Editorial

Manufacturing is a significant wealth generation sector, accounting for over 20% of the European Union's (EU's) gross domestic product. To compete successfully in the global market, European manufacturing industry needs to be underpinned by well-focused advanced production systems research. Because of the breadth of the field, commercial considerations and the multi-nationalism of the EU, production research activities within it have been naturally fragmented.

Under its Sixth Framework Programme (FP6), the EU introduced Networks of Excellence as a new ‘instrument’ to overcome fragmentation of European research and help shape the conduct of research in Europe. The operation of these networks is based on a joint programme of activities aimed principally at integrating the research activities of the network partners while also advancing knowledge on the topic.

The EU FP6 Network of Excellence (NoE) for Innovative Production Machines and Systems (I∗PROMS) was inaugurated in October 2004. I∗PROMS integrates the production research activities of 30 research centres from 14 countries in Europe. It addresses production research in an integrated manner to help shape this research area and overcome fragmentation. By creating an EU-wide research community concentrating on future manufacturing concepts, processes and systems, I∗PROMS acts as the main research hub within the EU for the whole area of production machines and systems.

I∗PROMS adopts the knowledge-based ‘Autonomous Factory’ vision for delivering increased competitiveness for manufacturing in 2020. The network focuses on intelligent and adaptive production machines and systems that meet dynamic business and value drivers through advanced Information and Communication Technology.

I∗PROMS promotes the development of common concepts, tools and techniques enabling the creation and operation of flexible, re-configurable, sustainable, fault-tolerant and eco- and user-friendly production systems. Such systems should react rapidly to changing customer needs, environmental requirements, design inputs and material/process/labour availability to manufacture high quality, cost-effective products.

I∗PROMS addresses six manufacturing challenges, namely, concurrent manufacturing, integration of human and technical resources, conversion of information to knowledge, environmental compatibility, reconfigurable enterprises and innovative manufacturing processes and products. Work on those themes is conducted by four interconnected clusters: advanced production machines, production automation and control, innovative design technology and production organisation and management. The research undertaken by these four I∗PROMS clusters cover a broad range of topics including new processes for new materials, miniaturisation, mechatronic modules, nanotechnology, modelling and simulation, product life cycle planning, flexible manufacturing systems, process integration, new process control and sensors concepts, intelligent manufacturing process/near-net shape processes, and substitution of harmful substances. Further information can be found at the I∗PROMS website (http://www.iproms.org).

The papers presented in this issue were selected from the I∗PROMS Virtual Conference on Intelligent Production Machines and Systems (http://conference.iproms.org). In manufacturing, as in many other domains, human intelligence plays an important role. As a consequence, systems that attempt to model and utilise aspects of human intelligence have been developed for different manufacturing related tasks throughout the product lifecycle, ranging from design to production to quality control and finally to the use and maintenance of the product. The articles in this special issue cover all of these aspects:

Design is addressed in the papers on:

• Early Analysis of Design Concepts using Rough Set Theory (D. Alisantoso and L.P. Khoo)

• Integration of Genetic Optimization and Neuro-Fuzzy Approximation in Parametric Engineering Design (K. Saridakis and A.J. Dentsoras)

Production machines and systems are covered by the articles on:

• Application of Genetic Algorithms to Robot Kinematics Calibration (K. Wang)

• A method for optimising the nesting of multiple, highly complex shapes using a modified Simulated Annealing algorithm (R. La Brooy)

The process of quality control through production inspection is dealt with by:

• Automated visual inspection system for wood defect classification using computational intelligence techniques (G.A. Ruz, P.A. Estevez, P.A. Ramirez)

• A flexible visual inspection system based on neural networks (P. Liatsis, J.Y. Goulermas, X.-J. Zeng and E. Milondis)

Use and maintenance of products is addressed by:

• An object-oriented framework for virtual intelligent product manuals (C. Pasantonopoulos, S.S. Dimov, R.M. Setchi, D.T. Pham)