SmartDispensing

A flexible robot system which can be programmed by demonstration is being developed to enable the watchmaking industry to automate the application of luminous compounds to watch indices and hands.

Factsheet

  • Lead school School of Engineering and Computer Science
  • Institute Institute for Human Centered Engineering (HUCE)
  • Research unit HUCE / Laboratory for Robotics
  • Strategic thematic field Thematic field "Humane Digital Transformation"
  • Funding organisation Innosuisse
  • Duration (planned) 01.03.2023 - 28.02.2026
  • Project management Prof. Dr. Sarah Dégallier Rochat
  • Head of project Prof. Dr. Sarah Dégallier Rochat
  • Partner IDIAP
  • Keywords Robotics, flexible robots, programming by demonstration, dispensing, watchmaking industry

Situation

For watch faces and hands to glow in the dark, they must be coated with a special paint. The luminescent compound Swiss Super-LumiNova® (SLN), made by the Swiss company RC Tritec AG, is generally used for this purpose. It is applied by hand – a repetitive task, and recruiting people to perform it is proving increasingly challenging. That’s why there is growing interest in automating this process in the watchmaking industry. A flexible automation solution is needed in view of the small batch sizes and wide variety of products. Researchers at BFH, in collaboration with the Idiap Research Institut and Ciposa SA, are working on a suitable system as part of an Innosuisse project. Ciposa SA, headquartered in canton Neuchâtel, specialises in micro-assembly. Over recent years, the company has developed several machines for the watchmaking industry that ensure the high-precision application of materials in the correct dosage.

Course of action

Researchers are adopting an innovative approach to the project, whereby robots are programmed by manually showing them what to do. The machine then records the worker’s movements and combines this information with measurement data from various sensors to achieve the required degree of precision. Machine learning will enable the robot to develop the correct strategy to applying the compound in any given situation.

This project contributes to the following SDGs

  • 8: Decent work and economic growth
  • 9: Industry, innovation and infrastructure