DLR develops concept for final assembly line with artificial intelligence

In a 2021 article by Dr. Ing. Lennart Weiss, of the DLR Institute for Composite Structures and Adaptive Systems (Braunschweig, Germany), manual compensation procedures — for example, shimming —and the resulting delays are a thing of the past. Each assembly can be joined immediately using artificial intelligence, because a clever pre-selection of the components reduces excessively large tolerances at the connection areas. Even high-rate production is possible thanks to the “plug and fly” principle. DLR envisions the complete introduction of this expert system into series production.

 

Independent assembly decision

In an expert system, the artificial intelligence selects individual parts that can be assembled independently. The connection dimensions and their tolerances determine whether the components can be assembled. The neural network evaluates the recorded parameters, draws on its "experience" and signals a positive or negative assembly process. The neural network achieves empirical values ​​through so-called monitored training cycles (supervised learning). This often requires a large number of independent assembly scenarios or data points. The greatest challenge is the limited availability of this training data for assembly processes in the final assembly line.

 

Computer-aided for small batch sizes

A two-stage process solves the problem in dealing with small lot sizes. Thefirst step of the process consists of training several neural networks. They represent various preselected approach functions. Their training for the best possible regression of the data points leads to the first "experiences". However, the influence of the preselection of shape functions on the regression result is too great. Therefore, in the second step, the algorithm is free to generate its own approach function based on its “experiences” from the first step and to carry out the training with this again.

The algorithm learns from its training experience and independently determines a regression function that is sufficiently predictive even for small batches. The artificial intelligence runs through this computer-aided process after each assembly, so that the wealth of experience grows with each positive or negative assembly process. This improves the prediction quality of the expert system with each assembly process carried out.

The learning expert system accompanies the final assembly line in series production. The knowledge gained can be used both for new flexible assembly concepts in the factory of the future and for a new construction philosophy for the designer of the future.

This article follows on from Weiss’ 2020 article, “Flexible Cooperation - with a learning expert system for intelligent assembly”.