In more details, the architecture is composed by five layers.
The first layer takes the input values and determines the membership functions belonging to them. The membership degrees of each function are computed by using the premise parameter set, namely .
Automated welding experiments confirm the effectiveness of the proposed human response model.
A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot.
ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements.
A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots.
The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated.
Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN).
The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined.
A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed.