scholarly journals Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2877 ◽  
Author(s):  
Pablo Alhama Blanco ◽  
Fares Abu-Dakka ◽  
Mohamed Abderrahim
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Robot Manipulators ◽  
Smart Cities ◽  
Industrial Robot ◽  
Online Communication ◽  
Intelligent Manufacturing ◽  
Communication Tool ◽  
Industrial Big Data

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation

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.

scholarly journals Conditions for the Development of Industry 4.0 from the Human Capital Technological Competences Perspective

2019 ◽  
Vol 2 (4) ◽  
pp. 159-165
Author(s):  
Patricia Avitia-Carlos ◽  
Carlos Gerardo Morales-García ◽  
José Luis Rodríguez-Verduzco ◽  
Bernabe Rodríguez Tapia ◽  
Norma Candolfi Arballo
Keyword(s):  
Human Capital ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Regional Economies ◽  
Industry Collaboration ◽  
Computer Based ◽  
Technological Competences ◽  
University Industry

The so-called Industry 4.0 supports its emergence and growth in the use of artificial intelligence techniques for the development of production systems whose capacity, efficiency and adaptability exceed the performance of current computer-based systems. Intelligent manufacturing corresponds to the digitization and interconnection of devices for the construction of production and supply chains that share a continuous flow of information. This revolution involves the development of technologies such as the Internet of Things, data analytics and cyber-systems aligned with machine learning, among others. The development and sustainability of these advanced manufacturing systems represent an area of opportunity for the growth of the technological competitiveness of regional economies. There are, however, training needs among engineering professionals for the development of specific updated technological competences; as well as a need of general conditions for the establishment of innovation networks between academia and the productive sector. This paper uses bibliographic techniques to examine existing literature and conducts a review on Industry 4.0. As a result, it presents an overview of the related technological trends and discuss the role of higher education institutions in the development of competitive human capital. There are also multiple areas of opportunity in the medium and long term to strength university-industry collaboration programs related to this adoption.

Smart Factory of Industry 4.0: Connection Infrastructure, Data Acquisition, Data Processing, and Applications

2019 ◽  
Author(s):  
Zhao Zhiqiang ◽  
Chua Wei Quan ◽  
Ding Xiaoming ◽  
Prabhu Vinayak Ashok
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Discrete Systems ◽  
Smart Factory ◽  
Hierarchical Architecture ◽  
Support Functions ◽  
Control Functions

Abstract Smart factory adopts cyber-physical technologies integrating independent discrete systems into a context-sensitive manufacturing environment to optimize manufacturing processes using decentralized information and real-time communication. This paper presents our work in the realization of a smart factory, which comprises of a four-layer hierarchical architecture, i.e. connection infrastructure, data acquisition, data processing and smart applications. In the connection infrastructure layer, all shopfloor machines are connected through diverse protocols, IoT sensors, PLC interfaces and DNC connectors. A centralized IoT gateway supports such a scalable and adaptable connection and ensures a reliable communication among all heterogeneous manufacturing systems. In the data acquisition layer, the real-time machine and job data are acquired from shopfloor systems. Machine data indicates machines’ working condition and job data reveals the production information. The data processing layer comprises of three modules, i.e. shopfloor monitoring, data visualization and data analytics, which monitor and visualize shopfloor activities and analyze the semantics of various data using AI-based TPM engines providing the scientific indicators for next-step decisions. The smart application layer provides with several decision-making and remote control functions for shopfloor productivity and efficiency, such as predictive maintenance, shopfloor management, machine & job optimization and digital twin. The smart factory system has been implemented in the manufacturing shopfloor at Nanyang Polytechnic. The results and validation show that the system can simultaneously collect and analyze the manufacturing data from shopfloor systems, and further communicate with and control the shopfloor systems with decision-support functions. The overall shopfloor efficiency and flexibility can be significantly improved towards a smart factory of Industry 4.0.

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 Developing of Industry 4.0 Applications

2017 ◽  
Vol 13 (10) ◽  
pp. 30 ◽  
Author(s):  
Juan David Contreras ◽  
Jose Isidro Garcia ◽  
Juan David Diaz
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Industrial Revolution ◽  
Intelligent Manufacturing ◽  
Reference Architecture ◽  
Physical Systems ◽  
Architecture Model ◽  
Smart Factories ◽  
Correct Implementation

<p class="0papertitle">The fourth industrial revolution or industry 4.0 has become a trend topic nowadays, this standard-based strategy integrates Smart Factories, Cyber-physical systems, Internet of Things, and Internet of Service with the aim of extended the capacities of the manufacturing systems. Although several authors have presented the advantages of this approach, few papers refer to an architecture that allows the correct implementation of industry 4.0 applications using the guidelines of the reference architecture model (RAMI 4.0). In this way, this article exposes the essential characteristics that allow a manufacturing system to be retrofitting as a correct industry 4.0 application. Specifically, an intelligent manufacturing system under a holonic approach was developed and implemented using standards like FDI, AutomationML and OPC UA according to the RAMI 4.0</p>

scholarly journals Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 151
Author(s):  
Samad M. E. Sepasgozar
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Industry 4.0 ◽  
Construction Projects ◽  
Data Exchange ◽  
Smart Cities ◽  
Information Modeling ◽  
Digital Data ◽  
Digital Twin ◽  
Industrial Sectors

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

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