Stents4Tomorrow

Development of a highly flexible, learning production process for the economical manufacture of patient-specific, braided cardiovascular implants (stents) of the highest quality using novel, intelligent, autonomous production equipment

Problem definition

For years, cardiovascular diseases have been the leading cause of death worldwide. In Germany, for example, this group of diseases accounts for 13.7 percent of medical costs, equivalent to approx. 338.2 billion euros, making it the largest item (2015/DESTATIS, 29.9.2017). Cardiovascular implants, also known as stents, are used to treat the causes of these diseases (see Figure 1). In principle, these stents are produced either by laser cutting or braiding. Compared to laser cutting, production via braiding has the advantage of lower wall thicknesses, smaller sizes and fewer post-processing steps. On the other hand, however, there is less flexibility in terms of the structure to be realized and a lower production speed.

Figure 1: Braided stent

Goals

The aim of the Stents4Tomorrow research project is to develop a new type of tunnel braiding machine for the resource-efficient, flexible and fast production of stents. Compared to current braiding machines, a modified physical operating principle for controlling the braiding paths and new types of production equipment will be used. The overall aim is to enable the braiding of patient-specific stents under high quality standards in a cost-effective manner.
In practice, new bobbins and a magnetic switch must be developed for the assembly of the tunnel braiding system in order to implement the modified operating principle. Software for controlling the machine and the braiding process as well as an energy management system are also required. Apart from this, errors in the braiding process are to be identified and rectified with the help of the learning module to be developed (see Figure 2).

Figure 2: Conceptual representation of the components to be researched and developed

Contribution of the KIT

KIT is significantly involved in the development of the learning module. The learning module is responsible for detecting and correcting braiding faults during the production process. Defect detection is camera-based and uses artificial intelligence. Corrective instructions for rectifying and, if necessary, preventing the defect are then derived from the detected defect and its localization.