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

这个i-Corps项目更广泛的影响/商业潜力是开发一种制造方法，可以减少50%(50%)的材料浪费。从昂贵的基材(钢、钛、皮革、复合材料)切割异形件的制造商使用Shape嵌套软件在基板毛坯和残留物上安装尽可能多的部件，以最大限度地减少浪费材料。然而，目前的形状排样软件往往达不到最优的材料使用。目前的产品按顺序嵌套形状，将每一块逐一放置在基板上。这项拟议的技术旨在更有效地改进2D基板上物品的放置。这项技术的基础是模拟多种相互依赖的物种进化成一个紧密联系、稳定和多产的生态系统。其结果是大大减少了浪费的材料。例如，在汽车行业，拟议中的技术可能会将车身部件从钢板上更紧密地嵌套在一起，从而减少所需的粗钢和废钢的数量，后者需要大量能源来熔化和重复使用。该技术可应用于布料和皮革(纺织品)、铝和钢铁(造船)或钛和复合材料(航空航天)。废品的减少可能会提高美国经济的效率，提高工人的生产率。减少对开采金属的需求可能会带来环境效益，对垃圾填埋空间的需求也会减少(因为修整废物往往无法使用)。更高效的原材料零部件制造可能提供可持续的资源管理，节省能源，降低温室气体排放，减少污染和对生态系统的压力，并使最终产品对消费者来说更负担得起。i-Corps项目基于开发一种算法，用于使用生态系统战略模拟合作的演变。该算法采用大规模并行同时考虑全局布局模式，不同于现有嵌套产品中采用的顺序布局和局部布局决策方法。数以百万计的物种(每个物种都可能在2D嵌套的平面衬底上放置一个形状的部件)竞争覆盖2D材料。出现了最大的一组合作(即非竞争)物种。从生态系统的角度来看，相比于个别的、局部成功的物种，生态系统更倾向于大规模的合作物种。共同进化选择的压力将种群推向全局最优的整体解。这种对解决方案组件协作组的强调通常会发现2D形状(或3D碎片的包装)的嵌套更紧密、更高效，而不是目前的顺序技术，即一次放置一个形状，或在另一个物品之前打包。最近对商业2D形状嵌套软件产品在一个问题上的比较研究表明，所提出的技术嵌套了12个部件，而商业产品只嵌套了10个或11个。建议的技术在速度和输出质量方面的性能可用于扩大规模，以解决工业中的更大嵌套问题。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，建议的方法依赖于图形处理单元(GPU)计算能力的指数增长。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.