Design of energy-optimized forestry crane (DEOS)
| Reference number | |
| Coordinator | Sveriges Lantbruksuniversitet - Sveriges Lantbruksuniversitet SLU Inst f skogens biomaterial & teknol |
| Funding from Vinnova | SEK 2 192 000 |
| Project duration | July 2021 - June 2023 |
| Status | Completed |
| Venture | Fossilfria Arbetsmaskiner - FFI |
| Call | Fossil-free mobile work machines - spring 2021 |
| End-of-project report | 2021-01799svenska.pdf(pdf, 994 kB) (In Swedish) |
Important results from the project
To speed up the development of forest machines with significantly reduced energy requirements, and thereby reduced climate emissions, through new crane design. The aim of this project was to contribute to the building of skills of participating companies and researchers in optimization-based design methodology, as well as to research advancement in both applied crane design and mechatronics. It also aimed to provide tangible benefit in the form of a new energy-optimized crane design, which is ready for the subsequent concept development planned by project members.
Expected long term effects
A new design can reduce energy consumption by 60%, while maintaining functionality. The design includes new mechanisms and passive elements. Forest cranes are oversized, and the size can be reduced by 33% while increasing manipulability. The project has resulted in the desired competence development, and taken an important step in the product development process towards cranes that contribute to energy efficiency, electrification and automation of forest machinery.
Approach and implementation
Crane operation data from 10 operators of different ages and of both sexes were collected. The drivers considered the study´s scenarios to be realistic, except that some logs were unusually far away. In the study, Cranab´s crane model FC12 was used, with a reach of 10.06 m and a weight of 1765 kg. Based on the collected data, the energy consumption and the size of the crane were minimized. The optimization-based design methodology took into account kinematic and dynamic analyses, meta-heuristic algorithms and manipulability.