Machine-vision-based electrode wear analysis for closed loop wire EDM process control

doi:10.1007/s40436-021-00373-y

Advances in Manufacturing ›› 2022, Vol. 10 ›› Issue (1): 131-142.doi: 10.1007/s40436-021-00373-y

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

P. M. Abhilash, D. Chakradhar

Department of Mechanical Engineering, Indian Institute of Technology Palakkad, Kerala, 678557, India

收稿日期:2021-02-23 修回日期:2021-04-24 出版日期:2022-03-25 发布日期:2022-02-23
通讯作者: P. M. Abhilash E-mail:abhilashpm184@gmail.com
基金资助:
The authors would like to thank the central instrumentation facility (CIF), IIT Palakkad, for providing the test facility and equipment.

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

P. M. Abhilash, D. Chakradhar

Department of Mechanical Engineering, Indian Institute of Technology Palakkad, Kerala, 678557, India

Received:2021-02-23 Revised:2021-04-24 Online:2022-03-25 Published:2022-02-23
Contact: P. M. Abhilash E-mail:abhilashpm184@gmail.com
Supported by:
The authors would like to thank the central instrumentation facility (CIF), IIT Palakkad, for providing the test facility and equipment.

摘要/Abstract

摘要： The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining (EDM) process control. Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures. Such process interruptions are undesirable because they affect cost efficiency, surface quality, and process sustainability. The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear. Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images. In the proposed method, the images are pre-processed and enhanced to obtain a binary image that is used to compute the wire wear ratio (WWR). The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time, servo voltage, and wire feed rate. The algorithm successfully predicted wire breakage events. In addition, the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits, thereby preventing wire breakage interruptions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00373-y

关键词: Wire electrical discharge machining (WEDM), Machine vision, Image processing, Wire wear, Wire electrode, Wire breakage, Inconel 718, Process control

Abstract: The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining (EDM) process control. Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures. Such process interruptions are undesirable because they affect cost efficiency, surface quality, and process sustainability. The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear. Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images. In the proposed method, the images are pre-processed and enhanced to obtain a binary image that is used to compute the wire wear ratio (WWR). The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time, servo voltage, and wire feed rate. The algorithm successfully predicted wire breakage events. In addition, the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits, thereby preventing wire breakage interruptions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00373-y

Key words: Wire electrical discharge machining (WEDM), Machine vision, Image processing, Wire wear, Wire electrode, Wire breakage, Inconel 718, Process control

P. M. Abhilash, D. Chakradhar. Machine-vision-based electrode wear analysis for closed loop wire EDM process control[J]. Advances in Manufacturing, 2022, 10(1): 131-142.

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Machine-vision-based electrode wear analysis for closed loop wire EDM process control

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

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