Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining

Paturi, Uma Maheshwera Reddy; Cheruku, Suryapavan; Pasunuri, Venkat Phani Kumar; Salike, Sriteja; Reddy, N.S.; Cheruku, Srija

doi:10.1016/j.mlwa.2021.100099

Cited by 31 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ey have concluded that, both the techniques have performed well; however, the ANN has produced best results rather than the ANFIS. ese works by Paturi et al [9] have evolved the model to predict surface roughness using machine learning techniques such as ANN, support-vector machines (SVM), and genetic algorithm (GA) in wire electro discharge machining (WEDM) of Inconel 718. e forecasted values by the ANN and SVM were compared with the response surface method (RSM) model based on correlation coefficient. e SVM model was found to be accurate rather than other methods.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Rajesh

Prabhuswamy

Raghavan

2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Rajesh

Prabhuswamy

Raghavan

2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“… References Year Machined materials Input parameters Response parameters Prediction method 23 2022 NiTi alloys, NiCu alloys and BCu alloys Ton, Toff, Gap current and gap voltage MRR RF, D.T., Gradient Boosting ANN 24 2020 Aluminum Voltage, Ton, Wire feed, dielectric pressure S.R Support vector method (SVM), Extreme learning machine, Weighted Extreme learning machine 25 2022 Shape memory alloys (SMA) Nitinol rods Ton, Toff and current S.R AlexNet, KNN, MNB and DenseNet 26 2022 Memory alloy of Cu-based shape Peak current (Ip), Ton, gap voltage and Toff D.D. and TWR GA and TLBO techniques 27 2021 Inconel 718 wire feed rate, Ip, Ton, Toff and servo voltage SR ANN, SVM 28 2021 EN31 tool steel Ton, Toff, Ip, Vg, flushing pressure (P) Tool shape prediction and S .R DT, R.F., linear model and ANN 29 2020 Hastelloy C-276 Wire tension, flushing pressure, Ton, Toff, servo voltage and wire feed rate Kerf width and S.R Gradient descent method 30 2022 ...…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

Kaigude,

Khedkar,

Jatti

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

show abstract

“…SR is essential feature for quality control procedures in manufacturing processes [ 23 ]. Paturi et al [ 24 ] examined Inconel 718's SR in WEDM. SVM, ANN, and GA AI models were created to optimize SR using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

Sana,

Farooq,

Anwar

et al. 2023

Heliyon

View full text Add to dashboard Cite

Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining

Cited by 31 publications

References 31 publications

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

Contact Info

Product

Resources

About