The process for submitting a paper to a special session is similar to submitting a regular paper. Please refer to type of submisson: Special Session Paper
In order to specify for which Special Session you want to submit, you will be asked for a code, which you can find in the table beneath. Additionally the matching between special Sessions is only successful, if for the submitted paper ONLY the keywords of the Special Session are selected in the right order. This means the first keyword in the table has priority 1, the second keyword priority 2 and so on. Please find those Keywords in the table beneath. If the wrong keywords or the wrong order are selcted an error message will be displayed and the submission is not successful.
Please note that all special session are in a first step placed in the subject area of the special session and not in the special session itself.
|Stochastic Modeling and Optimization in Healthcare System||xhekn||Xiang Zhong, Jie Song, Xiaolei Xiefirstname.lastname@example.org||Health Care Management|
|Automation in Logistics and Intelligent and Knowledge Based Transportation Systems||4xk93||Maria Pia Fanti, MengChu Zhou, Birgit Vogel-Heuser, Agostino Marcello Manginiemail@example.com||Logistics; Intelligent Transportation Systems; Optimization and Optimal Control|
|Advanced metaheuristics for smart shop scheduling||49a28||Ling Wang, Liang Gaofirstname.lastname@example.org||Planning, Scheduling and Coordination; Intelligent and Flexible Manufacturing; Optimization and Optimal Control|
|Decision making on data-driven production planning and scheduling||873vm||Zhi-Hai Zhang, Danyu Bai, Wei Liemail@example.com||Planning, Scheduling and Coordination|
|Architectures for autonomous Systems in Engineering and real-time Operation||symh5||Michael Weyrich, Daria Ryashentsevafirstname.lastname@example.org||Control Architectures and Programming; Agent-Based Systems; Cyber-physical Production Systems and Industry 4.0|
|Alarm systems analysis and improvement||t6u2x||Jean-Marc Faure, Yu Ding, Jonathan Shapiro, Tongwen Chen, Luiz Affonso Guedesemail@example.com||Big-Data and Data Mining; Discrete Event Dynamic Automation Systems; Factory Automation|
|Innovative engineering methods||g7q3n||Rainer Drathfirstname.lastname@example.org||Control Architectures and Programming; Cyber-physical Production Systems and Industry 4.0; Cloud Computing For Automation|
|Simulation Optimization for Cyber Physical Energy Systems||4661e||Qing-Shan Jia, Yijie Peng, Jie Songemail@example.com||Optimization and Optimal Control; Probability and Statistical Methods; Building Automation|
|Artificial intelligence applied to health-care||k2mjb||Vincent Augusto, Xiaolan Xie||Big-Data and Data Mining; Health Care Management; Automation in Life Sciences: Biotechnology, Pharmaceutical and Health Care|
|Machine learning for Cyber-Physical Production Systems||281r4||Marta Fullen, Benedikt Eiteneuer, Oliver Niggemannfirstname.lastname@example.org||AI-Based Methods; Cyber-physical Production Systems and Industry 4.0; Big-Data and Data Mining|
|Data Analytics and Process Modeling for System Improvement||aa3r2||Xi Zhang, Jianguo Wuemail@example.com||Big-Data and Data Mining; Probability and Statistical Methods; Process Control|
|Advance in Sustainable Manufacturing Automation||j5y81||Congbo Li, Ying (Gina) Tang, Zhigang Jiang, Tao Pengfirstname.lastname@example.org||Energy and Environment-aware Automation; Optimization and Optimal Control; Process Control|
|Engineering Methods and Tools for the Development of Collaboration-intensive Cyber Physical Systems||59xc4||Alexander Fayemail@example.com||Cyber-physical Production Systems and Industry 4.0|
|Innovative Tools and Methods for Automated, Accurate and Complex Tasks at Micro-Nanoscales||6i6bk||Philippe Lutz, Dan Popa, David Cappelleri, Cédric Clévyfirstname.lastname@example.org||Assembly; Automation at Micro-Nano Scales; Nanomanufacturing|
