Jack Beuth

Bio: Jack Beuth is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Residual stress & Ultimate tensile strength. The author has an hindex of 32, co-authored 78 publications receiving 3685 citations.

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Abstract: This overview highlights some of the key aspects regarding materials qualification needs across the additive manufacturing (AM) spectrum. AM technology has experienced considerable publicity and growth in the past few years with many successful insertions for non-mission-critical applications. However, to meet the full potential that AM has to offer, especially for flight-critical components (e.g., rotating parts, fracture-critical parts, etc.), qualification and certification efforts are necessary. While development of qualification standards will address some of these needs, this overview outlines some of the other key areas that will need to be considered in the qualification path, including various process-, microstructure-, and fracture-modeling activities in addition to integrating these with lifing activities targeting specific components. Ongoing work in the Advanced Manufacturing and Mechanical Reliability Center at Case Western Reserve University is focusing on fracture and fatigue testing to rapidly assess critical mechanical properties of some titanium alloys before and after post-processing, in addition to conducting nondestructive testing/evaluation using micro-computerized tomography at General Electric. Process mapping studies are being conducted at Carnegie Mellon University while large area microstructure characterization and informatics (EBSD and BSE) analyses are being conducted at Materials Resources LLC to enable future integration of these efforts via an Integrated Computational Materials Engineering approach to AM. Possible future pathways for materials qualification are provided.

Prediction of lack-of-fusion porosity for powder bed fusion

Abstract: A geometry-based simulation is used to predict porosity caused by insufficient overlap of melt pools (lack of fusion) in powder bed fusion. The inputs into the simulation are hatch spacing, layer thickness, and melt-pool cross-sectional area. Melt-pool areas used in the simulations can be obtained from experiments, or estimated with the analytical Rosenthal equation. The necessary material constants, including absorptivity for laser-based melting, have been collated for alloy steels, aluminum alloys and titanium alloys. Comparison with several data sets from the literature shows that the simulations correctly predict process conditions at which lack-of-fusion porosity becomes apparent, as well as the rate at which porosity increases with changes in process conditions such as beam speed, layer thickness and hatch spacing.

Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm

Abstract: Despite the rapid adoption of laser powder bed fusion (LPBF) Additive Manufacturing by industry, current processes remain largely open-loop, with limited real-time monitoring capabilities. While some machines offer powder bed visualization during builds, they lack automated analysis capability. This work presents an approach for in-situ monitoring and analysis of powder bed images with the potential to become a component of a real-time control system in an LPBF machine. Specifically, a computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process. Anomaly detection and classification are implemented using an unsupervised machine learning algorithm, operating on a moderately-sized training database of image patches. The performance of the final algorithm is evaluated, and its usefulness as a standalone software package is demonstrated with several case studies.

Synchrotron-Based X-ray Microtomography Characterization of the Effect of Processing Variables on Porosity Formation in Laser Power-Bed Additive Manufacturing of Ti-6Al-4V

Abstract: The porosity observed in additively manufactured (AM) parts is a potential concern for components intended to undergo high-cycle fatigue without post-processing to remove such defects. The morphology of pores can help identify their cause: irregularly shaped lack of fusion or key-holing pores can usually be linked to incorrect processing parameters, while spherical pores suggest trapped gas. Synchrotron-based x-ray microtomography was performed on laser powder-bed AM Ti-6Al-4V samples over a range of processing conditions to investigate the effects of processing parameters on porosity. The process mapping technique was used to control melt pool size. Tomography was also performed on the powder to measure porosity within the powder that may transfer to the parts. As observed previously in experiments with electron beam powder-bed fabrication, significant variations in porosity were found as a function of the processing parameters. A clear connection between processing parameters and resulting porosity formation mechanism was observed in that inadequate melt pool overlap resulted in lack-of-fusion pores whereas excess power density produced keyhole pores.

Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process

Abstract: Because many of the most important defects in Laser Powder Bed Fusion (L-PBF) occur at the size and timescales of the melt pool itself, the development of methodologies for monitoring the melt pool is critical. This works examines the possibility of in-situ detection of keyholing porosity and balling instabilities. Specifically, a visible-light high speed camera with a fixed field of view is used to study the morphology of L-PBF melt pools in the Inconel 718 material system. A scale-invariant description of melt pool morphology is constructed using Computer Vision techniques and unsupervised Machine Learning is used to differentiate between observed melt pools. By observing melt pools produced across process space, in-situ signatures are identified which may indicate flaws such as those observed ex-situ. This linkage of ex-situ and in-situ morphology enabled the use of supervised Machine Learning to classify melt pools observed (with the high speed camera) during fusion of non-bulk geometries such as overhangs.

Metal Additive Manufacturing: A Review

Abstract: This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.

The Superalloys: Fundamentals and Applications

Abstract: 1. Introduction 2. The physical metallurgy of nickel and its alloys 3. Single crystal superalloys for blade applications 4. Superalloys for turbine disc applications 5. Environmental degradation: the role of coatings 6. Summary and future trends.