Neurofuzzy c-Means Network-Based SCARA Robot for Head Gimbal Assembly (HGA) Circuit Inspection

作     者：Kiatwanidvilai, Somyot Praserttaweelap, Rawinun 

作者机构：King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Elect Engn Bangkok Thailand 

出 版 物：《COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE》 (计算智能与神经科学)

年 卷 期：2018年第2018卷第1期

页      面：1-10页

核心收录：

学科分类：0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：KMITL Research Funds RRI Thailand Research Fund Seagate Technology (Thailand) Co., Ltd 

主　　题：INDUSTRIAL robots HARD disks IMAGE processing BRANCH & bound algorithms K-means clustering 

摘      要：Decision and control of SCARA robot in HGA (head gimbal assembly) inspection line is a very challenge issue in hard disk drive (HDD) manufacturing. The HGA circuit called slider FOS is a part of HDD which is used for reading and writing data inside the disk with a very small dimension, i.e., 45 x 64 mu m. Accuracy plays an important role in this inspection, and classification of defects is very crucial to assign the action of the SCARA robot. The robot can move the inspected parts into the corresponding boxes, which are divided into 5 groups and those are Good, Bridging, Missing, Burn, and No connection. A general image processing technique, blob analysis, in conjunction with neurofuzzy c-means (NFC) clustering with branch and bound (BNB) technique to find the best structure in all possible candidates was proposed to increase the performance of the entire robotics system. The results from two clustering techniques which are K-means, Kohonen network, and neurofuzzy c-means were investigated to show the effectiveness of the proposed algorithm. Training results from the 30x microscope inspection with 300 samples show that the best accuracy for clustering is 99.67% achieved from the NFC clustering with the following features: area, moment of inertia, and perimeter, and the testing results show 92.21% accuracy for the conventional Kohonen network. The results exhibit the improvement on the clustering when the neural network was applied. This application is one of the progresses in neurorobotics in industrial applications. This system has been implemented successfully in the HDD production line at Seagate Technology (Thailand) Co. Ltd.