Grafische Darstellung eines Atoms mit Punkten eines neuronalen Netzes.

Services
Machine learning,
predictive analytics
and optimization for
real requirements

Grafische Darstellung eines Atoms mit Punkten eines neuronalen Netzes.

Services
Machine learning,
predictive analytics
and optimization for
real requirements

In order to implement AI methods such as machine learning, predictive analytics and optimization for your company, we work closely together with you. The best approach for you may be a consulting project, the licensing of one of our software tools, or the development of a customized solution specially designed for your task.

Together we will find the optimal approach

In order to implement AI methods such as machine learning, predictive analytics and optimization for your company, we work closely together with you. The best approach for you may be a consulting project, the licensing of one of our software tools, or the development of a customized solution specially designed for your task.

Together we will find the optimal approach

Consulting

Consulting

Darstellung von Personen und grafischen Knotenpunkte.

It often starts with a consulting project which includes, for example, analyzing and modeling your data with AI methods. Then we analyze your data and provide you with answers and insights. These can be influencing parameter settings that lead to quality deviations during the production process, or those that lead to the early failure of components.

Likewise, it is possible to find previously unknown interactions between process parameters, identify anomalies in the data, and derive predictions about optimal settings. These are just a few of many possible insights.

Also, the use of optimization techniques, the integration of data and the general approach to AI-based methods can be the subject of a consulting project.

It often starts with a consulting project which includes, for example, analyzing and modeling your data with AI methods. Then we analyze your data and provide you with answers and insights. These can be influencing parameter settings that lead to quality deviations during the production process, or those that lead to the early failure of components.

Likewise, it is possible to find previously unknown interactions between process parameters, identify anomalies in the data, and derive predictions about optimal settings. These are just a few of many possible insights.

Also, the use of optimization techniques, the integration of data and the general approach to AI-based methods can be the subject of a consulting project.

Contact us
Contact us
Darstellung von Personen und grafischen Knotenpunkte.

Software tools

Software tools

Screenshot von ClearVu Analytics.

For the analysis of data, model building with AutoML (automated machine learning) methods and optimization on the models, we have developed ClearVu Analytics. This software can be used either with a user-friendly graphical interface or as a Python package with a corresponding programming interface. For the AutoML in Excel, we have also made an entry-level solution available for you.

ClearVu Solution Spaces supports the development process by automating the procedure for municipal design by finding maximum valid ranges for the design parameters. This allows engineering designs with the greatest possible implementation and reusability potential.

For the analysis of data, model building with AutoML (automated machine learning) methods and optimization on the models, we have developed ClearVu Analytics. This software can be used either with a user-friendly graphical interface or as a Python package with a corresponding programming interface. For the AutoML in Excel, we have also made an entry-level solution available for you.

ClearVu Solution Spaces supports the development process by automating the procedure for municipal design by finding maximum valid ranges for the design parameters. This allows engineering designs with the greatest possible implementation and reusability potential.

Our software tools

Customized solution development

Customized solution development

Hand mit einer grafischen Darstellung eines Atoms mit Punkten eines neuronalen Netzes.

We develop solutions for you that make AI methods applicable to your requirements. Our focus lies on the specific application as well as the integration into the IT environment of your company. From tool chains in simulation-based development to the integration into production processes, we ensure the full performance of AI methods in your process flow.

We develop solutions for you that make AI methods applicable to your requirements. Our focus lies on the specific application as well as the integration into the IT environment of your company. From tool chains in simulation-based development to the integration into production processes, we ensure the full performance of AI methods in your process flow.

Contact us
Contact us
Hand mit einer grafischen Darstellung eines Atoms mit Punkten eines neuronalen Netzes.

Application areas

Icon für Machine Learning

Machine Learning

Machine Learning refers to a class of AI methods that can infer relationships from data. These correlations can be derived from the data as predictive models or as explicitly usable knowledge from the data.

Automatic Machine Learning (AutoML) is a development, in which the learning methods are automatically selected, validated and optimized for the respective data sets and tasks. Our ClearVu Analytics product family provides these functionalities.

Due to adaptive learning methods the models created in this way are continuously checked and automatically retrained in case of necessary adjustments. Machine Learning is a powerful tool for the automatic modeling of data and can be used in many areas, from development to production and logistics processes.

Icon für Process Optimization

Process optimization

The combination of artificial intelligence and optimization is ideally suited for optimizing production processes in terms of product quality, process stability, resource consumption or other criteria. For this purpose, process data are jointly analyzed and prediction models are derived using machine learning that can predict the target criterion – for example product quality – from the process data.

In combination with a nonlinear optimization method, process parameters can then be derived that enable further improvement of the target criterion. Conversely, it is also possible to identify rather disadvantageous parameter settings and then avoid them. If several target criteria are to be optimized simultaneously, this can be implemented using methods of so-called multi-objective optimization. The combination of machine learning with optimization is the key to successful process optimization.

Icon für Data Integration

Data preprocessing and
data integration

Machine learning, AI and optimization are based on data that can come from different sources and are often incomplete or insufficiently prepared for analysis. The automated preprocessing of this data can also be supported with machine learning techniques. The combination and integration of data from different sources is another important prerequisite for the application of learning and optimization methods.

We have the appropriate tools for both, which we adapt to your requirements so that AI methods can be used in the best possible way. We make the results of machine learning available in your IT environment through interfaces so that they can be used directly in the process.

Icon für Predictive Maintenance

Predictive maintenance

With the help of data-driven modeling using AutoML methods it is possible to derive predictive models for predictive maintenance from suitable data. By means of such models, it is possible to calculate the remaining useful life (RUL) of plant components.

This enables significantly improved planning, thus optimization of maintenance intervals and maintenance measures, and the avoidance of unplanned production downtimes.

Icon für Predictive Quality

Predictive quality

The quality of produced components often depends on a variety of parameters, which include the process settings, material properties and other influencing variables. Due to digitization, this data is increasingly available today. By means of data-driven modeling with AutoML methods, such data can be used to create predictive models for product quality depending on these influencing parameters. From this we can derive key parameters that influence product quality and, in combination with our optimization algorithms, recommendations for optimal parameter settings that further improve product quality, scrap rates or rework efforts.

Contact

How to contact us

Telephone

+49 231 97 00 340

E-Mail

Address

Joseph-von-Fraunhofer-Straße 20,
44227 Dortmund, Germany

Contact

How to contact us

Telephone

+49 231 97 00 340

E-Mail

Address

Joseph-von-Fraunhofer-Straße 20,
44227 Dortmund, Germany