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Dr. Rodolfo Haber
Centre for Automation and Robotics (UPM-CSIC), Consejo Superior de Investigaciones Científicas, Ctra. de Campo Real, km 0.200, Arganda del Rey, 28500 Madrid, Spain

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Research Keywords & Expertise

0 Cyber-Physical Systems
0 Industry 5.0
0 control and optimization
0 Continuous and discrete processes
0 Smart manufacturing processes

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Cyber-Physical Systems
Artificial cognitive systems

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Journal article
Published: 17 August 2021 in Computers in Industry
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This paper presents an approach for image classification based on an ensemble of convolutional neural networks and the application to a real case study of an industrial welding process. The ensemble consists of five convolutional neural networks, whose outputs are combined through a voting policy. In order to select appropriate network parameters (i.e., the number of convolutional layers and layers hyperparameters) and voting policy, an efficient search process was carried out by using an evolutionary algorithm. The proposed method is applied and validated in a case study focused on detecting misalignment of metal sheets to be joined through submerged arc welding process. After selecting the most convenient setup, the ensemble outperforms other seven strategies considered in a comparison in several metrics, while maintaining an adequate computational cost.

ACS Style

Yarens J. Cruz; Marcelino Rivas; Ramón Quiza; Alberto Villalonga; Rodolfo E. Haber; Gerardo Beruvides. Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process. Computers in Industry 2021, 133, 103530 .

AMA Style

Yarens J. Cruz, Marcelino Rivas, Ramón Quiza, Alberto Villalonga, Rodolfo E. Haber, Gerardo Beruvides. Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process. Computers in Industry. 2021; 133 ():103530.

Chicago/Turabian Style

Yarens J. Cruz; Marcelino Rivas; Ramón Quiza; Alberto Villalonga; Rodolfo E. Haber; Gerardo Beruvides. 2021. "Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process." Computers in Industry 133, no. : 103530.

Journal article
Published: 27 July 2021 in Sensors
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In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

ACS Style

Sebastian Bombiński; Joanna Kossakowska; Mirosław Nejman; Rodolfo Haber; Fernando Castaño; Robert Fularski. Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry. Sensors 2021, 21, 5086 .

AMA Style

Sebastian Bombiński, Joanna Kossakowska, Mirosław Nejman, Rodolfo Haber, Fernando Castaño, Robert Fularski. Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry. Sensors. 2021; 21 (15):5086.

Chicago/Turabian Style

Sebastian Bombiński; Joanna Kossakowska; Mirosław Nejman; Rodolfo Haber; Fernando Castaño; Robert Fularski. 2021. "Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry." Sensors 21, no. 15: 5086.

Journal article
Published: 13 April 2021 in Annual Reviews in Control
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Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process.

ACS Style

Alberto Villalonga; Elisa Negri; Giacomo Biscardo; Fernando Castano; Rodolfo E. Haber; Luca Fumagalli; Marco Macchi. A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annual Reviews in Control 2021, 51, 357 -373.

AMA Style

Alberto Villalonga, Elisa Negri, Giacomo Biscardo, Fernando Castano, Rodolfo E. Haber, Luca Fumagalli, Marco Macchi. A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annual Reviews in Control. 2021; 51 ():357-373.

Chicago/Turabian Style

Alberto Villalonga; Elisa Negri; Giacomo Biscardo; Fernando Castano; Rodolfo E. Haber; Luca Fumagalli; Marco Macchi. 2021. "A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins." Annual Reviews in Control 51, no. : 357-373.

Journal article
Published: 24 November 2020 in IEEE Access
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Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty’s method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.

ACS Style

Daniel Rivas; Ramon Quiza; Marcelino Rivas; Rodolfo E. Haber. Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process. IEEE Access 2020, 8, 212904 -212916.

AMA Style

Daniel Rivas, Ramon Quiza, Marcelino Rivas, Rodolfo E. Haber. Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process. IEEE Access. 2020; 8 (99):212904-212916.

Chicago/Turabian Style

Daniel Rivas; Ramon Quiza; Marcelino Rivas; Rodolfo E. Haber. 2020. "Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process." IEEE Access 8, no. 99: 212904-212916.

Journal article
Published: 12 August 2020 in Sensors
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One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

ACS Style

Yarens J. Cruz; Marcelino Rivas; Ramón Quiza; Gerardo Beruvides; Rodolfo E. Haber. Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques. Sensors 2020, 20, 4505 .

AMA Style

Yarens J. Cruz, Marcelino Rivas, Ramón Quiza, Gerardo Beruvides, Rodolfo E. Haber. Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques. Sensors. 2020; 20 (16):4505.

Chicago/Turabian Style

Yarens J. Cruz; Marcelino Rivas; Ramón Quiza; Gerardo Beruvides; Rodolfo E. Haber. 2020. "Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques." Sensors 20, no. 16: 4505.

Review
Published: 03 February 2020 in IEEE Transactions on Industrial Informatics
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Nowadays, reconfiguration and adaptation by means of optimal re-parametrization in industrial cyber-physical systems (ICPS) is one of the bottlenecks for the digital transformation of the manufacturing industry. This work proposes a cloud-to-edges-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules increasing the smartness and the autonomy for monitoring and control the behavior at shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro and micro milling operations.

ACS Style

Alberto Villalonga; Gerardo Beruvides; Fernando Castano; Rodolfo E. Haber. Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line. IEEE Transactions on Industrial Informatics 2020, 16, 5975 -5984.

AMA Style

Alberto Villalonga, Gerardo Beruvides, Fernando Castano, Rodolfo E. Haber. Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line. IEEE Transactions on Industrial Informatics. 2020; 16 (9):5975-5984.

Chicago/Turabian Style

Alberto Villalonga; Gerardo Beruvides; Fernando Castano; Rodolfo E. Haber. 2020. "Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line." IEEE Transactions on Industrial Informatics 16, no. 9: 5975-5984.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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In recent years, digitalization has taken an important role in the manufacturing industry. Digital twins (DT) are one of the key enabling technologies that are leading the digital transformation. Integrating DT with IoT and artificial intelligence enables the development of more accurate models to improve scheduling tasks, production performance indices, optimization and decision-making. This work proposes a distributed DT framework to improve decision making at local level in manufacturing processes. A decision-making module supported on an adaptive threshold procedure is designed and implemented. Finally, the proposed framework is evaluated on a pilot line, highlighting the behavior of the decision-making module for detecting possible faults, alerting the operator and notifying the manufacturing execution system to trigger actions of reconfiguration and scheduling.

ACS Style

A. Villalonga; E. Negri; L. Fumagalli; M. Macchi; F. Castaño; R. Haber. Local Decision Making based on Distributed Digital Twin Framework. IFAC-PapersOnLine 2020, 53, 10568 -10573.

AMA Style

A. Villalonga, E. Negri, L. Fumagalli, M. Macchi, F. Castaño, R. Haber. Local Decision Making based on Distributed Digital Twin Framework. IFAC-PapersOnLine. 2020; 53 (2):10568-10573.

Chicago/Turabian Style

A. Villalonga; E. Negri; L. Fumagalli; M. Macchi; F. Castaño; R. Haber. 2020. "Local Decision Making based on Distributed Digital Twin Framework." IFAC-PapersOnLine 53, no. 2: 10568-10573.

Journal article
Published: 04 November 2019 in Fuzzy Sets and Systems
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The fuzzy Kalman filter (FKF), introduced some years ago, is revisited. In the initial version, trapezoidal possibility distributions functions instead of gaussian probability distributions were proposed, and trapezius were modeled by four representative points. Nevertheless, and although the algorithm works properly, an implementation problem occurs when propagating uncertainty through a non-linear function in multi-variable systems, something that was solved by linearization. In this work we propose an alternative method to represent uncertainty, still using trapezoidal distributions, which avoids the previous inconvenience and eases the Kalman filter steps computation. We reformulate the FKF algorithm, presenting a new theoretical approach as well as validation tests in both simulation and a real mobile robot.

ACS Style

Fernando Matía; Víctor Jiménez; Biel P. Alvarado; Rodolfo Haber. The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation. Fuzzy Sets and Systems 2019, 402, 78 -104.

AMA Style

Fernando Matía, Víctor Jiménez, Biel P. Alvarado, Rodolfo Haber. The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation. Fuzzy Sets and Systems. 2019; 402 ():78-104.

Chicago/Turabian Style

Fernando Matía; Víctor Jiménez; Biel P. Alvarado; Rodolfo Haber. 2019. "The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation." Fuzzy Sets and Systems 402, no. : 78-104.

Review
Published: 27 September 2019 in Remote Sensing
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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

ACS Style

Fernando Castaño; Stanisław Strzelczak; Alberto Villalonga; Rodolfo E. Haber; Joanna Kossakowska. Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study. Remote Sensing 2019, 11, 2252 .

AMA Style

Fernando Castaño, Stanisław Strzelczak, Alberto Villalonga, Rodolfo E. Haber, Joanna Kossakowska. Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study. Remote Sensing. 2019; 11 (19):2252.

Chicago/Turabian Style

Fernando Castaño; Stanisław Strzelczak; Alberto Villalonga; Rodolfo E. Haber; Joanna Kossakowska. 2019. "Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study." Remote Sensing 11, no. 19: 2252.

Preprint
Published: 28 July 2019
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Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

ACS Style

Fernando Castaño; Alberto Villalonga; Rodolfo E. Haber; Joanna Kossakowska; Stanisław Strzelczak. Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System. 2019, 1 .

AMA Style

Fernando Castaño, Alberto Villalonga, Rodolfo E. Haber, Joanna Kossakowska, Stanisław Strzelczak. Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System. . 2019; ():1.

Chicago/Turabian Style

Fernando Castaño; Alberto Villalonga; Rodolfo E. Haber; Joanna Kossakowska; Stanisław Strzelczak. 2019. "Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System." , no. : 1.

Journal article
Published: 11 July 2019 in IEEE Access
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A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction. The main contributions of the study are the design of the digital twin and its implementation, assuming a two-mass drive system. The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction. The surface quality of certain manufactured components, such as hip and knee implants, depends on the smoothness and the accuracy of the real trajectory produced in the cutting process that is strongly influenced by the maximum position error. The simulations and experimental studies are presented using a real platform and two references for trajectory control, and a comparison of four digital twin-based optimization methods. The simulation study and the real-time experiments demonstrate the suitability of the digital twin-based optimization procedure and lay the foundations for the implementation of the proposed method at an industrial level.

ACS Style

Rodolfo Haber Guerra; Ramon Quiza; Alberto Villalonga; Javier Arenas; Fernando Castano. Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction. IEEE Access 2019, 7, 93462 -93472.

AMA Style

Rodolfo Haber Guerra, Ramon Quiza, Alberto Villalonga, Javier Arenas, Fernando Castano. Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction. IEEE Access. 2019; 7 ():93462-93472.

Chicago/Turabian Style

Rodolfo Haber Guerra; Ramon Quiza; Alberto Villalonga; Javier Arenas; Fernando Castano. 2019. "Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction." IEEE Access 7, no. : 93462-93472.

Journal article
Published: 06 September 2018 in IFAC-PapersOnLine
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Nowadays, new challenges are arising in the multiple industrial sectors focused on the application or adaptation of the digitalized solution with a high grade of optimization in the process, traceability, information & knowledge generation, products personalization, connectivity, customer feedback, etc. The aim is to provide an excellent product or service, increasing your productivity, competitiveness and presence in the market. In particular, manufacturing processes adopt solution with higher sensor system levels based on Cyber-Physical System approaches to obtain process parameters and control machine tools, robots and industrial drives on the shop floor, plant or global production stations. The present article introduces a condition-based monitoring architecture to manage alarm and events based on global information captured by multiple sensors integrated a family of CNC machine tools. The architecture is divided in two modules: a local system embedded in each machine and a cloud as a service system to supervise the local accuracy based on global system knowledge. Finally, an industrial CPS study case based on a bearing benchmark is simulated, validating the local condition-based monitoring stability in function of the update provides by the global module.

ACS Style

Alberto Villalonga; Gerardo Beruvides; Fernando Castaño; Rodolfo E. Haber; Marcelino Novo. Condition-based Monitoring Architecture for CNC Machine Tools based on Global Knowledge. IFAC-PapersOnLine 2018, 51, 200 -204.

AMA Style

Alberto Villalonga, Gerardo Beruvides, Fernando Castaño, Rodolfo E. Haber, Marcelino Novo. Condition-based Monitoring Architecture for CNC Machine Tools based on Global Knowledge. IFAC-PapersOnLine. 2018; 51 (11):200-204.

Chicago/Turabian Style

Alberto Villalonga; Gerardo Beruvides; Fernando Castaño; Rodolfo E. Haber; Marcelino Novo. 2018. "Condition-based Monitoring Architecture for CNC Machine Tools based on Global Knowledge." IFAC-PapersOnLine 51, no. 11: 200-204.

Conference paper
Published: 01 July 2018 in 2018 IEEE 16th International Conference on Industrial Informatics (INDIN)
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Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous interaction with different and a large amount of distributed and networked computing nodes, devices and human operators. These systems are critical to ensure the quality of production and the safety of persons working at the shop floor level. Furthermore, this situation is similar in other domains, such as logistics that, in turn, are connected and affect the overall production efficiency. In this context, this article presents some key steps for integrating three pillars of CPS (production line, logistics and facilities) into the current smart manufacturing environments in order to adopt an industrial Cyber-Physical Systems of Systems (CPSoS) paradigm. The approach is focused on the integration in several digital functionalities in a cloud-based platform to allow a real time multiple devices interaction, data analytics/sharing and machine learning-based global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety and energy efficiency and industrial productivity. Conceptually, isolated systems may enhance their capabilities by accessing to information of other systems. The approach introduces particular vision, main components, potential and challenges of the envisioned CPSoS. In addition, the description of one scenario for realizing the CPSoS vision is presented. The results herein presented will pave the way for the adoption of CPSoS that can be used as a pilot for further research on this emerging topic.

ACS Style

Borja Ramis Ferrer; Wael M. Mohammed; Jose L. Martinez Lastra; Alberto Villalonga; Gerardo Beruvides; Fernando Castano; Rodolfo E. Haber. Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) 2018, 792 -799.

AMA Style

Borja Ramis Ferrer, Wael M. Mohammed, Jose L. Martinez Lastra, Alberto Villalonga, Gerardo Beruvides, Fernando Castano, Rodolfo E. Haber. Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). 2018; ():792-799.

Chicago/Turabian Style

Borja Ramis Ferrer; Wael M. Mohammed; Jose L. Martinez Lastra; Alberto Villalonga; Gerardo Beruvides; Fernando Castano; Rodolfo E. Haber. 2018. "Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments." 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) , no. : 792-799.

Journal article
Published: 29 June 2018 in Sensors
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Nowadays, the preservation, maintenance, rehabilitation, and improvement of road networks are key issues. Pavement condition is highly affected by environmental factors such as temperature and humidity, hence the importance of building databases enriched with real-time information from monitoring systems that enable the analysis and modeling of the road properties. Information and communication technologies, and specifically wireless sensor networks and computational intelligence methods, are enabling the design of new monitoring systems. The main goal of this work is the design of a pavement monitoring system for measuring temperature at internal layers. The proposed solution is based on low-cost and robust temperature sensors, vehicle-to-infrastructure communications, allowing one to transmit information directly from probes to a moving auscultation vehicle, and a neural network-based model for prediction pavement temperature. User requirements drive probes’ design to a modular device, with easy installation, low cost, and reduced energy consumption. Results of the test and validation experiments show both the benefits and viability of the proposed system, which reflect in an accuracy improvement and reduction in routine test duration. Finally, data collected over a year is applied to assess the performance of BELLS3 models and the suggested neural network for predicting pavement temperature. The dynamic behavior of the predicted temperature and the mean absolute error of the neural network-based model are better than the BELL3 model, demonstrating the suitability of the proposed pavement monitoring system.

ACS Style

Jorge Godoy; Rodolfo Haber; Juan Jesús Muñoz; Fernando Matía; Álvaro García. Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications. Sensors 2018, 18, 2092 .

AMA Style

Jorge Godoy, Rodolfo Haber, Juan Jesús Muñoz, Fernando Matía, Álvaro García. Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications. Sensors. 2018; 18 (7):2092.

Chicago/Turabian Style

Jorge Godoy; Rodolfo Haber; Juan Jesús Muñoz; Fernando Matía; Álvaro García. 2018. "Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications." Sensors 18, no. 7: 2092.

Journal article
Published: 10 May 2018 in Sensors
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On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the ‘Internet of Things’ (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.

ACS Style

Fernando Castaño; Gerardo Beruvides; Alberto Villalonga; Rodolfo E. Haber. Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models. Sensors 2018, 18, 1508 .

AMA Style

Fernando Castaño, Gerardo Beruvides, Alberto Villalonga, Rodolfo E. Haber. Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models. Sensors. 2018; 18 (5):1508.

Chicago/Turabian Style

Fernando Castaño; Gerardo Beruvides; Alberto Villalonga; Rodolfo E. Haber. 2018. "Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models." Sensors 18, no. 5: 1508.

Journal article
Published: 26 March 2018 in IEEE Transactions on Industrial Informatics
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Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is challenging in micro-scale manufacturing processes because of intrinsic nonlinear behaviors and reduced dimensions. In this article, a decision-making system for selecting optimal parameters in micro-milling operations is designed and implemented using simple and, efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modelling by means of a multilayer perceptron; the two-objective optimization using the cross-entropy method and a decision-making procedure using a fuzzy inference system. In order to evaluate the proposed system, micro-milling processes of titanium-based and a tungsten-copper alloys are considered. The experimental study demonstrates the effectiveness of the proposed solution for automatically decision making based on simple soft-computing methods and its successfully application to a truly industrial challenge.

ACS Style

Iván La Fé; Gerardo Beruvides; Ramon Quiza; Rodolfo Haber; Marcelino Rivas. Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes. IEEE Transactions on Industrial Informatics 2018, 15, 800 -811.

AMA Style

Iván La Fé, Gerardo Beruvides, Ramon Quiza, Rodolfo Haber, Marcelino Rivas. Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes. IEEE Transactions on Industrial Informatics. 2018; 15 (2):800-811.

Chicago/Turabian Style

Iván La Fé; Gerardo Beruvides; Ramon Quiza; Rodolfo Haber; Marcelino Rivas. 2018. "Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes." IEEE Transactions on Industrial Informatics 15, no. 2: 800-811.

Preprint
Published: 28 February 2018
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Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold

ACS Style

Fernando Castano; Gerardo Beruvides; Alberto Villalonga; Rodolfo Haber. Self-Tuning Method for Increasing Reliability in Obstacle Detection based on Internet-of-Things LiDAR Sensor Models. 2018, 1 .

AMA Style

Fernando Castano, Gerardo Beruvides, Alberto Villalonga, Rodolfo Haber. Self-Tuning Method for Increasing Reliability in Obstacle Detection based on Internet-of-Things LiDAR Sensor Models. . 2018; ():1.

Chicago/Turabian Style

Fernando Castano; Gerardo Beruvides; Alberto Villalonga; Rodolfo Haber. 2018. "Self-Tuning Method for Increasing Reliability in Obstacle Detection based on Internet-of-Things LiDAR Sensor Models." , no. : 1.

Journal article
Published: 01 January 2018 in Procedia CIRP
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ACS Style

Gerardo Beruvides; Alberto Villalonga; Pasquale Franciosa; Darek Ceglarek; Rodolfo E. Haber. Fault pattern identification in multi-stage assembly processes with non-ideal sheet-metal parts based on reinforcement learning architecture. Procedia CIRP 2018, 67, 601 -606.

