Abstract: This study compares the effectiveness of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) in optimizing the thermal diffusivity of mild steel Tungsten Inert Gas (TIG) welds. The analysis evaluates the predictive accuracy and optimization efficiency of both techniques, providing insights into their suitability for modeling thermal behavior in welding applications. The set of tools, including power hacksaw cutting and grinding machines, mechanical vice, emery (sand) paper and sander was used to prepare the mild steel coupons for welding. The produced coupons were evaluated for their Thermal Diffusivity. The two expert systems used to determine the effect of the interaction of welding current, welding voltage and gas flowrate on the Thermal Diffusivity were the Response Surface Methodology and Artificial Neural Network. The models were validated using the model summary values between the experimental results compared to RSM (R2 = 94.49%) and ANN (R2 = 97.83%) values. This shows that ANN is a better predictor as compared to RSM.Abstract: This study compares the effectiveness of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) in optimizing the thermal diffusivity of mild steel Tungsten Inert Gas (TIG) welds. The analysis evaluates the predictive accuracy and optimization efficiency of both techniques, providing insights into their suitability for modeling the...Show More
Abstract: Testing of components and systems is a specialized discipline that often involves complex instrumentation schemes, dedicated test rigs, and meticulous interpretation of results after assessing measurement accuracies. Turbine blades, especially those made of super alloys like Nimonic, have been subjected to vibration testing for over seven decades for research, design validation, and quality control. In recent years, advancements in 3D printing have transformed manufacturing processes, evolving from plastic powders and filaments to metal powders, enabling the production of functional components for industrial applications. Small turbine blades have been successfully manufactured using additive manufacturing (AM) techniques, particularly for wind tunnel and vibration testing, to support design and performance evaluation. This paper presents the experimental investigations conducted on a 3D-printed gas turbine stage blade subjected to vibration testing. The study outlines the test methodologies, instrumentation, and data acquisition techniques employed to evaluate the dynamic behavior of the printed blade. Additionally, key precautions necessary to ensure reliable testing and accurate result interpretation are discussed. A significant aspect of this work is the correlation between vibration characteristics of 3D-printed blades and actual steel blades used in gas turbines. The paper explores predictive techniques that facilitate the estimation of dynamic parameters in real turbine blades based on results obtained from 3D-printed prototypes. The findings contribute to the growing understanding of how additive manufacturing can aid in early-stage design validation and provide insights into the feasibility of using 3D-printed components for experimental testing in turbomachinery applications.
Abstract: Testing of components and systems is a specialized discipline that often involves complex instrumentation schemes, dedicated test rigs, and meticulous interpretation of results after assessing measurement accuracies. Turbine blades, especially those made of super alloys like Nimonic, have been subjected to vibration testing for over seven decades f...Show More