I-Corps: Deep-Scale Evolution for Industrial Shape Nesting

这个I-Corps项目的更广泛的影响/商业潜力是开发一种制造方法，可以减少百分之五十（50%）的材料浪费。制造商从昂贵的基材（钢、钛、皮革、复合材料）上切割成型件，使用形状嵌套软件将尽可能多的零件安装在基材坯料和残余物上，以最大限度地减少材料浪费。然而，目前的形状套料软件往往福尔斯达不到最佳的材料使用。目前的产品按顺序嵌套形状，将每一片一片地放置在基板上。所提出的技术被设计为更有效地改善物品在2D基板上的放置。该技术基于多个相互依赖的物种模拟进化为紧密结合、稳定且富有成效的生态系统。 其结果是大大减少了浪费的材料。 例如，在汽车工业中，拟议的技术可能会更紧密地嵌套从钢板上切割下来的车身部件，减少所需的原料钢和废钢的数量，废钢需要大量的能源来熔化和再利用。 该技术可应用于布料和皮革（纺织品），铝和钢（造船）或钛和复合材料（航空航天）。 废料的减少可能会使美国经济更有效率，工人的生产力更高。 减少对开采金属的需求可能会带来环境效益，减少对填埋场的需求也会带来环境效益（因为修剪后的废物往往无法使用）。更有效地利用原材料制造零部件，可以提供可持续的资源管理，节省能源，减少温室气体排放，减少污染和对生态系统的压力，并使消费者更能负担得起最终产品，这个I-Corps项目的基础是开发一种算法，用于模拟使用生态系统战略的合作演变。 所提出的算法用于深层次的进化使用并行的同时考虑全球布局模式，一种新的方法，不同的顺序件放置和局部布局决策中使用的现有的排样产品。 数以百万计的物种（每个物种都可能在2D嵌套中将一个成形的块放置在平面基底上）竞争覆盖2D材料。最大的一组合作（即，非竞争性物种出现。 生态系统的观点更倾向于合作物种的大集合，而不是个体，局部成功的物种。协同进化选择压力推动种群向全局最优的整体解发展。 这种对解决方案组件的协作组的强调通常会发现，与当前的顺序技术（一次放置一个形状或在下一个之前包装一个项目）相比，2D形状（或3D部件的包装）的嵌套更紧密，更有效。 最近的研究比较商业二维形状嵌套软件产品的一个问题表明，提出的技术嵌套12件，而商业产品产品嵌套只有10或11。 所提出的技术在速度和输出质量方面的性能可用于按比例放大到来自工业的更大的排样问题。 基于种群的进化，该算法本质上是大规模并行的。 与深度学习神经网络一样，所提出的方法依赖于图形处理单元（GPU）计算能力的指数级增长。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of an approach to manufacturing that may reduce material waste by fifty percent (50%). Manufacturers cutting shaped pieces from expensive substrates (steel, titanium, leather, composites) use shape nesting software to fit as many pieces as possible on substrate blanks and remnants to minimize wasted material. However, current shape nesting software often falls short of optimal material usage. Current products nest shapes sequentially, placing each piece on the substrate one after another. The proposed technology is designed to improve the placement of items on a 2D substrate more efficiently. The technology is based on the simulated evolution of multiple, inter-dependent species into a tightly knit, stable and productive ecosystem. The result is a substantial reduction in wasted material. In the auto industry, for example, the proposed technology may more tightly nest body parts to be cut from steel sheets, reducing the amount of raw steel needed as well as the scrap steel, which requires substantial energy to melt and reuse. The technology may be applied to cloth and leather (textiles), aluminum and steel (shipbuilding), or titanium and composites (aerospace). The reductions in scrap may make the US economy more efficient and workers more productive. Reduced demand for mined metals may have environmental benefits, as will the reduced need for landfill space (since trim waste is often unusable). More efficient manufacturing of parts from raw materials may provide for sustainable resource management, save energy, lower greenhouse gas emissions, reduce pollution and the strain on ecosystems, and make final products more affordable for consumers.This I-Corps project is based on the development of an algorithm for simulating evolution of cooperation using an ecosystem strategy. The proposed algorithm for deep-scale evolution uses massively-parallel simultaneous consideration of global layout patterns, a new approach distinct from the sequential piece placement and local layout decisions used in available nesting products. Millions of species (each a possible placement of a shaped piece on a flat substrate in 2D nesting) compete to cover the 2D material. The biggest set of cooperating (i.e., non-competing) species emerges. The ecosystem perspective favors large sets of cooperative species over individual, locally successful species. Co-evolutionary selection pressure pushes the population toward globally optimal total solutions. This emphasis on cooperative groups of solution components often will find tighter, more efficient nestings of 2D shapes (or packings of 3D pieces) than current sequential techniques that place one shape at a time, or pack one item before the next. Recent studies comparing commercial 2D shape nesting software products on one problem showed that the proposed technology nested twelve pieces, while the commercial product offerings nested only ten or eleven. The performance of the proposed technology for both speed and output quality may be used to scale up to larger nesting problems from industry. Based on the evolution of populations, the algorithm is inherently massively parallel. As with deep learning neural networks, the proposed approach depends on the exponential growth in computing power of graphics processing units (GPUs).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.