|Multi-Disciplinary Engineering for Cyber-Physical Production Systems||2895q||Stefan Biffl, Kleanthis Thramboulidis, Jan Vollmar||Stefan.Biffl@tuwien.ac.at||Cyber-physical Production Systems and Industry 4.0; Domain-specific Software and Software Engineering; Mechatronics|
|Collaborative Intelligent Manufacturing||4kyh4||Weiming Shen, Liang Gaoemail@example.com||Intelligent and Flexible Manufacturing; Agent-Based Systems; Cyber-physical Production Systems and Industry 4.0|
|Intelligent Welding Manufacturing||t3atj||Yuming Zhang, Zhili Feng, Shan-Ben Chenfirstname.lastname@example.org||Intelligent and Flexible Manufacturing|
|Innovative Decision and Control Approaches for Smart Factories and Supply Chains||q9nw2||Francesco Basile, Mariagrazia Dotoli, Qing-Shan Jia, Spiridon Reveliotis, Carla Seatzuemail@example.com||Cyber-physical Production Systems and Industry 4.0; Intelligent and Flexible Manufacturing; Manufacturing, Maintenance and Supply Chains|
|Cybernized Manufacturing System Modeling, Informatics and Control for Service Orientation||2v3gs||Feng Ju, Yan Lu||Feng.Ju@asu.edu||Cyber-physical Production Systems and Industry 4.0; Intelligent and Flexible Manufacturing; Energy and Environment-aware Automation|
|Real-time Modeling, Monitoring, and Control of Advanced Manufacturing Systems||h9cx7||Feng Ju, Hao Yan, Jianguo Wufirstname.lastname@example.org||Cyber-physical Production Systems and Industry 4.0; Intelligent and Flexible Manufacturing; Probability and Statistical Methods|
|Intelligent Supporting Systems and Technologies for Manufacturing Industry||bybgv||Min-Hsiung Hung, Chao-Chun Chen, Haw-Ching Yangemail@example.com||Manufacturing, Maintenance and Supply Chains; Intelligent and Flexible Manufacturing; Cyber-physical Production Systems and Industry 4.0|
|Human-centered methodologies for smooth human-machine interaction||66y22||Valeria Villani, Lorenzo Sabattini, Hao Ding, Kathleen Richardsonfirstname.lastname@example.org||Human-Centered Automation; Human Factors and Human-in-the-Loop|
The desire for safety, high quality and efficiency is rapidly increasing in today’s healthcare system. While the development of information technology can help address many needs, theory, models and methods in Systems Engineering and Operations Research are strongly desired to offer a new perspective and provide decision support to healthcare operations. Moreover, they can provide valuable insights on the underlying fundamental rules that govern many healthcare problems. Indeed, the hospital IT can significantly empowers model-based decision making. The main objective of this special session is to present research that focuses on theoretical work and with successful implementations in hospital or healthcare systems.
The topics include but are not limited to: Emergency/critical care operations management, Operating Room (OR) management, Clinical Pathways, Hospital logistics, Healthcare demand forecasting, Home and outpatient healthcare services design, Healthcare facility location, etc.
This special session deals with the problem of making the modern Logistics and Transportation Systems smart and green. The increasing complexity of such systems and the availability of Artificial Intelligence and Information and Communication Technologies require the development of innovative models, control and optimization approaches and lead to the definition of novel problems with respect to the related literature. The goal is to provide novel intelligent services to achieve planning, scheduling and transport systems that are resource-efficient, climate- and environmentally-friendly, safe and seamless for the benefit of all citizens, the economy and society.
