Purpose–ThisworkaimsatproposinganovelInternetofThings(IoT)-basedandcloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomaliesoccurringinto the production. Anovelartificialintelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed. Design/methodology/approach– The proposed solution is a five-layer scalable and modular platform in Industry5.0perspective,wherethecruciallayeristheCloudCyberone.Thisembedsanovelanomalydetection solution, designed byleveragingcontrol charts,autoencoders(AE)longshort-termmemory(LSTM)andFuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities. Findings–Theproposedarchitecture,experimentallyvalidatedona manufacturingsysteminvolvedintothe production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels. Practical implications– Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic. Originality/value– The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance. Keywords Smart manufacturing, IoT, Cloud-assisted, Smart monitoring, AI algorithm, Anomaly detection, Industry 5.0, Zero defect manufacturing Paper type Research paper
An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation
Caiazzo B.;
2023-01-01
Abstract
Purpose–ThisworkaimsatproposinganovelInternetofThings(IoT)-basedandcloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomaliesoccurringinto the production. Anovelartificialintelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed. Design/methodology/approach– The proposed solution is a five-layer scalable and modular platform in Industry5.0perspective,wherethecruciallayeristheCloudCyberone.Thisembedsanovelanomalydetection solution, designed byleveragingcontrol charts,autoencoders(AE)longshort-termmemory(LSTM)andFuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities. Findings–Theproposedarchitecture,experimentallyvalidatedona manufacturingsysteminvolvedintothe production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels. Practical implications– Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic. Originality/value– The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance. Keywords Smart manufacturing, IoT, Cloud-assisted, Smart monitoring, AI algorithm, Anomaly detection, Industry 5.0, Zero defect manufacturing Paper type Research paperI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
