A real-time quality monitoring framework for steel friction stir welding using computational intelligence

In this article, we present a human-centric model-based framework, where we create a new process monitoring algorithm relying on discrete frequency-based analysis of process parameters. The algorithm is capable of providing real-time feedback to the process operator in linguistic form (natural language rule base). The proposed framework is applied to the friction stir welding process, to monitor in real-time for the first time the joining of shipbuilding steel plates (DH36). We take advantage of principles of human like information capture in granular computing (GrC) and computational intelligence (Cl) to (a) build a data-driven model to predict in real-time (during welding) quantitative part quality markers extracted from frequency spectra of the process variables (downward and traverse forces), and (b) we introduce a process monitoring algorithm that takes advantage of the developed model to provide continuous feedback to the operator - in linguistic format - on the performance of the process. We conclude the study by evaluating the proposed approach based on interval type-2 radial basis function neural network (IT2-RBF-NN) against a multilayer perceptron neural network (MLP-NN), and a type-1 radial basis function neural network (T1-RBF-NN). Simulation results show the effectiveness of the proposed approach to handle uncertainties and produce reasonable process performance predictions (similar to 80% accuracy in testing data) that could be used to further optimise the process. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.