Journal article
A Digital Thread Approach for Real-Time Defect Correction in Polymer Additive Manufacturing
Materials evaluation, v 84(7), 32
01 Jul 2026
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Abstract
Additive manufacturing (AM) processes offer versatile capabilities, but parts are often riddled with defects due to the inherent variability of process parameters. This research presents a closed-loop, in situ sensing-based feedback system for autonomous, real-time defect correction that actively adjusts operational parameters by fusing information from multiple sensor inputs. The approach is integral to a digital twin, creating a thread between the manufacturing system, sensing and control data, edge processing, and deep learning–based dynamic adaptations. To demonstrate this approach, a commercial polymer 3D printer outfitted with acoustic emission sensors and optical imaging was used to develop a testbed for manufacturing process monitoring and real-time control. Two principal elements were incorporated: a multimodal deep learning model for heterogeneous sensor inputs, and a digital thread enabling real-time use. Both time-series and image data were collected as exemplary cases of the variety of sensor types usable in similar monitoring and control approaches, while common defects—such as under- and overextrusion—were deliberately introduced into the G-code. The labeled datasets were used to train the deep learning models.
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Details
- Title
- A Digital Thread Approach for Real-Time Defect Correction in Polymer Additive Manufacturing
- Creators
- Sarah Malik - Drexel UniversityAntonios Kontsos (Corresponding Author) - Rowan University
- Publication Details
- Materials evaluation, v 84(7), 32
- Publisher
- The American Society for Nondestructive Testing
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Other Identifier
- 991022197045004721