Error assessment of laser cutting predictions by semi-supervised learning

作     者：Mustafa Zaidi Imran Amin Ahmad Hussain Nukman Yusoff 

作者机构：Department of Computing Shaheed Zulfikar Ali Bhutto Institute of Science & Technology (SZABIST) Department of Nuclear Engineering King Abdulaziz University Manufacturing Systems Integration Department of Mechanical Engineering University of Malaya 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2014年第21卷第10期

页      面：3736-3745页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080901[工学-物理电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CRER SZABIST Research Centre Higher Education Commission, Pakistan, HEC 

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

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.