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.

In order to solve the assembly sequence planning problem of fixtures, an ant colony algorithm is presented according to the principle that an assembly can be assembled if it can be disassembled. The disassembly constraint relations among the parts in a fixture are used to construct an integrated disassembly interference matrix. The key technologies are discussed, including feasible disassembly direction derivation based on an interference matrix, state transition probability of disassembly parts, pheromone updating rule of the ant colony algorithm and so on. The objective function takes the changing times of disassembly directions and tools. A program is realized in Matlab and verified by an example. The algorithm can effectively find the optimal assembly sequences for fixtures.