Corner cutting accuracy for thin-walled CFRPC parts using HS-WEDM

Abstract

Carbon Fiber-Reinforced polymer (CFRP) composite parts with thin-walled corners are in great demand in aircraft, cars, and precision instruments. Nonetheless, the fabrication of these parts is difficult due to their low stiffness. High-speed WEDM is an advanced technique for cutting thin CFRP components as it is a non-contact method for removing materials. Nonetheless, testing results demonstrate an unavoidable deformation in the thin-walled corners of the CFRP composite. The objective of this study is to improve the accuracy of corners in thin-walled CFRP composite parts. The research utilized a Taguchi L16 orthogonal array to investigate the influence of various process parameters, including pulse-on duration (Pon), pulse-off duration (Poff), and input current (I), as well as the parameter CFRP plate thickness (T), on corner inaccuracy. The CFRP thickness varied between 0.5, 1.0, 1.5, and 2.0 mm, and the corner angles examined were 30°, 60°, 90°, and 120°. Additionally, a second-order polynomial regression model was used to determine the correlation between the process parameters and corner inaccuracy at various corner angles. Also, a multi-response optimization technique using a composite desirability coupled with a generalized reduced gradient were used to find the optimal process combination across various CFRP thicknesses. According to the research findings, the most relevant process parameters impacting corner accuracy at different angles were the pulse-on duration and input current. To achieve accurate corners with different angles, the optimal process parameters were identified: Pon (40µs), Poff (15µs), and I (4A) for CFRP thicknesses 0.5 and 1.0mm, and Pon (45μs), Poff (30μs), and I (2A) for thicknesses 1.5 and 2.0mm.

Survivability Prediction of Open Source Software with Polynomial Regression

“…Due to its capacity to model complex nonlinear relationships, polynomial regression is extensively used across various fields, such as optimizing production processes, analyzing survival data, and managing energy systems. AbouHawa et al used polynomial regression to model the intricate relationship between process parameters and product quality, predicting the optimal production conditions [14]. Oliveira et al analyzed survival data to understand and predict the relationship between the survival rates of specific diseases or conditions and their covariates [15].…”