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An affordable data acquisition system for large-area additive manufacturing melt flow and interpass temperature measurement

Siivonen, Jere; Naakka, Kari; Häkkinen, Simo; Ituarte, Iñigo Flores; Salminen, Katri (2025)

 
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An_affordable_data_acquisition_system_for_large-area_additive_manufacturing_melt_flow_and_interpass_temperature_measurement.pdf (494.9Kt)
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Siivonen, Jere
Naakka, Kari
Häkkinen, Simo
Ituarte, Iñigo Flores
Salminen, Katri
2025

doi:10.1049/icp.2025.3627
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202603303604

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Peer reviewed
Tiivistelmä
Additive Manufacturing (AM) continues to evolve as a cornerstone of digital production. While significant progress has been made in small-scale AM systems, large-area AM remains constrained by lower technology readiness levels-particularly in process control and monitoring. These limitations are a barrier to broader adoption in sectors where sustainability and scalability are essential. This paper addresses the pressing need for affordable digital monitoring solutions by introducing a low-cost data acquisition system for thermal measurement in large-area AM using thermoplastic materials. The proposed solution targets temperature control challenges-namely, managing melt flow and interpass temperatures, both critical to ensuring component quality and minimizing production waste. We describe the development of a cost-effective thermal matrix camera setup, integrated with a streamlined data pipeline for real-time processing. The system is further enhanced through synchronization with robot kinematic data, enabling a comprehensive view of the process state. A demonstration case validates the feasibility of this solution, showcasing its potential for cost-sensitive manufacturing environments, especially SMEs aiming to implement low-cost automation strategies aligned with sustainability goals. The presented work directly supports the theme of low-cost digitalisation for sustainable automation, offering a practical and scalable approach for extending digital capabilities to large-area AM processes. Future research directions include enhancing system robustness, expanding sensing modalities, and integrating closed-loop control mechanisms.
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Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste