RELIABILITY PREDICTION AND PROCESS PARAMETER OPTIMIZATION OF WELDED JOINTS: ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC

Jaganathan Gokulachandran, Mohanavelu Thenarasu, Bhadrinath Pothkanoori, Madhavarao Seshadri Narassima, Erfan Babaee Tirkolaee

DOI Number
10.22190/FUME240903003J
First page
Last page

Abstract


Reliability prediction is an upcoming method used in most industries today to correctly estimate and predict each component’s life in a day-to-day application. This field has proven extremely helpful in evolving various methods such as preventive maintenance and non-destructive testing for various machinery and its parts. In this study, mild steel workpieces are welded together according to three parameters: weld current, weld speed, and weld angle. These parameters are varied based on the Taguchi L27 orthogonal array design of experiments (DOE) to conduct the experiments. The workpieces are then subjected to tensile testing to determine the tensile strength values as well as the failure time. The main objective of this research is to develop a comprehensive, methodical framework to assess the reliability and failure time of welded joints of mild steel material. According to the experimental values, artificial neural network (ANN) and fuzzy logic (FL) models are developed to predict reliability percentage error and failure time. Based on the findings and in the case of FL implementation, the percentage deviation between the experimental and predicted values is vast, while it is calculated small with the use of ANN as a more accurate approach. A sample is also found to have an experimental reliability of 89.5%, the highest among the L27 DOE array wherein the optimum weld strength can be achieved by incorporating 100 A weld current, 55º weld angle, and 1.17mm/s of weld speed, respectively.


Keywords

Welding, Design of Experiments, Artificial Neural Network, Fuzzy Logic, Reliability Prediction

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