Fatigue Assessment of Inconel 625 Produced by Directed Energy Deposition from Miniaturized Specimens

In recent years, the industrial application of Inconel 625 has grown significantly. This material is a nickel-base alloy, which is well known for its chemical resistance and mechanical properties, especially in high-temperature environments. The fatigue performance of parts produced via Metallic Additive Manufacturing (MAM) heavily rely on their manufacturing parameters. Therefore, it is important to characterize the properties of alloys produced by a given set of parameters. The present work proposes a methodology for characterization of the mechanical properties of MAM parts, including the material production parametrization by Laser Directed Energy Deposition (DED). The methodology consists of the testing of miniaturized specimens, after their production in DED, supported by a numerical model developed and validated by experimental data for stress calculation. An extensive mechanical characterization, with emphasis on high-cycle fatigue, of Inconel 625 produced via DED is herein discussed. The results obtained using miniaturized specimens were in good agreement with standard-sized specimens, therefore validating the applied methodology even in the case of some plastic effects. Regarding the high-cycle fatigue properties, the samples produced via DED presented good fatigue performance, comparable with other competing Metallic Additive Manufactured (MAMed) and conventionally manufactured materials.

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

Data-driven Prediction of Temperature Evolution in Metallic Additive Manufacturing Process

In this study, a data-driven deep learning model for fast and accurate prediction of temperature evolution and melting pool size of metallic additive manufacturing processes are developed. The study focuses on bulk experiments of the M4 high-speed steel material powder manufactured by Direct Energy Deposition. Under non-optimized process parameters, many deposited layers (above 30) generate large changes of microstructure through the sample depth caused by the high sensitivity of the cladding material on the thermal history. A 2D finite element analysis (FEA) of the bulk sample, validated in a previous study by experimental measurements, is able to achieve numerical data defining the temperature field evolution under different process settings. A Feed-forward neural networks (FFNN) approach is trained to reproduce the temperature fields generated from FEA. Hence, the trained FFNN is used to predict the history of the temperature fields for new process parameter sets not included in the initial dataset. Besides the input energy, nodal coordinates, and time, five additional features relating layer number, laser location, and distance from the laser to sampling point are considered to enhance prediction accuracy. The results indicate that the temperature evolution is predicted well by the FFNN with an accuracy of 99% within 12 seconds.

A non-metallic additive for diamond-wire-sawn multi-crystalline silicon texturing

Diamond-wire-sawn (DWS) technology has been widely used in the photovoltaic industry. When using the HF/HNO3/H2O acid etching solution for texturing of DWS multi-crystalline silicon(mc-Si), the aid of additive is required to improve the reactivity of the mc-Si surface in the acid texturing solution. It also needs to enhance the nucleation and uniform growth of the texturing surface. This paper proposes a non-metallic additive for DWS mc-Si texturing. Sodium polyacrylate is added to the HF/HNO3/H2O acid etching solution to reduce the reflectance of DWS mc-Si and improve surface morphology. Compared to the textured wafers without additive, the surface of the wafers using this method is uniformly distributed with pits whose size is 0.5 μm×1 μm. And the weighted average reflectance of the textured wafers can be reduced from 33.32% to 23.9% in the wavelength range of 350–1100 nm, with the lowest reflectance of 19.8% reached at 950 nm. It shows a promising application prospect.

Building Block Synthesis of Self-Supported Three-Dimensional Compliant Elements for Metallic Additive Manufacturing

Compliant mechanisms gain motion through the elastic deformation of the monolithic flexible elements. The geometric design freedom of metallic additive manufacturing enables the fabrication of complex and three-dimensional (3D) compliant elements within mechanisms previously too complicated to produce. However, the design of metallic additive manufactured mechanisms faces various challenges of manufacturing restrictions, such as avoiding critical overhanging geometries and minimizing the amount of support structure, which has been reported in a few cases. This paper presents a synthesis approach for translational compliant elements, involving building blocks based on leaf-type springs and covering building orientations between 0 deg and 90 deg. In particular, this range is approached by the synthesis of self-supported 3D building blocks with orientations of 0 deg, 45 deg and 90 deg. The compliant elements are built based on linear and circular plane curves and compared numerically according to their mechanical performance to create preferable building blocks. The applicability of the presented procedure and the manufacturability of the compliant mechanisms are proven by printing individual 3D building blocks and their serial aggregation with laser-based powder bed fusion. Consequently, several prototypes are demonstrated, including a bistable switch mechanism and a large displaceable rotational spring joint. In addition, a small-scale highly maneuverable segment of a surgical instrument with a grasping mechanism at the distal end is proposed.

In-situ development of a sandwich microstructure with enhanced ductility by laser reheating of a laser melted titanium alloy

Metallic additive manufacturing, particularly selective laser melting (SLM), usually involves rapid heating and cooling and steep thermal gradients within melt pools, making it extremely difficult to achieve effective control over microstructure. In this study, we propose a new in-situ approach which involves laser reheating/re-melting of SLM-processed layers to engineer metallic materials. The approach involves alternate laser melting of a powder layer at a high laser power and laser reheating of the newly formed solidified layer at a low or medium laser power. This strategy was applied to Ti-6Al-4V with a range of laser powers being used to reheat/re-melt solidified layers. It was found that the SLM-processed sample without undergoing laser reheating consist of a pure martensitic needle structure whereas those that were subjected to laser reheating/re-melting all consist of horizontal (α + β) bands embedded in martensitic α′ matrix, leading to development of a sandwich microstructure in these samples. Within the (α + β) bands, β exist as nano-sized precipitates or laths and have a Burgers orientation relationship with α matrix, i.e., {0001}⍺//{110}β and ⟨11$$\stackrel{-}{2}$$ 2 - 0⟩⍺//⟨111⟩β. The width of (α + β) banded structure increased first with increased laser power to a highest value and then decreased with further increased laser power. With the presence of these banded structures, both high strengths and enhanced ductility have been achieved in the SLM-processed samples. The current findings pave the way for the novel laser reheating approach for in-situ microstructural engineering and control during metallic additive manufacturing.

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.