Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; In order to model the effect of processing variables including primary gas flow rate, stand-off distance, powder flow rate, and arc current on the plasma spraying coating properties including thickness, porosity and micro-hardness, adaptive neural fuzzy inference system (ANFIS) and neural network based models are proposed to understand the spraying process and estimate process parameters. In order to overcome the difficulty of small size of sample data, bootstrap method is applied for the resampling technique and cross validation is applied for the performance evaluation. The ANFIS model and NN model are compared on the performance metrics of 1) mean square error (MSE), and determination coefficient (R2). The comparisons illustrated that ANFIS based modeling showed significant superiority than the other approach. This may be due to the fact that ANFIS combines the strength of NN's learning capability and fuzzy logic's knowledge interpretation ability. With this ANFIS model and identified control rules, feedback control strategy can be effectively implemented to regulate the coating quality in plasma spraying proces