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

arXiv: https://arxiv.org/abs/2304.05685 · HTML: https://mlsanity.com/paper/2304.05685/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Digital Twins in Additive Manufacturing: A Systematic Review](https://mlsanity.com/paper/2409.00877/) (2024)
- [Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing](https://mlsanity.com/paper/2408.05307/) (2024)
- [Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing](https://mlsanity.com/paper/2408.06891/) (2024)
- [Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning](https://mlsanity.com/paper/2609.19347/) (2026)

## Abstract (arXiv)

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.