Kaitwanidvilai, Somyot

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Kaitwanidvilai, S.

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School of Engineering

somyot.ka@kmitl.ac.th

Now showing 1 - 10 of 21

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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems
(2025-06-01)
Yanyong, Sarucha
;
Kaitwanidvilai, Somyot
This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.
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Dual-stage Classification Framework for Detecting Rare or Unseen Patterns Based on Novelty Detection and Supervised Learning
(2024-01-01)
Rungtalay, Natthakritta
;
Kaitwanidvilai, Somyot
In this article, we propose a dual-stage classification framework designed for identifying rare or unseen patterns in the hard disk drive (HDD) industrial test process. The proposed framework integrates novelty detection and supervised learning methodologies to effectively address the challenges associated with imbalanced datasets and the detection of infrequent or unseen patterns within continuously changing environments. By employing novelty detection as the first-stage classifier followed by supervised learning as the second-stage classifier, the proposed method demonstrates an increased capacity to adapt to fluctuating environments, consequently enhancing the overall accuracy of process classification in practical manufacturing settings. To strengthen the robustness of novelty detection methods, an ensemble model technique is employed. Notably, the accuracy of the novelty detection methods in the first stage can be further enhanced with the incorporation of supervised learning techniques, particularly when a sufficiently large number of labeled samples are amassed. The proposed method consistently maintains accuracy, even in the face of changing environments, as it demonstrates the ability to adapt to data drift without necessitating the acquisition of new labeled data in the initial stage. This adaptability makes it particularly well suited for managing imbalanced datasets, rendering it highly practical for industrial applications. In a comprehensive case study conducted within the HDD industry, the framework exhibits immediate adaptability to rapidly changing environments while preserving high accuracy. This highlights the practical effectiveness of the proposed dual-stage classification framework in addressing the unique challenges posed by industrial scenarios.
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PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads
(2025-01-01)
Punyammaree, Chaiwat
;
Kaitwanidvilai, Somyot
This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. A U-Net model optimized via Particle Swarm Optimization (PSO) is developed to enhance segmentation performance by automatically tuning hyperparameters. The methodology includes optimized SEM image acquisition, preprocessing (patch-based subdivision and expert annotation), PSO-driven hyperparameter selection, and post-processing with thresholding and connected component analysis for pit counting. Experimental results demonstrate that the PSO-optimized U-Net significantly outperforms standard U-Net, SegNet, and LinkNet models, achieving an F1-score of 79.60%, an IoU of 86.51%, and an accuracy of 99.77%. Additionally, the proposed method achieves 86.9% counting accuracy, surpassing human experts (72.7%) while processing images 15 times faster (180 seconds vs. 2700 seconds per image). These findings highlight the potential of PSO-optimized deep learning for improving HDD quality control by providing an accurate, efficient, and standardized solution for corrosion pit detection, ultimately reducing the risk of HDD failure and data loss.
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Influence of Recorded Pattern on Background Interference impact in Magnetic Recording
(2021-11-05)
Chaiduangsri, Nuttapon
;
Kaitwanidvilai, Somyot
;
Tongsomporn, Damrongsak
This paper discusses the impact of background interference on a recorded pattern for heat-assisted magnetic recording technology (HAMR). Several patterns of the background track were examined, with the log bit error rate and signal to noise ratio measured via a spin-stand tester using HAMR head and media. It was found that the low frequency pattern gave the highest BER and SNR loss due to the strong magnetic field from the adjacent tracks. Similar to its practical use, the PRBS pattern also showed high interference. These observations may be used to support HDD areal density growth.
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Message from dean of faculty of engineering, kmitl
(2020-10-25)
Kaitwanidvilai, Somyot
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Adaptive Force Control Using a Standard Deviation-based Hybrid Approach
(2023-01-01)
Songthai, Maethinee
;
Yanyong, Sarucha
;
Kaitwanidvilai, Somyot
A good tracking response is essential for a force control system to achieve high performance in object handling. One of the significant challenges of the force control system is that the plant dynamics are directly dependent on the environment and the mechanical properties of the object. It's well-known that the mechanical properties of an object in manufacturing vary based on the type of product, gripper tools, and environment; hence, a non-adaptive controller may not be efficiently adopted. To address this issue, this paper proposes impedance control with hybrid adaptive algorithms to ensure the actuator system tracks the desired force command accurately. Particle Swarm Optimization (PSO) is adopted to adapt the impedance control parameters, thereby applying the capabilities of the learning system. A hybrid adaptive force control with a fitness function of the standard deviation and summation of error is proposed. The tracking response of the conventional adaptive controller was examined and compared with the proposed controller. The simulation results demonstrate the effectiveness of the proposed system.
