Assembly sequence planning method for optimum assembly accuracy of complex products based on modified teaching–learning based optimization algorithm - Zheng, Yu; Chen, Liang; Wu, Dianliang; Jiang, Peng; Bao, Jinsong (International journal of advanced manufacturing technology, 2023)
As an important part of the intelligent manufacturing, assembly sequence planning is closely related to the product quality, production efficiency, and cost. In this paper, a generalized assembly sequence planning method is proposed based on a modified teaching–learning based optimization algorithm. According to the assembly information, the interference relationship matrices can be established. Combined with the large volume differences of complex product parts, by using variable step sizes, the step dynamic interference analysis method is modified to test the interference relationship of parts with different sizes. By analyzing the interference relationship and tolerance propagation of parts, the dimensional tolerance unit is defined and a method is established to automatically generate the assembly dimensional tolerance transfer chain. Based on the interference matrices and dimensional transfer chain, the feasible assembly solution generation method is devised to generate feasible assembly sequences, and the accuracy of these sequences can be calculated. To optimize the assembly sequences with the assembly accuracy as the objective, the teaching–learning based optimization algorithm is modified by introducing the multiple learning mechanism including the teaching, learning, and remediation stages. The updating number of the assembly sequence can be increased through the multiple learning mechanism, and the optimization speed of the algorithm can be improved. The proposed method is finally verified by planning the assembly sequence of a subassembly of the pumping unit. The experiments show that the proposed method stops iterating after reaching the optimal accuracy. By introducing the multiple learning mechanism, more feasible solutions are generated in the learning stage, and compared to the single learning mechanism, the multiple learning mechanism has the faster computing speed.