Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-26

AUTHORS

Makbul Hajad, Viboon Tangwarodomnukun, Chorkaew Jaturanonda, Chaiya Dumkum

ABSTRACT

A simulated annealing algorithm combined with an adaptive large neighborhood search (ALNS) has been proposed in this paper to minimize the laser cutting path in a two-dimensional cutting process. The proposed algorithm was capable of finding a near-optimum cutting path from a given layout of cut profiles. In this study, the layout was taken from an image, in which image processing algorithms were employed to extract cut profiles from the input image and to assign coordinates to the contours’ pixels. The optimization algorithm was based on generalized traveling salesman problem (GTSP), where all pixels of the input image were considered as the potential piercing locations. A laser beam made a single visit and then did a complete cut of each profile consecutively. The simulation results revealed that the proposed algorithm can successfully solve several datasets from GTSP-Lib database with a good solution quality. A compromise between the path distance and computing time was achievable by considering only 30% of the total pixels of the input image examined in this study. In addition, the cutting path generated by the proposed method was shorter than that recommended by the commercial CAM software and other previous works in terms of path distance with the same profile sample. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-019-03569-6

DOI

http://dx.doi.org/10.1007/s00170-019-03569-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112986374


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