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Integration of AI in Computer Aided Design (CAx)
Challenge
The search for identical and similar parts becomes more and more challenging with increasing product complexity and number of variants.
Solution
Development of a knowledge database with CAD models for intelligent common parts management.
Added value
Reuse of components to save manufacturing, development and storage costs and to reduce the number of parts in the inventory database in the medium term.
The international agricultural machinery manufacturer CLAAS GmbH & Co. KGaA is testing a special use case for the integration of AI in Computer Aided Design (CAx) in the AI marketplace. Against the background of constantly increasing product complexity and number of variants, the reuse of components offers an important opportunity to save manufacturing, development and storage costs.
Product complexity can make finding similar parts difficult
However, the search for identical and similar parts is a major challenge, since these were often developed in a project-specific context and were therefore not considered for reuse as a standard part. Furthermore, identical parts often cannot be found because the master data is not maintained correctly. If you have found "too many" similar parts, it takes human intelligence today, which means a lot of time, to reduce the search results to a usable level.
AI methods help to identify similar parts
The aim of the project is therefore to develop an intelligent common parts management and to implement it as a prototype. For this purpose, CAD models are first examined for their geometry and later also for their function and then classified. Missing master data or other meta data are also added to create a knowledge database for the models. Thanks to this data, it is possible to use AI methods such as Case Based Reasoning (CBR) to use to identify common parts based on geometry and in terms of their identify functionality.
With the help of the CBR process, CLAAS can also incorporate feedback on the components and thus collect knowledge about their reuse, adaptation or rejection in the production process. This knowledge can in turn help to improve the performance of tools.
In this way, CLAAS will improve the search for similar parts in order to reduce the number of parts in the inventory database or keep it manageable in the medium term. For example, potential identical parts or evolution stages can be suggested to the developer during the design phase, which reduces the design effort. The AI solution from the project will finally be made available to other users on the AI marketplace in generic form.
