Abstract:
Nesting is pivotal in maximizing material use and productivity within manufacturing industries and involves the ordering, rotational placement, and translation placement of 2D irregular patterns onto raw material sheets. Despite the industrial significance, few methodologies tackle the challenging rotational placement problem due to its computational complexity. Unlike traditional search-based heuristics and meta-heuristics methods, this research pioneers a Deep Reinforcement Learning (DRL)-based framework that acquires a learning-based policy for flexible rotational placement and combines it with two rule-based policies to ensure a comprehensive nesting solution. Empowered by a bespoke Deep Learning (DL)-based geometric semantics extractor module, our approach achieves a 97%improvement in computation time and a 11%enhancement in material utilization compared to an open-source nesting software on a dataset from the sheet metal industry. Additionally, it shows competitive industry-practical performance against prevailing nesting algorithms on open datasets while being at least six times faster in computation time. Furthermore, this paper introduces a novel metric for geometrical irregularity, enriching the analysis and evaluation of nesting problems.
