Industry 4.0 and Measurement

The transfer of concepts and ideas from Industry 4.0 to industrial practice in the steel industry and other process industry sectors is a key topic in applied research.
The BFI began very early on to use modern IT methods in solving emerging tasks in product and quality tracking, root cause analysis of quality defects, process automation and material allocation. Selected examples are provided here:

Vertical integration and networked production

Smart Coil

Smart Coil

By choosing suitable database concepts (SQL or NoSQL), solutions were developed for factory-wide saving of all process and product data. This of course depends on prior mapping of entire material genealogies. In various projects this extensive body of data was then used for through-process support of decision-making (see IConSys) or root cause analysis of surface defects, also on a through-process basis (see EvalHD).

Self-organisation

Selbstorganisation in der Produktion

Self-organisation in production

To enable searches for an alternative order for a product (in this case, a coil) which was out-of-spec, a “virtual marketplace” was created with the help of a software agent (see I2MSteel). The basis for this was semantic descriptions of the process chain and mathematical models. The number of matches achieved in allocation was significantly higher than it would have been when using a conventional approach.
In a project that is still running, the self-organising workflow is being created for the production of a coil from the hot strip mill via the coil shower to the pickling line (see SoProd). The aim here is to reduce energy consumption while increasing the flexibility of the production operations.

Automation of process chains

Using suitable information flows between the plants in a longish process chain, solutions are devised for cross-process automation of process chains. Examples include the temperature management in a steel mill, from the tapping of a melt in the electric arc furnace or converter to the casting of the metal in a continuous caster (see TotOptLiS), or the through-process homogenisation of tensile strength and yield stress across the strip length (see StrengthControl). The process models involved are self-learning, using advanced algorithms to adapt to changing process conditions (see PerMonLiSt).

Big Data and Big Data Analytics

Big Data and Big Data Analytics

Big Data and Big Data Analytics

Extensive industrial data from production operations has already been furnishing the basis for detailed analysis for a long time. These analyses are performed using methods like data mining and big data analytics (e.g. streaming, deep learning). One of the BFI’s particular specialisations is handling industrial data.
Application areas include the analysis of large volumes of surface inspection data (see EvalHD), fast searches for similar process situations among process signals recorded over large time spans (see SitErk), or combining semantics with data mining to identify the root causes of quality defects among high-resolution process signals (see Presed).

Cyber physical systems

Auto-flying drone

Auto-flying drone

Among the different kinds of assistance systems, one variety is cyber physical systems as the basis for Industry 4.0. Within the scope of a current project (see DromoSPlan), the BFI is developing an auto-flying drone equipped with a camera and various sensors to monitor pipelines in the steel industry.

Measuring and Testing Techniques

Without sufficient online information on the state of the production process and the quality parameters of the product, it is impossible to adjust individual plants or realise the kind of Industry 4.0 solutions just mentioned. The BFI already has a long history of delivering innovative ideas and solutions for measuring and testing techniques and ranks among the world leaders when it comes to technology for flatness measurement of flat steel products. Here is a selection of the areas we cover in measuring and testing.

Open eye monitoring

Open eye monitoring

  • Fibre optics sensors for measuring the temperature in metallic melts (see MeltCon) or the temperature distribution in the mould pipe (see FOMTM).
  • Process monitoring using different cameras (visual and infrared) and image processing techniques. Stirring efficiency of melt treatment in the stirring station (see DynStir) and vacuum treatment plant
Model of a virtual blast furnace shaft

Model of a virtual blast furnace shaft

(see EffiDynVac), deslagging (see OptDeSlag) and slag carryover, analysis of process zones and permeability in the blast furnace shaft (see TopTemp, Stackmonitor).

  • Ultrasonic techniques for measuring the iron and acid content in pickling baths
  • Monitoring of the solids concentration in process waters using magnetic techniques (solids sensor)
BFI Measuring Roll

BFI Measuring Roll

  • Flatness measurement on untensioned strip using TopPlan and on tensioned strip using the BFI Shapemeter Roll; new feature: integrated temperature measurement
  • Scale detection and measurement of very thin layers on strip material
  • Measurement of the calorific value of gas (see DynGas)
BFI_FIDUS

BFI_FIDUS

  • Measurement of surface and internal defects using ultrasound on slabs (see NDTSlab), strip, wire rod and bar material (see FIDUS).

Projects from topic

Quality4.0 – Transparent product quality supervision in the age of Industry 4.0

Situation: Progressive digitalization is changing the game of many industrial sectors. One of the main aspects of todays’ Industry 4.0 […]

MACO Pilot – Optimisation of the mixed-acid online monitoring and control in stainless steel pickling plants

Initial situation Economic pressure on European steel sector demands high flexible and favourable production Especially the pickling step is of […]

CheckSIS – Performance assessment for automatic surface inspection systems

Initial situation: In modern steel production, automatic surface inspection systems (ASIS) are commonly used to detect and classify surface defects […]

TotOptLis – Through-process optimisation of liquid steelmaking

Initial situation: Process control in secondary metallurgy is based on static operating instructions and manual interventions, taking into account measurements […]