About

Jack Beuth is a Professor of Mechanical Engineering at Carnegie Mellon University, where he has been a faculty member since receiving his Ph.D. in Engineering Sciences from Harvard University in 1992. His research focuses on manufacturing, solid mechanics, and fracture mechanics, with over 75 publications in areas such as additive manufacturing, interfacial mechanics, and thin film mechanics. Beuth's current work includes modeling additive manufacturing processes and micro-scale mechanics, developing process map approaches to understand the influence of process variables on characteristics like melt pool geometry, microstructure, and residual stress. His modeling research has provided insights into process control, expanding operational ranges, and comparing different additive manufacturing processes.

Research topics

• Materials science
• Metallurgy
• Composite material
• Optics
• Computer Science
• Nanotechnology

Selected publications

• Geometric deviations and their effects in thin-plate lattice structures fabricated via LPBF

  Additive manufacturing · 2026-02-11 · 1 citations

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• AdditiveGDL: Generative deep learning for predicting local thermal distributions in metal 3D-printed layers

  Research Square · 2026-01-27

  preprintOpen accessSenior author
  PublisherDOI

• AlSi10Mg plate-lattice structures fabricated by laser powder bed fusion exhibiting high specific energy absorption

  Materials & Design · 2025-07-15 · 10 citations

  articleOpen accessSenior authorCorresponding

  • AlSi10Mg plate-lattice structures with plate thicknesses 150 µm–500 µm are successfully fabricated with laser powder bed fusion. • Plate-lattice plates remain axially loaded post-yielding, leading to competitively high stresses maintained for large compressive strains. • The simple cubic plate-lattice has the highest specific energy absorption of 27.2 J/g at a density of 1.23 g/cc. Theoretical studies have shown that plate-lattice structures exhibit exceptional mechanical properties such as high strength-to-weight ratios. Their fabrication, however, is challenging and has only been realized for metals via the Laser Powder Bed Fusion (LPBF) process. A deeper understanding of the deformation mechanisms of LPBF fabricated plate-lattice structures, including their post-yielding and energy absorption characteristics, is needed to evaluate their applicability in defense, aerospace, and biomedical industries. In this study, AlSi10Mg plate-lattice structures with four unit cell topologies were fabricated using LPBF and tested in quasi-static compression to determine mechanical properties, deformation behaviors, and energy absorption capabilities. Microcomputer tomography revealed surface variations resulting from adhered powder and dross formation were comparable in scale to plate thicknesses. Tested plate-lattices experience primarily stretch-dominant deformation consistent with theoretical Gibson-Ashby models. Stretch-dominant deformation is maintained for large compressive strains post-yielding until brittle fracture occurs in unit cell layers or diagonal bands, leading to high strength localized. For the simple cubic geometry, high yield stresses that were maintained post-yielding resulted in the highest specific energy absorption yet observed in lattice materials, reaching up to 27.2 J/g at a density of 1.23 g/cc. This research highlights AlSi10Mg plate-lattices as excellent candidates for light-weight energy absorption applications.

  PublisherDOI

• Process mapping for laser hot wire additive manufacturing of Ti6Al4V

  Rapid Prototyping Journal · 2025-02-24

  articleSenior author

  Purpose Laser hot wire additive manufacturing (LHWAM) is a newer technology within the space of large-scale directed energy deposition (DED) additive manufacturing (AM) processes. This study aims to map known AM flaw types such as lack of fusion and keyholing, as well as a dripping flaw unique to hot wire processes, across process parameter space using a small number of single-track experiments. Design/methodology/approach A semianalytical model was calibrated using a small initial set of experimental data. Lack of fusion and keyholing flaws were mapped across process space using existing models. The dripping flaw was modeled via analytical methods calibrated with experimental data, and then mapped across processing space. Further experimental data beyond the small initial set was used to evaluate the accuracy of the process maps developed. A website and executable were deployed to users of the process for convenient rapid process parameter selection. Findings With the process maps generated during this work, users can easily and rapidly generate desirable parameter sets for a range of conditions, enabling the intelligent utilization of the entire stable processing regime. Practical implications The methodology developed can be applied to other LHWAM machines or DED processes to rapidly and inexpensively generate a systematic understanding of processing space for build planning. Originality/value LHWAM shows advantages over other large-scale DED processes, but a systematic physically informed study of the key flaw regions across process space had not been conducted, limiting more widespread use of the process and creating a gap that this study fills.

