scholarly journals An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5480 ◽  
Author(s):  
Panagiotis Trakadas ◽  
Pieter Simoens ◽  
Panagiotis Gkonis ◽  
Lambros Sarakis ◽  
Angelos Angelopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Business Intelligence ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Shop Floor ◽  
Efficient Production ◽  
Human In The Loop ◽  
Industrial Iot ◽  
Potential Applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

scholarly journals Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 628
Author(s):  
Michail J. Beliatis ◽  
Kasper Jensen ◽  
Lars Ellegaard ◽  
Annabeth Aagaard ◽  
Mirko Presser
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Careful Consideration ◽  
Added Value ◽  
Shop Floor ◽  
Circuit Boards ◽  
Next Generation ◽  
Road Map ◽  
Industrial Iot ◽  
Production Cycles

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

scholarly journals Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
Keyword(s):  
Strong Correlation ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Initial Study ◽  
Efficient Production ◽  
Product Management ◽  
Nonlinear Behaviour ◽  
Manufacturing Companies ◽  
Manufacturing Performance ◽  
Manufacturing Productivity

Manufacturing systems, in pursuit of cost, time and flexibility optimisation are becoming more and more complex, exhibiting a dynamic and nonlinear behaviour. Unpredictability is a distinct characteristic of such behaviour and effects production planning significantly. Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. This challenge must be met with appropriate decision-making by manufacturing companies to secure competitive advantage without compromising sustainability. Assessing complexity realises the reduction and management of complexity sources which contribute to lowering associated engineering costs and time, improves productivity and increases profitability. Therefore, this study was undertaken to investigate the priority level and current achievement of manufacturing performance in Malaysia’s manufacturing industry and the complexity drivers on manufacturing productivity performance. The results showed that Malaysia’s manufacturing industry prioritised product quality and they managed achieved a good on-time delivery performance. However, for other manufacturing performance, there was a difference where the current achievement of manufacturing performances in Malaysia’s manufacturing industry is slightly lower than the priority given to them. The strong correlation of significant value for priority status was observed between efficient production levelling (finished goods) and finish product management while the strong correlation of significant value for current achievement was minimised the number of workstation and factory transportation system. This indicates that complexity drivers have an impact towards manufacturing performance. Consequently, it is necessary to identify complexity drivers to achieve well manufacturing performance.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

scholarly journals Human and Social Dimensions in CPS & IoT based Automated Production Systems

2021 ◽  
Author(s):  
Hind Bril El-Haouzi ◽  
Etienne Valette ◽  
Bettina-Johanna Krings ◽  
António Brandão Moniz
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Human Dimensions ◽  
Automated Production ◽  
Human In The Loop ◽  
Research Fields ◽  
Human Operators ◽  
Conceptual Evolution

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap where human factor was seen as an important source of errors and disruptions. Today the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised the awareness about the central role humans have to play in manufacturing systems, to the design of which they must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different Human dimensions associated with CPS and IoT and focuses on their conceptual evolution of automatization to improve the sociability of such automated production systems and consequently puts again the human in the loop. Hereby, our aim is to take stock of current research trends, and to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. As results, different models of sociability as way to integrate human into the broad sense and/or the development of future automated production systems, were identified from the literature and analysed.

Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

2018 ◽  
Vol 18 ◽  
pp. 20-23 ◽  
Author(s):  
Jay Lee ◽  
Hossein Davari ◽  
Jaskaran Singh ◽  
Vibhor Pandhare
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Manufacturing Systems

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2020 ◽  
pp. 31-52 ◽  
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

scholarly journals FASTEN IIoT: An Open Real-Time Platform for Vertical, Horizontal and End-To-End Integration

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5499
Author(s):  
Felipe S. Costa ◽  
Silvia M. Nassar ◽  
Sergio Gusmeroli ◽  
Ralph Schultz ◽  
André G. S. Conceição ◽  
...  
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Business Models ◽  
Production Systems ◽  
Transformation Process ◽  
Digital Transformation ◽  
Shop Floor ◽  
Decentralized Decision Making ◽  
End To End

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

scholarly journals Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

2019 ◽  
Vol 9 (16) ◽  
pp. 3325 ◽  
Author(s):  
Tran ◽  
Park ◽  
Nguyen ◽  
Hoang
Keyword(s):  
Radio Frequency Identification ◽  
Mass Production ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Shop Floor ◽  
Self Healing ◽  
Manufacturing Environment ◽  
Information And Communication ◽  
Ict Infrastructure

The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.

A NeuroEndocrine-Inspired Manufacturing System Using the Potential Field Concept

2011 ◽  
Vol 201-203 ◽  
pp. 1741-1747
Author(s):  
Kun Zheng ◽  
Dun Bing Tang ◽  
Wen Bin Gu
Keyword(s):  
Potential Field ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Environmental Changes ◽  
Point Of View ◽  
Shop Floor ◽  
System Control ◽  
Hormone Regulation ◽  
Test Bed

The manufacturing industry must have manufacturing systems that deal with the agile response to the appearance and changing conditions.As biological organisms are quite capable of adapting to environmental changes and stimulus, bio-inspired concepts have been recognized much suitable for adaptive manufacturing system control. This paper, therefore, proposes a NeuroEndocrine-Inspired Manufacturing System (NEIMS) using the potential field concept. The proposed NEIMS control architecture is inherited from neuro-control and hormone-regulation principles to agilely deal with the frequent occurrence of unexpected disturbances at the shop floor level. Hormone-regulation can impel system to be equilibrium through a potential field approach. From the cybernetics point of view, the control model of NEIMS has been described in detail. And a test bed has been set up to enable the NEIMS simulation.

scholarly journals Formal Tools for Management of Manufacturing Systems: A Multi Agents System Approach

2019 ◽  
Vol 13 (5) ◽  
pp. 691-699
Author(s):  
Doriana M. D’Addona ◽  
Alessandro A. Bruzzone ◽  
◽  
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Cloud Manufacturing ◽  
Technological Innovations ◽  
Real Sector ◽  
Multi Agent System ◽  
Industrial Internet ◽  
Multi Agent ◽  
Multi Agents

To overcome the consequences of the 2008 crisis on the real sector, especially manufacturing, Industry 4.0 gives guidelines to drive production by emphasizing technological innovations, such as industrial internet, cloud manufacturing, etc. The proposed paper focuses on cognitive manufacturing within the framework of the emergent synthesis paradigm. Specifically, the structuring process by which the manufacturing assets are organized to provide the finished goods is analyzed. The study is carried out by considering the analogies between manufacturing and other inventive processes supported by formal tools such as formal languages, semantic webs, and multi agent system.

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