A1 Journal article – refereed
Organizing the nozzle magazine of a gantry-type PCB assembly machine




List of Authors: Knuutila T, Suomi T, Emet S, Johnsson M, Nevalainen OS
Publisher: SPRINGER LONDON LTD
Publication year: 2013
Journal: International Journal of Advanced Manufacturing Technology
Journal name in source: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Journal acronym: INT J ADV MANUF TECH
Number in series: 5-8
Volume number: 68
Issue number: 5-8
ISSN: 0268-3768

Abstract
The present work studies the operation control of so-called collect-and-place component placement machines. These kinds of machines are suited for the flexible manufacturing of various printed circuit board products. These machines operate in cycles where a set of components is first collected from the component feeders to the vacuum nozzles of the component placement head. The head then moves on the circuit board and places the components to their appropriate locations. Different component types require the use of different nozzle types, but the placement head has only a limited capacity for nozzles. Hence, the ability to change nozzles every now and then allows the manipulation of a great variety of component types with the same machine. This is accomplished by storing a larger selection of nozzles in a separate nozzle magazine from where the nozzle collection of the placement head can be updated. The cost of changing the nozzle setup is, however, relatively large compared to the time costs of other operations in the placement cycle. What complicates things more is that the nozzle change cost is affected by the organization of nozzles in the magazine, too. The aim of this work is to determine the contents of the nozzle magazine in such a way that the change operation times are as small as possible. We develop two heuristics (a genetic algorithm and a swarm optimization algorithm) for this purpose and evaluate their performance on sample problems. Both heuristic approaches are capable of processing realistic production problems, in particular the genetic algorithm finds near-optimal results for small problem instances and outperforms clearly our other approaches for larger problems.

Last updated on 2019-29-01 at 12:56