Quality analysis modelling for development of a process controller in resistance spot welding using neural networks techniques
| dc.contributor.author | Oba, Pius Nwachukwu | |
| dc.date.accessioned | 2006-11-14T10:35:21Z | |
| dc.date.available | 2006-11-14T10:35:21Z | |
| dc.date.issued | 2006-11-14T10:35:21Z | |
| dc.description | Student Number : 9811923K - PhD thesis - School of Mechanical Engineering - Faculty of Engineering and the Built Environment | en |
| dc.description.abstract | Methods are presented for obtaining models used for predicting welded sample resistance and effective weld current (RMS) for desired weld diameter (weld quality) in the resistance spot welding process. These models were used to design predictive controllers for the welding process. A suitable process model forms an important step in the development and design of process controllers for achieving good weld quality with good reproducibility. Effective current, dynamic resistance and applied electrode force are identified as important input parameters necessary to predict the output weld diameter. These input parameters are used for the process model and design of a predictive controller. A three parameter empirical model with dependent and independent variables was used for curve fitting the nonlinear halfwave dynamic resistance. The estimates of the parameters were used to develop charts for determining overall resistance of samples for any desired weld diameter. Estimating resistance for samples welded in the machines from which dataset obtained were used to plot the chart yielded accurate results. However using these charts to estimate sample resistance for new and unknown machines yielded high estimation error. To improve the prediction accuracy the same set of data generated from the model were used to train four different neural network types. These were the Generalised Feed Forward (GFF) neural network, Multilayer Perceptron (MLP) network, Radial Basis Function (RBF) and Recurrent neural network (RNN). Of the four network types trained, the MLP had the least mean square error for training and cross validation of 0.00037 and 0.00039 respectively with linear correlation coefficient in testing of 0.999 and maximum estimation error range from 0.1% to 3%. A prediction accuracy of about 97% to 99.9%. This model was selected for the design and implementation of the controller for predicting overall sample resistance. Using this predicted overall sample resistance, and applied electrode force, a second model was developed for predicting required effective weld current for any desired weld diameter. The prediction accuracy of this model was in the range of 94% to 99%. The neural network predictive controller was designed using the MLP neural network models. The controller outputs effective current for any desired weld diameter and is observed to track the desired output accurately with same prediction accuracy of the model used which was about 94% to 99%. The controller works by utilizing the neural network output embedded in Microsoft Excel as a digital link library and is able to generate outputs for given inputs on activating the process by the push of a command button. | en |
| dc.format.extent | 2873915 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10539/1676 | |
| dc.language.iso | en | en |
| dc.subject | welded sample resistance | en |
| dc.subject | effective weld current | en |
| dc.subject | RMS | en |
| dc.subject | Effective current | en |
| dc.subject | dynamic resistance | en |
| dc.subject | applied electrode | en |
| dc.subject | Generalised Feed Forward neural network | en |
| dc.subject | Multilayer Perceptron network | en |
| dc.title | Quality analysis modelling for development of a process controller in resistance spot welding using neural networks techniques | en |
| dc.type | Thesis | en |