Research on the optimal design technology of a digital assembly sequence based on an internet of things data collection framework

Abstract

The assembly of a machine tool modular fixture with a complex structure, flexible layout and poor regularity characteristics is used as an example in this paper to establish an IOMT system for optimizing the assembly sequence of a machine tool modular fixture based on IOT technology driven by modern intelligent identification and acquisition technology, with the aim of addressing the shortcomings of existing assembly technology in the traditional serial design method. From the perspective of an inverse sequence of an assembly sequence, and combined with the disassembly sequence data extracted by the perceptual assembly layer control unit under the environment of the IOT, the disassembly sequence data can be mathematically expressed by using three mainstream heuristic algorithms (i.e. the artificial fish swarm algorithm, the genetic algorithm, and the ant colony algorithm), while considering the disassembly quality, disassembly cycle and cost. Then, the three algorithm models are used to analyse the example of a machine tool modular fixture, and the calculation efficiency and solution accuracy are comprehensively evaluated; Simulation results show that the ant colony algorithm has the highest computational efficiency and the highest accuracy in a complex data environment. Therefore, the construction of an IOMT system for optimizing the assembly sequence of a machine tool modular fixture can be realized by using the ant colony algorithm.