The efficiency and sustainability of both the manufacturing of actual and future products is still very much determined by the processes that shape and assemble the components of these products.
Innovative products and advanced materials (including nano-materials) are emerging but are not yet developing to their full advantage since robust manufacturing methods to deliver these products and materials are not developed for large scale. Research is needed to ensure that novel manufacturing processes can efficiently exploit the potential of novel products for a wide range of applications.
Integration of non-conventional technologies (e.g. laser, ultrasonic) towards the development of new multifunctional manufacturing processes (including in process concept: inspection, thermal treatment, stress relieving, machining, joining
Manufacturing systems include machines, modules and components that integrate mechanics, materials processing technologies, electronics, and computing capabilities (ICT technologies) to perform desired tasks according to expectations. Mechatronic systems do not only interface with materials, parts and products, they also co-operate safely with factory workers and communicate with other systems in the factory.
Also they connect to manufacturing execution and monitoring systems on a higher factory and management level.
Hence manufacturing systems are becoming smarter in order to generate high value (quality, productivity) while consuming less energy and generating less waste. They feature high levels of autonomy and cognitive capabilities, largely inspired by and making use of robotic technologies.
The needs for reconfigurability and the ability to produce smaller lot sizes of personalized products require not only smart mechatronics but also for higher efficiency and effectiveness in the planning and engineering of such manufacturing systems.
Control technologies will be further exploiting the increasing computational power and intelligence in order to come forward to the demands of increased speed and precision in manufacturing. Advanced control strategies will allow the use of lighter actuators and structural elements for obtaining very rigid and accurate solutions, replacing slower and more energy-intensive approaches. Learning controllers adapt the behaviour of systems to changing environments or system degradation, taking into account constraints and considering alternatives, hereby relying on robust industrial real-time communication technologies, system modelling approaches and distributed intelligence architectures.
Continuous monitoring of the condition and performance of the manufacturing system on component and machine level, enables sustainable and competive manufacturing, also by introducing autonomous diagnosis capabilities and context-awareness. Detecting, measuring and monitoring the variables, events and situations will increase the performance and reliability of manufacturing systems. This involves advanced metrology, calibration and sensing, signal processing and model-based virtual sensing for a wide range of applications, e.g. event pattern detection, diagnostics, anomaly detection, prognostics and predictive maintenance.
Intelligent machinery components will enable the deployment of safe, energy-efficient, accurate and flexible or reconfigurable production systems. This includes the introduction of smart actuators and the use of advanced end-effectors composed of passive and active materials for complex part manipulation or assembly. Energy technologies are gaining importance, such as (super)capacitors, pneumatic storage devices, batteries and energy harvesting technologies.
Production equipment does not yet take full advantage of the benefits that new and advanced materials offer, and factories of the future will need more advanced equipment to meet the requirements for energy efficiency and environmental targets and to meet new demands for a connected world. The future will therefore see modern, lightweight, long-lasting/flexible and smart equipment able to produce current and future products for existing and new markets. There will be a step change in the construction of such equipment, leading to a sustainable manufacturing base able to deliver high added value products and customised production. Increased smartness in the manufacturing equipment also enables a systems approach with machines able to learn from each other and impacting on the human-machine interface.
Smarter equipment and manufacturing systems with self-diagnosis (temperature, vibrations, noise) and embedded sensing, memory or active architecture, with functional materials allowing them to adjust work processes and operations to variances in structure, shape and material composition (right first time manufacture).. Capture of machine data through this inherent ‘smartness’ for communication between machines (for M2M), at factory level and through supply chains for a systems approach to manufacturing and meeting customer demand.
New equipment components taking advantage of new designs and advanced materials (e.g. gears and transmissions providing longer lifetime of equipment, active surfaces that can embed and release lubricant when needed (higher pressures or temperatures))
According to the Factories of the Future 2020 roadmap issued in 2013 (see https://www.effra.eu/factories-future-roadmap), digital manufacturing platforms are described as distributed and collaborative applications, implemented through mash-ups of services implemented by different small and large ICT and manufacturing vendors. The cloud will be the “agora” for provisioning customised functionalities through services that are reliable, secure, and guarantee performance. Open standards will ensure the full inter-operability in terms of data and applications.
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:
Sensors, to convert physical parameters to electrical signals.
Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values.
Analog-to-digital converters, to convert conditioned sensor signals to digital values.
Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages
So, as a summary, Data acquisition is in itself a vast group of protocols, technologies, sensors, hardware and software…
Simulation (often referred to as digital twins) is the imitation of the operation of a real-world process or system. The act of simulating something first requires that a model be developed; this model represents the key characteristics, behaviors and functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time. (from https://en.wikipedia.org/wiki/Simulation)
Complex environments need to be consistently described by semantic models in order to correlate information, describe the dynamics, and forecast their behaviour. Knowledge from different sources (e.g. human, experience, research) will be made available and fully exploited by dedicated modelling and simulation tools.
Integration of the modelling and simulation methods of manufacturing processes in a MDO (Multidisciplinary Design Optimization) to permit an holistic approach and to guarantee fast and costless results
Achieving the goal of sustainable manufacturing requires methods and tools for modelling, simulating and forecasting the behaviour of production processes, resources, systems, and factories during their life-cycle phases. New methods and tools are needed for the design and management of integrated product-process-production system that are well embedded into their social, environmental and economical context.
A holistic and coherent virtual model of the factory and its production machinery will result from the contribution and integration of modelling, simulation and forecasting methods and tools that can strategically support the manufacturing-related activities during all the phases of the real factory life-cycle (e.g. site and network planning, conceptual design, technology selection and process planning, resource design and component selection, layout planning, implementation, ramp-up, operation/execution, maintenance, end-of-life).
