Lincoln Global, Inc.

John R Procario

Badri K Narayanan

Michael A Kottman

Embodiments of systems and methods for characterizing weldments are disclosed. One embodiment includes a method of generating an algorithm for classifying weldments as meeting or not meeting a specification. Training data is read by a machine learning system. The training data includes cross-sectional images of training weldments, truth data indicating whether the training weldments meet the specification or not, and training weld data associated with creating the training weldments. The machine learning system trains up an algorithm using the training data such that the resultant algorithm can classify a subsequent test weldment as meeting the specification or not meeting the specification when a cross-sectional image of the test weldment and test weld data used to create the test weldment are read and processed by the classification algorithm as trained.

Machine learning for weldment classification and correlation

Embodiments of the present invention are directed to systems and methods of predicted a welding property for a given welding operation using at least one electrode. Embodiments determine a predicted weld deposit property and compare the predicted property to a desired property to determine whether or not a selected electrode for the given welding operation can achieve the desired weld deposit.

System and method for positive metal identification and intelligent consumable identification

A system and method is provided for joining pipes where a keyhole is formed in each of the pipe ends and then a filler metal is placed in the keyholes to join the pipes to each other. The filler metal can be deposited using a laser hot wire process.

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Strongsville, OH, United States of America

John R Procario