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Industry 4.0 - I (25 June / 14:00 - Room 01)

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25 June / 14:00 - Room 01:

Digitalization innovation in automotive strip production

K. Van Teutem
(Fives DMS S.A., France)

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Title: Digitalization innovation in automotive strip production


Author:
K. Van Teutem

Company:
Fives DMS S.A., France

Co-Authors:

Abstract:
Today’s steel processing plants are becoming "smart" and more agile. Digital tools offer solutions that facilitate production system management, quality control and maintenance by means of data capture and analysis, mathematical and statistical modelling of production processes, digital control and process feedback. Digital technologies link the complete steel making processes that enable production to be adapted in real time to requirements and resources. This paper examines the latest developments in digital quality control methodology as applied to automotive grade strip steel production including through process control to upstream steel making processes.

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25 June / 14:20 - Room 01:

Cyber-attacks for breakdown or intentional quality reduction - how secure is the European steel production in the era of digitalisation?

M. Neuer
(VDEh-Betriebsforschungsinstitut GmbH, Germany)

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Close25 June, Room 01 ( 14:20 )
Title: Cyber-attacks for breakdown or intentional quality reduction - how secure is the European steel production in the era of digitalisation?


Author:
M. Neuer

Company:
VDEh-Betriebsforschungsinstitut GmbH, Germany

Co-Authors:
A. Wolff

Abstract:
Cyber-attacks on the European steel producers have become a real threat, where massive production breakdowns are rare, but very expensive events. Companies prepare for these problems, often on the IT level, where firewalls, internet restrictions or staff compliance rules are enforced to secure the on premise networks. For the staff this topic often regards to the right handling of malicious Emails or non-compliant USB sticks which address some of the most urgent security issues. But the threat is much more fundamental. Intentional attacks are targeting not only production systems with the aim to stop the process or destroy machinery, moreover, they secretly reduce the product quality and try to remain undetected as long as possible. To do this, an attack must be explicitly target the automation layer of a plant. The talk will elaborate about this threat using simple examples that demonstrate attack vectors and working principles, without disclosing details on processes. It also shows counteractions as well as mitigation and secure shutdown strategies to prevent any larger damages to machinery or continued downgrading of product quality.

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25 June / 14:40 - Room 01:

Digital twin of an integrated steel plant in m.simtop – strategic operations planning and cost optimization in the digitalization era

B. Weiss
(Primetals Technologies Austria GmbH, Austria)

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Close25 June, Room 01 ( 14:40 )
Title: Digital twin of an integrated steel plant in m.simtop – strategic operations planning and cost optimization in the digitalization era


Author:
B. Weiss

Company:
Primetals Technologies Austria GmbH, Austria

Co-Authors:
A. Spanlang, W. Wukovits

Abstract:
Iron and steel making requires a wide range of different raw materials significantly influencing process performance. This demands a continuous optimisation of process routes with respect to energy efficiency as well as environmental emissions. Steadily changing raw material prices and qualities, market situations and product variations are challenging integrated steel plant operators in production planning and cost optimization.
On the basis of state of the art process simulation practices as applied in petrochemical and oil and gas industries was decided to develop a comprehensive metallurgical flow sheet model library for simulation and optimization of integrated steel plants. Intensive development efforts were taken in order to migrate existing well established calculation and engineering routines as well as integrate newly developed models. The generated model library enables the setup of mass and energy balances for integrated steel plants in hand with professional simulation and optimisation capabilities. Development and evaluation of new process concepts as well as investigations of impacts of raw material changes and trace material distributions can be performed in one platform. By using this process integration system, it is possible to compare different iron and steelmaking routes within one standardized environment. In this publication an insight will be given in depicting of an integrated steel plant operation on the example of a real operation simulation along with an analysis of selected trace materials via the full ironmaking production route. Additionally the functionalities for raw material planning by usage of optimization techniques and consideration of core KPIs will be shown. The effect of applied functionalities on operation figures and operation costs will be clearly illustrated.

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25 June / 15:00 - Room 01:

Application of advanced artificial intelligence in the manufacturing execution system for metals industry

A. Klein
(SMS group GmbH, Germany)

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Title: Application of advanced artificial intelligence in the manufacturing execution system for metals industry


Author:
A. Klein

Company:
SMS group GmbH, Germany

Co-Authors:
W. Runde, T. Ohm, K. Ptaszyk, I. Bleskov, M. Passon, M. Hütt

Abstract:
Manufacturing Execution Systems (MES) provide powerful capabilities for production scheduling.

At X-Pact® MES 4.0 from SMS group the capacity scheduling is performed by the Advanced Planning System, which reserves the required durations for production of each product. These predictions are made by the Technical Order Generator, where technological rules are applied to calculate the maximum possible process speeds. In the Production Sequencing System, sequences are formed by selecting slabs or strips from the order backlog. It also optimizes the production order within a sequence for maximum yield.

SMS group improved its MES by application of state-of-the-art artificial intelligence methods. The main benefits are easier handling and improved results of the sequencing step in the Production Sequencing System. The selection of product items for a sequence is done automatically, which saves time and improves yield. For this selection a combination of technological rules with advanced machine learning methods is applied.

Another improvement is the continuous monitoring of remarkable coincidences between production situations and production efficiency or quality. In case that suspicious events are identified, the system generates messages that help to identify the root cause of the yield or quality losses. This is done by applying statistical and artificial intelligence methods.

Finally the scheduling precision is improved by continuous and autonomous usage of machine learning methods. Different data models are trained and evaluated. The best one for each product type and process step is then deployed in the live system. The technological rules in the Technical Order Generator are supported by learning from the historic production data.

Industry 4.0 - II (25 June / 15:50 - Room 01)

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25 June / 15:50 - Room 01:

Capturing, analyzing and documenting big data for continuous process improvement

U. Lettau
(IBA AG, Germany)

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Close25 June, Room 01 ( 15:50 )
Title: Capturing, analyzing and documenting big data for continuous process improvement


Author:
U. Lettau

Company:
IBA AG, Germany

Co-Authors:

Abstract:
Due to the convergence of automation technology with IT technologies, there will be changes in the industrial environment, which will greatly affect many industrial sectors due to new business models. In future, more complex products will be required, which must be manufactured economically and with very high quality in very small batch sizes in order to meet the requirements of global markets in terms of sustainability, flexibility and efficiency. The resulting complexity of manufacturing technology can only be mastered with data-based approaches over the entire life cycle of a plant.

The presentation introduces a four-step data model that enables production, process and quality data to be delivered on a digital platform. This enables plant operators to make complex production processes transparent. Also, it is then possible to autonomously monitor, analyze and optimize the plant production.

Based on the experience that different user groups need data in completely different representations, a unified procedure is shown, which is based on a central acquisition of raw data directly at their sources of origin (controls, sensors, measuring instruments ...) and the subsequent calculation of information for different purposes in the form of characteristic values or performance indicators. Thus a consistent system of different performance indicators can be provided. Deviations of these characteristic values from the normal behavior indicate abnormalities in the production process at a very early stage and can be used to initiate a predictive maintenance. In order to find the root cause of the conspicuous behavior it is possible to drill down on the raw data and thus allow an in depth analysis by experts.

Finally, it will be shown how to understand and control effects that cannot be detected by the sensors of the automation system by means of video recordings that are in synchrony with the measured data.

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25 June / 16:10 - Room 01:

Smart Maintenance Solutions for the metallurgical industry

C. Häusler
(SMS group GmbH, Germany)

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Title: Smart Maintenance Solutions for the metallurgical industry


Author:
C. Häusler

Company:
SMS group GmbH, Germany

Co-Authors:

Abstract:
In 2015 acatech published a position paper „Smart Maintenance for Smart Factories, POSITION 10/2015”. They in general explained the effect and targets of digitalization for the plant of the future as well as the roll of maintenance in particular. Even a self-optimizing plant has to be maintained and repaired by a maintenance team, but by the use of modern methods and tools the quality and efficiency can be increased sustainably by smart maintenance.
How can a general model of a Smart Factory including Smart Maintenance be transferred to the steel industry? The concept of SMS Smart Maintenance Solutions delivers an approach. This paper is going to explain the concept and will describe a possible way by the use of existing tools and solutions.

Keywords: Smart Maintenance, digitalization, learning steel plant, Smart Factory

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25 June / 16:30 - Room 01:

Quo Vadis, automation? - From intelligent products and machines to machine learning control

M. Neuer
(VDEh-Betriebsforschungsinstitut GmbH, Germany)

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Close25 June, Room 01 ( 16:30 )
Title: Quo Vadis, automation? - From intelligent products and machines to machine learning control


Author:
M. Neuer

Company:
VDEh-Betriebsforschungsinstitut GmbH, Germany

Co-Authors:
J. Kremeyer, M. Loos, A. Wolff

Abstract:
In the challenging international steel market, European steel producers must face strong competition. Technological leadership, especially regarding production process optimization helps companies to be ahead of the market game. One key enabler is modern process automation, which aims for yield increase, better energy efficiency, constant or improved quality and of course cost-effective production. Throughout recent years and driven by the Industry 4.0 umbrella, several new concepts emerged: digital twins of products and machines, smart logistic optimization approaches, Big Data analytics and finally machine learning, all of which are highly relevant for the underlying control layers. They allow for a new kind of through-process optimization for steel industry, that is in the focus of recent research projects. These technologies lead to the fusion of dynamic scheduling and rescheduling approaches, process control, process optimization and machine learning. The paper will show how information from product oriented data storages can be utilized for automation purposes. It presents existing applications of digital twins and their optimization potential for the daily steel production, including the replay of large numbers of twins for machine learning.

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25 June / 16:50 - Room 01:

Advanced systems for future steel grade development

A. Rimnac
(Primetals Technologies Austria GmbH, Austria)

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Title: Advanced systems for future steel grade development


Author:
A. Rimnac

Company:
Primetals Technologies Austria GmbH, Austria

Co-Authors:
W. Hackl, N. Champion, T. Pfatschbacher

Abstract:
The world steel market is in a phase of maturity. The situation has changed from a supplier driven to a customer driven market. Customer requirements are increasing and margins are getting tighter. This challenge is being addressed with increasing levels of digitization and a more service orientated customer focus. Alongside this the delivery of higher quality products, introduction of new steel grades and further customization of high-end products to cover niche markets are of prime importance to maintain or increase market shares. To successfully operate in this challenging business environment the role of the research and development department is becoming even more important. The development cycle time to market for new products needs to be minimized but increasing pressure on development budgets requires even more efficient and effective R&D work.
Addressing these challenges in terms of the operational practice is achieved with a combination of physical simulation, numerical simulation and digitalization all of which are driven by metallurgical know-how as a complete system. This combination provides increased understanding as the basis for efficient development and production of new steel grades.
Increasingly the role of the plant supplier is to develop and provide solutions to support steel producers in achieving these objectives for fast and efficient process development, including mechatronic systems, metallurgical know-how and simulation technologies.
This paper will discuss the structure and requirements for this integrated system approach described above and the current technologies to deliver it. The benefits of a digitized product development cycle fostering the intrinsic ideas of Industrie 4.0 to the customers operations in terms of efficiencies in development of new, higher value products and in the use of metallurgical know-how will also be presented.

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25 June / 17:10 - Room 01:

Internet of Materials: verified origin and properties based on blockchain technology

S. Grüll
(S1Seven GmbH, Austria)

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Close25 June, Room 01 ( 17:10 )
Title: Internet of Materials: verified origin and properties based on blockchain technology


Author:
S. Grüll

Company:
S1Seven GmbH, Austria

Co-Authors:

Abstract:
STEEL but SMART® is unleashing blockchain’s potential to steel industry and a digitally integrated, collaborative supply chain.

The EN 10204 specifies the various types of inspection certificates that must be provided to each delivery to prove compliance with the terms of the order and European standards. These certificates contain technical data on the product and mandatory test results. In all subsequent stages of further processing, these certificates are the central point of quality assurance and declaration of performance in commercial transaction.

Through blockchain technology it is possible to provide this certificates electronically and fully machine readable with all the features of a tamper proof document and more: From the genesis set of data, subsets are being created as material moves along the supply chain. This means the data stays connected, cross-company and beyond the usual corporate boundaries. Redundancies of double and triple testing of one and the same material are replaced by a verified log on existing results and documented procedures. In case of quality issues with a specific batch of material, steel producers can actively track down and recall applications where the affected material has been used elsewhere.

In addition to the set of data required for an inspection certificate, there is already much more information collected at each step of production and testing. This additional data represents outstanding value for downstream steel processor - enabling optimized engineering, processing and use of steel. This data can be processed with STEEL but SMART® the same way so not only the full DNA of the physical product gets available digitally but the innovative and unique product attributes become transparent resulting in visible competitive advantages. This not only refers to the product performance itself but also to the it’s production process and its environmental footprint.