The topics of interest include, but are not limited to:
- Novel automation, information and data architectures on the design of logistics systems and supply chains
- Intelligent transportation and distribution systems: theory and application models
- Automation and Informatics for Intelligent Transportation Systems
- Smart logistics and green mobility
- Blockchain in Logistics and Intelligent Transportation Systems
- Logistics for Healthcare Systems
- Internet of Things applications in logistics and intelligent transportation systems
- Novel engineering methods for evolving logistic systems
- Automatic Methods for software synthesis and verification
- Agents vs. service oriented architectures
- Port, supply chain and warehouse of the future
Manufacturing industry is the material basis of main industrial body and the engine for the rapid growth of economy as well as an important guarantee for overall national power. Production scheduling is one of the most common and significant problems faced by the manufacturing industry, which is to allocate limited resources to tasks over time and to determine the sequence of operations so that the constraints of the manufacturing system are met and the performance criteria are optimized as well. Advanced scheduling theories and technologies play important roles in smart manufacturing systems under Industry 4.0 to improve product adapting ability and competitiveness in the dynamically changing market with the goal of low consumption, clean and flexible production. Due to a variety of complexities in manufacturing systems, metaheuristics have been successfully applied to the
classical shop scheduling problems and the generalized problems as well as the practical systems. This special session intends to give the state-of-the-art of the advanced metaheuristics-based optimization research that satisfies the needs of smart manufacturing scheduling systems. Interdisciplinary methodologies may be given based on the innovative intelligent computing and optimization techniques for complex scheduling problems. The aim of this special session is to reflect the most recent developments of metaheuristics for shop scheduling. The topics of interest include, but are not limited to:
- Knowledge-based metaheuristics for planning and scheduling;
- Data-driven metaheuristics for planning and scheduling;
- Metaheuristics for open shop, flow shop, job shop, flexible shop, distributed shop, assembly line, etc;
- Metaheuristics for green shop scheduling;
- Metaheuristics for multi-objective shop scheduling;
- Metaheuristics for stochastic/fuzzy/interval/dynamic shop scheduling;
- Metaheuristics for shop scheduling in practical systems.
In cyber physical production systems, various production data can be acquired based on advanced sensor technologies, which provide a base to implement intelligent manufacturing. This presents new opportunities to investigate novel solution approaches to make efficient decisions such as production planning and scheduling. The goal of the session is to organize the related themes on data-driven decision making approaches for production planning and scheduling. This session will be a great opportunity to share excellent research work with the colleagues. The proposal is related but not limited to the following topics of CASE 2018: Intelligent manufacturing systems and big data in applications.
The topics include but are not limited to: Cyber physical production systems and industry 4.0 (Self x-systems, Agents vs. service oriented systems, Intelligent products and manufacturing systems, Networked control systems, Modelcoupling and co-Simulation, Standardization of interfaces, capabilities, communication, architecture), Cloud computing for automation, Big data and data mining (Architecture, pre-processing, data-curation, algorithm, Application examples: challenges, strength and weaknesses, Business models, Knowledge acquisition, Knowledge modeling -semantic technologies-, Knowledge-based engineering assistance), Automation and control (Automotive-, manufacturing-, additive manufacturing industry, Automation in meso, micro and nano-scale), Domain specific
software & systems engineering, Mechatronics, Discrete event systems, Model evolution (systems and software), Human in the loop (in engineering and operation), Building automation.
Cyber Physical Production Systems (CPPS) and ‘Industrie 4.0’ require complex IT architectures which work in real time. These systems will be deployed to multiple domains and based on different architectural concepts. As of to date, available reference models are either based on a very generic level, focusing on layer concepts for function, or they provide very specialized and domain-specific service oriented structures. This Special Session on “Architectures for autonomous Systems in Engineering and real-time Operation” aims at bringing concepts and results of empirical research together. In this way, architectures based on distributed control systems are the proven way in software design to cope with the complexity of systems that are getting more
complicated with a time. This session seeks to present architectures for autonomous systems in analogy to the approach of design patterns in software engineering.
Papers are addressing the multiple approaches:
- Design structures for Multi Agent Systems
- Analysis of roles and responsibilities aiming towards patterns of self-organization systems
- Architectures and frameworks for decentralized autonomous components with application in engineering and real-time operation
- IT architectural design methodologies for autonomous systems applied in different domains
The Special Session of IEEE CASE 2018 is going to provide an underlying basis in terms of architectural patterns to the themes of the conference in cyber physical production systems and ‘Industrie 4.0’ as well as cloud computing for automation.
According to ANSI/ISA 18.2, an alarm is “An audible and/or visible means of indicating to the operator an equipment malfunction, process deviation or abnormal condition requiring a timely response.” Alarm systems are interfaces between the controlled process and the operators and therefore play a significant role for availability and safety of critical processes, like chemical or power plants.
