Mechanical Engineering Commons^™

Transcribing Braille Code: Learning Equations Across Platforms, Deegan Atha, Courtney Balogh

Purdue Journal of Service-Learning and International Engagement

Deegan Atha, a graduating senior in electrical engineering and a future engineer, is interested in human-centered design and developing technology that helps students engage and be successful in STEM.

Courtney Balogh, a junior in mechanical engineering, is interested in human-centered design and the importance it plays in product development. Deegan and Courtney are members of the Purdue EPICS project, Learning Equations Across Platforms (LEAP). They partnered with the Indiana School for the Blind and Visually Impaired (ISBVI) to develop a braille transcription device and web application that converts braille to print in real time.

Purdue Air Sense: A Methodology For Improving The Accuracy Of Ambient Aerosol Mass Concentration And Size Distribution Measurement With Low-Cost Optical Sensing Techniques, Rishabh Ramsisaria, Satya Sundar Patra, Brandon Emil Boor

The Summer Undergraduate Research Fellowship (SURF) Symposium

There is a global lack of a means for monitoring air pollutant levels at a local level due to expensive and bulky instrument requirements. It is important to monitor toxic gas levels, as well as particulate matter levels, in the atmosphere to study their effects on human health and to further develop city- and community-level air pollution solutions. In this study, with the means of a Raspberry Pi, low-cost Alphasense Optical Particle Counter and gas sensors, and methodical calibration techniques, we built a portable 3-D printed module powered by clean electricity generated by an on-board Voltaic solar cell that measures …

Micro-Manipulation Using Learned Model, Matthew A. Lyng, Benjamin V. Johnson, David J. Cappelleri

The Summer Undergraduate Research Fellowship (SURF) Symposium

Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that …