A cost-function driven adaptive welding framework for multi-pass robotic welding

Loukas, Charalampos and Williams, Veronica and Jones, Richard and Vasilev, Momchil and MacLeod, Charles Norman and Dobie, Gordon and Sibson, Jim and Pierce, Gareth and Gachagan, Anthony (2021) A cost-function driven adaptive welding framework for multi-pass robotic welding. Journal of Manufacturing Processes, 67. pp. 545-561. ISSN 1526-6125 (https://doi.org/10.1016/j.jmapro.2021.05.004)

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Abstract

Manual teaching of robot paths and welding parameters for multi-pass robotic welding is a cumbersome and time-consuming task, which decreases the flexibility, adaptability, and potential of such systems. This paper introduces and presents a new automated weld parameter and pass deposition sequencing framework, which builds on the current state of the art developments and enables automatic planning of multi-pass welding for single-sided V-groove geometries. By integrating a novel cost-function concept that permutates and identifies the welding parameters for each layer through a user-driven weighting, the framework delivers the minimum number of passes, filler material and welding arc time based on application requirements. A mathematical model relating the cross-section area of beads with the pose of the torch and weaving width was built upon to allow full-process automated welding parameter generation and adaption for different geometric characteristics of the groove. The concept methodology and framework were then developed and verified experimentally, through robotically deployed Metal Active Gas (MAG) welding. For a given representative joint, the arc welding time and amount of filler wire were found to be 32.9 % and 26.18 % lower respectively, than the worst-case available welding parameter combination, delivering a corresponding decrease in direct automated welding manufacturing costs. Lastly, an ultrasonic inspection was undertaken to verify the consistent quality of the weldments validating the framework outcome and enabling welding pass automation through robotic systems.

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