A knowledge-based control system for the robust manufacturing of deep drawn parts

Throughout a single batch of deep drawing parts the settings of the press have to be adjusted to account for several influences. These can be divided in influences originating through the process, like heating of the tools or aggregation of the lubricant in the tool, and influences originating in the manufacturing of the blanks, like scattering material properties within a coil or between different coils. In the present paper, a method is shown to minimize the effects of both types of influences. The first step in building up a knowledge based control is the quantification of the influences. This is done by running a virtual process tryout based on FEM simulations in order to predict the influence of the scattering material and process properties on the process outcome. For an effective feed forward control based on the variant system, the blank properties are measured during the cutting stage and every part is labeled with a unique identification. The yield strength and ultimate tensile strength are measured by an eddy current system, while the blank thickness is measured via laser triangulation. As the knowledge of the blank properties alone is not sufficient, a feedback loop is introduced to compensate for the non-blank related influences. For the feedback control, an optical measurement system is proposed, which is able to calculate the draw-in at pre-defined points. The relevant measuring points are defined by evaluation of the correlation between draw-in and changing properties in the virtual process tryout. Both control mechanisms are solely using the usual available and adjustable press settings. In the presented case, the position of the blank as well as the different blankholder forces were chosen. Finally the applicability of the proposed method is evaluated virtually. (C) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Conference on the Technology of Plasticity.