About

Nathan W. Hartman is the Dauch Family Professor of Advanced Manufacturing in the School of Engineering Technology at Purdue University and serves as the Director of the Purdue University Digital Enterprise Center. His research focuses on the product lifecycle management and the digital transformation of manufacturing, including process and methodology for creating model-based definitions to support the product lifecycle, data standards and interoperability for both machine and human use, and workforce development strategies for next-generation manufacturing. He has been awarded over $14 million in research funding from industrial, federal, and government sources, collaborating with industry partners such as Rolls Royce, Cummins, Boeing, GM, and others. Hartman also leads initiatives in education and workforce development, including online professional certificate programs in PLM, MBD, and TDP, and directs the Indiana Next-generation Manufacturing Competitiveness Center (IN-MaC), which has supported numerous companies and research consortia to advance manufacturing innovation. With a background that includes industry experience at Fairfield Manufacturing, Caterpillar, and Rand Worldwide, Hartman holds a Doctorate from North Carolina State University and both Bachelor's and Master's degrees from Purdue University. He has held various academic and administrative roles at Purdue, including Department Head of Computer Graphics Technology and Associate Head, and has contributed significantly to research, education, and industry engagement in manufacturing technology.

Research topics

• Computer Science
• Engineering
• Computer graphics (images)
• World Wide Web
• Software engineering
• Computer Security
• Political Science
• Artificial Intelligence
• Sociology
• Mathematics
• Human–computer interaction
• Mathematics education
• Programming language
• Engineering management
• Engineering drawing
• Systems engineering
• Pedagogy
• Mechanical engineering
• Law
• Distributed computing

Selected publications

• Leveraging the power of eye-tracking for virtual prototype evaluation: a comparison between virtual reality and photorealistic images

  Displays · 2026-01-10

  articleOpen access

  • Combining eye-tracking with self-report methods provides valuable insights into product perception and user behavior. • Integrating eye-tracking with self-report questionnaires may enhance the product design process by providing more comprehensive and accurate feedback from the user. • The presentation medium affects both product perception and user observation patterns. • The user’s background (e.g., university of origin) can also influence product assessment. • A joint assessment can mitigate the impact of the viewing medium on user perception, leading to less biased feedback. Most of the information we gather from our environment is obtained from sight, hence, visual evaluation is vital for assessing products. However, designers have traditionally relied on self-report questionnaires for this purpose, which have proven to be insufficient in some cases. Consequently, physiological measures are being employed to gain a deeper understanding of the cognitive and perceptual processes involved in product evaluation, and, thanks to their integration in Virtual Reality (VR) headsets, they have become a powerful tool for virtual prototype assessment. Still, using virtual prototypes raises some concerns, as previous studies have found that the medium can influence product perception. These results rely solely on self-report techniques, highlighting the need to explore the use of ET for product assessment, which is the main objective of this research. We present two case studies where a group of people assessed through two display mediums (CS-1) a set of furniture comprising a general scene using a ranking-type evaluation (i.e., joint assessment) and (CS-2) two armchairs individually using the Semantic Differential technique. Moreover, the dwell time of the Areas of Interest (AOIs) defined was recorded. Primarily, our results showed that, despite VR being sensitive to aesthetic differences between designs of the same product typology, the medium may still influence the perception of specific product attributes —e.g., fragility (p MODERN < 0.001, p TRADITIONAL = 0.002)—, and observation of specific AOIs —e.g., AOI1 (p MODERN = 0.003, p TRADITIONAL < 0.001), AOI9 and AOI10 (p < 0.001). At the same time, no differences were found in the perception of the general scene, whereas dwell time was influenced for AOI1 (p = 0.003), AOI4 (p = 0.006), and AOI5 (<.001). Additionally, the university of origin may also be a factor influencing product evaluation, while confidence in the response was not affected by the medium. Hence, this study contributes to a deeper understanding of how the medium influences product perception by employing ET with self-report methods, offering valuable insights into user behavior.

