Experimental and thermal investigation with optimization on friction stir welding of nylon 6A using Taguchi and microstructural analysis

Friction stir welding is an environmentally friendly process of joining due to the non-usage of flux, or any shield gas. Therefore, this article proposes an experimental and thermal investigation with optimization technique for studying the process of FSW on nylon 6A or polycaprolactam polymer composite plates. Specifically, the influence of input operating process parameters such as tool rotational speed (TRS), feed rate, and pitch values on the output response parameters like ultimate tensile strength (UTS), and hardness of welded joints is examined. In addition, L27 orthogonal array of Taguchi approach is employed for the optimization of design experiments of FSW parameters. The experimental setup is carried out with various process parameter combinations like 500, 1000, and 1500 rpm as TRS, 30, 40, and 50 mm as feed rate by varying the pitch values as 1, 2, and 3 mm. Further, the analysis of variance (ANOVA) also employed for finding the significant parameters of input process using the regression analysis equations. Finally, microstructural analysis is used to assess the mixing or dispersing uniformity of composites effectively. The experimental and optimum FSW parameters for maximum UTS are obtained at a feed rate of 30 mm/min, tool pitch of 3 mm, and the TRS of 500 rpm.

Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks

Electric discharge machining (EDM) process is one of the earliest and most extensively used unconventional machining processes. It is a noncontact machining process that uses a series of electric discharges to remove material from an electrically conductive workpiece. This article is aimed to do a comprehensive experimental and thermal investigation of the EDM, which can predict the machining characteristic and then optimize the output parameters with a newly integrated neural network-based methodology for modelling and optimal selection of process variables involved in powder mixed EDM (PMEDM) process. To compare and investigate the effects caused by powder of differently thermo physical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory. Peak current, pulse period, and source voltage are selected as the independent input parameters to evaluate the process performance in terms of material removal rate (MRR) and surface roughness (Ra). In addition, finite element method (FEM) is utilized for thermal analysis on EDM of stainless-steel 630 (SS630) grade. Further, back propagated neural network (BPNN) with feed forward architecture with analysis of variance (ANOVA) is used to find the best fit and approximate solutions to optimization and search problems. Finally, confirmation test results of experimental MRR are compared using the values of MRR obtained using FEM and ANN. Similarly, the test results of experimental Ra also compared with obtained Ra using ANN.