AMA Style

Gerardo Beruvides, Alberto Villalonga, Pasquale Franciosa, Darek Ceglarek, Rodolfo E. Haber. Fault pattern identification in multi-stage assembly processes with non-ideal sheet-metal parts based on reinforcement learning architecture. Procedia CIRP. 2018; 67 ():601-606.

Chicago/Turabian Style

Gerardo Beruvides; Alberto Villalonga; Pasquale Franciosa; Darek Ceglarek; Rodolfo E. Haber. 2018. "Fault pattern identification in multi-stage assembly processes with non-ideal sheet-metal parts based on reinforcement learning architecture." Procedia CIRP 67, no. : 601-606.

Research article
Published: 17 December 2017 in Complexity
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The complexity of machining processes relies on the inherent physical mechanisms governing these processes including nonlinear, emergent, and time-variant behavior. The measurement of surface roughness is a critical step done offline by expensive quality control procedures. The surface roughness prediction using an online efficient computational method is a difficult task due to the complexity of machining processes. The paradigm of hybrid incremental modeling makes it possible to address the complexity and nonlinear behavior of machining processes. Parametrization of models is, however, one bottleneck for full deployment of solutions, and the optimal setting of model parameters becomes an essential task. This paper presents a method based on simulated annealing for optimal parameters tuning of the hybrid incremental model. The hybrid incremental modeling plus simulated annealing is applied for predicting the surface roughness in milling processes. Two comparative studies to assess the accuracy and overall quality of the proposed strategy are carried out. The first comparative demonstrates that the proposed strategy is more accurate than theoretical, energy-based, and Taguchi models for predicting surface roughness. The second study also corroborates that hybrid incremental model plus simulated annealing is better than a Bayesian network and a multilayer perceptron for correctly predicting the surface roughness.

ACS Style

Gerardo Beruvides; Fernando Castaño; Rodolfo E. Haber; Ramon Quiza; Alberto Villalonga. Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization. Complexity 2017, 2017, 1 -11.

AMA Style

Gerardo Beruvides, Fernando Castaño, Rodolfo E. Haber, Ramon Quiza, Alberto Villalonga. Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization. Complexity. 2017; 2017 ():1-11.

Chicago/Turabian Style

Gerardo Beruvides; Fernando Castaño; Rodolfo E. Haber; Ramon Quiza; Alberto Villalonga. 2017. "Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization." Complexity 2017, no. : 1-11.

Proceedings
Published: 14 November 2017 in Proceedings
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Nowadays, the preservation, the maintenance, the rehabilitation and improvement of the road network are key issues. Many of the parameters that define the road surface conditions are influenced by various environmental factors, mainly temperature. Hence the importance of having databases enriched form real-time monitoring systems that enable the analysis and modeling of the properties of the road. The main goal of this work is the design and development of a road monitoring system for temperature measurement at different pavement depths, capable of transmitting the information to a moving vehicle. The practical realization required a modular device, of easy installation, low cost and with reduced energy consumption. The proposed monitoring system makes easy the auscultation procedure, improving the reliability of the measures collected which, in turn is the basement to estimate the useful life of the pavement. The results of the tests and validation of the proposed prototype in either static system with two types of pavement (asphalt and concrete), and in real driving situations, demonstrate the good performance and accuracy of the proposed monitoring system.

ACS Style

Álvaro García; Rodolfo Haber; Jorge Godoy; Juan Jesús Muñoz. Wireless Monitoring of Pavement Temperature Based on Low Cost Computing Platform. Proceedings 2017, 2, 146 .

AMA Style

Álvaro García, Rodolfo Haber, Jorge Godoy, Juan Jesús Muñoz. Wireless Monitoring of Pavement Temperature Based on Low Cost Computing Platform. Proceedings. 2017; 2 (3):146.

Chicago/Turabian Style

Álvaro García; Rodolfo Haber; Jorge Godoy; Juan Jesús Muñoz. 2017. "Wireless Monitoring of Pavement Temperature Based on Low Cost Computing Platform." Proceedings 2, no. 3: 146.