Interrelated process-geometry-microstructure relationships for wire-feed laser additive manufacturing

Wire-feed laser additive manufacturing is an emerging fabrication technique capable of highly automated large-scale volume production that can reduce both material waste and overall cost while improving product lead times. Quality assurance necessary for implementation into critical structural applications. However, the large number process variables along with associated traditional trial error methods makes this difficult. This study investigates interrelated process-geometry-microstructure relations based on learning from experiments will enable improved quality control consistent surface, geometric microstructural features part. Specifically, experimental dataset across multiple output characteristics in terms bead quality, shape (i.g. height, width, fusion zone depth, etc.), microstructures are collected. The predicted then captured by virtue data-driven machine models. properties printed beads visualized extensive range processing space within a 3-dimensional contour map. insights impacts morphology, comprehensively investigated improvement during processes.