The digital transition of these systems, that permits to define an alarm only by setting thresholds on software variables and not by using a specific sensor, as well as the possibility to monitor the process variables by means of low-cost, smart and wireless sensors, have led to an increasing number of alarms, however. This increase impacts negatively the performances of the alarm systems; the operators are often overwhelmed by the far too many alarms to be handled, especially during the alarm floods. Several contributions to this issue have been proposed in the latter years. To analyze then improve the existing alarm systems, these contributions are based on statistical or model-based (graph-based and discrete event models) approaches, data mining techniques, etc. The aim of this session is to provide an opportunity for presenting the novel results from academia and industry in these domains and to favor exchanges in order to develop, in a near future, methods for decreasing alarm floods and more efficient alarm systems.
The following topics (but not necessarily only these ones) are expected to be addressed in this session:
- Alarms correlation
- Alarms similarities
- Redundant alarms detection
- Cause-effect analysis
- Alarm floods analysis and reduction
- Human-machine interfaces for alarm management
- Data mining for alarm systems improvement
- Alarm rationalization
- Alarm management performance increase (delay-timer, threshold, alarm accuracy)
- Case studies from chemical, pharmaceutical, oil and power industries
The goal of this special session is dedicated to innovative engineering methods. Engineering is a key topic in all main industries. Continuous improvements in engineering processes and engineering methods are important to manage the growing complexity of automation systems under growing quality requirements versus cost and time pressure. This session is dedicated to Engineering methods across the lifecycle of an industrial automation system across industrial domains as process or manufacturing industry. It covers seamless data exchange across the engineering phases e.g. via AutomationML/CAEX, electronic data modelling to overcome the era of printed diagrams, data mining or model quality checks of electronic data models of engineering data, preparation and
feeding engineering data into digital twins, simulation-based engineering methods as virtual engineering or virtual commissioning, model driven engineering, methods to gain engineering efficiency as modular automation or agile engineering, measurement of engineering efficiency, interdisciplinary engineering, modelling of communication systems and future engineering concepts in the context of Industry 4.0. Observing automation conferences in the last 10 years (GMA Kongress, ETFA etc.), there is always a significant number of Engineering Methodology related papers, this session is therefore an umbrella for a key topic in industry.
The recent advances in information technology have made it possible to embed cheap and powerful sensing, information processing, and decision making devices in almost every aspects of our daily life, from healthcare to energy systems. In this session, we focus on an important part of the infrastructure in our modern society, which is the cyber physical energy systems (CPES). In these systems, the information on the demand and supply of energy becomes richer and richer. The update becomes more and more frequent. On the one hand, these advances in information technology offers new opportunities to better serve the user and to improve the overall system performance. On the other hand, the large size of these optimization problems creates new challenges for simulation and optimization. We propose to organize a special session on Simulation Optimization for Cyber Physical Energy Systems in the upcoming CASE2018. In this session, we will review the recent advances in simulation optimization, and discuss the opportunities as well as challenges during decision making in CPES. This session will provide a good opportunity for researches from different disciplines to interact with each other. This includes but does not limit to researchers from simulation optimization, smart grid, smart buildings, discrete event dynamic systems, Markov decision processes, and reinforcement learning. Just to name a few. Such a combination would provide a perfect match for the diverse background of audiences in CASE.
The adoption of artificial intelligence (AI) in health-care is on the rise and solving a variety of problems for patients, hospitals and healthcare industry overall. An important goal of medicine is to develop quantitative models for patients that can be used to predict health status, as well as to help prevent disease or disability. In this context, many sources of health data can be used, such as electronic health records (EHRs) or medicoadministrative data. Recent studies have shown that secondary use of EHRs has enabled data-driven prediction of drug effects and interactions, identification certain pathologies, discovery of comorbidity clusters and clinical pathway, and identification of crucial medical examinations for systematic screening. However, predictive models and tools based on machine learning techniques have not been widely and reliably used in clinical decision support systems. Health data is challenging to represent and model due to its high dimensionality, noise, heterogeneity, sparse-ness, incompleteness, random errors, and systematic biases. These challenges have made it difficult for machine learning methods to identify patterns that produce predictive clinical models for real-world applications1. This special session aims at identifying crucial research progress in artificial intelligence applied to healthcare. Scientists, researchers and engineers are invited to submit their current research related with artificial intelligence: recent developments in theory, computational studies, optimization, data mining, machine learning or combination of these.
Topics of interest include, but are not limited to:
- Modeling of health data and medical decision
- Optimization of machine learning approaches applied to health-care
- Big data and data mining applied to health-care
- Architecture, pre-prosessing, data-curation, algorithm
- Knowledge acquisition in health-care
- Medical workflow analysis
- Chronic disease management
- Clinical decision support systems
Cyber-Physical Production Systems (CPPS) are a key topic in the domain of manufacturing industry.
Numerous projects focus on bringing smart “Factories of the Future” based on CPPSs into reality, including Industry 4.0 initiatives and the European Union calls. At the core of CPPSs lie advances in the fields of machine learning, data mining and artificial intelligence, creating adaptable and learning factories. These abilities are used to develop solutions that increase efficiency, safety and reliability, as well as reduce the costs and downtime of CPPSs. Machine learning gives a wide spectrum of tools to improve CPPSs. Fully or partially data-driven approaches become an alternative to manual engineering efforts. Supervised, semi-supervised and unsupervised algorithms can also operate directly on the process data to detect anomalies. These tools open the door to industrial systems capable of self-diagnosis, self-configuration, self-optimisation, condition monitoring and predictive maintenance. We invite all engineers and researchers interested in machine learning applications for Cyber-Physical Production Systems to discuss their work in this special session at CASE 2018. Special session topics include, but are not limited to:
- Machine learning for CPPS and Industry 4.0,
- Data-driven and model-based approaches for monitoring and diagnosis,
- Unsupervised and supervised learning from process data,
- Anomaly detection, predictive maintenance, condition monitoring, optimisation,
- Intelligent control, simulation, virtual shadows, virtual commissioning,
- Cyber-Physical Production System and Factory of the Future applications and success stories.
Fast development of data acquisition technologies makes data now being routinely collected from many complex systems such as advanced manufacturing processes, social networks, physiological systems, and so on. Constant practices of processing the data streams by innovative methods of data analysis and modeling have greatly improved the services and functionalities of systems. The goal of this session is mainly to present the edge-cutting methodologies in data analytics of complex systems.
The topics include but are not limited to:
- Innovative Statistical Modeling for Complex Systems
- Data Fusion and Analysis for System Improvement
- Statistical Process Control in Complex Systems
- Monitoring, diagnostics, and prognostics in Complex Systems
- Industrial and System Informatics
- Data Visualization and Exploratory Data Analysis
- Relevant Innovative Applications
The increasing environmental and economic concerns have aroused the attention of industries,
governments and academia to the development of sustainable manufacturing, resulting in many new
regulations, models, and algorithms. This invited session aims at presenting the state-of-the-art
development and applications in this area, and bringing academy and industry practitioners to share
their findings and visions Interested topics include (but not limited to):
- Low-carbon Manufacturing
- Green Supply Chain
- Green Product Design
- Energy Efficient Manufacturing
- Artificial intelligent, Big data and Cyber-physical systems for sustainable manufacturing
- Regulatory Issues
- Environmental implications of e-commence and logistics
- Market-based information tools for green procurement
- Enterprise integration and decision support
- Life-cycle assessment: case studies, streamlined methods, and data needs
- Industrial applications and case studies
SS – 13 Engineering Methods and Tools for the Development of Collaboration-intensive Cyber Physical Systems
Collaboration-intensive cyber-physical systems are aware of their context and are able to communicate information about themselves and their context with each other to achieve common goals. These abilities unlock a variety of new applications in various domains, such as factory automation, autonomous logistic systems, autonomous driving and energy supply. However, the transition from conventional CPSs to collaboration-intensive CPSs results in an increasing complexity regarding their engineering. This complexity cannot entirely be handled with existing engineering methods and tools. This Special Session on “Engineering Methods and Tools for the Development of Collaboration-intensive Cyber- Physical Systems” provides a forum for the latest research dedicated to the development of collaboration-intensive CPSs. This theme is within the scope of several calls and research projects currently funded by the European Commission, such as FAR-EDGE and PERFoRM, and by national funding bodies, such as the project “CrESt” (Collaborative Embedded Systems) that should benefit from this special session. Topics include, but are not limited to, the following research topics and technologies with respect to collaboration-intensive CPSs:
- Model-based systems engineering (MBSE)
- Context modeling for CPS
- Engineering of light- and heavyweight ontologies
- Virtual commissioning
- Failure mode and effect analysis (FMEA)
- Flexible, adaptive and dynamic system architectures
- Distributed ledger technology for autonomous systems
- Edge Computing for CPS
- Validation and verification
- Runtime reasoning
- Variability at runtime
- Modeling frameworks
- Tool chains, platforms and frameworks
Investigations at micro and nano scales through robotics have recently received increasing interest from both industrial and scientific communities. Such an approach offers the opportunity to design innovative miniaturized products but also to better understand phenomenon in a unique and original way. Much of the previous research in this area proposed proof of concept tools such as actuators, sensors, gripping principles and tools, control, manipulation, and so on. However, achieving high performance manipulation, assembly or characterization tasks at small scales remains a challenge.
The aim of this special session is to provide most recent and original results showing the successful realization of tasks at the micro or at the micro scale and related tools and methodologies. It will notably target discussions about current most challenging research topics in this field, such as high transparency teleoperation systems, dexterous gripping of single objects, collaborative control approaches and automating tasks to reach high-positioning accuracy, high-quality feedback to the user, highly repeatable and high-throughput tasks.
Topics to be covered include, but are not limited to:
- Untethered robotics
- Tethered robotics
- Dexterous gripping with or without contact
- Visual servoing
- Force control
- Hybrid force-position control
- Precision robot calibration
- Accurate sensor fusion
- Collaborative control of microrobots
- Transparent teleoperation for micro and nano manipulation
- Virtual and augmented reality environments for micro and nano scales
- Applications to manipulation of: integrated optics, near field probes, sensors, nanophotonics, iological cells, tissue, natural objects such as fibers, grains, o nanotubes, nanolamella, metamaterials, nanofibers…
Industrial engineering is a multi-disciplinary endeavor that is moving towards an interdisciplinary and knowledge driven approach in all application areas, including the engineering of Cyber-Physical Production Systems (CPPS). The demand for flexible production systems imposes the need for stronger integration of the models of the various disciplines, and the consequent upgrade of methods, and tools involved in their development and operation. Engineers from several disciplines have to develop the constituent components of the CPPS cooperatively by exchanging engineering information describing the system and its components from different viewpoints and on various levels of detail. Within this interdisciplinary and information-driven approach, models of different kinds and their interrelations become key assets that should be treated as first-class citizens in the engineering process. Challenges of CPPS can only be tackled by a cooperation of the relevant research communities. In this context, model-driven approaches envision improving engineering quality and reducing engineering efforts. The goal of this special session is to bridge the gap between the three scientific communities involved in a multidisciplinary engineering of products, and production systems, especially in the domain of CPPSs with a focus on model-based engineering. The special session is strongly related to the following topics of CASE 2018: Cyber physical production systems and industry 4.0, knowledge-based automation, Mechatronics, Domain specific software & systems engineering.
Collaborative intelligent manufacturing finds its root in the need to develop efficient and adaptive production and distribution systems that can simultaneously meet the expectations of ever changing market demand and recover from disturbances. The proposed Special Session on Collaborative Intelligent Manufacturing will focus on methodologies and techniques for the application of intelligence science (including artificial intelligence, computational intelligence, and swarm intelligence), data science (including data mining, machine learning, and Big Data analytics), and emerging information and communication technologies (including Cloud Computing/Edge Computing/Fog Computing, Service-Oriented Computing, Evolutionary Computing, Internet of Things or Cyber-Physical Systems, and Blockchains) in the design, modeling, simulation, planning, and optimization of product, process, production, assembly, supply chain, and logistics. The proposed SS covers the full range of collaborative intelligent manufacturing, including:
- Agent-based collaborative intelligent manufacturing
- Sensor-fusion for intelligent machining and inspection
- Internet of Things / Cyber-Physical Systems in intelligent manufacturing
- Big Data in intelligent manufacturing
- Collaborative intelligent process planning and scheduling
- Human-machine interactions, human-robot / robot-robot collaboration in manufacturing
- Smart factory of future
- Collaborative and virtual enterprises
- Smart supply chains and logistics
- Intelligent manufacturing standardization
- Preventive and predictive maintenance of manufacturing equipment
- Cybersecurity in collaborative intelligent manufacturing
Welding is a major manufacturing method in fabricating high-value added product. Its automation is critical for competitive manufacturing as the working condition is harsh and skilled welders are in continuous shortage. According to the International Federation of Robotics, 50 percent of all robots used are for welding. Since manufacturing conditions are subject to changes, effective and extended use of robots and machines in welding/manufacturing largely depends on if capabilities are available to effectively sense and control the processes resulting in desirable intelligent welding manufacturing.
This special session intends to provide a platform for researchers around the world to share their newest results in different areas related to intelligent welding. Topics include but are not limited to weld seam tracking, robotic welding, weld penetration monitoring, weld pool monitoring, human welder modeling and control, dynamic modeling of welding processes, adaptive/nonlinear/robust control of welding processes.
Decision and control approaches for industrial systems and supply chain networks have been important research streams over the last fifty years. In view of the increasing importance of cyber physical production systems and industry 4.0 concepts, aspects such as close monitoring of these systems, performance evaluation, sustainability, risk management, coordination, resilience, and adaptation have become very important. Since decision and control theory methods are eminently suitable for quantitative analysis of these issues in dynamic environments, there has been a great deal of activity in exploiting these tools to address smart factories and supply chains.
This Special Session on “Innovative Decision and Control Approaches for Smart Factories and Supply Chains” is devoted to the recalled recent developments, and its aim is to attract high-quality papers dealing with dynamic industrial systems and supply chain problems with the help of innovative and smart decision and control approaches. In particular, this Session focuses on problems that are still open and currently find application in most industrial areas, namely: 1) operational management and control of the internal logistics and production; 2) operational management and control of the external logistics; 3) strategic problems regarding the smart factory or supply chain design.
Modern manufacturing, named Smart Manufacturing, is resulted from the convergence of advanced manufacturing capabilities with information and communication technology (ICT). Smart Manufacturing systems are characterized by the collaboration of human beings with fully or partly autonomous machines in factories, and integration of customers and business partners in business and value‐added processes. In this new manufacturing paradigm, systems are becoming increasingly complex, and face more frequent and unpredictable changes and disruptions, in which timely decision making is pressingly needed. The rise of the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) technology and approaches is driving increased efforts to take advantage of the real-time information
from interconnected systems of sensing technology, communication network and physical processes. By utilizing advanced modeling methodology and information analytics, manufacturing systems will be able to perform more efficiently, agile, collaboratively and resiliently. This session focuses on innovative analytical approaches and methods for design, modeling and control of advanced manufacturing systems. The aim of this special session is to bring academic researchers and industry professionals together to review the latest advances and explore future direction in this filed. Specific topics of interest include but are not limited to:
- Smart manufacturing architecture
- Service oriented manufacturing system
- Smart manufacturing informatics and knowledge management
- Advanced modeling for manufacturing process and systems
- Data analytics for real-time production planning, control and management
- Advances in cloud manufacturing
- Mass customization and “lot size 1” production
- Human-machine interaction for production optimization
- Industry 4.0, cloud, and IoT for smart production
Modern manufacturing is resulted from the convergence of complex system layout design and the large-scale real-time sensing system. In this new manufacturing paradigm, systems are becoming increasingly complex, face more frequent and unpredictable and changes and disruptions, in which timely decision making is pressingly needed. The rise of the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) technology and approaches are driving increased efforts to take advantage of the real-time sensing information from interconnected systems of sensing networks and physical processes. Especially, sensing information from different stage, processes, machines becomes
in the larger scale, heterogeneous types, and much higher dimension. By utilizing advanced modeling methodology and information analytics to address these challenges, manufacturing systems will be able to perform more efficiently, reliably, agile, collaboratively and resiliently. This session focuses on innovative analytical approaches and methods for design, sensing, modeling, and control of advanced manufacturing systems. The aim of this special session is to bring academic researchers and industry professionals together to review the latest advances and explore future direction in this filed. Specific topics of interest include but are not limited to:
- Advanced modeling for manufacturing process and systems
- Real-time process monitoring, control, and system evaluation
- Data analytics for real-time production planning and management
- Smart sensing for manufacturing process
- Advanced methodology dealing with bit data or high-dimensional data in the manufacturing system
- Quality control in manufacturing systems
- Advances in cloud manufacturing
- Energy efficient manufacturing systems
