Browsing by Keyword "Industry 4.0"
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Item Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0(2019-06) Para, Jesus; Del Ser, Javier; Nebro, Antonio J.; Zurutuza, Urko; Herrera, Francisco; IAIndustry 4.0 is revolutionizing decision making processes within the manufacturing industry. Among the technological portfolio enabling this revolution, the late literature has capitalized on the potential of data analytics for improving the production cycle at different stages, from resource provisioning to planning, delivery and storage. However, such a promising role of data analytics has been so far explored without a proper, quantitative inspection of the cost-improvement trade-off, nor has the process of acquiring sensors and extracting valuable information from their captured data formalized in a series of methodological steps. This paper introduces the Analyze, Sense, Preprocess, Predict, Implement and Deploy (ASPPID) methodology, an iterative decision workflow that spans from the acquisition of sensing equipment to the quantitative assessment of the contribution of their captured data to enhance the production step under focus. By placing the data scientist at the core of the workflow, this methodology helps improvement teams make informed decisions about which parts of the process need to be sensed, and how to exploit this information towards a verifiable improvement of the production cycle. The implementation of this methodology is exemplified in a real use case within the automotive industry, where the detection of defects in an annealing process can be modeled as a classification problem over a highly imbalanced dataset. Results obtained after applying the proposed ASPPID methodology show that the scrap ratio is reduced by sensing the correct part of the process at minimal investment costs, thus highlighting the crucial role of the data scientist in the management team of manufacturing plants.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 Case study of the experience capturer evaluation tool in the design process of an industrial HMI(2020-08) Aranburu, Erik; Lasa, Ganix; Gerrikagoitia, Jon Kepa; Mazmela, Maitane; Tecnalia Research & InnovationIn the absence of user experience evaluation tools for industrial human-machine interfaces (HMI), a specific tool called eXperience Capturer (XC) has been created. It is a multi-method user-centred tool that evaluates the pragmatic and experiential aspects of employees' interaction with industrial HMIs during the three phases of experience. In this article, a case study is shown where the XC tool is used in an industrial HMI design process. The results show that evaluation using the XC tool facilitates the creation of a new design that improves the experience of employees during interaction, increasing their autonomy, competence, closeness to the system, safety and stimulation.Item Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0(2019-10) Diez-Olivan, Alberto; Del Ser, Javier; Galar, Diego; Sierra, Basilio; Tecnalia Research & Innovation; IAThe so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.Item DECENTRALIZED INDUSTRY 5.0: BLOCKCHAIN’S CONTRIBUTION TO INDUSTRIAL SUSTAINABILITY, RESILIENCE, AND HUMAN-CENTRICITY(2023-11) Lage, Oscar; Saiz-Santos, María; Zarzuelo, José Manuel; CIBERSEC&DLTThe emerging Industry 5.0 presents new challenges related to sustainability, resilience, and worker welfare in industrial companies; challenges that are vital for industrial and social development in the coming years. We believe that blockchain can help to face these challenges thanks to the trust and security it provides in complex ecosystems such as industrial supply chains. To verify this hypothesis, this paper performs a Systematic Literature Review (SLR) based on two stages. A first stage focused on the analysis of publications related to blockchain and Industry 5.0. Being such a novel term and with limited literature, we complemented the analysis with a second stage in which we analyzed the benefits of blockchain on each of the three pillars of Industry 5.0: sustainability, resilience, and human-centricity. The results of the SLR confirm our initial hypothesis and provide details on how, and to what extent, blockchain can add value to each of the three challenges of Industry 5.0, empowering at least nine use cases that will contribute to those challenges.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 Evolutionary LSTM-FCN networks for pattern classification in industrial processes(2020-05) Ortego, Patxi; Diez-Olivan, Alberto; Del Ser, Javier; Veiga, Fernando; Penalva, Mariluz; Sierra, Basilio; Tecnalia Research & Innovation; IA; FABRIC_INTELThe Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are gaining a lot of attention over the last decade due to their capability of successfully modeling nonlinear feature interactions. However, they have not been yet fully applied for pattern classification tasks in time series data within the digital industry. In this paper, a novel approach based on an evolutionary algorithm for optimizing the networks hyperparameters and on the resulting deep learning model for pattern classification is proposed. In order to demonstrate the applicability of this method, a test scenario that involves a process related to blind fastener installation in the aeronautical industry is provided. The results achieved with the proposed approach are compared with shallow models and it is demonstrated that the proposed method obtains better results with an accuracy value of 95%.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(2021-05-21) Morella, Paula; Lambán, María Pilar; Royo, Jesús Antonio; Sánchez, Juan Carlos; INDUSTRY_THINGS; SGAmong 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 PLAHS: A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process[Formula presented](2023-11) Navajas-Guerrero, Adriana; Portillo, Eva; Manjarres, Diana; IAIn real-life industry it is difficult to have fully-labelled datasets due to lack of time, resources or knowledge. In this sense, this paper proposes the design and development of a Partial Labelling Autonomous Hyper-heuristic System PLAHS, a solution that autonomously labels partially labelled databases and evaluates the yielded labelling solution by means of a novel Trustworthiness Metric (TM). The proposal combines a hyper-heuristic inspired approach with a Semi Supervised Learning Clustering (SSLC) methodology that optimizes the parameters of different clustering algorithms, based on a novel semi-supervised metric named Partially Supervised Optimization Metric (PSOM). The proposal has been tested with promising and excellent results on both a real use case for labelling work orders in a cold stamping press, and 13 databases from the UCI (multivariate data) and UCR (time series data) repositories.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 Protocol for Identity Management in Industrial IoT based on Hyperledger Indy(2022) Regueiro, Cristina; Gutierrez-Agüero, Iván; Anguita, Sergio; de Diego, Santiago; Lage, Oscar; CIBERSEC&DLT; Tecnalia Research & InnovationThis paper presents a protocol for Identity Management in Industrial IoT enabled devices, that is based on the principles of Self-Sovereign Identity. The Industry 4.0 transformation has led to the Industry sector digitalization and one of its major challenges is to uniquely identify the Industrial Internet of Things unattended devices. The digital identity management must allow increasing the security and control, and it has been evolving towards a model where the device acquires the responsibility for managing its own data through Self-Sovereign Identity. This paper studies why the Self-Sovereign Identity approach is suitable for the industrial IoT particularities, properly justifying its use. Furthermore, it analyzes the actors and roles involved in an industrial identity environment, and it addresses a protocol that defines how data should be exchanged over an Hyperledger Indy public permissioned Distributed Ledger Technology network as Sovrin. The paper applies the proposal to a reference use case, filling the gaps that are not currently specified in the literature for a successful Industrial Internet of Things identity management operation.Item A reliable turning process by the early use of a deep simulation model at several manufacturing stages(2017-06-01) Urbikain, Gorka; Alvarez, Alvaro; de Lacalle, Luis Norberto López; Arsuaga, Mikel; Alonso, Miguel A.; Veiga, Fernando; Tecnalia Research & InnovationThe future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep-knowledge and models" that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.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.