Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB

Artificial Intelligence and Machine Learning for Industrial Production

One significant feature of Industrie 4.0 is the consistent networking and penetration of all fac-tory components as well as complete value-added chains with sensor technology, embedded systems and communication technology – which is called cyber-physical systems. This results in large amounts of data, usually generated by machines, ranging from planning the products to be manufactured and the production resources to actual production and the uses of the products. This data forms the basis of modern and powerful analysis and evaluation methods, which are called “artificial intelligence” (AI) today. AI procedures are capable of “coping with new situations successfully, processing new data or new information, drawing conclusions from the available knowledge, thus generating new knowledge (…) or solving new task.” [1] Today, it is widely accepted that AI is a key technology that allows all users to make use of large potentials for improvement in all stages of the value chain.

Even though current trials certify that Germany holds a good position in AI research, they also state that AI applications are far more competitive. China is making tremendous investment in Artifical Intelligence – and Chinese companies will enter the German market for AI applica-tions in production in a few years. Therefore, the Federal Government is absolutely right to state, in the context of its AI strategy, that they intend to make Germany and Europe a lead-ing AI location. [2] Industrial production is one of the most important fields of application in this context. Using Smart Factories and specific and challenging practical cases from our customers in industrial manufacturing, we have already started to develop innovative AI methods and tools, which will be briefly presented in the following sections.

[1] PaiCE (Ed.): Study Potentials of Artificial Intelligence in the Manufacturing Sector in Germany(in German language only)

[2] Strategy Artificial Intelligence oft he Federal Government, see www.ki-strategie-deutschland.de(in German language only)

Our services

Expand all Close all

Machine Learning

In production processes, we use machine learning to generate “knowledge“ from “experience” in a very general sense. Learning algorithms develop a complex model from sample data with the largest possible degree of representation. Subsequently, this model can be applied to new and potentially unknown data of the same kind. Machine learning is an appropriate method whenever processes are too complicated to describe in an analytic way, but there is a sufficient amount of sample data such as sensor data or images. [1] The models are matched with the data flow from operational business and ultimately enable forecasts or recommendations and decisions.

Examples of how machine learning can improve quality and reduce time or costs:

  • Discovering anomalies in the behavior of machines or components because the procedures reliably discover deviancies from the normal behavior of a process and consequently enable predictive maintenance.
  • Making better decisions in complex situations because the models can identify the connections spanning several manufacturing stages to enhance their ability to serve as assistant systems.
  • Adapting manufacturing and assembly processes to current situations quickly because clear correlations between measuring results and process parameters allow for automated control.

Further areas of application of machine learning that we are developing for our customers are human-robot cooperation, autonomous intralogistics, and self-organization in manufacturing.

We support you in selecting the right learning and modeling algorithms, defining, editing, and storing representative training data, generating meaningful models from the training data, and then comparing these models with runtime data. All these tasks require appropriate sensor technology, software tools, and architectures. We support you in establishing these issues in a future-proof and sustainable way.

Research on machine learning is proceeding. For example, the relevant issues are machine learning with extremely large or very small amounts of data, the combination of machine learning with physical or expert knowledge as well as security and transparency of machine learning models.

[1] Fraunhofer Gesellschaft (Hrsg.): Machine learning - an analysis of skills, research and application. Munich, 2018

Project highlights

 

Lighthouse project ML4P

In the Fraunhofer lighthouse project, six institutes under the leadership of Fraunhofer IOSB have developed a process model and software tools for the targeted and systematic application of ML methods in the field of industrial production.

 

To the project page
 

CC-KING

At the Karlsruhe Competence Center for AI Systems Engineering (CC-KING), we drive the systematic application of AI and ML methods in various engineering domains - from basic research to practice-oriented consulting.

More about CC-KING
 

© Fraunhofer IOSB / M. Zentsch

Karlsruher Forschungsfabrik

The new Karlsruher Forschungsfabrik® for AI-integrated production is entirely dedicated to leveraging the potential of AI and ML methods for production processes - whether established or new and “immature”.

More about the Forschungsfabrik

Qualification of data scientists and data analysts

In collaboration with the Fraunhofer Academy and the Fraunhofer Alliance Big Data and AI we offer a certified training program as well as method and industry-specific training.