ANFIS modelling of mean gap voltage variation to predict wire breakages during wire EDM of Inconel 718

The current work aims to study the influence of discharge energy and debris accumulation on wire break failure and surface integrity aspects like surface roughness, subsurface hardness, and geometrical errors during the wire EDM of Inconel 718. The responses considered for analyzing the geometrical accuracies are flatness error, roundness error and cylindricity error. Four different wire electrodes were considered for the study based on the wire strength and coatings, namely hard zinc coated brass, half-hard zinc coated brass, hard uncoated brass, and half-hard uncoated brass wire electrodes. The geometric accuracy of the machined parts is closely related to the choice of wire electrode.High tensile strength hard wires were observed to machine the surfaces with maximum accuracy. Also, the zinc coated wires were observed to cut faster compared to the uncoated wire electrodes. The study revealed that there exists a strong influence of discharge energy on the surface integrity of wire electric discharge machined surface. The zinc and copper elemental contamination was minimal for uncoated wire electrodes at lowest discharge energy mode. Wire wear phenomena was analysed for both coated and uncoated wires. Discharge energy had a significant impact on the electrode wear of coated and uncoated wire electrode.

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“…14 (d)). Similar pulse cycle trends are observed in the recent literature during condition monitoring of wire EDM [35][36][37].…”

Section: Wire Breakagessupporting

confidence: 87%

Effect of wire material and discharge energy on productivity and surface integrity of WEDM-processed Inconel 718

Abhilash

Chakradhar

2022

Advances in Materials and Processing Technologies

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“…Such models will thus be less accurate in predicting wire breakages. Abhilash and chakradhar (2020a) has developed an adaptive neurofuzzy inference system (ANFIS) powered decision support system to predict wire breakages. The prediction is based on a threshold value of mean gap voltage variation above which the wire breakage was experimentally observed.…”

Section: Wire Break Prediction Systemsmentioning

confidence: 99%

Performance monitoring and failure prediction system for wire electric discharge machining process through multiple sensor signals

Abhilash

Chakradhar

2022

Machining Science and Technology

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The study aims to develop a pulse classification algorithm to understand wire electric discharge machining (wire EDM) process stability and performance based on the discharge pulse characteristics. Also, a process data driven failure prediction system is proposed. The wire EDM monitoring system includes high sampling rate differential probes and current probes. The features extracted through pulse train analysis were spark discharge energy, ignition delay time, spark frequency, and proportion of various discharges. A pulse discrimination algorithm was proposed which classifies the discharges into open circuit sparks, arc discharges, short circuit sparks, and normal sparks. It was observed that, higher proportions of short circuit pulses resulted in inferior part quality. The differences in the pulse cycle during stable and unstable machining was studied, based on the extracted features. It was found that the discharge frequency and the proportion of arc and short circuit pulses were extremely high before the wire breakages. An artificial neural network (ANN) model was developed to predict the process responses, like cutting speed and surface roughness, from the process data. Also, an intelligent algorithm was developed based on the extracted in-process data to predict the unstable conditions leading to machining failures.The accuracy of the algorithm was confirmed to be very high by conducting confirmation tests.

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“…28 Due to their inherent ability to model high dimensional data with non-linearity, machine learning approaches have been highly successful in matching WEDM process outputs with input attributes. 29–36 The literature presented in the preceding paragraphs suggests that there are plenty of reports available to indicate the correlation of geometrical inaccuracies with the input parameters such as pulse on time ( T on ), pulse off time ( T off ), and pulse peak current ( I p ). Hence, the goal of this research is to see how random fluctuations in process variables like servo voltage (SV), servo feed rate (SF), dielectric water pressure (WP), and wire tension (WT) affect two geometric responses: corner error (CE) and undercut (UC).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic investigation of geometric responses in Wire EDM machined complex-shaped profile: A machine learning based approach

Saha

Gupta

Maity

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Wire electric discharge machining (WEDM) has gained tremendous market share due to its potential to create complex profiles and ease in machining exotic materials. The inherent problem of wire lag, however, has a significant impact on the accuracy and precision of complex profiles in WEDM. Furthermore, if parametric uncertainties are not taken into account during machining of complex profiles, problems like geometrical inaccuracy and imprecision are likely to worsen. Hence, in the present study, we focused on the role of parametric uncertainties on geometrical parameters such as corner error (CE) and undercut (UC) of complicated profile. In this regard, the machine learning (ML) driven framework is proposed, wherein the Gaussian process regression model is integrated with the experimental responses gathered from the WEDM. The sample-space to train and validate the ML model is constructed by performing the WEDM for the experimental plan, which is designed on the basis of face-centered central composite design. The constructed ML model is rigorously tested and validated to ensure its predictive accuracy. The ML-based WEDM framework is further extended to perform data-driven uncertainty quantification and sensitivity analysis to reveal the likelihood of variations in geometrical precision due to inherent parametric uncertainties. The constructed model is further deployed for the data-driven sensitivity analysis and to reveal the probabilistic behavior of responses in the presence of parametric uncertainties. The results of this study will help to develop and expand the WEDM robust control technique.

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ANFIS modelling of mean gap voltage variation to predict wire breakages during wire EDM of Inconel 718

Cited by 30 publications

References 14 publications

Effect of wire material and discharge energy on productivity and surface integrity of WEDM-processed Inconel 718

Effect of wire material and discharge energy on productivity and surface integrity of WEDM-processed Inconel 718

Performance monitoring and failure prediction system for wire electric discharge machining process through multiple sensor signals

Probabilistic investigation of geometric responses in Wire EDM machined complex-shaped profile: A machine learning based approach

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