Automatic inspection for remotely manufactured fuel elements

Two classification techniques, standard control charts and artificial neural networks, are studied as a means for automating the visual inspection of the welding of end plugs onto the top of remotely manufactured reprocessed nuclear fuel element jackets. Classificatory data are obtained through measurements performed on pre- and post-weld images captured with a remote camera and processed by an off-the-shelf vision system. The two classification methods are applied in the classification of 167 dummy stainless steel (HT9) fuel jackets yielding comparable results