Publications

Effects of Gender, Effort, and Spatial Visualization Abilities in an Engineering Graphics Class

Published in Proceedings of the 2019 ASEE Annual Conference and Exposition, 2019

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Recommended citation: H. Budinoff, A. Ford, and S. McMains. "Effects of Gender, Effort, and Spatial Visualization Abilities in an Engineering Graphics Class" in Proceedings of the 2019 ASEE Annual Conference and Exposition, Tampa, FL, USA, June 15-19, 2019. http://hannahbudinoff.com/files/Budinoff_Ford_McMains_2019_ASEE_preprint.pdf

Underrepresented and International Student Success and Confidence in a Small, Lab-based CAD Class

Published in Proceedings of the 73rd American Society for Engineering Education Engineering Design Graphics Division MidYear Conference, 2019

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Recommended citation: H. Budinoff and S. McMains. " Underrepresented and International Student Success and Confidence in a Small, Lab-based CAD Class, " in Proceedings of the 73rd American Society for Engineering Education Engineering Design Graphics Division MidYear Conference, Berkeley, CA, January 6-8, 2019. https://hannahbudinoff.com/files/EDGD_2019_Underrepresent_International.pdf

Measuring and Predicting Achievable Tolerances for AM Processes

Published in Proceedings of the 3rd International Symposium on Academic Makerspaces, 2018

Abstract: In this study, we focus on experimentally testing mathmatical approximations of error associated with geometric tolerances. Experiments are performed using different types of 3D printers.

Recommended citation: Y. Sun, H. Budinoff, and S. McMains. (2018). "Measuring and Predicting Achievable Tolerances for AM Processes." Proceedings of the 3rd International Symposium on Academic Makerspaces. https://drive.google.com/open?id=1BEF4-l0ahugJzrPumh05RdsDWAfST0HY

Prediction and visualization of achievable orientation tolerances for additive manufacturing

Published in Procedia CIRP, 2018

Abstract: In additive manufacturing, process parameters can have a large influence on the quality of the produced part, making it diffcult to understand what tolerances are actually achievable. We present a system that can rapidly analyze part geometry and predict parallelism, perpendicularity, and angularity geometric deviations for planar surfaces, based on layer thickness and build direction. Our system can analyze multiple distinct features and their corresponding tolerances and datums to identify build directions where all specified tolerances can be achieved. This tool can be used to select an optimal build direction and to analyze whether specified tolerances are manufacturable using additive manufacturing.

Recommended citation: H. Budinoff and S. McMains, 2018, "Prediction and visualization of achievable orientation tolerances for additive manufacturing." Procedia CIRP, 75, pp. 81-86. https://www.sciencedirect.com/science/article/pii/S2212827118304967

Aptitude, Effort, and Achievement in an Introductory Engineering Design Graphics Class

Published in Proceedings of the 71st American Society for Engineering Education Engineering Design Graphics Division MidYear Conference, 2016

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Recommended citation: H. Budinoff and S. McMains. "Aptitude, Effort, and Achievement in an Introductory Engineering Design Graphics Class," in Proceedings of the 71st American Society for Engineering Education Engineering Design Graphics Division MidYear Conference, Nashua, NH, October 16-18, 2016. https://sites.asee.org/edgd/wp-content/uploads/sites/22/2017/12/Part14-Budinoff-and-McMains.pdf

Predicting Energy Usage During Milling Based on Workpiece Properties

Published in UC Berkeley Master's Report, 2015

Abstract: The work presented in this thesis focuses on improving material selection by developing a better understanding of the factors that drive energy consumption in machining. Choice of material, like choice of manufacturing process, depends on many factors, especially the desired function of the part. However, there currently is not enough research for designers to make informed decisions when considering the importance of energy consumption. A designer trying to choose between two different aluminum alloys with similar functional properties currently has no way of knowing which material would be more energy efficient. New materials also present potential challenges: it is currently difficult to estimate the energy required to machine a novel material with a particular set of properties without prior experimentation.

Recommended citation: H. Budinoff, 2015. "Predicting Energy Usage During Milling Based on Workpiece Properties," Master's report, Department of Mechanical Engineering, University of California, Berkeley.