Additive manufacturing of metallic components – Process, structure and properties

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

The laser–matter interaction and solidification phenomena associated with laser additive manufacturing (LAM) remain unclear, slowing its process development and optimisation. Here, through in situ and operando high-speed synchrotron X-ray imaging, we reveal the underlying physical phenomena during the deposition of the first and second layer melt tracks. We show that the laser-induced gas/vapour jet promotes the formation of melt tracks and denuded zones via spattering (at a velocity of 1 m s−1). We also uncover mechanisms of pore migration by Marangoni-driven flow (recirculating at a velocity of 0.4 m s−1), pore dissolution and dispersion by laser re-melting. We develop a mechanism map for predicting the evolution of melt features, changes in melt track morphology from a continuous hemi-cylindrical track to disconnected beads with decreasing linear energy density and improved molten pool wetting with increasing laser power. Our results clarify aspects of the physics behind LAM, which are critical for its development. Additive manufacturing of metals is now widely available, but the interaction of the metal powder with the laser remains unclear. Here, the authors use X-rays to image melt features and pore behaviour during laser melting of powders.

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In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing

Additive manufacturing of biomaterials.

Laser powder bed fusion additive manufacturing is an emerging 3D printing technique for the fabrication of advanced metal components. Widespread adoption of it and similar additive technologies is hampered by poor understanding of laser-metal interactions under such extreme thermal regimes. Here, we elucidate the mechanism of pore formation and liquid-solid interface dynamics during typical laser powder bed fusion conditions using in situ X-ray imaging and multi-physics simulations. Pores are revealed to form during changes in laser scan velocity due to the rapid formation then collapse of deep keyhole depressions in the surface which traps inert shielding gas in the solidifying metal. We develop a universal mitigation strategy which eliminates this pore formation process and improves the geometric quality of melt tracks. Our results provide insight into the physics of laser-metal interaction and demonstrate the potential for science-based approaches to improve confidence in components produced by laser powder bed fusion. Laser-matter interactions during laser powder bed fusion additive manufacturing remain poorly understood. Here, the authors combine in situ X-ray imaging and finite element simulations to show how detrimental pores form under printing conditions and develop a strategy to suppress them.

/pdf/dynamics-of-pore-formation-during-laser-powder-bed-fusion-31pwqc4nq7.pdf

Dynamics of pore formation during laser powder bed fusion additive manufacturing.

Additive manufacturing (AM) is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. Although process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the 630measurement science
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 necessary to make in-process measurement and real-time control possible in AM. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of AM processes. As a precursor, this report presents a review on the AM control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion (PBF) process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control.

/pdf/measurement-science-needs-for-real-time-control-of-additive-10kt5jw6lw.pdf

Measurement science needs for real-time control of additive manufacturing powder-bed fusion processes

In this article we describe the design of a universal ultra-precision positioning platform to be used in the development of many different nano-manufacturing processes. The system incorporates the concept of employing different interchangeable manufacturing and characterization tools with one ultra-precision positioning system. A multi-linear-motor-driven XY planar stage floating on a thin film of air is used to traverse a substrate through a 10 mm × 10 mm travel range with an XY linear position resolution of less than 1 nm and an angular resolution about the Z axis of 0.05 μrad over a range of about a degree (determined by plane mirror interferometer optics). A piezoelectric transducer (PZT) driven tripod positioning system is used to align each manufacturing and characterization tool with the substrate through a travel range of 40 μm along the Z axis and 245 μrad of rotation about the X and Y axes. The mechanical design, an overview of the system error analysis, preliminary component performance results, and the results of the first micro-imprint fabricated with the machine are presented.

Multi-scale Alignment and Positioning System – MAPS

We provide evidence for a high-pressure phase transformation (HPPT) in the ceramic material silicon nitride. This HPPT is inferred by a high-pressure diamond anvil cell, Raman spectroscopy, scanning/transmission electron microscopy, and optical and acoustic microscope inspection. In the case of silicon nitride, the HPPT involves a ductile or metallike behavior that is observed in severe deformation processes, such as nanoindentation and micromachining. This pressure-induced plasticity is believed to be similar to that found in silicon and germanium with its origin in the high-pressure metallic β-Sn phase formation.

High-pressure phase transformation of silicon nitride

Recent research has shown that many semiconductor and ceramic materials can be machined in ductile fashion under very high pressures and at low depths of cut. In a previous study, the authors reported results from numerical simulations of orthogonal machining of Silicon Nitride using a pressure‐independent material model with von Mises yield criterion. Experimental evidence strongly suggests that the brittle‐to‐ductile transition of Silicon Nitride is pressure‐dependent. Specifically, experiments indicate that the ductile regime machining is possible when the hydrostatic pressure within the workpiece is of the same order of magnitude as the hardness. In the present work, the ductile behavior of Silicon Nitride is studied by carrying out single point diamond turning operation on Silicon Nitride samples at depths of cut ranging from 250nm to 10μm. Force and surface roughness data collected from machining tests are presented. Chip morphology is also investigated to determine the depth of cut at which brittle‐ductile transition takes place. Concurrently, the machining process is also simulated using commercial machining software AdvantEdge and the pressure‐sensitive Drucker‐Prager yield criterion. Numerical simulations are performed for depths of cut ranging from 1μm to 40μm at different rake angles. Results from the simulations and experiments for micro‐ and submicron‐depths of cuts are discussed.

Experimental and Numerical Investigation of Ductile Regime Machining of Silicon Nitride

Standards communities are developing robust machining and instrumented performance tests to evaluate the accuracy of 5-axis machining centers through the direct and indirect measurement of errors affecting the simultaneous motion of all five axes. The combined effect of the numerous geometric errors and servo errors complicates diagnostic analysis of the measurement results and estimation of the individual errors. To better understand the effect of the individual errors on the measurement results, we developed a reconfigurable five-axis data driven virtual machine tool (DDVMT) error simulator. The DDVMT is a generalized model that incorporates machine tool information models, geometric error models, controller models, and (standardized) machine tool metrology test methods and analyses into one modeling scheme. This paper describes the data driven virtual machine tool and demonstrates its ability to simulate the effects of geometric errors on multi-axis performance tests.

Ronnie R. Fesperman

Papers

Measurement science needs for real-time control of additive manufacturing powder-bed fusion processes

Multi-scale Alignment and Positioning System – MAPS

High-pressure phase transformation of silicon nitride

Experimental and Numerical Investigation of Ductile Regime Machining of Silicon Nitride

Reconfigurable data driven virtual machine tool: Geometric error modeling and evaluation