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Wire-feed laser additive manufacturing is an emerging fabrication techniquecapable of highly automated large-scale volume production that can reduce bothmaterial waste and overall cost while improving product lead times. Qualityassurance is necessary for implementation into critical structuralapplications. However, the large number of process variables along with thecost associated with traditional trial and error methods makes this difficult.This study investigates a comprehensive quality framework based on learningfrom experimental data that will enable improved quality control along withconsistent microstructural features of the part. Specifically, a comprehensiveexperimental data across multiple process variables and output characteristicsin terms of overall bead quality, geometric shape (i.g. bead height, width,fusion zone depth, etc.), and microstructural features are collected. Thepredicted process-geometry-microstructure relations are then captured by virtueof data-driven machine learning models. The properties of printed beads arevisualized based on an extensive range of processing space within a3-dimensional contour map. The insights and impacts of process variables onbead morphology, geometric and microstructural features are comprehensivelyinvestigated for quality improvement during manufacturing processes.

Comprehensive Quality Investigations of Wire-feed Additive Manufacturing by Learning of Experimental Data