Application of Machine Learning in Product Structure for Mass Customisation: Model Structure Validation with Large Language Model

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Insinööritieteiden korkeakoulu | Bachelor's thesis
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Date

2024-12-27

Department

Major/Subject

Laskennallinen tekniikka

Mcode

ENG3082

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

29+9

Series

Abstract

This thesis investigates the application of machine learning, specifically fine-tuned large language models (LLMs), in validating product structures for mass customisation. The research focuses on kitchen ventilation systems, employing GPT-4o to streamline product structure validation. A dataset of 250 samples, including correct and misconfigured examples, was used to train the model. The study evaluates the impact of prompt engineering, dataset size, and fine-tuning strategies, highlighting the challenges of limited data and input constraints. Results indicate that the fine-tuned model effectively identifies misconfigurations, provides actionable recommendations, and understands the principles of mass customisation. The findings demonstrate the potential of LLMs in enhancing knowledge transfer and complexity management in product configuration systems. While limitations such as dataset size and model scalability remain, this research establishes a foundation for extending machine learning solutions to broader manufacturing contexts.

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Supervisor

St-Pierre, Luc

Thesis advisor

St-Pierre, Luc

Keywords

large language model, machine learning, mass customisation, product structure

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https://urn.fi/URN:NBN:fi:aalto-202502182445

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[kand] Insinööritieteiden korkeakoulu / ENG