Error assessment of laser cutting predictions by semi-supervised learning  

作　　者：Mustafa Zaidi Imran Amin Ahmad Hussain Nukman Yusoff 

机构地区：[1]Department of Computing, Shaheed Zulfikar Ali Bhutto Institute of Science & Technology (SZABIST) [2]Department of Nuclear Engineering, King Abdulaziz University [3]Manufacturing Systems Integration, Department of Mechanical Engineering, University of Malaya

出　　处：《Journal of Central South University》2014年第10期3736-3745,共10页中南大学学报（英文版）

摘　　要：Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.

关 键 词：semi-supervised learning training algorithm kerf width edge quality laser cutting process artificial neural network（ANN） 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN249[自动化与计算机技术—控制科学与工程]