In-situ inspection method based on digital data model of weld

A method for in-situ inspection of weld quality, the method comprising:

• obtaining a plurality of sequenced images of an in-progress welding process, wherein the plurality of sequenced images captures temporal patterns related to characteristics of a weld pool for an entire weld event for the welding process forming an as-welded region;
• generating a multi-dimensional data input based on the plurality of sequenced images and one or more weld process control parameters, wherein generating the multi-dimensional data input includes (i) detecting features and patterns from the plurality of sequenced images, (ii) assembling data across the entire weld event, (iii) assigning significance to the patterns, (iv) integrating the one or more weld process control parameters, and (v) extracting features of statistical significance, wherein the patterns include one or more of:
• a shape of a weld pool, a size of the weld pool, a contour of the weld pool, a depth of the weld pool, a thermal gradient of the weld pool, a change over time of the weld pool, and a uniformity of the weld pool, wherein assembling data across the entire weld event comprises applying temporal cross-correlations to align the plurality of sequenced images to geometry, and wherein generating the multi-dimensional data input comprises converting temporal image trends to stationary signals by taking a temporal derivative of the plurality of sequenced images;
• generating defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input, wherein the analytics information includes predictive insights on quality features of the in-progress welding process, wherein the quality features include a type of weld defect and a location of the weld defect, wherein the one or more computer vision techniques includes applying a convolutional neural network with 3D convolutions on sequential, lagged batches of the plurality of sequenced images, wherein the 3D convolutions consider pixel position, intensity, and color/spectral band;
• and
• generating a 3-D visualization of the as-welded region, based on the analytics information and the plurality of sequenced images, wherein the 3-D visualization displays the quality features for virtual inspection of the as-welded region.