LUNAM Université Ecole Centrale de Nantes

Computers & Industrial Engineering

The key role of information and communication technologies (ICT) to improve manufacturing productivity within the paradigm of factory of the future is often proved. These tools are used in a wide range of product lifecycle activities, from the early design phase to product recycling. Generally, the assistance tools are mainly dedicated to the management board and fewer initiatives focus on the operational needs of the worker at the shop-floor level. This paper proposes a context-aware knowledge-based system dedicated to support the actors of the factory by the right information at the right time and in the appropriate format regarding their context of work and level of expertise. Particularly, specific assistance functionalities are dedicated to the workers in charge of the machine configuration and the realization of manufacturing operations. PGD-based (Proper Generalized Decomposition) algorithms are used for real time simulation of industrial processes and machine configuration. At the conceptual level, a semantic model is proposedas key enablersfor the structuration of the knowledge-based system.

2008

1-everything in heads 2-mostly in heads 3-in heads and dokumented 4-mostly documented 5-everything documented Abstract—This paper focuses on the systematic methodology for incorporating intelligence and development methodology for knowledge acquisition system in an automated manufacturing environment. The intelligence is incorporated in the shape of technology data catalogue that contains the knowledge about production system as a whole. The knowledge acquisition system is implemented in the form of a multiuser scalable interface into remote human machine interface devices (e.g. Personal Digital Assistants) with a purpose of extracting concrete and precise information and knowledge about manufacturing systems and processes in highly automated manufacturing environment. The extraction of precise knowledge as well as organized access to the knowledge will facilitates the operators, technicians and engineers for making faster, safer and simpler on-process modifications and parameters o...

2013

When the question about the "factory of the future" is discussed, it is often mentioned the use of innovative approach for knowledge acquisition and exploitation interfering in the different phases of the product lifecycle. Consequently, the use of innovative Knowledge Management Systems based on Information and Communications Technology (ICT) in the factory environment has become a must. Our research work will focus on the development of a "digital factory assistant" to help factory's actors in their daily tasks and in particular tasks that demand to make a decision.

Procedia CIRP, 2014

In the modern world we are continuously surrounded by information. The human brain has to analyse and interpret this information to transform into useable knowledge that is then used in decision making activities. The advent and implementation of Industry 4.0 will make it a requirement for systems within factories to interact and share large quantities of information with each other. This large volume of information will make it even more difficult for the human resources within the factory to sift through the large amount of information required since there is a limit to the information that our brains can cope with. Just in time information retrieval (JITIR) within the digital factory environment aims to provide support to the human stakeholders in the system by proactively yet non-intrusively providing the required information at the right time based on the users context. This paper will therefore provide an insight into the cognitive difficulties experienced by humans in the digital factory and how JITIR can tackle these challenges. By validating the JITIR concept, several industry scenarios have been evaluated: an exemplary model, concerning the machine tool industry, is presented in the paper. The results of this research are a set of guidelines for the development of a digital factory support tool.

Wcsc 2013 Proceedings of the 3rd World Conference on Soft Computing, 2013

Technology in the manufacturing sector has seen rapid change, transforming from stand alone, manual processes to smart, integrated systems. We have witnessed the migration of relay-based systems to advance SCADA systems, manual production to fully automated, and hand written reports to interactive computer-based dashboards. We are now seeing the emergence of smart products manufactured in smart plants and the evolution of smart services in manufacturing. Future manufacturing systems will be distinguished by intelligent machines, automation and human factors' integration. This talk will focus on how knowledge can be embedded in processes and products through the use of simulation and modelling tools to streamline future smart production systems and improve product quality. The implications to future smart manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.

Procedia Computer Science, 2016

This paper presents a framework for monitoring, analysing and decision making for a smart manufacturing environment. We maintain that this approach could play a vital role in developing an architecture and implementation of Industry 4.0. The proposed model has features like experience based knowledge representation and semantic analysis of engineering objects and manufacturing process. It is also capable of continuous real time visualization of key performance indicators (KPI's) and supports M2M communications over novel protocols like OPC-UA. Our model covers the industrial manufacturing cycle right from capturing raw data at machine level, converting it into useful information, doing semantics analysis and performs real time KPI visualization.

Mechanik, 2017

The paper illuminates the innovation in software – technical issues related to the concept of intelligent factory presented at TMTS show (Taiwan 2016). Programs and systems for monitoring and management of machines and production lines. And independent actions taken by machine manufacturers to adapt their products to the idea of Industry 4.0.

Manufacturing System, 2012

Numerous and significant challenges are currently being faced by manufacturing companies. Product customization demands are constantly growing, customers are expecting shorter delivery times, lower prices, smaller production batches and higher quality. These factors result in significant increase in complexity of production processes and the necessity for continuous optimization. In order to fulfil market demands, managing the production processes require effective support from computer systems and continuous monitoring of manufacturing resources, e.g. machines and employees. In order to provide reliable and accurate data for factory management personnel the computer systems should be integrated with production resources located on the factory floor. Currently, most production systems are characterized by centralized solutions in organizational and software fields. These systems are no longer appropriate, as they are adapted to high volume, low variety and low flexibility production processes. In order to fulfil current demands, enterprises should reduce batch sizes, delivery times, and product life-cycles and increase product variety. In traditional manufacturing systems this would create an unacceptable decrease in efficiency due to high replacements costs, for example. Modern computer systems devoted to manufacturing must be scalable, reconfigurable, expandable and open in the structure. The systems should enable an on-line monitoring, control and maximization of the total use of manufacturing resources as well as support human interactions with the system, especially on the factory floor. Due to vast amounts of data collected by the systems, they should automatically process data about the manufacturing processes, human operators, equipment and material requirements as well as discover valuable knowledge for the factory's management personnel. The new generation of manufacturing systems which utilizes artificial intelligence techniques for data analyses is referred to as Intelligent Manufacturing Systems (IMS). IMS industrial implementation requires computer and factory automation systems characterized by a distributed structure, direct communication with manufacturing resources and the application of sophisticated embedded devices on a factory floor. Many concepts in the field of organizational structures for manufacturing have been proposed in recent years to make IMS a reality. It seems that the most promising concepts www.intechopen.com

2008

On the base of a running national research project on digital enterprises and production networks, the paper illustrates how the concepts of intelligent manufacturing systems in view of digital enterprises can contribute to the realization of a digital factory. It describes the main targets, steps and results of the third cluster of the above introduced project, called "Monitoring of complex production systems" The cluster incorporates three main work areas representing the continuous development starting with basic and applied research, followed by research and development (R&D) assignments, ending in the marketoriented demonstrations of the cluster results.

Advances in Intelligent Systems and Computing, 2017

Scheduling production instructions in a manufacturing facility is key to assure a efficient process that assures the desired product quantities are produced in time, with quality and with the right resources. An efficient production avoids the creation of downstream delays, and early completion which both can be detrimental if storage space is limited and contracted quantities are important. Therefore, the production, planning and control of manufacturing is increasingly more difficult as family products increases. This paper presents an ongoing Ambient Intelligent decision support system development that aims to provide assistance on the creation on standard work procedures that assure production quantity and efficiency by means of ambient intelligence, optimization heuristics and machine learning in the context of a large organization.