Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network

Abas, Muhammad; Naeem, Khawar; Habib, Tufail; Khan, Imran; Farooq, Umer; Khalid, Qazi Salman; Rahman, Khalid

doi:10.1007/s13369-020-05103-3

Cited by 7 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Group the data. The data samples with small sample size can be grouped based on the leave-one-out method [19]. n known reliability data were divided into (n−m) groups, and each group contains (m + 1) values.…”

Section: B Basic Methods and Procedures Of Neural Network Predictionmentioning

confidence: 99%

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Yan

2021

IEEE Access

View full text Add to dashboard Cite

Aiming at the large error of traditional reliability prediction method, and the defects of BP neural network prediction method, a new method of optimized cascade feedforward neural network was proposed based on Adam algorithm to predict the reliability of CNC machine tool spindles. A three-layer optimized cascade feedforward neural network model for reliability prediction was established based on the first n th reliability value and the mean time between failure corresponding to the (n+1) th reliability value as the input variables of the neural network. Besides, error comparison analysis was performed on the test set data using the existing relevant reliability data for simulation training. As per the research results, the absolute value of the maximum relative error of the reliability prediction value obtained by this method is 2.53% which is less than 3%, and the prediction method shows high accuracy. Compared with BP neural network, it has the advantages of faster learning speed and better nonlinear fitting ability. Therefore, this method is feasible to be utilized for the prediction of the reliability of CNC machine tool spindles, and provides a reference for improving the accuracy of CNC machine tool reliability prediction.INDEX TERMS Optimized cascade feedforward neural network, CNC machine tool spindle, predict, reliability.

show abstract

Section: B Basic Methods and Procedures Of Neural Network Predictionmentioning

confidence: 99%

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Yan

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Peak current was found to have the greatest impact on performance measures. Payal et al [21] reported multi-optimized input parameters for EDM of Inconel 825 using GRA coupled with PCA together, resulting in improved performance parameters [22]. Researchers working to reduce the heat-affected zone (HAZ) in welding, which is prone to cracking.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Laser Beam Welding Performance Parameters on Nimonic 80A Superalloy: A Study on Experimentation, TGRA, and PCA

Saurabh,

Chand,

Yadav

2024

Soldag. insp.

View full text Add to dashboard Cite

This article describes the effect of CO 2 LBW performance parameters for the welded joint of 1.5 mm thin sheet of Nimonic 80A superalloy. Superalloy 80A used in high temperature application as aero-engine components-blade, rings, exhaust gas turbine part, nuclear tube support and automobile valve. The study focused on optimizing the welding process for the welded joints. Here, LP, WS, FD, and shielding GR will be optimised to improve the weld quality of Nimnic 80A material. Two different optimization techniques were used. The first method is SOO, which is based on the S/N ratio. The aim of this method is to find the Single-optimal values of process parameters. The second method employed MOO, combining the Taguchi grey relational analysis (TGRA) with the principal component analysis (PCA) technique. The researchers used these methods in an effort to identify the set of parameters for obtaining improved weld quality. Authors find best setting parameters p3-v3-f3-g1, with the specific parameter values are LP 2400 W, WS 1800 mm/min, FD 18 mm, and GR 10 lpm. The optimized parameters have yield significant improvements in the depth of penetration 6.03%, weld bead 8.74%, Vickers hardness 12.5%, and tensile strength 16.48% by using TGRA coupled with PCA.

show abstract

Integrating resistance-based sensing into fused filament fabricated mechanical metamaterial structure

Khan,

Yaseen,

Ahmad

et al. 2024

Prog Addit Manuf

View full text Add to dashboard Cite

Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network

Cited by 7 publications

References 37 publications

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Optimizing Laser Beam Welding Performance Parameters on Nimonic 80A Superalloy: A Study on Experimentation, TGRA, and PCA

Integrating resistance-based sensing into fused filament fabricated mechanical metamaterial structure

Contact Info

Product

Resources

About