An adaptive clustering-based genetic algorithm for the dual-gantry pick-and-place machine optimization. (August 2018)

• Record Type: Journal Article
• Authors: He, Tian; Li, Debiao; Yoon, Sang Won
• Is Part Of: Advanced engineering informatics. Volume 37(2018)
• Page Start: 66
• Page End: 78
• Publication Date: 2018-08
• Subjects: Printed circuit board assembly -- Dual-gantry pick-and-place machine -- Component allocation -- Clustering -- Genetic algorithm; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.04.007
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Multiple nozzle types and the component-nozzle compatibility are considered. A clustering-based genetic algorithm is developed to solve the entire problem. A search-based heuristic is proposed to allocate nozzles to the two gantries. An adaptive clustering is developed to allocate components to each gantry cycle. The synchronized gantry operations in dual-gantry machines are analyzed. Abstract: This research proposes an adaptive clustering-based genetic algorithm (ACGA) to optimize the pick-and-place operation of a dual-gantry component placement machine, which has two independent gantries that alternately place components onto a printed circuit board (PCB). The proposed optimization problem consists of several highly interrelated sub-problems, such as component allocation, nozzle and feeder setups, pick-and-place sequences, etc. In the proposed ACGA, the nozzle and component allocation decisions are made before the evolutionary search of a genetic algorithm to improve the algorithm efficiency. First, the nozzle allocation problem is modeled as a nonlinear integer programming problem and solved by a search-based heuristic that minimizes the total number of the dual-gantry cycles. Then, an adaptive clustering approach is developed to allocate components to each gantry cycle by evaluating the gantry traveling distances over the PCB and the component feeders. Numerical experiments compare the proposed ACGA to another clustering-based genetic algorithm LCO and a heuristic algorithm mPhase in the literature using 30 industrial PCB samples. The experiment results show that the proposed ACGA algorithm reduces the total gantry moving distance by 5.71% and 4.07% on average compared to the LCO and mPhase algorithms, respectively.