Optimization of servo accuracy of Y axis of dicing saw based on iterative learning control

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-04-06 DOI:10.1007/s13198-024-02318-7
Jun Shi, Peiyi Zhang, Hechao Hou, Weifeng Cao, Lintao Zhou
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Abstract

Dicing saw is a key equipment in chip packaging, in which the servo performance of each axis affects the scribing accuracy. Since the Y-axis is used to locate the micron-level cutting street, its servo positioning accuracy is required to be very high. In this paper, a variable forgetting factor fuzzy iterative learning control (VFF-FILC) with tracking differentiator is proposed for the high-precision localization of the Y-axis electromechanical servo system of the dual-axis wheel dicing saw model 8230 manufactured by Advanced Dicing Technologies. The method combines fuzzy control with iterative learning control to overcome the problem of poor anti-interference ability of traditional PID control. VFF-FILC reduces the overshoot and build-up time, and also improves the tracking performance by adaptively adjusting the learning rate of the ILC algorithm according to the tracking error of the system. To address the problem of noise interference with the Y-axis servo system, tracking differentiator is used to process the input position signal. In order to verify the superiority of the proposed design, it is compared with three conventional controllers in MATLAB/SIMULINK platform and anti-interference experiments are conducted. The results show that the VFF-FILC reduces the rise time by 28.57% and the overshoot by 88.23% compared to the PID controller, which proves the superiority of the proposed method in the Y-axis servo system of the wheel dicing saw.

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基于迭代学习控制的切割锯 Y 轴伺服精度优化
摘要 切割锯是芯片封装的关键设备,其中各轴的伺服性能影响着划片精度。由于 Y 轴用于定位微米级切割街,因此其伺服定位精度要求非常高。本文提出了一种带有跟踪微分器的可变遗忘因子模糊迭代学习控制(VFF-FILC),用于 Advanced Dicing Technologies 公司生产的 8230 型双轴轮式切割锯 Y 轴机电伺服系统的高精度定位。该方法将模糊控制与迭代学习控制相结合,克服了传统 PID 控制抗干扰能力差的问题。VFF-FILC 根据系统的跟踪误差自适应地调整 ILC 算法的学习率,从而减少了过冲和建立时间,并提高了跟踪性能。为了解决 Y 轴伺服系统的噪声干扰问题,使用了跟踪微分器来处理输入位置信号。为了验证所提设计的优越性,在 MATLAB/SIMULINK 平台上将其与三个传统控制器进行了比较,并进行了抗干扰实验。结果表明,VFF-FILC 与 PID 控制器相比,上升时间减少了 28.57%,过冲减少了 88.23%,这证明了所提方法在砂轮切割锯 Y 轴伺服系统中的优越性。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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