing the surface active element concentration and laser power, the weld pool flow becomes highly unstable and can no longer be accurately modeled with a flat surface assumption. More accurate predictions of weld pool physics can be made if the free surface, solidification stage, and three-dimensionality are taken into account. This reduces the need for the use of unphysical parameter fittings widely reported in literature.

Marangoni driven free surface flows in liquid weld pools

Effect of enhanced heat and mass transport and flow reversal during cool down on weld pool shapes in laser spot welding of steel

Particle image velocimetry (PIV) was applied to estimate the velocity field on a weld pool surface with an oxide layer. A positive surface tension gradient drives an inward flow pattern from the boundary to centre of the weld pool, resulting in particles collecting on the centre line of the pool at the surface. Unsteady flow motions were observed by experimental visualisation of the particle flow, and the computed velocity field shows strong unsteady interfacial movement during inward flow. These unsteady flow motions are related to clockwise and counterclockwise vortices on the weld pool surface, which in turn affect both mass flow and energy transport. Both the qualitative flow motion and quantitative flow velocity are described in this work, which contributes to explaining the characteristic unsteady fluid flow in the weld pool, the measurements also provide experimental data for validation of numerical fluid flow models of the weld pool.

Unsteady interfacial phenomena during inward weld pool flow with an active surface oxide

The oscillation of a weld pool surface makes the fluid flow motion quite complex. Two-dimensional results cannot reflect enough information to quantitatively describe the fluid flow in the weld pool; however, there are few direct three-dimensional results available. In this paper, we describe a three-dimensional reconstruction method to measure weld pool surface features based on a single high-speed camera. A stereo adapter was added in front of the high-speed camera lens to obtain two images in the same frame from different view points at the same time. According to machine vision theory, three-dimensional parameters can be reconstructed based on two such images. In this work, three-dimensional velocity fields have been obtained using this method. Based on the calibration technique employed, the associated error is estimated to be less than 11.4%. Quantitative experimental results are useful for understanding the flow pattern, and possibly for controlling the flow of liquid in the weld pool.

/pdf/a-stereo-vision-method-for-tracking-particle-flow-on-the-2t2078r27m.pdf

Z. S. Saldi

Papers

The effect of oxygen on transitional Marangoni flow in laser spot welding

Marangoni driven free surface flows in liquid weld pools

Effect of enhanced heat and mass transport and flow reversal during cool down on weld pool shapes in laser spot welding of steel

Unsteady interfacial phenomena during inward weld pool flow with an active surface oxide

A stereo vision method for tracking particle flow on the weld pool surface