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

TL;DR

This paper presents a data-driven framework for quality assurance in wire-feed laser additive manufacturing, using experimental data and machine learning to optimize process variables and ensure consistent microstructure and bead quality.

Contribution

It introduces a comprehensive experimental dataset and machine learning models to predict and control quality attributes in wire-feed additive manufacturing.

Findings

Process variables significantly influence bead morphology and microstructure.

Machine learning models effectively predict quality outcomes based on process parameters.

3D contour maps visualize the relationship between process space and quality features.

Abstract

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 is necessary for implementation into critical structural applications. However, the large number of process variables along with the cost associated with traditional trial and error methods makes this difficult. This study investigates a comprehensive quality framework based on learning from experimental data that will enable improved quality control along with consistent microstructural features of the part. Specifically, a comprehensive experimental data across multiple process variables and output characteristics in terms of overall bead quality, geometric shape (i.g. bead height, width, fusion zone depth, etc.), and microstructural features are collected. The predicted process-geometry-microstructure relations are then captured by virtue of data-driven machine learning models. The properties of printed beads are visualized based on an extensive range of processing space within a 3-dimensional contour map. The insights and impacts of process variables on bead morphology, geometric and microstructural features are comprehensively investigated for quality improvement during manufacturing processes.