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AI-based production optimization with MLnext

AI-based production optimization with MLnext Intelligent data evaluation for resource-saving and efficient production processes.

Artificial intelligence presented abstractly on black background

The challenge

Dashboard with overview of production data

Data expertise for digital production systems

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. Nowadays, huge amounts of data can be found in the machines and systems of a production facility. When it comes to evaluation, the data provides insights into the consumption of various resources such as (compressed) air or water, for example. The data analysis based on machine learning offers a variety of advantages compared to manual data analysis. In addition to automated and therefore much faster data evaluation, users benefit from the scalability of individual machines, complete systems, and even entire production processes.

The solution

AI-based recommended actions for designing smart production processes

With the MLnext smart solutions, Phoenix Contact offers an easy way to create and deploy machine learning models for production. For example, this means that neural networks can be used to automatically detect problems in production processes and link to recommended actions. In Phoenix Contact’s PLCnext Factory, the use of MLnext-based solutions has already boosted productivity by 10% in a very short time. It has also been possible to realize shorter implementation cycles for new solutions, resulting in a faster return on investment (ROI). 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.

Maximum flexibility with MLnext

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 predictive maintenance or process optimization of production plants, for example. With the MLnext solutions, Phoenix Contact’s electronics production in the PLCnext Factory was the first to implement the machine-based use of artificial intelligence across the board. Since then, it has been possible to record the states of the components within the machine more easily. Based on this result, further data analyses for condition-based maintenance (predictive maintenance) will be carried out in the future.

Your advantages

Ready-to-use solution without the need for prior knowledge, as neural networks are created automatically
The parameterization instead of programming enables error-free and fast adaptation of the solution
Transparency due to automatic logging and visualization of model creation and execution
Quick identification of the best model due to an integrated, intuitive model comparison

MLnext Framework

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 are realized. All findings will be incorporated into the future design of the MLnext solutions.

MLnext Execution

MLnext Execution is a platform-independent software solution for neural network execution. The solution is used flexibly, whether at the control level, on 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 by 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 the runtime behavior. It provides information about the functionality and execution times. In addition, new solutions are 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.

MLnext Creation

MLnext Creation enables the creation and parameterization of 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 is adjusted afterwards.

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Use MLnext in your production

Use the trial version to familiarize yourself with MLnext.

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