Prediction of Vehicle Component Weights using Multi-Output Regression
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
With the ongoing digitalisation, many companies are exploring opportunities
to improve operational efficiency through machine learning.
Volvo Cars is part of this trend, and their Weight Management and
Optimization team seeks an automated way of managing data for component
weights of vehicles. The team performs data harmonisation as
a preliminary step, prior to production, which is currently computed
manually in Excel. This is inefficient and highly time-consuming, however,
machine learning models are believed to facilitate the process.
Hence, this thesis aims to investigate the feasibility of implementing a
machine learning model for automated data harmonisation, in combination
with a pipeline for managing the weight data and calculations.
The problem was formulated as a multi-output regression problem,
and neural network models and ensemble models were deemed appropriate
for the purpose. A significant part of this thesis involved
experimenting with different preprocessing techniques and model configurations
to find the optimal model performance. The results of the experiments indicated that the ensemble models
outperformed the neural network models, where XGBoost was the top
candidate. The best-performing XGBoost model achieved a validation
accuracy of 84% which was promising, however, the test accuracy was
modest, reaching only 22%. This performance was not considered sufficient
for the model to be implemented as a part of the final pipeline,
since the effort required for deployment of the tool was believed to
exceed the benefits. Nevertheless, there remains potential to optimise
the model further, and this study can be considered a guideline for
further work within this field. The results do not suggest that the use
of ensemble models for predicting vehicle component weights is unsuitable,
but rather that the optimal way of structuring this problem
was not found.
Beskrivning
Ämne/nyckelord
Machine Learning, Multi-Output Prediction, Neural Networks, Ensemble Models, XGBoost