Employees viewing production data on a tablet

AI-based production optimization with MLnext Resource-saving and efficient production processes with intelligent data evaluation

Data is the foundation of digitalization, but it is becoming increasingly complex to use and evaluate due to the growing networks and increased security requirements. The correct handling of data is an essential component for the digitalization of production systems. For example, to detect anomalies in the factory in time, the MLnext solutions offer the possibility to evaluate your data through machine learning as well as to make optimizations quickly, easily, and precisely.

Visualization of production data

More sustainable production thanks to intelligent data evaluation AI-based recommendations for action for the design of smart production processes

Nowadays, huge amounts of data can be found in the machines and systems of a production facility. This provides the basis for the implementation of various digitalization solutions. When it comes to evaluation, the data provides insights into the consumption of various resources such as the media air and water, for example. The data analysis based on machine learning offers a variety of advantages compared to manual data analysis. In addition to automated data evaluation, users of this approach can benefit from scalability for entire production processes.

With the smart solutions of MLnext, Phoenix Contact offers an easy way to create neural networks to link the data within production. Identifying potentials enables the optimal design of personnel planning and machine allocation.

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Your advantages Tailored to your use case and your application

With MLnext, you benefit from various advantages:

  • No prior knowledge required for automated neural network creation
  • Configuration of the solutions instead of programming
  • Automated logging of model creation and execution
  • Easy model comparison thanks to a reporting system that is intuitive and always available
Employee evaluates production data

Maximum flexibility with MLnext From the controller through to use in the cloud

The model learns which optimization measures can be used based on past consumption activities. Current changes, such as a drop in ambient temperature in a system, are automatically taken into account and included in the evaluation. 

In practice, there are no limits to the use of MLnext solutions. Possible areas of application could include leakage detection or process optimization in the manufacturing industry.

The electronics production PLCnext Factory from Phoenix Contact has automated the use of artificial intelligence close to the machine for the first time across the board with MLnext solutions. Since then, it has been possible to record the states of the components within the machine more easily. They form the basis for implementing condition-based maintenance (predictive maintenance) through further data analysis.

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MLnext Framework Data analysis implemented easily

For a quick and easy overview of the data, the MLnext framework is suitable for high-level language programmers. Users thus also benefit from a quick selection of the relevant data. In compliance with currently valid data security standards, the MLnext framework provides an open source basis that can be executed through PLCnext Technology or other hardware-independent platforms.

The programming library constantly implements a variety of (new) approaches from research that are used in the PLCnext Factory and have already led to productivity increases of 10%. Shorter implementation cycles could also be realized. All findings will be incorporated into the future design of the MLnext solutions.

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MLnext Execution Easy teach-in and execution of algorithms

MLnext Execution is a platform-independent neural network execution software platform. The solution can be used flexibly – whether on the control level, a local IT server, or in the cloud. Developed solutions can be tested, improved, and compared in the environment. Here, the necessary data flow is created through the configuration files. The origin of the data is defined so that the pre-processing, prediction, post-processing, and storage of the data sets can be performed later. The intuitive web interface monitors runtime behavior and provides information about the functionality and execution times.

In addition, new solutions can be developed easily on the same platform without interrupting the processes already integrated. Changes can be made flexibly by the user, service partner, or the experts from Phoenix Contact.
The ability to extend data flows during runtime provides users with a high degree of flexibility.

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MLnext Creation On the path to fully automated Deep Learning

MLnext Creation enables the creation and parameterization of the neural networks. Since the application works on the basis of a configuration file, no programming knowledge is required. Through automatic logging, all processing steps are recorded individually and generate standardized reporting. To choose the ideal neural network for the application, several models can also be quickly compared. Based on the reports, the configuration file can be adjusted afterwards.

Smart optimization of production

Whether anomaly detection, locating leaks, or optimizing processes, MLnext provides a simple and targeted way of uncovering hidden potential. Find out more in the Infopaper.

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