Objetivo The main benefit of NEUROWELD comes from the reduction of the number of weld defects and thus improving the security and reducing the repair cost as well as the costs of welding procedures development in laboratory by optimising the number of tests to be performed.So, such a development should increase the effectiveness of European end-users during their work for developing a welding process and during their welding operations. It will have an impact on the price of welded constructions, on the integrity of welded structures and will permit to progress in the way of the fully automatic welding process in hazardous environment and manufacturing applications.The following steps have been carried out under this EC project in order to successfully develop a NEUROWELD prototype which has been validated in industrial conditions using on-site production conditions ;- Development of an sensor capable to look the weld pool and measure the groove geometry (gap, Hilo) and the data processing algorithms.- Development of the Neural Network Controller used in auto-adaptive configuration,.- Integration of the Neural Network control system with a Fronius' automatic welding machine.- Teaching of the Neural Network and validation of the methodology with TIG, MIG/MAG and TIME twin on stainless steel, carbon steel and Aluminium.- Development of an appropriate methodology for qualifying welding procedure and systems, which use this kind of auto-adaptive control.Further to different tests on different metals using the complete NEUROWELD system for TIG, MIG/MAG as for T.I.M.E twin, the validations have been demonstrated that the concept of welding control by Neural Network is able to work correctly and to offer the best results for good and repetitive welds.All welding qualification results, especially with MAG on carbon steel and MIG on Aluminium, have shown that the Neural Network prototype is able to manage welding conditions for a repetitive quality of welding with several welding processes. It depends only on the initial trials in order to train the Neural Network and constitute the strongest data as a high powerful memory for the future enfaced welding situations.Concerning the impact on welding codes, a new approach can consist in defining a better range of approval, through a new concept. This concept consists in testing, during the qualification of the welding process and on-line tests, the mini-maxi gap values, eventual mini-maxi hilo values, in order to qualify that welding process with all guarantees of high welding quality, even, if the conditions of welding are very poor due to bad penetration of pieces or if, due to risk environment, good penetration would take too more time before welding.This new way for the qualification of auto-adaptive welding process can offer the manufacturer a reduction of costs, if the preparation of pieces to be welded can be made without the same quality level as for classical automatic welding.Further to the completion of the NEUROWELD programme, the ALSTOM-LHB partner has planned to equip its production site in Salgzitter with a first NEUROWELD equipment in order to train its operators to use it under production welding conditions of train roof. The most dramatic problem encountered when using automatic welding in all situations associated with problems of alignment and cast variations between parts to be welded is major penetration defects, especially for the critical root pass. Whereas a skilled welder may easily modify the welding parameters (welding speed, torch oscillation, etc.) in real time as a function of his perception of the weld pool, automatic welding is only possible if the machining and matching tolerances are accurate. These tolerances are difficult or costly to achieve, especially in the case of remotely controlled operation under hostile environments. In the manufacturing industries, off:line programming of robots for small production batches is a much time consuming phase of the production (up to 8 and 12 hours of factory time plus cost of welding tes runs on components). The objective of this project is to extend the automation of the TIG welding process by incorporating a weld pool measurement sensor and adaptive weld parameter generator to achieve welds with no penetration defect. The present state of the art offers no technical solutions meeting the project objectives; a thorough survey has been made for more than 4 years, which indicated that the attempts ??de so far both within Europe and outside have either stagnated at the laboratory stage or enabled only the joint fit up preparation to be accommodated; identical casts have been successfully welded but, when attempting to join parts of same grade but of different cast origins, penetration defects develop. The technical objectives are: 1. Automatically generate in real time welding parameters such as to guarantee complete and accurate penetration of the root pass with different casts and non perfect fit up, 2. Eliminate the need for extensive preparatory laboratory coupon welding runs; This is the most drastic drawback of knowledge based algorithms which require up to 3 months of experimental welding work to construct a data base for one single configuration (grade, thickness, bevel geometry, position). This can be reduced by a factor of 20, through the outcome of this research. 