About

Hannah Budinoff is an assistant professor of systems and industrial engineering at the University of Arizona. Her research interests include additive manufacturing, geometric manufacturability analysis, design for manufacturing, sustainable design, and engineering education. She completed her PhD in mechanical engineering at the University of California, Berkeley in 2019, where she was awarded an NSF Graduate Research Fellowship. She oversees the Manufacturing and Design Exploration (MADE) research group at the University of Arizona, contributing to advancements in manufacturing processes and design methodologies. Her work emphasizes improving outcomes in additive manufacturing, developing tools for manufacturability analysis, and enhancing engineering education through innovative approaches.

Research topics

• Computer Science
• Engineering
• Engineering drawing
• Mechanical engineering
• Manufacturing engineering
• Geometry
• Industrial engineering
• Mathematics
• Simulation
• Business
• Software engineering
• Economics
• Medicine

Selected publications

• Reverse engineering of additively manufactured parts: integrating 3D scanning and simulation-driven distortion compensation

  Rapid Prototyping Journal · 2026-01-30

  article

  Purpose Reverse engineering (RE) can be used to derive a three-dimensional (3D) model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing RE techniques can be readily applied, but parts produced with additive manufacturing (AM) can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to compensate for distortions of reverse-engineered additively manufactured components. Design/methodology/approach This framework uses iterative finite element simulations to predict geometric distortions and iteratively estimate the key dimensional characteristics of the part while accounting for process-induced distortion. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion AM. Findings Using the proposed computer-aided design (CAD)-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method. Originality/value This paper presents a remanufacturing framework combining RE and AM, leveraging geometric feature-based part compensation through process simulation, better capturing the design intent needed for RE. The approach in the present study can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during RE. The authors evaluate the merits of STL-based and CAD-based approaches by quantifying the accumulated errors induced at the different steps of the proposed approach and analyzing the impact of varying part geometries.

  PublisherDOI

• Reverse engineering of additively manufactured parts: integrating 3D scanning and simulation-driven distortion compensation

  Rapid Prototyping Journal · 2026-01-30 · 1 citations

  articleOpen access

  Purpose Reverse engineering (RE) can be used to derive a three-dimensional (3D) model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing RE techniques can be readily applied, but parts produced with additive manufacturing (AM) can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to compensate for distortions of reverse-engineered additively manufactured components. Design/methodology/approach This framework uses iterative finite element simulations to predict geometric distortions and iteratively estimate the key dimensional characteristics of the part while accounting for process-induced distortion. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion AM. Findings Using the proposed computer-aided design (CAD)-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method. Originality/value This paper presents a remanufacturing framework combining RE and AM, leveraging geometric feature-based part compensation through process simulation, better capturing the design intent needed for RE. The approach in the present study can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during RE. The authors evaluate the merits of STL-based and CAD-based approaches by quantifying the accumulated errors induced at the different steps of the proposed approach and analyzing the impact of varying part geometries.

  PublisherOA PDFDOI

• Bridging the Gap: Integrating Entrepreneurial Thinking and New Product Development into Manufacturing Education

  2025-08-21

  article
  PublisherDOI

• Distortion Minimization in Reverse Engineering for Additive Manufacturing: An Integrated 3D Scanning and Simulation Framework

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Translating Evidence on Asset-based Pedagogies into Engineering Education Practice

  2025-08-21

  articleSenior author
  PublisherDOI

• Linking Geometric Features to Distortion Risk in Laser Powder Bed Fusion via Clustering

  Journal of Computing and Information Science in Engineering · 2025-10-09 · 1 citations

  article

  Abstract This article investigates the relationship between geometric features of 3D geometries and distortion in laser-based powder bed fusion (LPBF) additive manufacturing processes. We used 97 shape descriptors, including several unique descriptors created specifically for this application, to extract critical geometric attributes from a set of 510 3D models. Simulations of the LPBF process predicted distributions of distortion after printing. Clustering analysis was applied to group diverse geometries based on the distribution of distortion magnitude across the part, identifying three distinct clusters of low, medium, and high distortion risk for our evaluated 3D models. Our findings reveal specific geometric characteristics that are strongly associated with high process-induced distortion from LPBF, including overhanging length, part size, skewness and kurtosis of the material distribution, and the ratio of the width of the parts’ base to its maximum width. Our approach can help identify patterns in simulation data for diverse sets of 3D models and extract meaningful geometric characteristics that lead to differences among groups of models.

  PublisherDOI

• Input part shape representation/deep learning architecture and dataset analysis for additive manufacturing part quality predictions

  Manufacturing Letters · 2025-08-01

  articleOpen access

  Deep learning (DL) models have revolutionized automation in fields such as image classification and segmentation. In traditional computer science fields, necessary training dataset size and quality, input resolution, and input shape representation/DL architecture pairings have been carefully selected for specific tasks. Predicting additive manufacturing (AM) part quality is increasingly important as more AM parts are made as end-use parts, but these predictions are often time and resource intensive. This research compares four DL pipelines’ performance, across different dataset sizes and input resolutions, at predicting AM print quality. We train our DL pipelines on varied, real world data and systematically evaluate each model’s predictive performance, training time, and sensitivity to hyperparameter tuning across different dataset sizes and input resolutions. We build and train voxel, depth image, and distance field 3D CNN and point cloud transformer pipelines that get far superior results to a baseline model. The distance field 3D CNN model achieves the best performance, 9.62% error, predicting AM print quality compared to 24.96% error for our baseline model. We find that dataset size and input resolution both impact model performance and hyperparameter sensitivity, but that dataset size has a greater impact on model performance than input resolution for the DL pipelines we test. We gain initial insight into what shape representation/DL pipelines are promising for improving AM part quality and performance predictions. Finally, this research demonstrates a systematic way to fairly compare multiple DL pipelines to a baseline model and evaluate the impacts of changing individual variables in the DL pipeline.

