294 research outputs found

    Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

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    Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

    Global Machinability of Al-Mg-Si Extrusions

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    Development of hybrid micro machining approaches and test-bed

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    High precision miniature and micro products which possess 3D complex structures or free-form surfaces are now being widely used in industry. These micro products require to be fabricated by several machining processes and the integration of these various machining processes onto one machine becomes necessary since this will help reduce realignment errors and also increase the machining efficiency. This thesis describes the development and testing of several hybrid machining approaches for machines which are typically used to produce micro products such as micro fluidic moulds, solar concentrator moulds, micro grooves in brittle materials and micro structured milling cutters. These are: (a) micro milling and laser deburring; (b) micro grinding involving laser pre-heating; (c) micro milling and laser polishing. The hybrid micro milling/ laser deburring process was tested during the fabrication of a micro fluidic injection mould. Micro burrs on the channel of micro fluidic mould generated during micro milling were completely removed by developed laser deburring process. This approach can achieve a good surface finish on a micro fluidic mould. The hybrid laser assisted micro grinding process was investigated by fabricating a set of micro grooves on brittle materials, including Al2O3 and Si3N4. The workpiece was pre-heated by laser to increase its temperature above that of the brittle to ductile transition phase interface. It was found that lower cutting forces were apparent in the grinding process when used to machine brittle materials. It was also found that laser assisted grinding helped achieve a very good surface finish and reduced subsurface damage. The final hybrid machining approach tested involved micro milling and laser polishing to fabricate solar concentrator moulds. Such a mould requires a good surface finish in order to accurately guide light focusing on a target. The laser polishing process was successfully used to remove any unwanted cutting marks generated by a previous micro milling process. Abstract iii As a novel extension to this hybrid machine world, a focussed ion beam (FIB) fabrication approach was researched regarding the generation of microstructures on the rake faces of milling cutters with the aim of reducing cutter cutting forces and increasing tool life. The tool wear resistance performance of these microstructured tools was evaluated through three sets of slot milling trials on a NAK80 specimen with the results indicating that micro structured micro milling cutters of this kind can effectively improve the tool wear resistance performance. A microstructure in a direction perpendicular to the cutting edge was found to be the best structure for deferring tool wear and obtaining prolonged tool life. This approach can potentially be further integrated into a hybrid precision machine such that micro structure cutters can be fabricated in-situ using a laser machining process. The conceptual design of a 5-axis hybrid machine which incorporates micro milling, grinding and laser machining has been proposed as a test-bed for the above hybrid micro machining approach. Through finite element analysis, the best configuration was found to be a closed-loop vertical machine which has one rotary stage on the worktable and another on machining head. In this thesis, the effectiveness of these novel hybrid machining approaches have been fully demonstrated through machining several microproducts. Recommendations for future work are suggested to focus on further scientific understanding of hybrid machining processes, the development of a laser repairing approach and the integration of a controller for the proposed hybrid machine

    Industry 4.0

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    This book shows a vision of the present and future of Industry 4.0 and identifies and examines the most pressing research issue in Industry 4.0. Containing the contributions of leading researchers and academics, this book includes recent publications in key areas of interest, for example: a review on the Industry 4.0: What is the Industry 4.0, the pillars of Industry 4.0, current and future trends, technologies, taxonomy, and some case studies (A.U.T.O 4.0, stabilization of digitized process). This book also provides an essential tool in the process of migration to Industry 4.0. The book is suitable as a text for graduate students and professionals in the industrial sector and general engineering areas. The book is organized into two sections: 1. Reviews 2. Case Studies Industry 4.0 is likely to play an important role in the future society. This book is a good reference on Industry 4.0 and includes some case studies. Each chapter is written by expert researchers in the sector, and the topics are broad; from the concept or definition of Industry 4.0 to a future society 5.0

    Industry 4.0

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    This book shows a vision of the present and future of Industry 4.0 and identifies and examines the most pressing research issue in Industry 4.0. Containing the contributions of leading researchers and academics, this book includes recent publications in key areas of interest, for example: a review on the Industry 4.0: What is the Industry 4.0, the pillars of Industry 4.0, current and future trends, technologies, taxonomy, and some case studies (A.U.T.O 4.0, stabilization of digitized process). This book also provides an essential tool in the process of migration to Industry 4.0. The book is suitable as a text for graduate students and professionals in the industrial sector and general engineering areas. The book is organized into two sections: 1. Reviews 2. Case Studies Industry 4.0 is likely to play an important role in the future society. This book is a good reference on Industry 4.0 and includes some case studies. Each chapter is written by expert researchers in the sector, and the topics are broad; from the concept or definition of Industry 4.0 to a future society 5.0

    Using ontology engineering for understanding needs and allocating resources in web-based industrial virtual collaboration systems

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    In many interactions in cross-industrial and inter-industrial collaboration, analysis and understanding of relative specialist and non-specialist language is one of the most pressing challenges when trying to build multi-party, multi-disciplinary collaboration system. Hence, identifying the scope of the language used and then understanding the relationships between the language entities are key problems. In computer science, ontologies are used to provide a common vocabulary for a domain of interest together with descriptions of the meaning of terms and relationships between them, like in an encyclopedia. These, however, often lack the fuzziness required for human orientated systems. This paper uses an engineering sector business collaboration system (www.wmccm.co.uk) as a case study to illustrate the issues. The purpose of this paper is to introduce a novel ontology engineering methodology, which generates structurally enriched cross domain ontologies economically, quickly and reliably. A semantic relationship analysis of the Google Search Engine Index was devised and evaluated. Using Semantic analysis seems to generate a viable list of subject terms. A social network analysis of the semantically derived terms was conducted to generate a decision support network with rich relationships between terms. The derived ontology was quicker to generate, provided richer internal relationships and relied far less on expert contribution. More importantly, it improved the collaboration matching capability of WMCCM

    The potential of additive manufacturing in the smart factory industrial 4.0: A review

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    Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

    Industry 4.0

    Get PDF
    This book shows a vision of the present and future of Industry 4.0 and identifies and examines the most pressing research issue in Industry 4.0. Containing the contributions of leading researchers and academics, this book includes recent publications in key areas of interest, for example: a review on the Industry 4.0: What is the Industry 4.0, the pillars of Industry 4.0, current and future trends, technologies, taxonomy, and some case studies (A.U.T.O 4.0, stabilization of digitized process). This book also provides an essential tool in the process of migration to Industry 4.0. The book is suitable as a text for graduate students and professionals in the industrial sector and general engineering areas. The book is organized into two sections: 1. Reviews 2. Case Studies Industry 4.0 is likely to play an important role in the future society. This book is a good reference on Industry 4.0 and includes some case studies. Each chapter is written by expert researchers in the sector, and the topics are broad; from the concept or definition of Industry 4.0 to a future society 5.0

    An early-stage decision-support framework for the implementation of intelligent automation

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    The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div
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