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

Abhilash, P. M.; Chakradhar, D.

doi:10.1080/10910344.2022.2044856

Cited by 13 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lower energy mode shows a significant surface roughness improvement of 46% and 74% when considering S z and S a , respectively. The higher energy mode produces a considerably higher number of short circuit pulses during the spark erosion which causes deeper craters and surface defects [28,29]. The short-circuit pulses are caused due to the ineffective flushing of debris from the inter-electrode gap.…”

Section: Polishing Under Different Energy Regimesmentioning

confidence: 99%

Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components

Abhilash

Ahmed

2023

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

The metal additive manufacturing (AM) process has proven its capability to produce complex, near-net-shape products with minimal wastage. However, due to its poor surface quality, most applications demand the post-processing of AM-built components. This study proposes a method that combines convolutional neural network (CNN) classification followed by electrical discharge-assisted post-processing to improve the surface quality of AMed components. The polishing depth and passes were decided based on the surface classification. Through comparison, polishing under a low-energy regime was found to perform better than the high-energy regimes with a significant improvement of 74% in surface finish. Also, lower energy polishing reduced the occurrences of short-circuit discharges and elemental migration. A 5-fold cross-validation was performed to validate the models, and the results showed that the CNN model predicts the surface condition with 96% accuracy. Also, the proposed approach improved the surface finish substantially from 97.3 to 12.62 μm.

show abstract

Section: Polishing Under Different Energy Regimesmentioning

confidence: 99%

Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components

Abhilash

Ahmed

2023

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…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:

View full text Add to dashboard Cite

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.

show abstract

Effects of current intensities on material removal rate, electrode wear rate, and surface roughness of machining high carbon high chromium steel

Rakesh,

Gupta,

Mahto

et al. 2024

Materialwissenschaft Werkst

View full text Add to dashboard Cite

The electrical discharge machining is very successful and generally recognized for producing complicated shapes and small openings with exceptional precision. The objective of this study is to assess the impact of different electrical discharge machining parameters on the attributes of the machining process. The evaluation of the machining process was conducted based on the workpiece‘s material removal rate, the rate at which the electrodes wear, and the level of surface roughness. Simultaneously achieving a high material removal rate, low electrode wear rate, and high surface roughness is not possible with a specific combination of approaches due to their contradicting nature. Frequently, it is necessary to independently apply many measures instantaneously in order to assess their impact on various responses. This research investigates the machining of high carbon high chromium steel making use of electrical discharge machining with aluminum, copper, and graphite electrodes. The study focuses on the impact of different current intensities on the rate of material removal, electrode wear, and surface roughness. The experimental results demonstrate that the highest material removal rate (mmcubicmin−1) is achieved at a current density of 12 A when using a copper electrode (54.67), as opposed to aluminum electrode (22.91) and graphite electrode (29.43).

show abstract

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

Cited by 13 publications

References 40 publications

Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components

Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components

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

Effects of current intensities on material removal rate, electrode wear rate, and surface roughness of machining high carbon high chromium steel

Contact Info

Product

Resources

About