  PublisherDOI

• Inhomogeneities in directed energy deposition of refractory metals with widely different melting temperatures and mass densities

  Additive manufacturing · 2025-09-01

  articleOpen access
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• Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion

  Additive manufacturing · 2025-12-27

  articleOpen access
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• Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Fast-response machine learning surrogate model of spatter transport in a laser powder bed fusion machine

  Additive manufacturing · 2025-09-01

  articleOpen accessSenior author

  In laser powder bed fusion (L-PBF), it is still difficult to produce defect-free parts. Spatter particles are one cause of lack-of-fusion (LOF) defects, which develop when spatter particles land on a part and become incorporated into the melt-pool. Preventing spatter-induced defects could thus be possible by planning for spatter contamination in the build planning phase. Several prior works have shown the promise of using computational fluid dynamics coupled with the discrete phase method (CFD-DPM) to predict the landing locations of spatter particles, but these models are too slow and complex for practical use in build planning. The current work thus proposes a machine learning surrogate model of a CFD-DPM model which performs hundreds of times faster than the original model with a root-mean-square error (RMSE) of 6.8 mm. This model is trained on Inconel 718 spatter particles within the EOS M290 L-PBF machine, but the approach is general and could be extended to other materials and L-PBF machines with retraining of the surrogate model. The model’s learned feature importance is evaluated through a SHAP analysis, finding that it follows previous analyses conducted with the original CFD-DPM model. The model’s accuracy and speed open the possibility for interactively and automatically planning builds around spatter contamination, both of which would improve consistency among machine users and could help reduce the amount of spatter contamination during L-PBF builds. • A surrogate model of spatter transport is sucessfully developed and applied. • The surrogate model achieves an average error of 6.8 mm compared to original model. • A prototype of a spatter-aware build planning application is presented and discussed.

  PublisherDOI

• High-speed imaging investigation and process mapping of the plume behavior in laser powder bed fusion

  Additive manufacturing · 2025-05-01 · 2 citations

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• AdditiveGDL: generative deep learning for predicting local thermal distributions in metal 3D-printed layers

  Journal of Intelligent Manufacturing · 2025-07-10 · 1 citations

  articleSenior author
  PublisherDOI

Recent grants

• GOALI/Collaborative Research: Process Development across Alloy Systems for Powder Bed Additive Manufacturing

  NSF · $110k · 2013–2016

• GOALI/Collaborative Research: Integrated Microstructure and Melt Pool Dimension Control for Electron Beam Additive Manufacturing

  NSF · $360k · 2011–2015

• Nanomechanical Material Size Effects Using an In-Situ, On-Chip Test Platform

  NSF · $439k · 2010–2015

Frequent coauthors

• Sneha Prabha Narra

  19 shared

• Jonathan A. Malen

  17 shared

• Amir Barati Farimani

  17 shared

• Anthony D. Rollett

  Carnegie Mellon University

  14 shared

• Francis Ogoke

  14 shared

• Nathan Klingbeil

  Wright State University

  13 shared

• Cristina H. Amon

  University of Toronto

  11 shared

• Guadalupe Quirarte

  Carnegie Mellon University

  10 shared

Education

• Ph.D., Engineering Sciences

  Harvard University

  1992

• M.S., Engineering Sciences

  Harvard University

  1989

• M.S., Engineering Science and Mechanics

  Virginia Institute of Technology

  1987

• B.S., Engineering Science and Mechanics

  Virginia Institute of Technology

  1984

Awards & honors

• Ralph R. Teetor Educational Award (1998)
• George Tallman and Florence Barrett Ladd Development Profess…
• ASME Curriculum Innovation Award (2005)
• Benjamin Richard Teare Teaching Award from the College of En…