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Optimizing Wafer Classification in Industrial Manufacturing Using Particle Swarm Optimization and Deep Learning
(2025-01-01)
Suwannoot, Pisit
;
Konghuayrob, Poom
In this study, we examine the application of convolutional neural networks (CNNs) for wafer pattern classification, with a focus on enhancing training efficiency and model performance. To achieve this, particle swarm optimization (PSO) is employed to improve the model performance while reducing its complexity, a critical factor in production environments. By minimizing the number of layers, the proposed method accelerates training, reduces resource consumption, and enhances defect detection accuracy. Wafer failure patterns are classified into four categories: vertical, rectangular, edge, and horizontal. The approach achieves an impressive F1-score of 0.988, significantly surpassing the traditional CNN’s score of 0.83. By integrating PSO, the method considerably improves the visual inspection process for hard disk drives, contributing to high-quality production. This optimization not only streamlines workflows but also enables manufacturers to address issues more rapidly, aligning with Industry 4.0’s objectives of automation and intelligent monitoring.
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Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process
(2024-01-01)
Kanjanapruthipong, Worawit
;
Prasitmeeboon, Pitcha
;
Konghuayrob, Poom
In contemporary hard disk drive (HDD) manufacturing processes, after the assembly of the HDD from the production line, a series of diverse calibration procedures are necessary to ensure standardization. These include capacity calibration, which determines the storage space in terabytes (TB) presently available, and flying height (FH) calibration, which evaluates the distance between the head and the disk by applying electric current to the heater coil element to achieve the desired FH, thus optimizing the writing and reading performance and tailoring it to each HDD. Additionally, electric current is saved in a digital-to-analog converter (DAC) unit for the utilization of a read/write head, while a preamp collaborates with the drive firmware to convert the electric current in the DAC unit to milliwatts. In the present scenario, multiple calibrations of flying heights (FHs), specifically flying height 1 (FH1) and flying height 2 (FH2), are performed. Each FH calibration requires a testing time of approximately 5 h owing to the separation of measurement points into 240 locations across the disk surface, referred to as test zones, with a total of 20 heads. The primary objective of this study is to reduce the testing time by using a combination of deep neural network (DNN) and particle swarm optimization techniques to predict the DAC profiles of FH2 as it approaches FH1, where FH1 is the input for the DNN model.
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The Development of Adaptive Gray Level Mapping Combined Partical Wwarm Optimization for Measuring the Dimeter Size of Automotive Nut
(2020-04-01)
Pondech, Wichai
;
Saenthon, Anakkapon
;
Konghuayrob, Poom
In line assembly process, it is necessary to use the accurate tools to inspect the work piece. In order to measure the width, thickness or depth of the nut used in the commercial car, previously in Thai Steel Cable Company used the caliper to measure size of the automotive nut operated by human as an sample test inspection. Even the result on this technique is high accuracy, however; there are some disadvantages based on this method such as human error, idle time and also fatigue by the human. Therefore, this research focus on the development of the new measurement technique that utilized industrial camera together with the image processing algorithm to measure the entire nut with 100% inspection test. Circular Hough Transform (CHT) is applied to be the basic concept used for finding the circle position and measuring nut diameter. Although the CHT technique can measure diameter of the interested nut, but the result's accuracy is not acceptable due to measuring error from the various range of light condition. This research proposed the new technique, Adaptive Gray Level Mapping algorithm (AGLM) to increase the quality of the input picture before measuring the radius by CHT technique. Moreover, the beta in AGLM is optimized by particle swarm optimization (PSO) technique that applies 50% of nut data and other is used for validate. The results show the effectiveness of the proposed AGLM combined PSO that increase the accuracy of the visual measuring method via compare to the conventional threshold with CHT technique.

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