case-based reasoning

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”.

This paper describes an approach for knowledge based product configuration through the integration of experience and knowledge from the product use phase. The goal of this approach is to enable a faster and software-controlled harmonization of the different customer’s and the manufacturer’s point of view during the pre contract phase. Therefore a case-based reasoning method was developed to be integrated in a rule based product configuration system.

The self-learning search engine improves the search for relevant information in enterprises by using a profile of the searcher and a context, that has to be chosen for each search request. Case-based reasoning is used to figure out which information sources contain relevant information for the given combination of profile and context. These information sources are further preferred in enterprise search.