Training

Industrie 4.0 with the digitisation of products and processes offers companies a large pool of information for process optimization. In many cases these information cannot be used directly in the textile industry, as raw materials are subject to natural fluctuations and the influencing factors and interactions of many product and process parameters are only partially known. In this contribution, an approach is presented that combines information from production with the experience of the employees and thus supports product and process optimization. The approach is based on the machine learning method “Case-Based Reasoning”.

With the industrial internet the digitization of communication processes receives a new impulse. By application of social networks within firms, the collaboration of and knowledge transfer between workers can be supported and rationalized. This paper focuses on Enterprise Social Networks and discusses the challenges and opportunities when implementing these digital communication tools. On basis of a real world case study we illustrate some good practices.

In the context of this article, a methodology for an efficient transfer of knowledge from research into industrial practice regarding cyber-physical production systems is presented. The methodology serves above all to sensitize small and medium-sized (SME) enterprises to the possible potentials of the so-called Industry 4.0. The starting point for this is the need-oriented and individual specification of knowledge required for a practical knowledge transfer and the development of tailor-made competence profiles of future employees in smart SMEs

Currently the implementation of digital technologies in response to important competition requirements is promoted in many places. Consequently, the working environment of employees in operative logistics is going to change significantly. This article provides an overview of the possible uses of future-oriented technologies in different logistics processes as well as the thereby changing subtasks and competence requirements of operative employees in the working world 4.0.

This article deals with the presentation of a concept that shows new possibilities for a holistic improvement in the company’s internal resource efficiency by using Industry 4.0. Subsequently, the structure of a resource management system will be shown. To attain a holistic improvement of the resource efficiency, the viewing frame will be extended to the whole product lifecycle to show which potentials a coupling along the entire value-added chain provides.

The Green Factory Bavaria is a research project, in which a platform at several locations in Bavaria is developed, in order to increase the resource efficiency in manufacturing companies. The platform shall serve as research-, demonstration- and training purposes. In Augsburg a process chain was developed, which consists of an additive manufacturing step, a cleaning and a packaging step. The research foci of those areas as well as the training concept are going to be presented in this article.

Today data processing becomes more and more complex concerning the amount of data to be processed, the data dimension and correlation and the relationship between derived information and inputdata. This is the case especially in sensing and measuring processes. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue. Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete. If reasoning of system states is primarily desired, Machine Learning can be an alternative. Tradionally, sensor data is acquired and delivered to and processed by a central processing unit. In this paper, the deployment of distributed Machine Learning using mobile Agents forming self-organizing systems is discussed and posing the ...

Economy, enterprises and employees are sustainably affected by digital transformation. The new opportunities of education and training to evaluate learner behavior digitally are of particular interest. Learning analytics can be used for specific optimization of learning forms and content for the benefit of learners and of the training company.