Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process

Sharmin Sultana Sheuly, Mobyen Uddin Ahmed, Shahina Begum

2023

Abstract

Good performance of the Machine Learning (ML) model is an important requirement associated with ML-integrated manufacturing. An increase in performance improvement methods such as hyperparameter tuning, data size increment, feature extraction, and architecture change leads to random attempts while improving performance. This can result in unnecessary consumption of time and performance improvement solely depending on luck. In the proposed study, a quantitative performance analysis on the case study of chip detection is performed from six perspectives: hyperparameter change, feature extraction method, data size increment, and concatenated Artificial Neural Network (ANN) architecture. The focus of the analysis is to create a consolidated knowledge of factors affecting ML model performance in turning process quality prediction. Metal peels such as chips are designed at the time of metal cutting (turning process) and the shape of these chips indicates the quality of the turning process. The result of the proposed study shows that following a fixed recipe does not always improve performance. In the case of performance improvement, data quality plays the main role. Additionally, the choice of an ML algorithm and hyperparameter tuning plays an essential role in performance.

Paper Citation

in Harvard Style

Sheuly S., Ahmed M. and Begum S. (2023). Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 368-379. DOI: 10.5220/0011800100003393

in Bibtex Style

@conference{icaart23,
author={Sharmin Sultana Sheuly and Mobyen Uddin Ahmed and Shahina Begum},
title={Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={368-379},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011800100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process
SN - 978-989-758-623-1
AU - Sheuly S.
AU - Ahmed M.
AU - Begum S.
PY - 2023
SP - 368
EP - 379
DO - 10.5220/0011800100003393