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    Optimal A∗ Path Planning with Ant Colony Optimization on Multi-Robot Task Allocation for Manufacturing Model
    (2021-04-23)
    Praserttaweelap, Rawinun
    ;
    Kiatwanidvilai, Somyot
    This paper presents the optimal path planning by using A∗ with ant colony optimization based on the multi-robot systems. The purpose of this research is to design an appropriate path planning model for manufacturing. The safety path and the processing time are the priority of the manufacturing. The main contribution of this study is the A∗ for path planning with the ant colony optimization for the lowest risk of collision (RC) searching. The 5 robots condition is the best condition for the minimum RC and the minimum processing time. The simulation results represent the suitable path planning prediction model for manufacturing systems.
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    Optimal path planning on multi-robot task allocation for manufacturing with artificial bee colony algorithm
    (2021-01-01)
    Praserttaweelap, Rawinun
    ;
    Kiatwanidvilai, Somyot
    This research presents the optimal path planning for manufacturing. The new contribution is the safety path searching by using artificial bee colony algorithm in multi-robot systems. The new RC function was introduced in this paper to represent the risk of collision path. The best safety path searching is the low risk of collision path from automatic searching on RC function. This research designs the artificial bee colony algorithm for the safety path searching based on the manufacturing environment. The proposed is to find the optimal safety path with fast convergence. With the built-in error, the simulation results illustrate the effectiveness of the optimal path planning in multi-robot systems for manufacturing.
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    Safety path planning with obstacle avoidance using particle swarm optimization for agv in manufacturing layout
    (2019-02-01)
    Praserttaweelap, Rawinun
    ;
    Kaitwanidvilai, Somyot
    ;
    Aoyama, Hisayuki
    In robotic systems, path planning is the one of important processes for robot motion. The best path planning is required for shortest path searching that can make fast movement of robot. However, the real environment is not only the path from point to point but it has obstacles which are the one of constraints for best path searching. The obstacle avoidance is concerned to avoid the crashing between robot and obstacle under environment. In Hard Disk Drive manufacturing, the first priority is safety constraint for non-collision and second priority is shortest path for processing time saving. This research designed the algorithm for path planning and obstacle avoidance for AGV in Hard Disk Drive Manufacturing of Seagate Technology (Thailand) Ltd by using particle swarm optimization. The fitness function on particle swarm optimization process for particle searching has been integrated with obstacle avoidance function to find the best path for robot without collision and total distance to find the shortest path. This algorithm is applied to verifying the model performance. The simulation results of this research are done by MATLAB 2016b and illustrate the good performance on different cases with controlled parameter.
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    Neurofuzzy c-Means Network-Based SCARA Robot for Head Gimbal Assembly (HGA) Circuit Inspection
    (2018-01-01)
    Kiatwanidvilai, Somyot
    ;
    Praserttaweelap, Rawinun
    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.
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    Head Gimbal Assembly circuit with vision technique and Fuzzy C-Means Clustering
    (2015-12-23)
    Praserttaweelap, Rawinun
    ;
    Kiatwanidvilai, Somyot
    In Hard Disk Drive (HDD) Industry, the automation system is the one key for manufacturing process. Head Gimbal Assembly (HGA) is a part of HDD which have the reader and writer circuit. The HGA circuit is very important for read/write process. This research proposes the new vision technique and clustering by Fuzzy C-Means algorithm for HGA circuit inspection in 3 groups. HGA circuits in 3 groups are good, bridging, and missing group. The bridging and missing groups are the defect group. Blob analysis is the one of vision technique that it can measure the properties of image. The measurement properties from blob analysis are used in clustering technique. Fuzzy C-Means Clustering is the clustering technique which is grouped the measurement data into the cluster group based on the natural grouping of data. From the experiment results of this research, the clustering performance from Fuzzy C-Means Clustering is 99.11% accuracy based on the measurement properties in blob analysis with 225 samples.
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    Vision inspection with k-means clustering on head gimbal assembly defect
    (2015-01-01)
    Praserttaweelap, Rawinun
    ;
    Kiatwanidvilai, Somyot
    Head Gimbal Assembly (HGA) is an important feature of read and write process in a Hard Disk Drive (HDD). Currently, HGA circuit inspections are done using human operators under microscope; the vision processing for inspection in automated systems is required. This research work proposes an algorithm for detection the HGA circuit defect by using the blob detection, and then analysis the properties of blob tool. By the measurement properties of the blob tool, the K-Means Clustering can specify the data in each group in 95.45% accuracy with 110 samples.