Research Area Overviews

AME's research and innovation areas are driven by the key challenges faced by industry and combine rapidly developing enabling technologies with the leading academic expertise offered by the Institute.

This area involves factory design, data collection and management, operation and planning, from real time to long term optimisation approaches. It contains the following topics:

  1. Development and implementation of more effective design for manufacturing techniques based on virtual models and simulation tools allowing gathering feedback from process monitoring and quality inspection systems and translating it in concrete suggestions for improving the product design in view of its manufacturing, with consequent cost reduction and quality improvement.
  2. Factory floor and physical world connectivity. Methods and tools are required to properly design and manage production systems that are becoming more and more complex and need to be adaptable to changing requirements (customisation, flexible).
  3. Systems for complete traceability of tools, production progress and products in real time.
  4. Cybersecurity and secured concepts for digital manufacturing communications and cloud computing.
  5. Virtual reality and augmented reality simulators for planning and operation of manufacturing systems.
  6. AI and ML solutions to address challenges such as intelligent planning and scheduling, flexible production line design (lot-size-one), more autonomous machine operation (for example to inspect and repair itself without any human intervention)
  7. Real machine and process data during production across the supply chain needs and situations (global data for industrial and economical aspects for business decision making.

The research area encompasses innovative manufacturing equipment at component and system levels, mechatronics, control and monitoring systems. The following list includes the key topics for research:

  1. Real time measurement systems, sensors and algorithms for process diagnosis and control. This includes any type of sensor and development of complex multisensory networks including multi-sensor fusion strategies for process monitoring, integrated with cognitive systems for intelligent and self-optimising production equipment.
  2. Data monitoring for the whole manufacturing life cycle: Real time process optimisation, Production strategies optimisation, Energy consumption optimisation, Machine components’ life prediction, life increase and replacement and reuse strategies definition and optimisation of machines and systems by the feed-back information integrated into digital twin models.
  3. Development of sensors and systems for process diagnostics, multi-objective, multi-variable models. “Hybrid” models combining the “physical” simulation with the real data obtained from the human experience and the data provided by dedicated monitoring systems are also needed.
  4. Integrated knowledge-based systems supporting the product and process archetypes approach, with self-learning capabilities for semi-automatic design rules update.

The research area encompasses the critical aspects of measurement systems in the context of digital and integrated engineering and manufacturing systems.
The following list includes the key topics for research:

  1. Digital twins, virtual sensors, sensor interfacing and measurement standards including the standardisation and optimisation of industrial sensors.
    Industrial Internet of Things (IIoT) enabled uncertainty budgets for sensors and measurement systems.
  2. The role of metrology in the loop. Metrology considerations for connected end-to-end engineering processes.
  3. Measurement applications based on IIoT systems.
  4. Measurement systems based on wireless sensor networks and IIoT.
  5. Wearables-based measurement systems and Body Sensor Networks.
  6. Measurement data management for digital manufacturing and engineering systems.

The PCP area focuses on improving our understanding of humans, workers and the workplaces and enhancing the role of people in manufacturing by improving their interaction with software and machines to improve productivity levels. The research domain includes the following topics:

  1. Developing functions with cognitive abilities that contribute to a reduction of programming and configuration requirements. There are clear benefits in reducing the time and skill needed to reconfigure and adapt systems to new processes. This includes ICT solutions for the interaction between machine and human.
  2. Concepts for smart use of virtual or augmented reality improving operations/process flow visualisation systems and human enhancement.
  3. Friendly, inclusive and healthier work environments (noises, emissions, vibrations, loads, repetitive tasks, ergonomics).
  4. Tools to ease human-systems interaction, such as virtual reality or augmented reality, semantic interfaces adapted to the skills of the momentary operator, and tools to include the knowledge provided by the experience of the operators to the machines will be required.
  5. New machine architectures, including collaborative integration of robots for flexibility and multi-processing capabilities. AI supporting a human to make better decisions in highly controlled environments with low risk tolerance.
  6. Ergonomic human-robot collaboration, for human performance improvement and error minimisation. Coexistence of robots integrated with manual processes.

Advanced Manufacturing Processes includes all types of innovative processing for either new and current material or products. It includes the following research topics:

  1. New and emerging advanced materials and processes.
  2. The application of new materials (lighter, active, intelligent) gives an opportunity to overcome the performance limits of current machines.
  3. Resource utilisations and more efficient materials removal processes for advanced metallic alloys and other materials.
  4. New part functionalities through surface manufacturing processes.
  5. Advanced joining and manufacturing for disassembly and recycling/reuse of advanced material combinations.
  6. Innovative physical, chemical and physicochemical processes.

Research is required to understand how advanced manufacturing technologies can enable the implementation of circularity and ultimately scale up the recirculation of resources. The key research areas include:

  1. Digital Material Passports for optimising the recirculation of materials in production systems that require information regarding material composition, optimal productivity, and how-to disassembly and recycle the component or product in question. Digital material passports (e.g. QR codes; Smart tags etc.) is a key mechanism to capture this vital information that would enable manufacturers to recover, re-use and recycle valuable resources.
  2. Industrial Symbiosis using Digitisation. Connecting Industry 4.0 systems and internet-connected technologies is central to creating real-time digital marketplaces. Scaling up implementation of real-time digital marketplaces requires I4.0 systems, standardisation, data security, and maintaining system stability ICT systems are interconnected.
  3. Circular Metrics and Mapping Tools for Manufacturers. Measuring progress in the circular economy requires standardised frameworks and indicators based on life-cycle analysis, which takes into consideration resource flows from raw material extraction, design, distribution and multiple use-phases to ultimate disposal at end of life.
  4. Systems for sustainability in terms of energy and resource consumption and impact in the environment. This includes design aimed at manufacturing, assembly, disassembly, remanufacturing, reuse and recycling. Also, recyclability of new materials, hybrid processing strategies for minimum resource consumption and reduction of the carbon footprint.

Involving partners across the manufacturing value chain will increase in importance, from product process design to manufacturing associated innovative services. Research themes in this area include the following topics:

  1. Adapting Manufacturing business models and strategies to suit new interconnected value chains. This includes not only engineering and technical transformation but also its impact on business strategies and policies and procedures.
  2. Identifying, generating, protecting and capturing intangible asset value in digital manufacturing businesses. A number of changes are required in order to transform manufacturing businesses from mostly tangible asset based businesses to intangible asset businesses which rely on knowledge and information to generate high value.
  3. Distributed ledgers, Blockchain and smart contracts for autonomous and connected value chains.
  4. Modular systems, reconfigurable machines and processes for efficient business models adaptation to customer demands.
  5. Customisation of products and processes in real time across the value chain.
  6. Services for product operation (e.g. maintenance, reliability, upgrades), and end-of-life use (e.g. re-manufacturing, recycling, disposal). New tools must be provided for enabling and fostering the dynamic composition of enterprise networks, in particular SMEs.
  7. User-centred to user well-being centred design. The well-being of the user could therefore become a winning strategy. More detailed behaviour modelling can promote the development of innovative solutions, aiming at user comfort, safety, performance, style. Innovative solutions and new business models for manufacturing.
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