Additive manufacturing of metallic components – Process, structure and properties

This article reviews published data on the mechanical properties of additively manufactured metallic materials. The additive manufacturing techniques utilized to generate samples covered in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, EBF3). Although only a limited number of metallic alloy systems are currently available for additive manufacturing (e.g., Ti-6Al-4V, TiAl, stainless steel, Inconel 625/718, and Al-Si-10Mg), the bulk of the published mechanical properties information has been generated on Ti-6Al-4V. However, summary tables for published mechanical properties and/or key figures are included for each of the alloys listed above, grouped by the additive technique used to generate the data. Published values for mechanical properties obtained from hardness, tension/compression, fracture toughness, fatigue crack growth, and high cycle fatigue are included for as-built, heat-treated, and/or HIP conditions, when available. The effects of test...

Metal Additive Manufacturing: A Review of Mechanical Properties

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

https://dr.ntu.edu.sg/bitstream/10356/88603/1/Anisotropy%20and%20heterogeneity%20of%20microstructure%20and%20mechanical%20properties%20in%20metal%20additive%20manufacturing-%20A%20critical%20review.pdf

Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review

Atomic Spectra Database

Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects during the build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning. During the build process, multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera. For each neighborhood in the resulting layerwise image stack, multi-dimensional visual features were extracted and evaluated using binary classification techniques, i.e. a linear support vector machine (SVM). Through binary classification, neighborhoods are then categorized as either a flaw, i.e. an undesirable interruption in the typical structure of the material, or a nominal build condition. Ground truth labels, i.e. the true location of flaws and nominal build areas, which are needed to train the binary classifiers, were obtained from post-build high-resolution 3D CT scan data. In CT scans, discontinuities, e.g. incomplete fusion, porosity, cracks, or inclusions, were identified using automated analysis tools or manual inspection. The xyz locations of the CT data were transferred into the layerwise image domain using an affine transformation, which was estimated using reference points embedded in the part. After the classifier had been properly trained, in situ defect detection accuracies greater than 80% were demonstrated during cross-validation experiments.

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition

Lack-of-fusion flaws can occur in powder bed fusion (PBF) additive manufacturing of metal components. This paper demonstrates a method for detecting such flaws by monitoring the fabrication of every layer before and after laser scanning with high resolution optical imaging. A binary template is created from the sliced 3D model of the part. Using this template the optical image data is indexed to the part geometry. The indexed image data is used to detect anomalies in the powder layer before laser scanning and in the solidified material after scanning. Lack-of-fusion defects are identified from optical data by correlating multiple images with different lighting conditions and from multiple layers. To test the algorithms intentional defects are created inside a test part at different locations and are successfully detected with a high true positive rate.

Flaw detection in powder bed fusion using optical imaging

Purpose – The purpose of this paper is to surveys classic and recently developed strategies for quality monitoring and real-time control of laser-based, directed-energy deposition.Additive manufacturing of metal parts is a complex undertaking. During deposition, many of the process variables that contribute to overall build quality – such as travel speed, feedstock flow pattern, energy distribution, gas pressure, etc. – are subject to perturbations from systematic fluctuations and random external disturbances. Design/methodology/approach – Sensing and control of laser-based, directed-energy metal deposition is presented as an evolution of methods developed for welding and cladding processes. Methods are categorized as sensing and control of machine variables and sensing and control of build attributes. Within both categories, classic methods are presented and followed by a survey of novel developments. Findings – Additive manufacturing would not be possible without highly automated, computer-based control...

A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing

Additive manufacturing (AM) offers the ability to fabricate complex metallic structures and shapes in a layer-by-layer process. However, overhanging surfaces often require support structures to be fabricated and minimize thermally induced distortion. Unlike polymer AM processes, soluble sacrificial support materials have not been identified and characterized for metallic materials, and, as a result, support structures in 3D printed metals must be removed using additional machining operations. In this work, we demonstrate that sacrificial metal supports can be fabricated by taking advantage of differences in the chemical and electrochemical stability between different metals. As a demonstration, a stainless steel bridged structure with a 90° overhang was fabricated using a carbon steel sacrificial support that was later removed through electrochemical etching in 41 wt.% nitric acid with bubbling O2. Open circuit potentials and potentiodynamic polarization curves were gathered to verify etch select...

Abdalla R. Nassar

Papers

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition

Flaw detection in powder bed fusion using optical imaging

A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing

Dissolvable Metal Supports for 3D Direct Metal Printing