TY - GEN
T1 - Utilizing Transfer Learning Models to Classify Absence Defects on Aluminum Plates Using Feature-Based Approaches
AU - Pandian, Kiran
AU - Zhen, Lim Weng
AU - PP Abdul Majeed, Anwar
AU - Teh, Sze Hong
AU - Goon, Koon Yin
AU - Razman, Mohd Azraai Mohd
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Screw – the small but important elements used in various industry. Its presence plays a significant role as it securely holds the product in place, preventing loosening or collision with the case. Such occurrences could lead to the displacement of small components or compartments, resulting in product failure. The advent of Industry 4.0 has contributed to reducing labor costs and human errors. This research aims to develop a robust classification model for machine vision detection, specifically for identifying the absence or presence of a screw. The model can be integrated into relevant robotics applications. To collect the customized dataset, a 6-degree-of-freedom (DOF) robot. The collected dataset was then categorized into two groups: absent and present. For the training process, a pretrained dataset called ImageNet was employed to facilitate the training process. Transfer learning techniques were used to extract the features required for different machine learning models. Each machine learning model underwent hyperparameter tuning to achieve the highest classification accuracy. The data was divided into training, validation, and testing sets using a sampling ratio of 60:20:20, respectively, before being fed into the various machine learning models.
AB - Screw – the small but important elements used in various industry. Its presence plays a significant role as it securely holds the product in place, preventing loosening or collision with the case. Such occurrences could lead to the displacement of small components or compartments, resulting in product failure. The advent of Industry 4.0 has contributed to reducing labor costs and human errors. This research aims to develop a robust classification model for machine vision detection, specifically for identifying the absence or presence of a screw. The model can be integrated into relevant robotics applications. To collect the customized dataset, a 6-degree-of-freedom (DOF) robot. The collected dataset was then categorized into two groups: absent and present. For the training process, a pretrained dataset called ImageNet was employed to facilitate the training process. Transfer learning techniques were used to extract the features required for different machine learning models. Each machine learning model underwent hyperparameter tuning to achieve the highest classification accuracy. The data was divided into training, validation, and testing sets using a sampling ratio of 60:20:20, respectively, before being fed into the various machine learning models.
KW - Hyperparameter Tunning
KW - Machine Learning
KW - Machine Vision
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=105002721757&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_43
DO - 10.1007/978-981-96-3949-6_43
M3 - Conference Proceeding
AN - SCOPUS:105002721757
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 513
EP - 520
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Y2 - 22 August 2024 through 23 August 2024
ER -