Project Details

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Automated material Testing System: Integrating sensors and AI to automatically detect specimen characteristics

Company: Tinius Olsen Testing Machine Company

Major(s):
Primary: MATSE
Secondary: ME
Optional: CMPSC

Non-Disclosure Agreement: YES

Intellectual Property: YES

Are you ready to use cutting-edge technology to solve a real-world engineering challenge? We're looking for passionate students to join us in building a working prototype of a smart system that will revolutionize the field of material testing. Imagine a Universal Testing Machine (UTM) that can automatically identify a specimen just by looking at it. Currently, operators must manually input details for every single test—a process that is time-consuming, repetitive, and prone to human error. Our project aims to solve this by creating an automated system that is more consistent and efficient than a human operator, saving valuable time and improving data quality across the board. The goal is to develop a working prototype of an automated sensor and AI system capable of identifying and characterizing material specimens for a Universal Testing Machine. The UTM operator simply holds a specimen up to the sensor and it detects the following characteristics of the specimen: • The type of specimen; test coupon, rebar, rope, lap joint, etc. • The material; metal, plastic, rubber, composite, etc. • The shape of the specimen; round, flat, dog-bone, etc. • The dimensions of the specimen; width, thickness, length, length of gauge section, etc. During the project, you will: • Design and build a multi-sensor prototype that can be integrated into a UTM to analyze test specimens. The prototype will integrate different sensor technologies (e.g. cameras, inductive sensors, load cells) to gather comprehensive data on specimen properties. You will develop and train a machine learning model to accurately infer a specimen’s characteristics (type, material, shape, and dimensions) from the combined sensor data. • Rigorously test the prototype to demonstrate its ability to identify and characterize specimens with higher accuracy and consistency than manual methods. • Prepare and deliver a comprehensive final report, a working prototype demonstration, and a presentation of our findings.