Using an Improved Differential Evolution for Scheduling Optimization of Dual-Gantry Multi-Head Surface-Mount Placement Machine

Abstract

The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a dual-gantry multi-head surface-mount placement machine. It considers five inter-related problems: (i) component allocation; (ii) automatic nozzle changer assignment; (iii) feeder arrangement; and (iv) pick-and-place sequence; it incorporates a practical restriction related to (v) component height. The paper proposes a solution to each problem: (i) equalizing “workloads” assigned to the gantries, (ii) using quantity ratio method, (iii) using two similarity measurement mechanisms in a modified differential evolution algorithm with a random-key encoding mapping method that addresses component height restriction, (iv) and a combination of nearest-neighbor search and 2-opt method to plan each placing operation. This study reports an experiment that involved the processing of 10 printed circuit boards and compared the performance of a modified differential evolution algorithm with well-known algorithms including differential evolution, particle swarm optimization, and genetic algorithm. The results reveal that the number of picks, moving distance of picking components, and total assembly time with the modified differential evolution algorithm are less than other algorithms.

A Review of Intelligent Assembly Technology of Small Electronic Equipment

“…Under the premise that the position of the components on the feeder was determined, it achieved a higher convergence rate and higher accuracy than the hybrid leapfrog algorithm. Lin et al [ 97 ] summarized the five significant issues faced by task planning for dual-arm placement machines and established a mathematical model. They balanced the load by reasonably partitioning PCBs, determined the number of various types of nozzles on the ANC (automatic nozzle changer) using the quantity ratio method, and proposed an improved differential evolution algorithm and a random-key encoding mapping method to optimize the feeder position.…”

Design and Research of Intelligent Assembly and Welding Equipment for Three-Dimensional Circuit

“…By using contact detection and pressure detection, we established position, speed, and pressure change curves, analyzed the factors affecting the C/T time of the pressure assembly, and integrated a force/position coupling smooth motion control technology based on fuzzy PID control to control the steady-state pressure accuracy and prevent pressure over-shoot [26][27][28]. The mechanical structure of the entire module is shown in Figure 10, which consists of a ZR force control module, linear module, a pressure sensor, a CCD camera, a loading device, a feeder, and a calibration mechanism [29][30][31].…”

“…Figure 10, which consists of a ZR force control module, linear module, a pressure sensor, a CCD camera, a loading device, a feeder, and a calibration mechanism [29][30][31].…”