Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services
Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services
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DOI：
10.1177/0954405417708217
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
J. Gopsill;B. Hicks
中科院分区：
文献类型：
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作者：
J. Gopsill;B. Hicks
挤出式3D打印机的销售增长迅速，市场价值预计在未来十年内将增加两倍。这种快速增长可归因于能力的阶跃变化以及机械，工业和土木工程过程中对3D打印零件需求的增加。相应地，一个新的技术原型平台-通常被称为制造实验室-已经出现，通过提供按需3D打印和原型服务，为当地教育，创业，创新和发明提供刺激。按需3D打印和原型设计服务（以下简称为3D管理打印服务）的有效性的核心是它们能够处理多个用户，这些用户对制造过程有不同的知识和理解，并能够扩展3D打印机的数量，以最大限度地提高服务的生产率。本文考虑的正是这种生产力挑战，更具体地说，是打印的可伸缩性和调度。通过改变可用打印机的数量和用户需求水平，对不同规模的3D管理打印服务的四种调度策略进行建模，研究了规模和调度策略对生产率的影响。两种最常见的方法（先到先服务和在线连续队列）和两种替代方案的基础上床空间优化（首次适合降低高度和首次适合降低高度与遗传算法）已被认为是。通过蒙特-卡罗模拟和策略的比较，表明增加3D管理打印服务的规模提高了3D管理打印服务保持生产力的峰值生产力和用户需求范围。此外，替代策略能够使3D管理打印服务的峰值生产率翻倍，并增加3D管理打印服务保持生产力的用户需求范围。
Sales of extrusion 3D printers have seen a rapid growth and the market value is expected to triple over the next decade. This rapid growth can be attributed to a step change in capability and an increase in demand for 3D printed parts within mechanical, industrial and civil engineering processes. Correspondingly, a new technical prototyping platform – commonly referred to as Fabrication Laboratories – has emerged to provide a stimulus for local education, entrepreneurship, innovation and invention through the provision of on-demand 3D printing and prototyping services. Central to the effectiveness of the on-demand 3D printing and prototyping services – hereby referred to as 3D managed print services – is their ability to handle multiple users with varying knowledge and understanding of the manufacturing processes and scaling numbers of 3D printers in order to maximise productivity of the service. It is this challenge of productivity and more specifically the scalability and scheduling of prints that is considered in this article. The effect of scale and scheduling strategies on productivity is investigated through the modelling of four scheduling strategies for 3D managed print service of varying scales by altering the number of available printers and level of user demand. The two most common approaches (first-come first-serve and on-line continuous queue) and two alternatives based on bed space optimisation (first-fit decreasing height and first-fit decreasing height with a genetic algorithm) have been considered. Through Monte-Carlo simulation and comparison of the strategies, it is shown that increasing the scale of 3D managed print service improves the peak productivity and range of user demands at which the 3D managed print service remain productive. In addition, the alternative strategies are able to double the peak productivity of 3D managed print service as well as increase the user demand range where the 3D managed print service remains productive.