Analysis of Characteristic Process Parameters to Identify Unstable Process Conditions during Wire EDM

Bergs, Thomas; Tombul, U.; Herrig, T.; Olivier, M.; Klink, Andreas; Klocke, Fritz

doi:10.1016/j.promfg.2018.11.018

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…Huge thermal energy is created, and it melts material and vaporizes the material from the sample. e modification of pulse energy and current durations in the dielectric medium can determine the dimensional accuracy and quality of the machining samples [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Optimization on Material Removal Rate and Surface Roughness of Stainless Steel 304 Wire Cut EDM by Response Surface Methodology

Seshaiah¹,

Sampathkumar²,

Mathanbabu³

et al. 2022

Advances in Materials Science and Engineering

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In this work, wire cut electrical discharge machining (WEDM) is used for the material removing processes; it is utilized for machining conductive parts where it is required to produce complicated shapes, new profiles, new geometry, new product development, and high-accuracy components. This machining process is best suitable for high-end applications such as aerospace, automations, automobile, and medical devices. At present, most of the industrial sectors choose the WEDM process because it is used to develop products in a very short development cycle and at a better economic rate. In this paper, the selected complex geometry of the metal sample was eroded away from the wire during the WEDM process, which eliminates mechanical tensions during machining. The effect of different WEDM operation variables set as wire speed, wire tension, discharge current, dielectric flow rate, and pulse on and off time on the parameter, stainless steel 304 material removing rate (MRR) using RSM, has been studied. The MRR will be maximized if the optimum sets of operational variations are used and also achieve a superior surface finish.

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Section: Introductionmentioning

confidence: 99%

Optimization on Material Removal Rate and Surface Roughness of Stainless Steel 304 Wire Cut EDM by Response Surface Methodology

Seshaiah¹,

Sampathkumar²,

Mathanbabu³

et al. 2022

Advances in Materials Science and Engineering

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“…Caggiano et al [17] has also developed an advanced signal processing methodology to extract discharge features by pattern recognition to identify unstable machining conditions. Bergs et al [18] analysed the discharge characteristics to predict unstable machining behaviour.…”

Section: Introductionmentioning

confidence: 99%

Wire EDM failure prediction and process control based on sensor fusion and pulse train analysis

Abhilash

Chakradhar²

2021

Int J Adv Manuf Technol

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The study aims to develop a neural network classification model to predict machining failures during wire electric discharge machining. Also, a process control algorithm retunes the process parameters based on remaining useful time before failure. In the proposed methodology, an artificial neural network (ANN) classifier receives four inprocess discharge characteristics as input. These extracted features are discharge energy, spark frequency, open spark ratio and short circuit ratio. Output classes are labelled normal machining, wire breakage, and spark absence. 108 experiments were conducted according to a full factorial design to train the classifier model, with 90 % classification accuracy. An ANN model was trained to predict the remaining useful time before failure, based on which process parameters are retuned to restore the machining stability. The algorithm was successful in ensuring continuous failure-free machining.

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“…However, the process characteristics are material dependent, and it is difficult to optimize the process overall. In addition, the stochastic nature of the process and complex interactions make the process 2 challenging to control [7]. One common cause of process interruption is wire breakage.…”

Section: Introductionmentioning

confidence: 99%

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

Abhilash

Chakradhar

2021

Adv. Manuf.

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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. 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.

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Analysis of Characteristic Process Parameters to Identify Unstable Process Conditions during Wire EDM

Cited by 15 publications

References 20 publications

Optimization on Material Removal Rate and Surface Roughness of Stainless Steel 304 Wire Cut EDM by Response Surface Methodology

Optimization on Material Removal Rate and Surface Roughness of Stainless Steel 304 Wire Cut EDM by Response Surface Methodology

Wire EDM failure prediction and process control based on sensor fusion and pulse train analysis

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

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