Within the next decade or so, the machine floor will see connectivity reach new heights with immense amounts of data being driven through networks and on demand – at the touch of a finger. Article contributed by Schaeffler Technologies AG.
In recent decades and years, the main aims of “mechatronisation” of subsystems and rolling bearings have been high levels of functional integration, high power density, downsizing and reducing the number of components and interfaces. With Industry 4.0, the scope has widened: Communicative products and the digitalisation of components which provide completely new additional functions are a new point of focus. Amongst other things, the aim is to have a real time view of production.
The performance and quality of production machines are fundamentally and definitively determined by the bearing supports. Data on the current condition as well as the future behaviour of subsystems supported by bearings, such as spindles, guidance systems, rotary and swivel axes are a fundamental basis for the required real time view.
In its role as supplier and development partner for technical drive components, Schaeffler is pursuing a digitalisation strategy across all product groups with the aim of providing data from the most diverse of processes via sensors, networking and analysis in order to be able to offer customers clear-cut added value. Schaeffler’s activities in the field of machine tool digitalisation provide the whole sector with the potential to convert opportunities and overcome challenges through close collaboration between manufacturer, supplier and user.
Schaeffler and Deckel Maho Pfronten have developed the “Machine tools 4.0” concept based on a specific investment project together with other partners which connects existing technology, from sensors to the cloud, to new digitised components and represents a real step towards digitised production. Two prototypes have been built based on the fourth generation of the DMC 80 FD duoBLOCK machining centre.
One will be put into volume production in the precision bearing cell at Schaeffler’s plant in Höchstadt. The second prototype was seen at the DMG Mori booth at EMO 2015. This is in pursuit of a specific digitalisation strategy with the aim of providing data from the most diverse of processes via sensors, networking and analysis.
As a user of machine tools, this Industry 4.0 initiative has a direct relevance in Schaeffler’s own production. The company’s activities in the field of machine tool digitalisation provide the whole sector with the potential to convert opportunities and overcome challenges through close collaboration between manufacturer, supplier and user.
Bearing Supports As Data Sources
Bearing supports are decisive components for machine tool performance as they are decisive not only for the functional capacity of the machine but also for the quality of the work piece. Data which can be attributed to the current condition as well as the future behaviour of components become an important source of information for the machine operator.
Existing sensors can be used or suitable equipment retrofitted. At times, it could advantageous or, in fact, the only sensible option, to integrate the sensor directly into components as this is the only point at which some parameters can be determined.
The prototypes for this innovative project have additional sensors integrated into nearly all the bearing positions relevant to the machining process in order to measure vibration, forces, temperatures and pressures and obtain the best possible information on the condition of the machine.
Making a machine ready for analytics means being able to assess the recorded data, saving it and drawing conclusions from them. In order to make all data accessible, the machine is provided with an internal network to which all additional sensors, actuators and evaluation units are connected. A gateway provides a link to the cloud. In order to ensure data can be exchanged with the machine’s control system, Profibus is integrated into the programmable logic controller (PLC) for time-critical and process data and the open platform communications unified architecture (OPC UA) protocol is used for further information from the human machine interface (HMI).
The data from the machine are saved locally in the gateway and copied into the cloud. This ensures the machine’s data history is available without having to connect to the network. Calculations can be completed in the cloud via web services or apps.
Analysing The Data
Analysis of large volumes of data has assumed a new significance compared to existing data analysis, which principally has a 1:1 ratio as far as output is concerned. This assumes that, in addition to the actual measured values, patterns occur in a sufficiently large number of measured values/data which can be correlated with other data.
These provide a new level of quality in terms of what they can reveal with respect to, for example, the bearings’ condition and therefore machine condition (data-based added value). The patterns can be automatically recognised by suitable algorithms and any necessary recommended actions carried out. Decentralised functional units are required which can operate both autonomously and as an integral part of the network.
This allows local intelligence to assess the data locally. Additional evaluations which require more computing power can be retrieved from the cloud. An analytical evaluation based on data from all connected machines is carried out in the cloud and not locally on the machine.
Schaeffler is therefore designing a horizontal network along the value-added chain, similar to the vertical integration of sensors into the cloud, in order to learn how complexity and requirements for products and services can be accommodated in production.
Integration Into Production
The possibilities offered by digitalisation are not limited to manufacturing machines. The manufacturing environment can also benefit from a continuous flow of data. This avoids isolated solutions which could require manual intervention. Vertical integration is also required to connect to the enterprise resource planning (ERP) system for automatic order processing.
An important element in “big data” is an unambiguous identification of individual components. To this end, a marking unit is integrated which gives each component a unique code using a data matrix code. This stays with the components throughout the manufacturing process and forms part of the unit ID. This allows the component’s history to be analysed and provides traceability.
Determining the forces on the tool centre point (TCP) allows further optimisation of the machine loading as well as the process itself:
Displacement at the TCP due to loads that occur during machining can be determined using a mathematical model and potential correction measures can be fed back to the control system in real time. The actual machining forces occurring can be determined in advance using machining simulations. These form a nominal value which may not deviate outside a predetermined range as this would indicate an unacceptable condition.
In addition to measuring actual energy usage and assigning this to each machining stage, it is also possible to determine future energy demands through process simulation. When combined with existing data, a more accurate prediction of energy requirements can be produced which allows demand-driven purchasing of energy as well as production planning by minimising energy peaks across the business.
Condition of the machine
The condition of the machine is recorded using classic vibration monitoring processes. The condition of the lubricant is also measured and evaluated at different points. Demand-driven lubrication guarantees functional capacity as well as careful use of resources without influencing machine performance. It is also possible to provide a relative prediction of the further development of the bearing condition.
Load collectives in the machine can be clearly understood by classifying the machining processes. For example, a nominal residual service life of the bearing position can be calculated online using the Schaeffler bearing calculation program Bearinx via a web service. The aim is to use simulations of planned machining tasks and the resultant foreseeable operating life of the individual components to control production in such a way as to allow essential maintenance to be planned in advance in order to maximise machine availability.
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