Multi-head surface mounting placement optimisation based on adaptive multi-point crossover operator

Using different sequencing of component pick-and-place on a Surface Mount Technology (SMT) machine significantly impacts the distance required for head movement. The optimisation problem of surface mounting process optimisation in multi-head gantry-type SMT machine is generally considered to be an NP-hard problem. Therefore, study divides the surface mounting process optimisation into three sub-problems and propose a two-stage optimisation algorithm. In the first stage, a balanced distribution strategy (BDS) is introduced to address the component allocation problem, and a nearest neighbour algorithm (NNA) is proposed to solve the initial feeder allocation problem and component pick-and-place sequencing problems. Due to the large scale of the problem, finding the optimal solution within a reasonable time frame is challenging. Therefore, in the second stage, a novel genetic operator is proposed to further optimise the feeder sequence and the component pick-and-place sequence. Experimental results demonstrate that the proposed algorithm achieves high precision and speed. Specifically, compared with the minimum criterion genetic algorithm, the average distance is reduced by 4.15%, and compared with the multi-swarm discrete firefly algorithm, the average distance is reduced by 7.02%.