A key step in remanufacturing is disassembly of the "core'' or the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the cores. Rust, corrosion, deformation, and missing parts may require disassembly plans to be changed and adapted frequently. Conventional industrial automation that usually serves in repetitive and structured activities may fail when it is applied to disassembly. This research investigates the flexible sequencing of robotic disassembly in the presence of failed automation operations and develops online recovery by incorporating backup actions. It starts with modeling the time and success rate of a backup action. The expected disassembly time and completion rate of a disassembly plan are deduced according to the failure probability of both the operations and their backup actions. A biobjective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm. Two solution selection criteria are combined to produce potential offspring candidates in each evolutionary generation. Experimental results show that the backup actions allow efficient recovery from automation and can potentially improve the robustness of robotic disassembly.
|Journal||IEEE Transactions on Automation Science and Engineering|
|Early online date||29 Apr 2021|
|Publication status||E-pub ahead of print - 29 Apr 2021|
- Backup actions
- multiobjective evolutionary algorithm
- robotic disassembly
- sequence planning.
- Service robots
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering