Scope

The study scope is moving towards the implementation of Industry 4.0 principles in manufacturing systems while looking at Industry 4.0-based characteristics that lead to build the future smart factories. This scope is motivated in the welcome page of this TC.

The main research interests addressed in this scope are currently organized in the following topics’ list, defined both building on past experiences and future visions:

Intelligent Manufacturing System (IMS) Modeling and Experimentation
Integrated design and process planning of the manufacturing
Production and Logistics over Manufacturing Networking
Manufacturing Automation over Networks
Dependable Manufacturing Systems Control
Discrete Event Systems in Manufacturing
e-Manufacturing Technologies and Facilities
Advanced maintenance modelling for dependable systems
Intelligent Inspection and Predictive Quality Control
Prognostics and Health Management (PHM)
Digital Twins for advanced plant operations
Artificial Intelligence in the shop floor operations and flow control
Digitalization through the manufacturing chain from product design to production
Trustworthy human-system integration in manufacturing plant control
Intelligent Manufacturing System (IMS) for sustainable operations

The topics’ list is defined at the general level of the TC, while is further specified in the research agenda of the working groups affiliated to the TC.

Working Groups

Working Group Intelligent Manufacturing Systems (Acronym WG IMS)- Chair: Ahmad BARARI

The mission of the Intelligent Manufacturing Systems working group (IMS WG) is “to investigate and develop novel solutions for manufacturing control systems, fostered in the digital era within the cyber-physical manufacturing enterprises”. These solutions merge know-how from control and systems engineering, computer science and software engineering, industrial and manufacturing engineering, and lead to the construction of complex adaptive systems integrating data-driven approaches with the multi-physics based engineering research on the manufacturing technologies and processes. New scientific directions are discussed towards a Smart Manufacturing practice, promising scientific results relevant for the “Factory of the Future” or “Industry 4.0” visions.

In this perspective, Intelligence is a key enabler, developed to support plenty of characteristics aimed to enhance the capabilities of manufacturing systems in terms of their adaptability, agility, scalability, integration, self-capabilities (e.g. self-awareness, self-prescription, self-adjustment, …), resilience, robustness, and sustainability. Indeed, Intelligent Manufacturing is today gaining further momentum as part of the Industrial Transition towards Smart Factories built on the presence of Cyber-Physical Systems. The transition is particularly favourable to establish a manufacturing plant control that embraces a Smart Manufacturing practice exploiting the current development of the key enabling technologies such as, amongst others, augmented reality, additive manufacturing, blockchain, computer vision, and the big data analytics.

Within the IMS WG, we currently aim at focusing on the following scientific directions:

Defining the requirements for digitalization in the overall product development cycle, particularly considering the interactions and integration of the manufacturing processes and systems with their upstream and downstream activities including design, process planning, inspection, maintenance, and the control and management of the other plant operations;
Developing a generation of advanced manufacturing processes and systems to contribute to the needs of Industry 4.0 and the Smart Factories of the future;
Developing advanced analytical and modelling tools for intelligent, effective and efficient control of the manufacturing plant operations, inclusive of monitoring and control of production processes, production plans and shop-floor schedules, and considering the dynamic optimization of process and planning parameters while being field-synchronized with the manufacturing execution;
Developing an integral approach aimed at the inspection, monitoring and control of the product quality, data collection and data analytics in order to embed self-capabilities to manage quality in the manufacturing systems (self-awareness, -diagnosis, -prognosis, -prescription, and -adjustment);
Developing intelligent support systems for a trustworthy human-system integration and decision making in manufacturing plant control.

To this end, it is essential building collaborations with the industrial sectors world-wide for knowledge transformation, successful implementation, and experimental validation of the developed concepts and solutions in industrial settings.

In accordance with the mission statement, the research agenda includes a list of topics of interest:

Intelligent decision making for cost-effective manufacturing
Intelligent decision making for resource efficiency and circular economy
Smart quality assurance, integrated inspection, monitoring and control systems
Smart maintenance with predictive and prescriptive capabilities
Smart logistics and control methods for smart part logistics
Smart production and control methods for smart production
Design for manufacturing, concurrent and closed-loop engineering applications
IoT methods for manufacturing shop floors
Virtualization and simulation techniques for manufacturing decision making
Digital twins for decision making in plant operations in Industry 4.0 era
Artificial intelligence in the shop floor operations and flow control
Artificial intelligence for manufacturing systems & processes
Big data analytics for manufacturing systems & processes
Machine learning for manufacturing systems & processes
Bio-inspired technologies and methodologies applied in manufacturing control
Theory of complexity, swarm intelligence, robustness and self-adaptation in manufacturing control
Augmented reality for operator assistance
Co-bots and innovative robotic technologies and their implementations in manufacturing shop floors
Additive manufacturing for on-demand production and personalized products
Computer vision systems for manufacturing processes
Fusion of sensor information for manufacturing processes
Blockchain technology and its applications in manufacturing
Autonomy, autonomous vehicles, and drones in manufacturing
Self configuration and self-diagnosis in manufacturing systems
Self optimization models for scheduling manufacturing activities in the shop floor
Self organizing systems and emergent behavior for advanced plant operations

For news have a look in the news page.

