Knowledge representation and reasoning in the context of automated production planning

Abstract In the manufacturing industry the step of generating the assembly plans for products is crucial but very time and labour intensive as most of the work has to be done manually. Especially for products with high customisability this means, a new plan is required for every custom order. To reduce the complexity, a new hybrid approach for the generation of these assembly plans is introduced, based on knowledge representation and reasoning. The focus of the approach is to be deployable in real-world production instead of only focusing on research environments. Therefore, a knowledge base is generated, specifying the preconditions and details about different operations linked to geometrical features. To preserve these valuable information Answer Set Programming (ASP) is used to define rules. By this, the generic information of when to apply which operation can be separated from the product-specific information of how a product looks, and therefore, be stored centrally and reused for any product or even different applications without any changes. Performing a geometric analysis on the product model allows extracting important geometrical features, that can then be used to derive the necessary information from the corresponding rules. In all of this, the human intervention is crucial to cover even complex assemblies and to strengthen the acceptance of the method. To showcase the functionality of this approach, a simple reproducible example is given on the assembly of the IKEA Hyllis shelf as well as the PUBLIC Bikes Sprout bike.