Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction

TL;DR

This paper introduces a multisensor fusion digital twin for additive manufacturing that enables real-time defect detection and correction, improving process understanding and reducing waste.

Contribution

It develops a novel spatiotemporal data fusion method for multisensor data in AM, enabling in-situ quality prediction and defect correction.

Findings

Enhanced defect detection accuracy through multisensor fusion

Real-time identification of defect regions for correction

Improved process understanding of pore formation and laser interactions

Abstract

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser direct energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defecting correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.