Abstract
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 × 64 µm. 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.
Highlights
Robotics and Artificial Intelligence (AI) especially in neurorobotics play an important role in a number of manufacturing processes because of their fast processing time, good accuracy, intelligence, and high repeatability
hard disk drive (HDD) manufacturing processes are under development to be fully autonomous process by the implementation of Artificial Intelligence (AI) into the automation machine to replace the manual process from human
To find the best features to be used in the clustering technique, the popular technique, “Branch and Bound (BNB)” was applied. e branch and bound (BNB) process starts from the objective function, branches the big problem, and divides it into small group problems. en, the process analyzes the bound of problem and removes some results, which cannot provide the best results evaluated from the objective function. e process is repeated until finding the best solution
Summary
Robotics and AI especially in neurorobotics play an important role in a number of manufacturing processes because of their fast processing time, good accuracy, intelligence, and high repeatability. To enhance the performance and accuracy of this process, AI techniques such as neural network, fuzzy system, and unsupervised learning are attempted to apply to the manufacturing, in the visual inspection and control of robotics process. Is research aims to develop a neurofuzzy-based decision technique for this system to automatically control the SCARA robot for the HGA circuit inspection. Neurofuzzy c-means clustering [17], which applies the Euclidean distance to the clustering technique, was utilized in many applications such as brain tumor in MRI images [18], remote sensing images [19], data clustering with image segmentation [20], and processing time improvement without performance effect [21]. When the classification or groups of inspecting object has been decided, the action to move the SCARA robot will be the process to transfer the object to the corresponding box
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