Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

In this paper, we propose a new approach for the simulation-based support of tryout operations in deep drawing which can be schematically classified as automatic knowledge acquisition. The central idea is to identify information maximising sensor positions for draw-in as well as local blank holder force sensors by solving the column subset selection problem with respect to the sensor sensitivities. Inverse surrogate models are then trained using the selected sensor signals as predictors and the material and process parameters as targets. The final models are able to observe the drawing process by estimating current material and process parameters, which can then be compared to the target values to identify process corrections. The methodology is examined on an Audi A8L side panel frame using a set of 635 simulations, where 20 out of 21 material and process parameters can be estimated with an R2 value greater than 0.9. The result shows that the observational models are not only capable of estimating all but one process parameters with high accuracy, but also allow the determination of material parameters at the same time. Since no assumptions are made about the type of process, sensors, material or process parameters, the methodology proposed can also be applied to other manufacturing processes and use cases.

Automatic Knowledge Acquisition in the Form of Fuzzy Rules From Cases for Solving Classification Problem

The authors consider an approach to automatic knowledge acquisition through machine learning on the basis of integrating the two basic reasoning methods – case-based reasoning and rule-based reasoning. Case-based reasoning allows using high-performance database technology for storing and accumulating cases, while rule-based reasoning is the most developed technology for creating declarative knowledge base on the basis of strong logical approach. This allows realizing the transformation of the spiral of knowledge, leading to continuous improvement of the knowledge quality in the management system. In the chapter, they propose one method of obtaining rules from cases based on fuzzy logic. Here the method is considered for solving classification problem, but it also can be applied for solving regression problem. The research shows acceptable accuracy of cases classification even for small training samples. At the same time, smoother (quadratic) membership functions show on average classification accuracy.