Context awareness in process monitoring of additive manufacturing using a digital twin

Abstract

Wire Arc Additive Manufacturing allows the cost-effective manufacturing of customized, large-scale metal parts. As the post-process quality assurance of large parts is costly and time-consuming, process monitoring is inevitable. In the present study, a context-aware monitoring solution was investigated by integrating machine, temporal, and spatial context in the data analysis. By analyzing the voltage patterns of each cycle in the oscillating cold metal transfer process with a deep neural network, temporal context was included. Spatial context awareness was enabled by building a digital twin of the manufactured part using an Octree as spatial indexing data structure. By means of the spatial context awareness, two quality metrics—the defect expansion and the local anomaly density—were introduced. The defect expansion was tracked in-process by assigning detected defects to the same defect cluster in case of spatial correlation. The local anomaly density was derived by defining a spherical region of interest which enabled the detection of aggregations of anomalies. By means of the context aware monitoring system, defects were detected in-process with a higher sensitivity as common defect detectors for welding applications, showing less false-positives and false-negatives. A quantitative evaluation of defect expansion and densities of various defect types such as pore nests was enabled.

Intelligent Monitoring System for Real-Time Optimization of Ultra-Precision Manufacturing Processes

“…Challenges such as defect detection, process optimization, and automatic manufacturing need to be tackled in ultra-precision manufacturing processes . Furthermore, the importance of context awareness in process monitoring of additive manufacturing using digital twins is emphasized (Reisch et al, 2022). In conclusion, addressing challenges such as scalability, flexibility, zero-defect production, real-time analysis, and decision-making capabilities, along with implementing affordable AIassisted systems and context-aware monitoring using digital twins, are critical for the successful development and deployment of intelligent monitoring systems for real-time optimization of ultra-precision manufacturing processes.…”

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

“…As one of the main concerns of product production, quality improvement is an important aspect of digital twin applications. Reisch et al (2022) were able to achieve real-time defect detection during the machining of large metal parts through digital twin and make quantitative assessments to improve the sensitivity of defect detection thereby ensuring machining quality. Also, quality control methods driven by digital twin can be used to accurately predict product quality, evaluate processes and optimise process parameters related to product quality (Zhu & Ji, 2022).…”

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

“…The system intelligence can decide the type and extent of the additive/subtractive process based on the geometry and the metallurgical data in DT. Future prospect remains on predictive analytics by establishing a bidirectional connection between the DT and processes [ 264 ], thereby unlocking the M2M and H2M capabilities through the HMI [ 265 ]. In this respect, intelligent decision-making through augmented reality (AR) [ 266 ] can be a key enabling technology for intelligent human–machine interaction for controlling and monitoring the additive and subtractive manufacturing process.…”