Adaptive servo system for die-sinking micro-EDM driven by deep Q-network with online-offline combined data

Cheng Guo (), Hao Li, Longhui Luo, Long Ye, Zhiqiang Liang and Xiang Chen
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Cheng Guo: College of Mechatronics and Control Engineering, Shenzhen University
Hao Li: College of Mechatronics and Control Engineering, Shenzhen University
Longhui Luo: College of Mechatronics and Control Engineering, Shenzhen University
Long Ye: The University of Edinburgh
Zhiqiang Liang: Beijing Institute of Technology
Xiang Chen: Chinese Academy of Sciences

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 8, No 7, 5374 pages

Abstract: Abstract Die-sinking micro electrical discharge machining (micro-EDM) belongs to non-conventional manufacturing methods. However, the process mechanism is complex and it is difficult to describe the process by an accurate mathematical model. Deep reinforcement learning (DRL), the combination of neural network and reinforcement learning (RL), successfully achieves the direct mapping from high-dimension states to scores of different actions, which enables an end-to-end control scheme, from process feedback data to action strategies. Comparing to training methods in traditional deep learning (DL), part or even all datasets for DRL stem from online environment-interactive data, enabling the adaptive ability. This article introduces a RL algorithm based on Deep Q-Network (DQN) and embeds it in the servo system for die-sinking micro-EDM. Based on online-offline combined process data and a priori-knowledge based reward function, the experience tuple for DRL generates automatically after every servo motion step and the Q-network updates for servo strategies. The experiments verify that the proposed DQN driven adaptive servo system for die-sinking micro-EDM can maintain the discharge efficiency more aggressively and avoid short circuits to a much extent, greatly enhancing the machining efficiency.

Keywords: Micro-EDM; Die-sinking; Deep Q-network; Adaptive servo system (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02520-1

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