Virtual factory models need to be created before the real factory is implemented to better explore different design options, evaluate their performance and virtually commission the automation systems, thus saving time-to-production. Furthermore, virtual factory models will be maintained throughout the lifetime of the production to guarantee an effective and efficient connection with the shop floor. On the one hand, reconfiguration options need to be tested in the virtual factory thanks to modelling and simulation tools and then, after validation, implemented into the real factory in a shorter time. On the other hand, the evolution of the real factory will be reflected and stored into the virtual models of the factory.
Modelling, simulation and forecasting methods and tools for manufacturing may have a great impact on the whole factory hierarchy. At the low level of the hierarchy, methods and tools can improve the design and management of production machinery and processes to support advanced and sustainable manufacturing. Then, methods and tools are required to properly design and manage production systems that are becoming more and more complex. Finally, at the high level of the hierarchy, modelling and forecasting are needed to support long-term strategic decisions.
Real-world resources such as machinery, robots, lines, items and operators are an integral part of the information structure of production processes. All of them need to be connected to each other and to back-end systems and at the same time to be self-aware of the surrounding environment.
The Orion Context Broker Generic Enabler is the core and mandatory component of any “Powered by FIWARE” platform or solution. It enables to manage context information in a highly decentralized and large-scale manner. It provides the FIWARE NGSIv2 API which is a simple yet powerful Restful API enabling to perform updates, queries or subscribe to changes on context information.
The Cygnus Generic Enabler brings the means for managing the history of context that is created as a stream of data which can be injected into multiple data sinks, including some popular databases like PostgreSQL, MySQL, MongoDB or AWS DynamoDB as well as BigData platforms like Hadoop, Storm, Spark or Flink.
In extended enterprises and globalized markets, applications (e.g. Life Cycle Management, Supply Chain Management, Monitoring & Control, and Customer Relationship Management) will no longer operate in closed monolithic structures. Stakeholders and customers collaborating on a common application platform implemented with the cloud approach will bank on new software development and testing environments more oriented towards non-technical users and support development of business processes. Distributed applications with low footprints targeting large user base would be supported by enhanced Business Process Re-engineering tools for rapid development and deployment.
Advanced machine interaction with humans through ubiquity of mobile devices will enable users to receive relevant production and enterprise-specific information regardless of their geographical location and tailored to the context and the skills/responsibilities they own. Interactions with ICT infrastructures and equipment will be natural language-like
The “servitization wave” of manufacturing has already spread out to the advanced countries and many leading high-capital investment sectors (e.g. aerospace and automotive) are already competing in the international markets providing to their customers a composition of services for product operation (e.g. maintenance, reliability, upgrades), and end-of-life use (e.g. re-manufacturing, recycling, disposal). Especially SMEs are trying to compete in the international markets with their niche solutions, adding innovative services to their value propositions. Such innovative business models are based on a dynamic network of companies, continuously moving and changing in order to afford more and more complex compositions of services. In such a context, there is a strong need to create distributed, adaptive, and interoperable virtual enterprise environments supporting these undergoing processes. In order to do so, new tools must be provided for enabling and fostering the dynamic composition of enterprise networks. In particular, SMEs call for tools and instruments which follow them in their continuously re-shaping process, enabling collaboration and communication among the different actors of the product-service value chains. New IPR methods are also needed.
The rise of the transport cost, the need for higher efficiency and productivity, the customer demand for greener product, the higher instability of raw material and energy prices and the shortening of the lead-time production will push for a more critical assessment of the delocalisation strategy towards low cost countries. Service-led personalised products will require a new paradigm for western countries re-industrialisation (Globalisation 2.0), moving back manufacturing of selected products.
Finally, innovation should become a business model in itself and a continuously run business process (the factory innovation): increasing the competitiveness through the design of a new product requires the development of a company strategy where product and process innovation is seen as a permanent, widely distributed, multi level, social oriented and user centric activity. Collaboration among companies of different sectors to exploit multi-disciplinary cross fertilisation is also envisaged. New tools, methodology and approaches for the user experience intelligence (i.e. social networks, crowd sourcing, social science methods, qualitative and quantitative, to generate insights, models and demonstrations, etc.) need to be addressed and explored.
According to the new paradigm of sustainability, the importance of the user is increasing. The user is at the same time a customer, a citizen and a worker. The well-being of the user could therefore become a winning strategy both for B2B as well as B2C companies. More detailed modelling behaviour can help the development of innovative solutions, aiming at user comfort, safety, performance, style; this requires new competitive focus for the development of these innovative solutions and new business models to support a quick and dynamic response to market changes.
As products are today virtually designed and tested before being engineered for production, new business models need also to have tools to support the company to design and test them before they are implemented through products, services and manufacturing processes. The complexity of these tools is higher than that of tools for product development, due the need for holistic modelling of product and processes.
The European Factories of the Future are expected to provide global manufacturing competitiveness, but also to create a large amount of work opportunities for the European population. Future factory workers are therefore key resources for industrial competitiveness as well as important consumers. However, as previously stated, the changing demographics and high skill requirements faced by European industry pose new challenges. Workers with high knowledge and skills (“knowledge workers”) will be scarce resources. Research efforts within Horizon 2020 must address ways to increase the number of people available for, and interested in, manufacturing tasks. This includes the following important aspects of the human resources: - New technology-based approaches to accommodate age-related limitations, through ICT and automation - New technical, educational, and organisational ways to increase the attractiveness of factory work to the young potential workforce, the existing workforce, the potential immigrant workforce, and the older workforce - New approaches to skill- and competence development, as well as skill and knowledge management, to increase competitiveness and be part of the global knowledge society - New ways to organise and compensate factory knowledge workers - New factory human-centric work-environments based on safety and comfort - Ways to integrate future factory work in global and local societal agendas and social patterns