Date of Award

2025-08-01

Degree Name

Master of Science

Department

Mechanical Engineering

Advisor(s)

Angel Flores Abad

Abstract

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then reorders them based on how the user prioritizes different traits. By doing this, it helps reduce guesswork and streamlines the design process. The app was trained using a carefully built dataset that reflects realistic actuator performance profiles across common engineering tasks. By combining prediction and trade-off evaluation into one platform, this system doesn't just make recommendations - it helps engineers explore options, visualize differences, and make choices that align with their design goals. Overall, it aims to make early-stage decisions easier, clearer, and more aligned with what users actually care about.

Language

en

Provenance

Received from ProQuest

File Size

58 p.

File Format

application/pdf

Rights Holder

Alejandro Silva Au

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