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Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line
(2023-01-01)
Yanyong, Sarucha
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Parichatprecha, Rattapoohm
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
;
Konghuayrob, Poom
In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard.
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Implementation and Optimization of Two Degree of Freedom H∞ Loop Shaping Control for Average Current Mode Control Buck Converter using Genetic Algorithm
(2021-01-01)
Kaitwanidvilai, Somyot
;
Phurahong, Nuttapon
;
Suwan-Ngam, Warachart
In this paper, we focus on the implementation and optimization of two degree of freedom (2DOF) H∞ loop shaping control for the DC-DC buck converter. The output voltage is controlled using the current control mode called the average current mode control (ACMC). The technique using the fixed structure robust controller technique, as well as genetic algorithm (GA), is applied, resulting in the reduction of the controller order and optimal parameter for the robust proportional integral (PI) controller. In this paper, the performance of the proposed controller is compared with those using the conventional 2DOF H∞ loop shaping controller and other techniques. According to both simulation and experimental results, the robust controller designed by the proposed technique is simple, low order, and practical, yet still retains both performance and robustness.
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Decentralised and Centralised Fixed Structure H∞ Robust Loop Shaping for the MIMO Microsurgical Manipulation Based on PSOGSA
(2026-01-01)
Kaitwanidvilai, Somyot
;
Konghuayrob, Poom
;
Aoyama, Hisayuki
This paper presents decentralised and centralised fixed-structure H∞ robust control methods optimised by the Particle Swarm Optimisation and Gravitational Search Algorithm (PSOGSA) for a coupled multi-input multi-output (MIMO) microsurgical manipulator. The design framework explicitly considers uncertainties and disturbance constraints. Conventional H∞ loop-shaping controllers are typically of high order, complex, and difficult to implement in practice. To address this limitation, Proportional-Integral-Derivative (PID)-structured decentralised and centralised H∞ controllers are proposed, providing compact structures while retaining robustness. The novelty of this work lies in embedding H∞ robustness criteria into practical PID-based frameworks, bridging the gap between theoretical robust design and experimental implementation in microsurgical applications. The proposed controllers are evaluated against a reduced-order H∞ controller derived from Hankel norm approximation and a Ziegler–Nichols tuned PID controller, using both simulation and experimental studies. Results demonstrate that the proposed controllers achieve improved stability margins (0.449–0.521 compared with 0.436 for the reduced-order design), maintain low root-mean-square errors (≈0.067–0.089) and remain robust under voltage disturbances where conventional PID control fails. These findings confirm the contribution of a practical and efficient robust control strategy for enhancing the precision and reliability of microsurgical manipulators.
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Optimizing Product Quality Prediction in Smart Manufacturing Through Parameter Transfer Learning: A Case Study in Hard Disk Drive Manufacturing
(2025-04-01)
Kaitwanidvilai, Somyot
;
Sittisombut, Chaiwat
;
Huang, Yu
;
Bom, Sthitie
In recent years, the semiconductor industry has embraced advanced artificial intelligence (AI) techniques to facilitate intelligent manufacturing throughout their organizations, with particular emphasis on virtual metrology (VM) systems. Nonetheless, the practical application of data-driven virtual metrology for product quality inspection encounters notable hurdles, such as annotating inspections in highly dynamic industrial environments. This leads to complexities and significant expenses in data acquisition and VM model training. To address the challenges, we delved into transfer learning (TL). TL offers a valuable avenue for knowledge sharing and scaling AI models across various processes and factories. At the same time, research on transfer learning in VM systems remains limited. We propose a novel parameter transfer learning (PTL) architecture for VM systems and examine its application in industrial process automation. We implemented cross-factory and cross-recipe transfer learning to enhance VM performance and offer practical advice on adapting TL to meet individual needs and use cases. By leveraging extensive data from Seagate wafer factories, known for their large-scale and high-dimensional nature, we achieved significant PTL performance improvements across multiple performance metrics, with the true positive rate (TPR) increasing by 29% and false positive rate (FPR) decreasing by 43% in the cross-factory study. In contrast, in the cross-recipe study, TPR increased by 27.3% and FPR decreased by 6.5%. With our proposed PTL architecture and its performance achievements, insufficient data from the new manufacturing sites, new production lines and new products are addressed with shorter VM model training time and smaller computational power with strong final quality prediction confidence.

Kaitwanidvilai, Somyot

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