948 research outputs found

    On the Impact of the Customer Base on the Added Value through System-Oriented Service Delivery in Industrial Maintenance

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    Today, during service delivery, providers allocate their delivery resources such that their own delivery-dependent costs are minimal. However, during service delivery, costs arise not only for the provider, but for the customer, too. In industrial maintenance, for example, those costs arise---depending on service delivery---due to longer equipment unavailability. The concept of system-oriented service delivery aims at minimizing the total (customer and provider) delivery-dependent costs within the service system and promises a Pareto improvement over today’s practice. Hence, added value is created. However, so far, we have no understanding of the magnitude of and factors favoring high added value through system-oriented service delivery. Consequently, this work aims at filling this gap and widening knowledge on the added value through system-oriented service delivery. We present a simulation study to elaborate on the added value in dependency of the customer base constellation in an industrial maintenance illustrative scenario

    Analysis of production control methods for semiconductor research and development fabs using simulation

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    The importance of semiconductor device fabrication has been rising steadily over many years. Integrated circuit technology and innovation depends on successful research and development (R&D). R&D establishes the direction for prevailing technology in electronics and computers. To be a leader in the semiconductor industry, a company must bring technology to the market as soon as its application is deemed feasible. Using suitable production control methods for wafer fabrication in R&D fabs ensures reduction in cycle times and planned inventories, which in turn help to more quickly, transfer the new technology to the production fabs, where products are made on a commercial scale. This helps to minimize the time to market. The complex behavior of research fabs produces varying results when conventional production control methodologies are applied. Simulation modeling allows the study of the behavior of the research fab by providing statistical reports on performance measures. The goal of this research is to investigate production control methods in semiconductor R&D fabs. A representative R&D fab is modeled, where an appropriate production load is applied to the fab by using a representative product load. Simulation models are run with different levels of production volume, lot priorities, primary and secondary dispatching strategies and due date tightness as treatment combinations in a formally designed experiment. Fab performance is evaluated based on four performance measures, which include percent on time delivery, average cycle time, standard deviation of cycle time and average work-in-process. Statistical analyses are used to determine the best performing dispatching rules for given fab operating scenarios. Results indicate that the optimal combination of dispatching rules is dependent on specific fab characteristics. However, several dispatching rules are found to be robust across performance measures. A simulation study of the Semiconductor & Microsystems Fabrication Laboratory (SMFL) at the Rochester Institute of Technology (RIT) is used to verify the results

    Lean service model with Simulation in Arena to improve response time of the technical service in a Peruvian SME

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    The services sector has maintained sustained growth in recent decades, becoming one of the sectors contributing most to the global economy. However, the characteristics of these activities represent a challenge to companies in the sector, which seek to be more competitive in the presence of new technologies and a globalized world. In this context, the companies that offer field service for the maintenance of machinery and equipment present problems in planning their activities due to the uncertainty in demand, the distances to be covered, and to comply with the expected response time of the client. Therefore, to solve these problems, this article proposes applying manpower planning strategies and lean tools to reduce the response time in field service. The simulation models are based on these strategies and were structured and validated using Arena simulator. A 7% improvement in service level was obtained, considering the contractual response time offered to customers. The proposal will provide alternatives for field service planning and provide companies with tools to increase their competitiveness

    Supporting the design of automated guided vehicle systems in internal logistics

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    Applications of automated guided vehicle (AGV) systems are becoming increasingly widespread in internal logistics for performing transports automatically. Recent technological advancements in navigation and intelligence have improved the functionality of vehicles and together with attention to Industry 4.0 have created further interest in AGV systems in industry and academia. Research on AGV systems has mainly focused on technical aspects, but to support AGV system design and, thereby, be able to achieve the full potential from use of AGV systems in internal logistics, more knowledge is needed that takes further into consideration aspects related to humans and the organisation, alongside the technical aspects. The purpose of this thesis is to develop knowledge to support the design of AGV systems and three research questions are formulated. The thesis is based on three papers, two of which are based on multiple case studies and one study based on simulation modelling. The thesis results provide input to the design process for AGV systems in three main ways. First, in developing an understanding for which requirements influence an AGV systems and how the requirements can be met in the AGV system configuration. Second, regarding how the load capacity of AGVs impact the performance of the AGV system, and third by identifying challenges with respect to the work organisation and related to human factors when AGV systems are introduced in internal logistics settings

    Augmented reality in support of Industry 4.0—Implementation challenges and success factors

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    Industrial augmented reality (AR) is an integral part of Industry 4.0 concepts, as it enables workers to access digital information and overlay that information with the physical world. While not being broadly adopted in some applications, the compound annual growth rate of the industrial AR market is projected to grow rapidly. Hence, it is important to understand the issues arising from implementation of AR in industry. This study identifies critical success factors and challenges for industrial AR implementation projects, based on an industry survey. The broadly used technology, organisation, environment (TOE) framework is used as a theoretical basis for the quantitative part of the questionnaire. A complementary qualitative part is used to underpin and extend the findings. It is found that, while technological aspects are of importance, organisational issues are more relevant for industry, which has not been reflected to the same extent in literature.University of Cambridg

    Augmented reality in support of intelligent manufacturing – A systematic literature review

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    Industry increasingly moves towards digitally enabled ‘smart factories’ that utilise the internet of things (IoT) to realise intelligent manufacturing concepts like predictive maintenance or extensive machine to machine communication. A core technology to facilitate human integration in such a system is augmented reality (AR), which provides people with an interface to interact with the digital world of a smart factory. While AR is not ready yet for industrial deployment in some areas, it is already used in others. To provide an overview of research activities concerning AR in certain shop floor operations, a total of 96 relevant papers from 2011 to 2018 are reviewed. This paper presents the state of the art, the current challenges, and future directions of manufacturing related AR research through a systematic literature review and a citation network analysis. The results of this review indicate that the context of research concerning AR gets increasingly broader, especially by addressing challenges when implementing AR solutions.No funding was received

    Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

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    Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

    An integrative framework for cooperative production resources in smart manufacturing

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    Under the push of Industry 4.0 paradigm modern manufacturing companies are dealing with a significant digital transition, with the aim to better address the challenges posed by the growing complexity of globalized businesses (Hermann, Pentek, & Otto, Design principles for industrie 4.0 scenarios, 2016). One basic principle of this paradigm is that products, machines, systems and business are always connected to create an intelligent network along the entire factory\u2019s value chain. According to this vision, manufacturing resources are being transformed from monolithic entities into distributed components, which are loosely coupled and autonomous but nevertheless provided of the networking and connectivity capabilities enabled by the increasingly widespread Industrial Internet of Things technology. Under these conditions, they become capable of working together in a reliable and predictable manner, collaborating among themselves in a highly efficient way. Such a mechanism of synergistic collaboration is crucial for the correct evolution of any organization ranging from a multi-cellular organism to a complex modern manufacturing system (Moghaddam & Nof, 2017). Specifically of the last scenario, which is the field of our study, collaboration enables involved resources to exchange relevant information about the evolution of their context. These information can be in turn elaborated to make some decisions, and trigger some actions. In this way connected resources can modify their structure and configuration in response to specific business or operational variations (Alexopoulos, Makris, Xanthakis, Sipsas, & Chryssolouris, 2016). Such a model of \u201csocial\u201d and context-aware resources can contribute to the realization of a highly flexible, robust and responsive manufacturing system, which is an objective particularly relevant in the modern factories, as its inclusion in the scope of the priority research lines for the H2020 three-year period 2018-2020 can demonstrate (EFFRA, 2016). Interesting examples of these resources are self-organized logistics which can react to unexpected changes occurred in production or machines capable to predict failures on the basis of the contextual information and then trigger adjustments processes autonomously. This vision of collaborative and cooperative resources can be realized with the support of several studies in various fields ranging from information and communication technologies to artificial intelligence. An update state of the art highlights significant recent achievements that have been making these resources more intelligent and closer to the user needs. However, we are still far from an overall implementation of the vision, which is hindered by three major issues. The first one is the limited capability of a large part of the resources distributed within the shop floor to automatically interpret the exchanged information in a meaningful manner (semantic interoperability) (Atzori, Iera, & Morabito, 2010). This issue is mainly due to the high heterogeneity of data model formats adopted by the different resources used within the shop floor (Modoni, Doukas, Terkaj, Sacco, & Mourtzis, 2016). Another open issue is the lack of efficient methods to fully virtualize the physical resources (Rosen, von Wichert, Lo, & Bettenhausen, 2015), since only pairing physical resource with its digital counterpart that abstracts the complexity of the real world, it is possible to augment communication and collaboration capabilities of the physical component. The third issue is a side effect of the ongoing technological ICT evolutions affecting all the manufacturing companies and consists in the continuous growth of the number of threats and vulnerabilities, which can both jeopardize the cybersecurity of the overall manufacturing system (Wells, Camelio, Williams, & White, 2014). For this reason, aspects related with cyber-security should be considered at the early stage of the design of any ICT solution, in order to prevent potential threats and vulnerabilities. All three of the above mentioned open issues have been addressed in this research work with the aim to explore and identify a precise, secure and efficient model of collaboration among the production resources distributed within the shop floor. This document illustrates main outcomes of the research, focusing mainly on the Virtual Integrative Manufacturing Framework for resources Interaction (VICKI), a potential reference architecture for a middleware application enabling semantic-based cooperation among manufacturing resources. Specifically, this framework provides a technological and service-oriented infrastructure offering an event-driven mechanism that dynamically propagates the changing factors to the interested devices. The proposed system supports the coexistence and combination of physical components and their virtual counterparts in a network of interacting collaborative elements in constant connection, thus allowing to bring back the manufacturing system to a cooperative Cyber-physical Production System (CPPS) (Monostori, 2014). Within this network, the information coming from the productive chain can be promptly and seamlessly shared, distributed and understood by any actor operating in such a context. In order to overcome the problem of the limited interoperability among the connected resources, the framework leverages a common data model based on the Semantic Web technologies (SWT) (Berners-Lee, Hendler, & Lassila, 2001). The model provides a shared understanding on the vocabulary adopted by the distributed resources during their knowledge exchange. In this way, this model allows to integrate heterogeneous data streams into a coherent semantically enriched scheme that represents the evolution of the factory objects, their context and their smart reactions to all kind of situations. The semantic model is also machine-interpretable and re-usable. In addition to modeling, the virtualization of the overall manufacturing system is empowered by the adoption of an agent-based modeling, which contributes to hide and abstract the control functions complexity of the cooperating entities, thus providing the foundations to achieve a flexible and reconfigurable system. Finally, in order to mitigate the risk of internal and external attacks against the proposed infrastructure, it is explored the potential of a strategy based on the analysis and assessment of the manufacturing systems cyber-security aspects integrated into the context of the organization\u2019s business model. To test and validate the proposed framework, a demonstration scenarios has been identified, which are thought to represent different significant case studies of the factory\u2019s life cycle. To prove the correctness of the approach, the validation of an instance of the framework is carried out within a real case study. Moreover, as for data intensive systems such as the manufacturing system, the quality of service (QoS) requirements in terms of latency, efficiency, and scalability are stringent, an evaluation of these requirements is needed in a real case study by means of a defined benchmark, thus showing the impact of the data storage, of the connected resources and of their requests
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