sensXPERT, a leading innovator in advanced manufacturing technologies, announced that its groundbreaking development in digital manufacturing has transformed composite manufacturing for a large global aerospace manufacturer. The result of this is a newfound efficiency and transparency in production, delivering numerous benefits to its manufacturing results.
The aviation industry has long grappled with the challenge of balancing production efficiency with the need for quality assurance, particularly in the manufacturing of large composite parts used for fuselage support. With extended cycle times and in-mold material behavior challenges, manufacturers were facing increased energy consumption, higher costs, and slower production. sensXPERT’s Digital Mold, an innovative solution that brings data-driven optimization to composites manufacturing, has been harnessed to address these challenges.
In a recent case study presented at the SAMPE Europe Conference 2023 in Madrid, sensXPERT was able to showcase how its Digital Mold has transformed aviation sector manufacturing processes. The system provides real-time insights into in-mold material behavior, allowing for dynamic process adaptation and optimization of cycle end-times based on desired Degree of Cure (DoC) targets.
Dielectric sensors are strategically placed within the mold, capable of withstanding extreme temperatures and pressures and an edge device positioned outside the mold collects data from the sensors. The edge device interface (Web App) visualizes active processes and employs machine learning (ML) and predictive algorithms to determine the optimal curing point in real-time.Data collected during production cycles is transferred to the sensXPERT Digital Cloud Service, where it's stored, visualized, and compared with historical process data to assess various quality indicators.
The outcome? Manufacturers gain full transparency in molding cycles, can pinpoint optimal curing points, and detect potential in-situ process or material behavior deviations. With quality assurance testing now performed within the mold, manufacturers experience increased process stability and reduced final part defects.
In a recent case study, data from 20 production cycles was collected and used to train a machine learning model, specifically tailored to the aviation manufacturer's process. Using dielectric sensors, sensXPERT accurately predicted the outcome of the cycle in as little as 50 minutes, providing real-time process adaptation.
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30 November 2023