  PublisherDOI

• Adaptive GenAI-Empowered Manufacturing Training Using LLMs and GraphRAG

  2025-11-16

  article

  Abstract Generative AI (GenAI) presents transformative potential for workforce training by autonomously generating rich contextual content—such as text, code, images, and videos on large-scale pretrained foundation models such as OpenAI’s GPT, and Google’s Gemini. Beyond content generation, GenAI facilitates natural, multimodal interaction with humans through texts and voice, thereby enhancing accessibility and engagement for workers. Critically, GenAI also enables the continuous updating of training materials to an individual’s evolving skill level and learning trajectory, supporting personalized, self-paced training while ensuring alignment with the latest manufacturing technologies. Building on these capabilities, this study proposes an adaptive, GenAI-powered workforce training approach - Adaptive GenAI-Empowered Manufacturing Training (AGEMT) that integrates GenAI, particularly large language models (LLMs) and their incorporation within the Graph retrieval-augmented generation (GraphRAG), with curriculum design and personalized instructions to deliver highly personalized and adaptive learning experiences. Our results demonstrate that AGEMT, through its integration of a manufacturing-specific knowledge graph, LLM-based personalization, and a real-time QA interface, successfully translated complex documentation into interactive training support and improved task performance and decision-making in an engine kit assembly use case.

  PublisherDOI

• A systematic review of Digital Twin (DT) and virtual learning environments (VLE) for smart manufacturing education

  Manufacturing Letters · 2025-08-01 · 6 citations

  articleOpen accessSenior author

  The integration of Digital Twin (DT) technology with Virtual Learning Environments (VLEs) is transforming manufacturing education by enabling real-time, immersive, and adaptive learning experiences. The paper presents a systematic review of state-of-the-art applications, methodologies, and challenges in DT-VLE integration, with a focus on enhancing experiential learning and bridging the theory-to-practice gap in manufacturing education. The study systematically analyzes Simulation-based learning (SBL), Problem-based learning (PBL), Gamification based learning (GBL), and AR/VR-enhanced training, identifying their impact on student engagement, knowledge retention, and skill development in smart manufacturing education. Additionally, real-world case studies and empirical findings demonstrate that students using DT-VLE systems score up to 20% higher in problem-solving assessments and report 83% increased confidence in manufacturing concepts. Despite the evident benefits, challenges such as high implementation costs, scalability limitations, and data security concerns remain barriers to widespread adoption. To address these issues, the paper proposes a three-phase framework for DT-VLE integration, encompassing infrastructure development, adaptive course design, and industry collaborations. The findings suggest that cloud-based DT solutions, AI-powered adaptive learning pathways, and modular open-source tools can enhance scalability and accessibility, making DT-based manufacturing education more viable. The review provides a roadmap for future research and implementation, emphasizing Industry 5.0-driven smart learning environments that combine human-centric, AI-assisted, and data-driven training models. The proposed strategies aim to make digital twin education more efficient, scalable, and aligned with future workforce needs in smart manufacturing education.

  PublisherDOI

• LUNAR-XDT: An integrated framework for sustainable lunar manufacturing using digital twin (DT), extended reality (XR), and extreme design (XD) principles

  Manufacturing Letters · 2025-08-01

  articleOpen accessSenior author

  Digital Twin (DT), Extended Reality (XR), and Extreme Design (XD) enhance sustainable manufacturing for lunar operations by enabling automation, resource efficiency, and infrastructure resilience. DT facilitates predictive maintenance, real-time monitoring, and process optimization, while XD ensures structural durability in extreme lunar conditions. XR, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), improves astronaut training, remote operations, and maintenance. Four NASA challenge projects demonstrate the integration of these technologies: Inflatable Lunar Gantry Crane (ILGC) from the NASA’s Advanced Lightweight Lunar Gantry for Operations (ALLGO) Challenge, Lunar Water Abstraction and Transportation by Excavation of Regolith (L-WATER) from the NASA’s Break the Ice Lunar Challenge, Lunar Origami Inspired Ground Heliostat (L-LIGHT) from the NASA’s Lunar Tele-Operated Rover-Based Configurable Heliostat (Lunar TORCH) Challenge, and Lunar Transporter and Gantry (L-TAG) from the NASA’s Lunar Delivery Challenge. The proposed DT-XR-XD framework ( LUNAR-XDT [Lunar eXtended Digital Twin Framework for Sustainable Manufacturing & Exploration] framework ) focuses on three key areas: adaptive DT models for automation, XR-based astronaut training and maintenance, and XD-driven modular infrastructure for sustained lunar operations. The structured approach supports autonomous, scalable, and resource-efficient exploration, ensuring long-term operational feasibility for lunar missions.

