Domenico Spensieri

Koordinering av industriella robotar för cykeltidsoptimering

​Domenico Spensieri, industridoktorand vid avdelningen för Produktutveckling IMS, försvarar sin doktorsavhandling. Under sina år som anställd på Fraunhofer-Chalmers Center har han utvecklat metoder och verktyg för att minska cykeltiden i industriella robotceller, speciellt inriktade på fordonstillämpningar.​
Populärvetenskaplig sammanfattning (engelska)
Industry, today, short time-to-market, excellent product quality, large product customization and high production rates are among the main drivers of technological progress. Besides them, the improvement of industrial processes is important from a sustainability perspective, in terms of resources used, such as energy, machines and physical prototyping. 

The achievements of such goals may, nowadays, be reached also thanks to virtual methods, which make modeling, simulation and optimization of industrial processes possible. The work in this thesis may be positioned in this area and focuses on virtual product and production development for throughput improvement of processes in the automotive industry. Here, many of the processes are carried out by robots: for example, operations such as stud/spot welding, sealing, painting and inspection. 

Specifically, this thesis presents methods and tools to avoid collisions and minimize cycle time in multi-robot stations. It presents algorithms to assign operations to specific robots, decide in which order these operations should be carried out and tune robot velocities. The purpose is to generate optimal robot programs, which aim to achieve the overall goals defined above.

In summary, by requiring fewer iterations between different planning stages, by using automatic tools to optimize the process and by reducing physical prototyping, the research presented in this thesis (and the corresponding implementation in software platforms) aims to improve virtual product and production realization for robotic applications.


Sidansvarig Publicerad: må 15 nov 2021.