3. The system shall equally be capable of automatically generating torch movements and parameters for the filling passes such that the bead position, the bead shape and the fusion of the walls of the groove comply with quality requirements. The major research tasks are: O Development of a weld pool properties monitoring sensor based on infra red light analysis combined w ith vibration response of the molten metal. In order to make the system to operate in real time, the image processing will require to be completed in 40 ms, which is 25 times higher speed than at present. This will be achieved by the use of parallel processing systems to segment the image Either an INMOS transputer system or TEXAS C 40 system will be considered as the processing hardware. O Study of the correlations between the weld pool properties and the penetration, O Integration of the previously developed system for on line laser measurement of the joint fit up geometry, O Development of a neural network which can be trained with the original welding data base and then updated with the information obtained from the sensors, O Execution of a selected programme of TIG and MIG test welds on carbon steels, austenitic and duplex S.S. aluminium and titanium alloy to verily that the system is effectively capable of automatically producing welds with the specified penetration using specimens of different casts, for cach grade. The consortium comprises of: 7 partners from 5 countries of EU, 4 industrial end users, out of which 2 are SMEs; -> ARSOPI (PT), which is an SME specialised in fabrication of food, process, agro and petrochemical industry welded components in different materials: carbon,low alloy. austenitic and duplex stainless steels. aluminium and titanium. -> The UNIVERSITY OF LIVERPOOL (UK), having expertise in parallel processing (transputers), neural network based machine control systems, high speed image processing and weld pool properties sensors. -> COMEX NUCLEAIRE (FR), having the role of both an end user and welding systems developer, is an SME (Co ordinator). -> LINKE HOFMANN BUSCH (DE), an end user manut`acturing railways vehicles, metro coaches, etc. (Transport Industry Sector) -> FRONIUS (AT). end user. a manufacturer of TIG and MIG MAG welding machines and robots marketed woridwide. -> ISQ (PT). cxpert in welding metallurgy and robotic welding with valuable experience and a National Accredited Body for W elding, Inspection, Certification and Qualifications. -> BUREAU VERITAS (FR). will play a key role to make the project results effectively exploitable by the EU industry from a normative (standards) point of view. Ámbito científico engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systemsengineering and technologymechanical engineeringmanufacturing engineeringsubtractive manufacturingengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsengineering and technologymaterials engineeringmetallurgynatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programa(s) FP4-BRITE/EURAM 3 - Specific research and technological development programme in the field of industrial and materials technologies, 1994-1998 Tema(s) 0101 - Incorporation of new technologies into production systems Convocatoria de propuestas Data not available Régimen de financiación CSC - Cost-sharing contracts Coordinador N/A Aportación de la UE Sin datos Dirección Ver en el mapa Coste total Sin datos Participantes (6) Ordenar alfabéticamente Ordenar por aportación de la UE Ampliar todo Contraer todo ARSOPI - Industrias Metalurgicas Arlindo S.Pinho SA Portugal Aportación de la UE Sin datos Dirección Relva 3731 Vale de Cambra Ver en el mapa Coste total Sin datos Bureau Veritas S.A. Francia Aportación de la UE Sin datos Dirección 17 BIS,Place des Reflets 17 BIS 92400 Pari Courbevoie Ver en el mapa Coste total Sin datos Fronius Schweißmachinen KG Austria Austria Aportación de la UE Sin datos Dirección 15-17,Gewerbestraße 15-17 8754 Thalheim Ver en el mapa Coste total Sin datos Instituto de Soldadura e Qualidade Portugal Aportación de la UE Sin datos Dirección Tagus Park 2781 Oeiras Ver en el mapa Coste total Sin datos Linke-Hofmann-Busch GmbH Alemania Aportación de la UE Sin datos Dirección 38233 Salzgitter Ver en el mapa Coste total Sin datos THE UNIVERSITY OF LIVERPOOL Reino Unido Aportación de la UE Sin datos Dirección Brownlow Hill L69 3GJ LIVERPOOL Ver en el mapa Coste total Sin datos