An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

An end-to-end big data analytics platform for IoT-enabled smart factories : a case study of battery module assembly system for electric vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

Industry 4.0 and world class manufacturing integration: 100 technologies for a WCM-I4.0 matrix

In the last decade, technological progress has profoundly influenced the industrial world and all industrial sectors have been confronted with a change in technological paradigms. In such a context, this study aims to analyze the synergies between the technological world of Industry 4.0 and the purely organizational and managerial domain ofWorld Class Manufacturing, a model of Operational Excellence. The objective is relating the driving dimensions of the World Class Manufacturing (WCM) system to the technological macrocategories of Industry 4.0: this would allow the identification of which technological solution to leverage on, aiming at optimization in a given World Class Manufacturing pillar. The result is a "WCM-I4.0 matrix": a proposal to reconcile, exploit and trace the relations between the two complex concepts. The WCM-I4.0 matrix includes, by now, 100 Industry 4.0 technologies that best suits with the World Class Manufacturing pillars

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Forth Industrial Revolution (4 IR) : digital disruption of cyber-physical systems

Article focus of the disruptive character of technological innovations brought by Fourth Industrial Revolution (4IR), withits unprecedented scale and scope, and exponential speed of incoming innovations, described from the point view of 'unintended consequences' (cross cutting impact of disruptive technologies across many sectors and aspects of human life). With integration of technology innovations emerging in number of fields including advanced robotics, pervasive computing, artificial intelligence, nano-and bio-technologies, additive and smart manufacturing, Forth Industrial Revolution introduce new ways in which technology becomes embedded not only within the society, economy and culture, but also within human body and mind (described by integration of technologies, collectively referred to as cyber-physical systems). At the forefront of digital transformation, based on cyber physical systems, stands Industry 4.0, referring to recent technological advances, where internet and supporting technologies (embedded systems) are serving as framework to integrate physical objects, human actors, intelligent machines, production lines and processes across organizational boundaries to form new kind of intelligent, networked value chain, called smart factory. Article presents broader context of 'disruptive changes (innovations)' accompanying 4IR, that embrace both economical perspective of 'broaderrestructuring' of modern economy and society (described in second part of the article as transition from second to third and forth industrial revolution), and technological perspective of computer and informational science with advances in pervasive computing, algorithms and artificial intelligence (described in third part of article with different stages of web development : web 1.0, web 2.0, web 3.0, web 4.0). What's more important, article presents hardly ever described in literature, psychological and philosophical perspective, more or less subtle reconfiguration made under the influence of these technologies, determining physical (body), psychological (mind) and philosophical aspect of human existence (the very idea of what it means to be the human), fully depicted in the conclusion of the article. The core element (novelty) is the attempt to bring full understanding and acknowledgment of disruptive innovations', that "change not only of the what and the how things are done, but also the who we are", moving beyond economical or technological perspective, to embrace also psychological and philosophical one

Sustainable engineering challenges towards Industry 4.0: A comprehensive review

This article reviews Industry 4.0, its emerging phase, implementation, challenges, benefits, etc. It combines various fields where it has any influence and leaves some changes and where it requires some adaptation. Papers from the last 4 years are taken and analyzed, what is written about this topic in various countries with different backgrounds and economic development. Industry 4.0 affects the production environment by introducing new technologies which require a better-educated workforce so it affects education and requires some changes in curricula and ways of teaching. It brings new challenges and asks for a new approach from management to be able to handle fast and big changes in the business environment and to implement such innovation in production effectively

Industry 4.0 for SMEs

This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies

Distributed embedded system with internet GSM connectivity for intelligent e-monitoring of machine tools

Machining is one of the most important operations in many industrial environments. To prosper in today's competitive industrial world any machining system should be able to deliver the highest possible quality at the lowest possible costs, with very high reliability and flexibility. To fulfil these requirements the idea of e-Monitoring an industrial process was introduced by the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff University. It has considerable potential applications in industrial systems to not only monitor the health of the machines but also for data management and presentation for future decision making. The research presented in this thesis considers the evolution of two different low complexity signal analysis techniques which can be used for e-Monitoring the health of the cutters used in milling machine tools. The researched techniques are based in the time and frequency domains. The frequency domain analysis technique is based on the idea of using switched capacitor filters and microcontrollers to monitor the frequencies of interest in existing machine tool signals (spindle load and speed) thus avoiding the need for external sensors. The results of frequency domain analysis are used to assess the health of the cutter. The time domain analysis technique uses the same signals to analyse any variations within a tool rotation period and relate these to the health of the cutter. The results are integrated before final decision making which helps in reducing false alarms. The thesis goes on to logically describe the design and development of an on-line microcontroller based distributed intelligent e-Monitoring system for a milling machine tool model Kondia B500, using the proposed signal analysis techniques. Some additional features such as internet and GSM connectivity have also been added to the designed system. The designed system was interfaced to the machine tool and tested for its reliability which was found to be competitive with many other very expensive systems. The designed system can be fitted into a machine tool at the manufacturing stage or it could be interfaced to an existing machine tool for automatically detecting a tooth breakage

Industry 4.0 for SMEs

This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies