Making IoT Run

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

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Guest Editorial: Design Engineering in the Age of Industry 4.0

Journal of Mechanical Design ◽

10.1115/1.4051042 ◽

2021 ◽

pp. 1-4

Author(s):

Janet K. Allen ◽

Sesh Commuri ◽

Jianxin Jiao ◽

Jelena Milisavljevic-Syed ◽

Farrokh Mistree ◽

...

Keyword(s):

Industry 4.0 ◽

Industrial Revolution ◽

Production Systems ◽

Systems Design ◽

Smart Manufacturing ◽

Future Research ◽

Design Engineering ◽

Production Environment ◽

Special Issue ◽

Industrial Iot

Abstract This special issue is motivated by the trend of smart factories of the future towards the fourth Industrial Revolution, which makes it possible to better leverage capabilities and resources in a human-cyber-physical production environment. This emerging paradigm of Industry 4.0 poses new systems design problems at the interface of smart manufacturing, robust and flexible automation, distributed and reconfigurable production systems industrial IoT, and supply chain integration. Recent advances of design engineering in the age of Industry 4.0 are presented in this special issue. More than forty (40) papers were received and peer-reviewed, out of which thirteen (13) papers were selected for publication. These are both theoretical and practical, as well as state-of-the-art reviews, new perspectives, and outlook for future research directions in the field. The papers span a range of design aspects and Industry 4.0 technologies. There are three intersecting clusters in this category: design principles and techniques for Industry 4.0, smart manufacturing technologies, and machine learning and data-driven techniques for Industry 4.0.

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Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

SHS Web of Conferences ◽

10.1051/shsconf/202112904003 ◽

2021 ◽

Vol 129 ◽

pp. 04003

Author(s):

Elvira Nica ◽

Gheorghe H. Popescu ◽

George Lăzăroiu

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Big Data ◽

Internet Of Things ◽

Real Time ◽

Industry 4.0 ◽

Production Systems ◽

Smart Manufacturing ◽

Digital Twin ◽

Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

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Editorial Special Issue on Recent Advances in Artificial Intelligence for Smart Manufacturing u2013 Part II

Intelligent Automation & Soft Computing ◽

10.31209/2019.100000082 ◽

2019 ◽

pp. -1--1

Author(s):

Zheng Xu ◽

Qingyuan Zhou ◽

Zhiguo Yan

Keyword(s):

Artificial Intelligence ◽

Smart Manufacturing ◽

Special Issue ◽

Recent Advances ◽

Editorial Special Issue

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INTERDISCIPLINARY LINKS, INTEGRATIVE APPROACH,HEURISTIC METHODS AND ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF INNOVATION - PART 1

Knowledge International Journal ◽

10.35120/kij28051527h ◽

2018 ◽

Vol 28 (5) ◽

pp. 1527-1532

Author(s):

Hristo Patev

Keyword(s):

Artificial Intelligence ◽

Production Systems ◽

Innovation Management ◽

Integrative Approach ◽

Heuristic Methods ◽

Organizational Renewal ◽

Innovation Activities ◽

Management Of Innovation ◽

Inventive Activity ◽

High Degree

In this first work, out of the total of twenty-four, are considered: Integrative approach, interdisciplinary relations and transnational language in the technical and economic fundament of engineering and management, for the purpose of competitive innovation and successful business. Approaches to develop the innovation with a high degree of complexity. Interactive heuristic methods and algorithms for inventive activity, for inspiring and developing new industrial products and services for households and production systems. Implementing an effective business vocabulary for organizational renewal. Introduction of gaming and "art" methods in innovation management. Intensifying innovation activities through an attempt to introduce artificial intelligence into teamwork, with simultaneous implementation of an engineering and non-engineering approach.

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Implementation of MQTT protocol based network architecture for smart factory

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/09544054211014488 ◽

2021 ◽

pp. 095440542110144

Author(s):

Chia-Shin Yeh ◽

Shang-Liang Chen ◽

I-Ching Li

Keyword(s):

Machine Tool ◽

Gesture Recognition ◽

Manufacturing Process ◽

Network Architecture ◽

Information Management System ◽

Edge Computing ◽

Smart Factory ◽

Smart Manufacturing ◽

Industrial Iot ◽

Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

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A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

IEEE Access ◽

10.1109/access.2020.3045111 ◽

2020 ◽

Vol 8 ◽

pp. 223114-223129

Author(s):

Kevin Nagorny ◽

Sebastian Scholze ◽

Armando Walter Colombo ◽

Jose Barata Oliveira

Keyword(s):

Data Acquisition ◽

Smart Manufacturing ◽

Manufacturing Environments

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Artificial Intelligence in Biomedicine: A Legal Insight

BioTech ◽

10.3390/biotech10030015 ◽

2021 ◽

Vol 10 (3) ◽

pp. 15

Author(s):

Takis Vidalis

Keyword(s):

Artificial Intelligence ◽

Personal Data ◽

Health Data ◽

The Self ◽

Data Handling ◽

Direct Control ◽

Eu Legislation ◽

Attending Physicians ◽

Self Learning ◽

The Eu

The involvement of artificial intelligence in biomedicine promises better support for decision-making both in conventional and research medical practice. Yet two important issues emerge in relation to personal data handling, and the influence of AI on patient/doctor relationships. The development of AI algorithms presupposes extensive processing of big data in biobanks, for which procedures of compliance with data protection need to be ensured. This article addresses this problem in the framework of the EU legislation (GDPR) and explains the legal prerequisites pertinent to various categories of health data. Furthermore, the self-learning systems of AI may affect the fulfillment of medical duties, particularly if the attending physicians rely on unsupervised applications operating beyond their direct control. The article argues that the patient informed consent prerequisite plays a key role here, not only in conventional medical acts but also in clinical research procedures.

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Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽

10.3233/wor-203429 ◽

2021 ◽

pp. 1-11

Author(s):

Duan Pingli ◽

Bala Anand Muthu ◽

Seifedine Nimer Kadry

Keyword(s):

Internet Of Things ◽

Manufacturing Industry ◽

Production Systems ◽

Intelligent Manufacturing ◽

Smart Manufacturing ◽

The Internet ◽

Resource Assignment ◽

Industrial Internet Of Things ◽

Assignment Method ◽

Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

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