  PublisherDOI

• Manufacturability Characterization of Digital Light Processing of Bone Scaffolds

  2024-10-01

  article

  In recent years, additive manufacturing has been used in the biomedical field to manufacture biocompatible medical implants. One such application is the manufacturing of bone scaffolds to encourage the growth of bone cells in damaged bone areas. Additive manufacturing (AM) processes used to manufacture bone scaffolds include stereolithography and digital light processing (DLP). Both AM technologies can print biocompatible bone scaffolds by using compatible resin materials but DLP has better prospects for developments of biocompatiblc AM bone scaffolds because of short printing duration and higher quality prints of smaller objects. Prior research has focused on exploring printer settings, scaffold structure, and biocompatibility, but more research on scaffold quality and process yield is needed as this application moves towards commercialization. This study characterizes changes of DLP-printed bone scaffolds made with hydroxyapatite to observe the relationship between process inputs (exposure time) and output measurements (dimensional error and yield). Significant differences were observed between 37, 42, and 52 seconds exposure time for dimensional error of the printed and cured parts. The total yield varied from 33% to 100% depending on exposure setting. The study provides a better understanding on how changes in process parameters can affect the quality of DLP-printed scaffolds to enable further improvements in scaffold manufacturability.

  PublisherDOI

• Integrating Asset-based Practices into Engineering Design Instruction

  2024-02-06 · 4 citations

  article1st authorCorresponding

  This work in progress paper presents asset mapping activities as a strategy to foster the development of engineering identities, sense of belonging, and engineering self-efficacy in a diverse student population and presents evidence on feasibility of this strategy.Asset-based approaches highlight and leverage students' diverse assets for teaching and learning in course activities.Students' assets or strengths may include community networks, language and communication skills, tinkering skills, and most importantly, their lived experiences.One assetbased pedagogical strategy is asset mapping, where students identify and categorize how their own experiences and backgrounds can provide them with useful skills and insights.Asset mapping has frequently been deployed in community development to help the general public recognize and build on existing resources and capabilities.However, less is known about deploying asset mapping in the engineering classroom.In this paper, we describe implementing asset mapping activities in a first-year engineering course at the University of Arizona, a large land-grant, Hispanic-serving institution.In our College of Engineering, approximately 20% of students identify as Hispanic/Latinx and 30% identify as female.We quantify changes in students' engineering identity, sense of belonging in engineering, and engineering self-efficacy over one semester for students who participated in asset mapping activities (n=31) and compare these changes to students in a control section of the same course (n=38).In our pilot deployment of asset maps, students tended to identify mostly technical skills (e.g., data analysis, prototyping) in their initial asset maps and in subsequent course activities related to asset development.Implementation lessons learned relating to teaming, discussions of implicit bias, and valuing diverse assets in design projects are also presented.These findings can help the engineering education community implement asset-based approaches such as asset mapping.

  PublisherDOI

• Exploring the impact of design tool usage on design for additive manufacturing processes and outcomes

  Design Science · 2024-01-01 · 10 citations

  articleOpen access1st authorCorresponding

  Abstract Improving designers’ ability to identify manufacturing constraints during design can help reduce the time and cost involved in the development of new products. Different design for additive manufacturing (DfAM) tools exist, but the design outcomes produced using such tools are often evaluated without comparison to existing tools. This study addresses the research gap by directly comparing design performance using two design support tools: a worksheet listing DfAM principles and a manufacturability analysis software tool that analyzes compliance with the same principles. In a randomized-controlled study, 49 nonexpert designers completed a design task to improve the manufacturability of a 3D-printed part using either the software tool or the worksheet tool. In this study, design outcome data (creativity and manufacturability) and design process data (task load and time taken) were measured. We identified statistically significant differences in the number of manufacturability violations in the software and worksheet groups and the creativity of the designs with novel build orientations. Results demonstrated limitations associated with lists of principles and highlighted the potential of software in promoting creativity by encouraging the exploration of alternative build orientations. This study provides support for using software to help designers, particularly nonexpert designers who rely on trial and error during design, evaluate the manufacturability of their designs more effectively, thereby promoting concurrent engineering design practices.

  PublisherOA PDFDOI

Frequent coauthors

• Sara McMains

  University of California, Berkeley

  14 shared

• Ann Shivers-McNair

  12 shared

• Jannatul Bushra

  University of Arizona

  12 shared

• Edward Berger

  11 shared

• Vignesh Subbian

  4 shared

• Behzad Beigpourian

  University of Arizona

  4 shared

• Audrey C. Ford

  University of California, Berkeley

  4 shared

• Philipp Gutruf

  University of Arizona

  4 shared

Labs

• Manufacturing and Design Exploration (MADE) research groupPI

Awards & honors

• Remote Learning ABCs Competition
• New Engineering Educators Division of the American Society f…
• Apprentice Faculty Grant Educational Research and Methods Di…
• Best diversity paper nominee ASEE Annual Conference and Expo…
• Best Paper Award ASEE Design Graphics Division, Summer I 201…