In industrial processes, predictive maintenance or automated optical analysis of artifacts is fundamental to ensure high-quality products with low costs. However, this step is still done by sophisticated systems or human operators. Automating this process with low-cost solutions while keeping high product quality is one of the most challenging goals of the Industrial Internet of Things (IIoT). IIoT fosters an automation-based production model that uses machine data to enable faster, more flexible, and more efficient production lines [1], leading companies to produce higher-quality goods at lower costs.

In industrial processes, predictive maintenance or automated optical analysis of artifacts is fundamental to ensure high-quality products with low costs. However, this step is still done by sophisticated systems or human operators. Automating this process with low-cost solutions while keeping high product quality is one of the most challenging goals of the Industrial Internet of Things (IIoT). IIoT fosters an automation-based production model that uses machine data to enable faster, more flexible, and more efficient production lines [1], leading companies to produce higher-quality goods at lower costs.

Albanese, A., Brunelli, D. (2023). Industrial Visual Inspection with TinyML for High-Performance Quality Control. IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE, 26(8), 17-22 [10.1109/MIM.2023.10292593].

Industrial Visual Inspection with TinyML for High-Performance Quality Control

Brunelli, Davide
Supervision
2023

Abstract

In industrial processes, predictive maintenance or automated optical analysis of artifacts is fundamental to ensure high-quality products with low costs. However, this step is still done by sophisticated systems or human operators. Automating this process with low-cost solutions while keeping high product quality is one of the most challenging goals of the Industrial Internet of Things (IIoT). IIoT fosters an automation-based production model that uses machine data to enable faster, more flexible, and more efficient production lines [1], leading companies to produce higher-quality goods at lower costs.
2023
Albanese, A., Brunelli, D. (2023). Industrial Visual Inspection with TinyML for High-Performance Quality Control. IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE, 26(8), 17-22 [10.1109/MIM.2023.10292593].
Albanese, Andrea; Brunelli, Davide
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1041727
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