Browsing by Keyword "Industry 4.0"
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Item CAPACITATION OF FLEXIBLES FIXTURES FOR ITS USE IN HIGH QUALITY MACHINING PROCESSES: AN APPLICATION CASE OF THE INDUSTRY 4.0. PARADIGM: Un caso de aplicación del paradigma Industria 4.0(2018-11) Rubio-Mateos, Antonio; Rivero-Rastrero, Asun; Del Sol-Illana, Irene; Ukar-Arrien, Eneko; Lamikiz-Mentxaka, Aitzol; FABRIC_INTEL; SGThe incipient implementation of the Industry 4.0 paradigm has led to an increase in the machines sensoring level, in the processes optimization and, thus, in the product manufacturing with a higher added value. In this article a new aspect is described where, through the machine monitoring, the utilization of innovative elements as fixture is enabled for high quality machining processes. These innovative elements are characterized by the great flexibility offered by them as holding component and by their low costs. However, these elements lack the enough geometrical accuracy for applications where a high shape and surface quality product are needed. First of all, in order to have a clear vision of the singularities of each fixture type present on the state of the art, a nomenclature and a classification has been proposed based on their geometry characteristics. Hence, based on this classification, an analysis of the different fixturing solutions provided by the market has been made, enhancing the advantages of this solution against the existing ones. With the aim of demonstrating its suitability for certain machining applications, the behavior of these sort of flexible materials has been characterized. Besides, the sensors implementation has been analyzed in order to capacitate this solution for processes where tough tolerances on parts are demanded. Therefore, this survey demonstrates that, through the massive information gathering, not only an optimization of the existing technologies is obtained, but it is possible to develop innovative solutions that provide improved capacities to the already existing ones in the Industry.Item Determining and Applying Productive, Environmental and Economical Indicators and Indexes to a Cyber Physical System for Greening Process of Supply Chain(Springer Nature, 2021-09-09) Morella, Paula; Lambán, María Pilar; Royo, Jesús; Sánchez, Juan Carlos; Hernández Korner, Mario Enrique; SGSustainability is taking on increasing value in the industrial world. This article aims to develop and implement in a cyber-physical system two new KPIs capable of transferring the current concept of Supply Chain (SC) to a Green Supply Chain (GSC), which no longer addresses only the productive and economic issues of traditional SC, but also addresses issues of sustainability, relying on industry 4.0 to achieve this goal. Each KPI has a different function within this process. The first one allows selecting the most adequate scenario to be implemented in the SC from all those proposed, attending to these three issues at the same time (productive, economic and environmental). The second allows the monitoring, at the same time, of the machine tool in these three dimensions, so that, it is possible to analyze throughout the production period of the machine its productive, economic and environmental evolution.Item Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain(2020-10-18) Morella, Paula; Lambán, María Pilar; Royo, Jesús; Sánchez, Juan Carlos; Corrales, Lisbeth Del Carmen Ng; SGThis work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.Item Development of a new kpi for the economic quantification of six big losses and its implementation in a cyber physical system(2020-12-21) Morella, Paula; Lambán, María Pilar; Royo, Jesús; Sánchez, Juan Carlos; Latapia, Jaime; SG; SMART_MONThe purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.Item Digital Twins applied to the implementation of Safe-by-Design strategies in nano-processes for the reduction of airborne emission and occupational exposure to nano-forms(2021-06-22) López De Ipiña, Jesús M.; Aznar, Gabriel; Lopez, Alberto; Olite, Jorge; Koivisto, Joonas; Bartolini, Gianni; Costa, Anna; SMART_MON; INDUSTRY_THINGSDigital Twins (DTs) are one of the most promising enabling technologies for the deployment of the factory of the future and the Industry 4.0 framework. DTs could be labelled as an inherently Safe-by-Design (SbD) strategy and can be applied at different stages in the life cycle of a process. The EU-funded project ASINA has the ambition to promote coherent, applicable and scientifically sound SbD nano-practices. In particular, in the field of nanomanufacturing, ASINA intends to deliver innovative SbD solutions applied to process (P-SbD). In this context, ASINA will investigate the use of DTs as a disruptive digital technology for the prevention, prediction and control of nano-forms airborne emission and worker exposure. This paper introduces the concept of DT in the field of