This paper presents an optimized approach for predicting peak temperatures during Friction Stir Welding (FSW) of Al 6061 T6 alloys. COMSOL Multiphysics is used to perform finite element analysis (FEA) to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. A novel methodology integrating the Taguchi method, Analysis of Variance (ANOVA), and machine learning was employed to optimize parameters and predict peak temperatures. The influence of these parameters was investigated through L32 Taguchi array and ANOVA, revealing axial force and tool rotational speed as the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material's melting point, indicating the need for improved thermal control. The experimental work was compared with FEA results that confirmed a strong agreement between the two. A feed-forward backpropagation neural network (BPNN) was implemented, achieving an R² of 0.9903 and a mean squared error (MSE) of 1.2746 × 10^-7. BPNN predicted peak temperatures with an error of 1.01%, outperforming Taguchi (3.57%) and ANOVA (3.39%). These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality.

This paper presents optimization of peak temperatures achieved during friction stir welding (FSW) of Al-6061-T6 alloys. This research work employed a novel approach by investigating the effect of FSW welding process parameters on peak temperatures through the implementation of finite element analysis (FEA), the Taguchi method, analysis of variance (ANOVA), and machine learning (ML) algorithms. COMSOL 6.0 Multiphysics was used to perform FEA to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. The influence of these parameters was investigated using an L32 Taguchi array and analysis of variance (ANOVA), revealing that axial force and tool rotational speed were the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material’s melting point, indicating the need for improved thermal control. This was achieved by using three machine learning (ML) algorithms, i.e., Logistic Regression, k-Nearest Neighbors (k-NN), and Naive Bayes. A dataset of 324 data points was prepared using a factorial design to implement these algorithms. These algorithms predicted the welding conditions where the temperature exceeded the melting temperature of Al-6061-T6. It was found that the Logistic Regression classifier demonstrated the highest performance, achieving an accuracy of 98.14% as compared to Naive Bayes and k-NN classifiers. These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality.

This paper describes the work performed at the satellite research and development centre, Karachi to produce cold gas propulsion system for prototype of Pakistan Remote Sensing Satellite (PRSS), which is currently under development phase. This system mainly consists of one Aluminum 6061 propellant tank which stores 2 Kg of gaseous nitrogen as propellant at 100 bars. This tank is designed, developed and tested by structure/propulsion/thermal (S/P/T) section of SRDC-K. The nitrogen gas is supplied to eight 1 N Thrusters at a steady regulated pressure of 8 bars. It passes through a two-stage pressure regulator assembly, and a solenoid valve. Thrusters closing and opening time is less than 7 ms and specific impulse is 73 s. These thrusters are tested on Thrusters Static Bench developed by S/P/T section. This system has passed all the required qualification and acceptance tests. The cold gas propulsion system represents an important advance in propulsion technology suitable for small satellites. As a result of this work, the size, mass and power requirements has been reduced.