- Sensor fusion for manufacturing system
- Maintenance in manufacturing systems
- Prognostics and diagnostics for manufacturing process
Facing global competition, manufacturing industries around the world have a common trend towards
leveraging advanced information and communication technologies (ICT) and intelligent technologies to promote and innovate manufacturing capabilities for creating digitalized, virtualized, networked, and intelligent manufacturing systems so as to increase their competitiveness. On one hand, an intelligent manufacturing system relies on smarter equipment containing stand-alone intelligence, such as fault detection and classification, manufacturing precision conjecture, RUL prediction, and predictive maintenance, which can enhance equipment availability and production quality. On the other hand, an intelligent manufacturing system also requires intelligent supporting systems to work cooperatively as a whole to offer integrated intelligence for reaching the realm of smart factory. For example, in the vision of Industry 4.0, future smart factories will exploit cyber-physical production systems that integrate cyber-physical systems (CPS), Internet of Things (IoT), and cloud computing,
along with data-driven decision making, to integrate automation and digitization tightly for providing more efficient and intelligent production methods. Therefore, developing intelligent systems and technologies that can facilitate smarter machines and manufacturing systems fits the trend of intelligent manufacturing and is a key factor for manufacturing companies to increase their competitiveness. This special session for CASE 2018 solicits papers that involve topics of implementation scheme of predictive maintenance (PdM) for smart factories, intelligent prognosis based on AI, automated data selection for model creation of PdM, distributed Map-Reduce-style manufacturing-task processing on edge devices, dynamic dispatching and preventive maintenance for equipment, and intelligent VR-based maintenance system for CNC machines. These topics are relevant to intelligent supporting systems and technologies for achieving intelligent manufacturing. This special session aims to provide a forum for researchers, engineers, industrial practitioners,
and academics to discuss the state-of-the-art intelligent systems and technologies for smart factories. The session will be an opportunity to share both academic and industrial results and vision for the future of manufacturing industries.
SS – 22 Human-centered methodologies for smooth human-machine interaction
Modern industrial automatic and robotic production systems are becoming increasingly complex in order to comply with market demands and competitiveness. Thus, workers are requested to be more and more skilled to be able to operate such systems. As a resulting drawback, many difficulties are typically experienced by human operators, since an increasing burden is put on them, to supervise and interact with very complex systems, typically under challenging situational conditions, such as noisy environment, tight time constraints, fear of job loss, and/or psychological pressure due to the presence of supervisors. In the worst cases, some classes of workers, such as the elderly and those with low education or physical or cognitive impairments, are prevented from job positions that require high attentional skills, or, in the case they are granted any of such positions, their responsibilities and duties are very limited. If jobs of the future are not designed to embrace the wider public, then it will lead to a two-tier system with divisions based around class, sex and race. Indeed, the only workers able to compete in the future jobs market are those with the initial resources and freedom to embark on the skilled training of expensive university educations or informal networks that favour some groups above others. To invert such a policy, it is needed to reverse the design of complex production systems and adopt a responsible approach based on the anthropocentric design methodology. This consists in the process of ensuring that people’s needs are met, that the resulting system is understandable and usable, that it accomplishes the desired tasks, and that the experience of use is positive and enjoyable. In the context of industrial production, this amounts to reverse the paradigm from the current belief that “the human learns how the machine works” to the future scenario in which “the machine adapts to the human capability” and accommodates to her/his own time and features.
This special session aims at soliciting discussion on how the design of human-machine, and human-robot, systems can be enhanced by explicitly considering human factors in the feedback loop, with the ultimate goal of allowing a smooth interaction, accessible also to low skilled and vulnerable users.
Topics of interest include, but are not limited to:
- users profiling,
- measurement of human capabilities,
- users modelling,
- computational models of emotion,
- methodologies for adaptation of user interfaces,
- affective human-machine/robot interaction,
- usability testing,
- social and ethical implications of the introduction of complex interaction systems in industrial environment.
This special session relates to the topic of CASE 2018 on human in the loop (in engineering and operation).