ASME 2024 Student Hackathon

Background

The Computer & Information in Engineering (CIE) Division of the American Society of Mechanical Engineers (ASME) held past hackathon events at the IDETC/CIE 2020, 2021, 2022, 2023 Conferences. These hackathon events provide students and engineering practitioners with a unique opportunity to learn how data science and machine learning techniques can be leveraged to solve real-world engineering problems.

Given the previous resounding successes, the CIE Division will hold the ASME-CIE Hackathon again at the IDETC/CIE 2024 Conference in hybrid settings, both virtual and on-site, as a pre-conference event from Aug. 18-25, 2024.

Click to Register for the Hackathon

Important Dates:

• August 5, 2024, 11:59 pm EDT: Registration Deadline
• August 16, 2024, 12 – 3 pm EDT: Opening Ceremony and Virtual Hackathon Kick-off
• August 25, 2024: Hybrid Hackathon Closing
• August 25, 2024, 6 am EDT: due for Hackathon Deliverables
• August 25, 2024, 9 am – 1 pm EDT: Final Presentations
• August 25, 2024, 1 – 3 pm EDT: Hackathon Judging
• August 25, 2024, 4 – 6 pm EDT: Closing Ceremony

Hackathon Problems

• Problem Statement 1:
  National Institute of Standards and Technology (NIST)
  In the realm of additive manufacturing (AM), especially powder bed fusion, real-time monitoring plays a pivotal role in ensuring process integrity. Equipped with sophisticated in-situ sensors, modern AM machines, like those at the National Institute of Standards and Technology (NIST), generate vast datasets during each build, exemplified by our testbed’s co-axial camera which captures up to 20,000 frames per second. Despite this advanced capability, data transfer limitations can lead to occasional image loss. For instance, discrepancies have been noted where the camera log records more images than are actually retrieved—sometimes with a shortfall of up to 50 frames per 200,000 captured. This hackathon challenge centers on developing innovative methods to accurately locate these missing frames within melt pool monitoring (MPM) image sequences. Participants are invited to tackle this issue in two parts: the first assumes missing images can be re-identified without their original sequence, while the second deals with scenarios where the missing frames are irretrievably lost. Solutions will be assessed based on accuracy, creativity, and clarity, with results evaluated after a week. Link to full dataset
  
• Problem Statement 2:
  UES-AFRL
  The AFRL FactoryNet dataset, designed to enhance machine vision AI systems in the manufacturing sector, features a vast collection of images sourced from web scraping, machine shops, factories, and industry partners. This diversity presents significant challenges in scope definition, organization, and curation, particularly at its current stage—organizing and collecting human-generated labels. This hackathon problem challenges participants to navigate the complexities of multiple unstructured labels to extract the most value from the dataset. The task involves sanitizing and structuring these labels in a way that aids the development of image classification models. By consolidating freeform labels and demonstrating their utility through successful model training and validation, participants will highlight the dataset’s strengths and areas for improvement. The goal is to produce structured, meaningful classes that enhance the dataset’s applicability in AI-driven image recognition within manufacturing. Successful entries will clearly demonstrate the creation of effective classification models and the strategic organization of label data. Link to template file , Link to full dataset
  

Award Information

For each problem:

• First Place: $1,400
• Second Place: $700
• Third Place: $350

Eligibility

Undergraduate students, graduate students, postdocs, and non-students (e.g. professionals) are welcome to attend the Hackathon and experience the exciting competitions.

Hackathon Team and Presentation

• All participants must be registered by August 5, 2024 11:59 pm EDT. Everyone will be placed in a team up of 1-2 members. You may form a team based on your own preference. All implementations must be based on the original work.
• Each Hackathon team will continue their own meetings via their own chosen platform between 08/18/24 and 08/25/24.
• Each team needs to present their teamwork including the technical approach and submit the results to their own GitHub repository by the submission deadline.
• Team final presentation, results, and technical approaches will be evaluated based on a technical committee (separated with the Hackathon organizing committee).

Hackathon Organizers
Prof. Binyang Song (Virginia Tech) (chair), binyangs@vt.edu
Dr. Anh Tran (Sandia National Laboratories), anhtran@sandia.gov
Prof. Zhenghui Sha (University of Texas – Austin), zsha@austin.utxas.edu
Prof. Hyunwoong Ko (Arizona State University), hyunwoong.ko@asu.edu
Dr. Zhuo Yang (Georgetown University, National Institute of Standards and Technologies), zy253@georgetown.edu
Dr. Laxmi Poudel (GE Aerospace Research) laxmi.poudel@ge.com
Dr. Yan Lu (National Institute of Standards and Technologies), yan.lu@nist.gov