Working Group Advanced Maintenance Engineering, Services and Technology (Acronym WG AMEST)- Chair: Christos EMMANOULIDIS

The mission of the AMEST WG is “to create, share and promote new knowledge pertaining to the technology, engineering and management of advanced maintenance systems”.

The AMEST WG promotes interdisciplinary approaches in maintenance development, leveraging on a blend of technology, engineering and management methodologies to provide decisive contribution to production enterprises’ business goals. It focuses on the design, management and improvement of advanced maintenance systems considering different interconnected and emerging engineering methodologies and technologies. It fosters the importance of maintenance services and new business models, capitalising on the integration and advancement of Industry 4.0 technologies adoption in production plants, together with the establishment of new management approaches. Overall, blending technology, engineering and management is a relevant means in order “to go beyond”, by implementation of proactive strategies not only focused on maintenance objectives but also on the objectives of environmental protection, wealth and safety of people.

The ongoing Industrial Transitions towards digitalisation is reflected in the increased interest of the IFAC AMEST WG in highlight that the Maintenance and Asset Management functions remain one of the biggest beneficiaries of this digital revolution. We therefore strive to advance the knowledge and application practice of employing technologies such as Industrial Internet of Things (IIoT), Cyber Physical Systems (CPS), and Digital Twins, which offer major opportunities for companies in the manufacturing sector to improve their processes and services.

Within the AMEST WG, we currently aim at focusing on the following scientific directions:

Exploring the linkage between the real world and the virtual world enabled by IIoT, CPS and Digital Twin technologies and supported by advanced Data Analytics, Machine Learning and more broadly Artificial Intelligence.
Developing techniques to embed such intelligence across the whole data process chain in manufacturing plants, from the Edge Level with advanced sensing and production asset and processing monitoring, all the way to Cloud-based offerings, made increasingly possible through the Virtualisation of relevant activities and operations and the Digital Twinning of the manufacturing assets and process hierarchies.
Identifying the opportunities offered by the effective integration of human and technical (including AI) actors. These are of key interest as they result in the amplification and enhanced effectiveness of the capabilities of the complex sociotechnical systems that today’s manufacturing plants are.
Developing industrial systems that are transparent, trustworthy, sustainable, and optimised, delivered through the advanced maintenance function.
Defining business models that exploit demand-driven, agile, and product-service system offerings.
Exploring and defining the links between maintenance with other manufacturing functions such as production planning and control, services, as well as logistics and supply chain management, across the range of connected system and business actors in a networked production ecosystem.

In accordance with the mission statement, the research agenda includes a list of topics of interest:

Maintenance strategies, organizational and economical methods
Maintenance business model design, processes and technology development
Maintenance effectiveness evaluation: economic, environmental and social impacts
Maintenance performance improvement
Lifecycle management and lifecycle simulation
Maintenance within asset management
Dependability, trustworthiness, and integrity management
Total productive maintenance (TPM)
Integration of maintenance management and production planning and control, logistics, and supply chains
Maintenance and integrity management for sustainable manufacturing plant operations
Value-based maintenance
Maintenance services and impact of emerging business models on plant operations
Reliability, statistical approaches
Reliability and maintenance engineering
Maintenance and dependability, fault tolerance and management, contribution to safety
Diagnostics, prognostics, reasoning and decision support
Condition monitoring, sensors, signal analysis and failure analysis
Prognostics and health management (PHM)
Predictive and proactive maintenance
Digital Maintenance including:
- Maintenance data management; industrial data spaces, information modelling and interoperability, semantic technologies; maintenance metadata; big data analytics pertaining to maintenance, linked data and maintenance knoweldge
- Emerging technologies in maintenance, such as e-maintenance, automation and robotics in maintenance, advanced, autonomous and smart sensing technology, industrial internet of things, cyber-physical systems, edge computing, cloud technologies, augmented and virtual reality, machine learning and artificial intelligence.
- Digital Twins (DT) for advanced maintenance and integrated maintenance, simulation, and optimisation services
- Virtualisation of manufacturing plant operations and maintenance
Human factors in maintenance; Industry 4.0-enabled maintenance work design, innovative education, training and knowledge management

For news have a look in the news page.