nano-processes SbD and outlines the preliminary architecture of ASINA-DT, that will be developed and implemented by ASINA in one industrial scenario.Item Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring(2019) Canizo, Mikel; Conde, Angel; Charramendieta, Santiago; Minon, Raul; Cid-Fuentes, Raul G.; Onieva, Enrique; HPAThe emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.Item The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators(Multidisciplinary Digital Publishing Institute (MDPI), 2021-05-21) Morella, Paula; Lambán, María Pilar; Royo, Jesús Antonio; Sánchez, Juan CarlosAmong the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.Item Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes(2019-12-02) Outón, Jose Luis; Villaverde, Iván; Herrero, Héctor; Esnaola, Urko; Sierra, Basilio; ROBOTICA_FLEXThere is a paradigm shift in current manufacturing needs that is causing a change from the current mass-production-based approach to a mass customization approach where production volumes are smaller and more variable. Current processes are very adapted to the previous paradigm and lack the required flexibility to adapt to the new production needs. To solve this problem, an innovative industrial mobile manipulator is presented. The robot is equipped with a variety of sensors that allow it to perceive its surroundings and perform complex tasks in dynamic environments. Following the current needs of the industry, the robot is capable of autonomous navigation, safely avoiding obstacles. It is flexible enough to be able to perform a wide variety of tasks, being the change between tasks done easily thanks to skills-based programming and the ability to change tools autonomously. In addition, its security systems allow it to share the workspace with human operators. This prototype has been developed as part of THOMAS European project, and it has been tested and demonstrated in real-world manufacturing use cases.Item An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments(Springer, 2020-10-27) Mendia, Izaskun; Gil-Lopez, Sergio; Del Ser, Javier; Grau, Iñaki; Lejarazu, Adelaida; Maqueda, Erik; Perea, Eugenio; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Tecnalia Research & Innovation; IA; DIGITAL ENERGYThe concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.Item A novel approach for the detection of anomalous energy consumption patterns in industrial cyber-physical systems(2024-02) Mendia, Izaskun; Gil-Lopez, Sergio; Grau, Iñaki; Del Ser, Javier; Gil‐Lopez, Sergio; Tecnalia Research & Innovation; IAMost scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.Item Plant-wide interoperability and decoupled, data-driven process control with message bus communication(2022-03) Kannisto, Petri; Hästbacka, David; Gutiérrez, Teresa; Suominen, Olli; Vilkko, Matti; Craamer, Peter; CIRMETALConventional industrial communication systems suffer from rigidness, inflexibility and lack of scalability. The environment is heterogeneous as the systems exchange data with a variety communication protocols, some of which are proprietary. This makes it laborious and expensive to reconfigure or upgrade the systems. As the solution, this article proposes a message-bus-based communication architecture to enable information exchange between systems regardless of their geographical location and position within the functional hierarchy of the plant. The architecture not only enables communication to cross the conventional physical borders but also provides scalability to growing data volumes and network sizes. As proofs of concept, the article presents a prototype in three environments: a copper smelter, a steel plant and a distillation column. The results suggest that the message-bus-based approach has potential to renew industrial communications, a core part of the fourth industrial revolution.Item Technodata and the Need of a Responsible Industry 4.0(IGI Global, 2019) Tabarés Gutiérrez, Raúl; Echeverría Ezponda, Javier; Tecnalia Research & InnovationThe great transformation that will face European industry is driven by the need of digitizing the entire value chain around manufacturing for creating competitive advantages to maintain a dominant position in the global economy. This new paradigm is commonly known as Industry 4.0, and it has a significant policy support from the European Commission as well as different member states. However, this transition is full of uncertainties as the digitization of industry creates different concerns about employment, privacy, labor rights, and other issues related with this technological revolution. In this chapter, the authors trace back the origins of Industry 4.0 to the Web 2.0 phenomenon as well as they reflect upon the role of technodata and technofactories in a postindustrial society. Finally, they stress the need to reflect about developing a responsible digitization of industry that will consider societal concerns.