A nesting optimization method based on digital contour similarity matching for additive manufacturing

Yizhe Yang, Bingshan Liu (), Haochen Li, Xin Li, Gong Wang and Shan Li
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Yizhe Yang: Chinese Academy of Sciences
Bingshan Liu: Chinese Academy of Sciences
Haochen Li: Chinese Academy of Sciences
Xin Li: Chinese Academy of Sciences
Gong Wang: Chinese Academy of Sciences
Shan Li: Chinese Academy of Sciences

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 6, No 19, 2825-2847

Abstract: Abstract Additive manufacturing (AM) technology uses the layer-by-layer stacking method to print parts, which simplifies the process of complex parts. The requirements for batch printing in AM are continuously growing now. In order to improve the economic and time efficiency of AM, the printing layout needs to be optimized. However, considering the diversity of part construction directions and accuracy requirements, as well as the limitations of time and materials, the printing layout still lacks comprehensive optimization models and methods, and the existing placement algorithms have not effectively utilized the holes inside parts and gaps between parts. In this paper, a comprehensive weighted general optimization model for 2D nesting is proposed to maximize time and economic benefits in AM. Moreover, a contour similarity matching method based on chain code for part placement is proposed to solve the problems about utilizing holes and gaps for the compact layout, and the approximate optimal solution is obtained by integrating annealing evolution algorithm. Experiments are conducted to verify the effectiveness of the proposed algorithm for regular geometry and real-world part layout.

Keywords: Nesting/packing; Freeman chain code; Contour similarity matching; Annealing evolution algorithm; Additive manufacturing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-01967-4

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