Luke Scime

1.8k citations

18 papers · 1.4k indexed · 1 hit paper · h-index 10

Impact in

Automotive Engineering top 1%
- Additive Manufacturing and 3D Printing Technologies
Industrial and Manufacturing Engineering top 0.5%
- Industrial Vision Systems and Defect Detection
- Manufacturing Process and Optimization

Papers in

Automotive Engineering 10
- Additive Manufacturing and 3D Printing Technologies 10
Industrial and Manufacturing Engineering 7
- Industrial Vision Systems and Defect Detection 4
- Manufacturing Process and Optimization 2

Co-authors: Jack Beuth Vincent Paquit Derek Siddel Amirkoushyar Ziabari Zackary Snow Ryan Dehoff Derek Rose Kurt A. Terrani
Journals: Additive manufacturing (8 papers)Manufacturing Letters (3 papers)Microscopy and Microanalysis (1 paper)Materials (1 paper)Metallurgical and Materials Transactions A (1 paper)
Partner nations: United States

In The Last Decade

Luke Scime

18 papers receiving 1.3k citations

Hit Papers

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Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm 2017 · 365 citations

Peers

Countries citing papers authored by Luke Scime

Since Specialization

Citations

This map shows the geographic impact of Luke Scime's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luke Scime with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luke Scime more than expected).

Fields of papers citing papers by Luke Scime

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luke Scime. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luke Scime. The network helps show where Luke Scime may publish in the future.

Co-authorship network

The 17 scholars most cited alongside Luke Scime, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Luke Scime Line = papers co-authored together Luke Scime links everyone, so they are left out of the graph.

All Works

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18 of 18 papers shown

#	Work
1	A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network Materials Today Advances ·Stephen Taller, Luke Scime, فايزة محمد سعد	2024	1
2	Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts Additive manufacturing ·Gerry Knapp, Benjamin Stump, Luke Scime, Andrés Márquez Rossy, Öner Avınca, James A. Rienhardt, Alex Plotkowski	2023	6
3	Methods for rapid identification of anomalous layers in laser powder bed fusion Manufacturing Letters ·Luke Scime, J. Senst, Vincent Paquit	2023	2
4	Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning Additive manufacturing ·Zackary Snow, Luke Scime, Amirkoushyar Ziabari, 政文渋谷, Vincent Paquit	2023	19
5	A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts Materials ·Luke Scime, Öner Avınca, S. Patal, Nima Sahba, JanWillem Hoftijzer, James A. Rienhardt, Ryan P. Duncan, Zackary Snow, Ryan Dehoff, Vincent Paquit	2023	6
6	Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) ·Kamila Bielawska, Singanallur Venkatakrishnan, Luke Scime, Keerthic Aswin, Carlos OsÃ¡car, Ryan Dehoff, Vincent Paquit, Amirkoushyar Ziabari	2023	2
7	Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring Additive manufacturing ·Zackary Snow, Luke Scime, Amirkoushyar Ziabari, 政文渋谷, Vincent Paquit	2022	48
8	A scalable digital platform for the use of digital twins in additive manufacturing Manufacturing Letters ·Luke Scime, Shafia Zaffar, Vincent Paquit	2021	32
9	Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning Frontiers in Mechanical Engineering ·James A. Rienhardt, Derek Rose, Luke Scime, Ryan Dehoff, Vincent Paquit	2021	14
10	Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718 Microscopy and Microanalysis ·Stephen Taller, Luke Scime, Kurt A. Terrani	2021	5
11	Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation Additive manufacturing ·Luke Scime, Derek Siddel, S. van der Struijs, Vincent Paquit	2020	155
12	Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process Additive manufacturing ·Luke Scime, Jack Beuth	2019	132
13	Safety and Workflow Considerations for Modern Metal Additive Manufacturing Facilities JOM ·Luke Scime, Lintang Laila Rizqy, Jack Beuth, Waleed Giratalla, Michael J. Kelley	2018	7
14	Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process Additive manufacturing ·Luke Scime, Jack Beuth	2018	301
15	A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process Additive manufacturing ·Luke Scime, Jack Beuth	2018	263
16	Using coordinate transforms to improve the utility of a fixed field of view high speed camera for additive manufacturing applications Manufacturing Letters ·Luke Scime, 政文渋谷, Jack Beuth	2018	15
17	Integrated Control of Melt Pool Geometry and Microstructure in Laser Powder Bed Fusion of AlSi10Mg Metallurgical and Materials Transactions A ·Sneha Prabha Narra, Luke Scime, Jack Beuth	2018	7
18	Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm Additive manufacturing ·Luke Scime, Jack Beuth Hit paper breakdown →	2017	365

About Luke Scime

Luke Scime is a scholar working on Automotive Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Radiation and Surfaces, Coatings and Films, having authored 18 papers that have together received 1.4k indexed citations. Recurring topics across this work include Additive Manufacturing Materials and Processes (16 papers), Additive Manufacturing and 3D Printing Technologies (10 papers), Welding Techniques and Residual Stresses (7 papers), Industrial Vision Systems and Defect Detection (4 papers), Advanced X-ray and CT Imaging (4 papers), Machine Learning in Materials Science (3 papers), Manufacturing Process and Optimization (2 papers) and Nuclear Physics and Applications (2 papers). The work is most often cited by research in Automotive Engineering (858 citations), Industrial and Manufacturing Engineering (541 citations), Mechanical Engineering (1.2k citations), Computational Mechanics (92 citations) and Biomedical Engineering (155 citations). Luke Scime has collaborated with scholars based in United States. Frequent co-authors include Jack Beuth, Vincent Paquit, Derek Siddel, Amirkoushyar Ziabari, Zackary Snow, Ryan Dehoff, Derek Rose, Kurt A. Terrani, Sneha Prabha Narra and Stephen Taller. Their work appears in journals such as Additive manufacturing, Manufacturing Letters, Microscopy and Microanalysis, Materials and Metallurgical and Materials Transactions A.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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