  PublisherDOI

• Engagement in Practice: Bridging the Gap between Industry, Universities, and K-12 Outreach

  2025-08-21

  article
  PublisherDOI

• Using machine learning with supplemented NC code to predict machining energy

  Manufacturing Letters · 2025-08-01 · 2 citations

  articleOpen accessSenior author

  Manufacturing contributes a significant amount of value to the economy while consuming nearly one-third of the total energy produced within the nation. Computer-aided manufacturing tools arose to streamline the process of manufacturing parts, but they lack energy-conscious practices. With this gap, research has been done in the development of mechanistic and data-driven models to accurately predict the energy consumption of this process. However, validation carried out in the experimental methodology in many of the research models is ill-fit to represent their realistic counterparts. In this paper, a data-driven deep learning model is developed, which properly accounts for the complexities associated with CNC machining, as it compensates for variations in operations observed during CNC machining. Furthermore, this deep learning model makes predictions by processing supplemented NC programs sequentially. These programs include additional information regarding the material removal process. Four variants of the model are created to provide insights into the effects of supplementing the program with different material removal variables. The variables include depth of cut, width of cut, material removal rate, and the volume of material removed per numerically controlled instruction. The prediction capability of these four models are then compared using several statistical tests.

  PublisherDOI

• Development and Evaluation of a Vision Inspection System for Plastic Bottle Measurement

  Advances in science and technology · 2024-04-16 · 3 citations

  articleOpen accessSenior author

  To quickly adapt to the fast-changing conditions in the modern markets and the global economy, manufacturers are adopting digital manufacturing methods and tools, instead of traditional paper-based processes, to release higher quality products more quickly and at a lower cost. The pharmaceutical industry has a high production standard in the world. Delivering a defective product (or package) can lead to customer complaints and may even result in the entire product series being returned in severe cases. To reach out to the tiny space of products and achieve a high pharmaceutical product dimensional standard, manufacturers must introduce commercial vision inspection systems for the quality inspection process. However, conventional commercial inspection systems are often of a high cost, thus making them unaffordable for micro, small, and medium-sized enterprises (MSMEs), particularly in developing countries. This paper proposes a cost-effective vision inspection system that intelligently measures critical plastic bottle dimensions. The system comprises three 4K industrial cameras, two LED lights, a customized measurement platform, and a laptop, making it more affordable for MSMEs. Under the appropriate illumination setting, a plastic bottle is positioned on the stage and viewed by the laptop screen in real-time. The middle camera captures the bottle image, followed by a series of image processing operations to obtain the region of interest (ROI), such as the snap cap radius and height. Then, extract the target bottle edges with the Canny edge detector. Lastly, the system calculates the pixel-based distance and converts it to the measurement results for records or decision-making. The proposed method demonstrates reliable dimensional detection abilities, offering a potential solution to reduce human workload and improve inspection productivity in measuring pharmaceutical bottles.

  PublisherDOI

• Computer-Aided Design, Computer-Aided Engineering, and Visualization

  Springer handbooks · 2023-01-01 · 8 citations

  book-chapter
  PublisherDOI

• A Model-Based Visual Inspection System (MBVIS) for Critical Plastic Bottle Dimensional Measurements

  Computer-Aided Design and Applications · 2023-08-16 · 2 citations

  articleOpen accessSenior author

  Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

  PublisherDOI

• A digital engineering framework to facilitate automated data exchange between geometric inspection and structural analysis

  Advances in Engineering Software · 2023-06-02 · 10 citations

  article
  PublisherDOI

Frequent coauthors

• Theodore Branoff

  9 shared

• Gary Bertoline

  Purdue University West Lafayette

  6 shared

• Eric Wiebe

  6 shared

• Thomas Hedberg

  5 shared

• Jorge D. Camba

  Purdue University West Lafayette

  5 shared

• Michael D. Sangid

  Purdue University West Lafayette

  4 shared

• Hua Xu

  4 shared

• Ronald J. Glotzbach

  Purdue University System

  4 shared

Labs

• Digital Enterprise CenterPI

Education

• Doctor of Education, Technology Education

  North Carolina State University

  2003

• Master of Science, Technology

  Purdue University

  1997

• Bachelor of Science, Technical Graphics

  Purdue University

  1995

Awards & honors

• 2011 Purdue University Faculty Scholar ($50,000 stipend over…
• 2009 College of Technology Outstanding Faculty in Engagement…
• 2008 Award for Excellence in Distance Education – Best Noncr…
• 2006 Outstanding Non-tenured Faculty Award in the College of…
• 2006 Purdue Seeds of Success Award: Co-PI on NSF ATE grant M…