Presentation on theme: "1 Intelligent Manufacturing roadmap Towards the process industry lean factory of the 21st century Document prepared by the Intelligent Manufacturing WG."— Presentation transcript:
1 Intelligent Manufacturing roadmap Towards the process industry lean factory of the 21st century Document prepared by the Intelligent Manufacturing WG (WG1-Profit) L. Chefneux (ArcelorMittal, chairman) Members : S. Fera (Riva), N. Goldenberg (Siemens), W. Moonen (Corus), A. Mouchette (ArcelorMittal), H. Peters (BFI), C. Pietrosanti (CSM), L. Sancho (ArcelorMittal), G. Tourscher (ArcelorMittal) September 3rd 2009
2 Challenges for the process industry All industries have to control their production in order to serve the customer with the best product, just in time and at minimal cost, in a sustainable way Sustainable and Lean manufacturing are the objectives to be achieved This concept mainly developed in discrete manufacturing has to be extended to process industries ( continuous or semi-continuous ) In particular the materials producing industries (steel, aluminum, other non ferrous, ferroalloys, glass, paper, cement, chemical) are facing very important challenges : - CO2 emissions constraints - energy cost and scarcity - raw material cost and scarcity - logistic constraints - ecological footprint - sustainability Intelligent Manufacturing gives the integration tool permitting the global control of the whole production chain supporting the Lean manufacturing deployment
3 Definition of IM for material process industries Intelligent manufacturing is the progressive building of an integrated control of manufacturing chains including all technological aspects ( utilization of sensors, process control loops, IT systems, production scheduling …) with the addition of intelligence provided by modelling, advanced control including cognitive automation concepts, diagnostic and advanced maintenance tools, optimization and simulation, expert knowledge, artificial intelligence…, in good coherence and interaction with human intelligence TECHNICAL CONTENT / SCOPE
4 Intelligent Manufacturing CUSTOMERS Data processing Ind. Information Syst. Supervision, DB Data Mining, KPI. Modelling Physics Statistics, NN Mixed Products Quality & sustainability Quality Control Prediction, correction Production Chain & global company Simulation Costs, route & flow Optimization Operation Research Production Lines Advanced maintenance Automatic control Diagnostic Evolution of present facilities Engineering Intelligent Machines Line Process Suppliers Line Process Line Process Production Chain Measurements – Data Instrumentation, Sensors Validation, Reconciliation
5 Defining a criterion Based on the concept of integration of different bricks, the 10 generic topics Sustainable & Lean manufacturing Real time control Product driven Just in time Cost driven Use of sensors ( 1) Use of Data processing (2) Advanced maintenance (7) Product quality control (5) Advanced control (6) Diagnostic ( 3) Supply and Production chain management ( 8 ) Modeling, Simulation & Optimization ( 4) Knowledge use ( 9) Integration of processes (10) Sustainability driven
6 Labelling of an IM project A project could be labelled IM if it concerns at least THREE generic topics (two if CLEARLY relevant to factory wide aspects) with a significant impact, and dealing with the global control of minimum one big process step (e.g. BF, BOF, CC, HSM, HDG…) * *Each project asking for the label will pledge to contribute to the IM Roadmap communicating the needed information
7 Important notice : The main difficulty to implement the factory of the future ! the ambition must be shared and supported by the whole company. It will require huge investments and to integrate all the functions in the company : technical, financial, IT, quality, maintenance, logistics, purchasing, human resources, as well as R&D R&D is a modest player but indispensable. It has to show the way and to be the link between all the functions. A lot of innovations will come from these and from outside through external suppliers and consultants. All will be largely depending on the progress in the critical technologies linked with the knowledge driven society : ICT, sensors, AI, KM, micro-technologies We will focus here after on the R&D activities supporting this huge ambition !
8 ROADMAP A continuous progress with overlaps | | | | | critical technologies for a knowledge driven society : ICT, sensors, AI… whole factory wide supervised control supervised control of factory parts optimization of process groups single process modelling and optimization Short term Medium term Long term
9 Short term ( ) –Projects respecting our labelling criteria and dealing with Modelling and optimization of single processes Global control of a consistent sub-part of the production chain Demonstration of cascaded monitoring and control through some, at least two coupled production chain links on some specific topics (product quality, energy saving …) filling as far possible the identified deficiencies pinpointed by examining the recent RFCS labeled projects, as : data validation cost and energy modelling production scheduling advanced maintenance supply chain management All existing possibilities have to be used to support the efforts, RFCS for steel specific projects, FP for generic ones, with process industries or discrete manufacturing ones, without neglecting the national and regional levels !
10 Medium term ( ) –Global control of aggregated sub-parts, –Deployment of cascaded monitoring concepts and tools including the supply chain management and customer delivery IRON and STEEL PLANT Cold, coating & finishing PLANT HOT ROLLING Raw Material supplying Customer
11 Keep in Europe an important production capacity, creating added value and wealth Reference for our equipment suppliers Preserve employment, although contributing to decrease the workforce in the industry Socio-cultural impacts linked to the implementation of the knowledge driven society : development and sharing of know-how, change of management practices, HR aspects, education and long- life learning… SOCIO-ECONOMIC ASPECTS STAKEHOLDERS Steel industries Suppliers (heavy equipment, IT, high-tech SMEs) and sub-contractors Research centres, universities (steel, sensors, automation, IT, KM, human aspects) Innovative SME Other continuous process industries Discrete manufacturing industries
12 To clarify the concept, a success story as didactic example : ASIS Automatic Surface Inspection System
13 Application of ASIS some years ago ASIS: Automatic Surface Inspection Systems ASIS of different plants are running independently ASIS results are only locally stored and no connection to process data exist Very often ASIS are used as intelligent video recorder
14 Typical situation today
15 Identified problems ASIS are not measurement devices, because the accuracy (reliability) can not be guaranteed Results of ASIS from different suppliers are not directly comparable Usage of ASIS data is not systematic and not integrated in on-line quality control loops No general solutions for typical usage of ASIS data exist, like automatic coil allocation, automatic root cause analysis, etc.
16 Research project starting in July 2009
17 Improvements after successful project completion Continuous monitoring of all ASIS components Monitoring system gives hints for maintenance actions or adaptations of ASIS parameters ASIS data are more reliable and can be used for further purposes Task related surface KPIs for one process and the complete process chain are calculated automatically Based on the surface KPIs a systematic supervision of product quality and process performance is realised The basis for future on-line quality control of surface quality is ready