Automatically Predicting Assembly Plans for New 3D Product Designs with Machine LearningJohn McCarthy Stage
Manufacturers need to deliver more and more products and product variations in ever shorter cycles to be competitive, which requires more efficient and cost-effective product design and assembly planning processes. This presentations shows how to automatically predict assembly times and assembly plans for new 3D product designs using machine learning, thereby accelerating the product design and assembly process, lowering costs, and increasing profit margins. While standard machine learning methods only process linear input vectors or data tables and deliver only atomic values, i.e. categories in the case of classification or numbers in the case of regression, here we are faced with complex hierarchical 3D product designs with hundreds or thousands of parts, enriched with textual data, as input data and have to deliver a sequence of assembly steps as an output. The presentation shows how to solve this complex task efficiently and accurately with machine learning. Validations at Daimler Trucks and Miele showed accurate predictions.
Cédric Villani, Mathematician - member of the Academy of Sciences - 1st vice-president of the Parliamentary Office For Scientific and Technological Assessment (OPECST) – Member of Parliament Fields Medal,