An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

Author:

Caiazzo BiancaORCID,Murino Teresa,Petrillo AlbertoORCID,Piccirillo GianlucaORCID,Santini StefaniaORCID

Abstract

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the 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 implicationsThanks 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/valueThe 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.

Publisher

Emerald

Subject

Industrial and Manufacturing Engineering,Strategy and Management,Computer Science Applications,Control and Systems Engineering,Software

Reference68 articles.

1. Review of data preprocessing techniques in data mining;Journal of Engineering and Applied Sciences,2017

2. Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach;Journal of Intelligent Manufacturing,2019

3. Towards zero defect manufacturing paradigm: a review of the state-of-the-art methods and open challenges;Computers in Industry,2022

4. The role of surrogate models in the development of digital twins of dynamic systems;Applied Mathematical Modelling,2021

5. Solar cell surface defect inspection based on multispectral convolutional neural network;Journal of Intelligent Manufacturing,2020

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3