Autonomous agents for the World Wide Lab : artificial intelligence in the manufacturing industry
Liebenberg, Martin Roland; Lakemeyer, Gerhard (Thesis advisor); Jarke, Matthias (Thesis advisor); Hirt, Gerhard (Thesis advisor)
Aachen : RWTH Aachen University (2021, 2022)
Dissertation / PhD Thesis
Dissertation, RWTH Aachen University, 2021
The Internet of Production (IoP) is a research programme, where 30 interdisciplinary institutes work on revolutionising the manufacturing industry. A central concept of the IoP is the World Wide Lab (WWL) by which in a lab of labs the data of many manufacturing processes should be made available as if the data came from one’s own manufacturing processes. With this data, which we receive from the WWL, we want to build Digital Shadows that are condensed or aggregated data for a specific purpose, such as a reduced mathematical model or a trained neural network. An early vision of the usage of the IoP is a Google-like web search, where one can pose a manufacturing problem and get in return an answer with which one can improve one’s production process or build new products. In this thesis, we propose a solution to realise such a scenario based on Artificial Intelligence (AI) methods, which we call WWL Agents. Inspired by the ideas of the Semantic Web, these agents should automate the search for data, knowledge or Digital Shadows in the WWL for specific manufacturing problems, which we think is impractical to do manually. Furthermore, WWL Agents should apply the found information to build Digital Shadows or improve manufacturing processes. We examine the development of WWL Agents from three different perspectives. First, we consider it from the perspective of building Digital Shadows in a cross-domain collaboration. The second perspective relates to modelling the behaviour of WWL agents. Finally, we discuss the infrastructure required by a WWL Agent to provide semantic interoperability in the WWL. By these means we obtain a powerful concept by which the user can get the precise meaning of an answer and, through provenance information, knowledge about the origin of entities of the answer. Moreover, we demonstrate applications for WWL Agents in manufacturing in two different use cases where the agents plan production processes. In the first case of hot rolling, we show that, with local search, agents can find very quickly schedules, which could be used to repair failed rolling schedules during operation. In the second case of the production of fibre-reinforced plastics, we show how we can use state-of-the-art automatic general-purpose planners to plan these processes.