Micro-Outsourcing for Mechanical CAD/CAM

机械 CAD/CAM 微外包

• 批准号: EP/F067291/1
• 负责人: Jonathan Corney
• 金额: $ 12.03万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF067291%2F1
• 关键词: Micro; Outsourcing; Mechanical; CAD; CAM

Recently the use of Crowdsourcing to deliver HITs has demonstrated a feasible way of providing cheap, robust, content based, Image analysis. This proposal seeks funding to investigate if a similar approach can be used to solve the geometric reasoning problems found in Mechanical CAD/CAM.Micro-outsourcing, or crowdsourcing, is a neologism for the act of taking a task traditionally performed by an employee or contractor, and outsourcing it to an undefined, generally large group of people, in the form of an open call. For example, the public may be invited to develop a new technology, carry out a design task, refine an algorithm or help capture, systematize or analyze large amounts of data. A Human Intelligence Task (HIT) is a problem that humans find simple, but computers find extremely difficult. For example a HIT related to a photograph could be: Is there a dog in this photograph? Many manufacturing operations require geometric reasoning to sequence, or recognize, various patterns, or constraints, in 2D and 3D shapes. Finding the best solutions to these problems would increase the productivity of numerous industries and impact directly on their profits. However frequently these types of problems are effectively incomputable (i.e. NP-complete) and so current practise is for CAD/CAM software to generate good , rather than optimum, solutions. If a Crowdsourcing approach to such difficult problems proves to be effective it would demonstrate how many similar pattern recognition and optimization problems manufacturing industry could be solved and provide a compelling demonstration of how a digital economy can distribute work, as well as, data. For much of its history CAD/CAM research has been motivated by the desire to increase the intelligence of systems by means of algorithms that could compute shape properties readily apparent to humans (eg. location of thin sections or holes). However this has proved to be difficult and where progress has been made it has generally solved special cases (eg. 2.5D geometry) rather than providing generic solutions. Examples of geometric reasoning problems still on the research agenda after decades of academic effort are numerous, for example: path planning, component packing, process planning, partial symmetry detection and shape feature recognition. Essential the difficulty is one of endowing computers with the appreciation of an object's overall form that humans gain so effortlessly. Interestingly similar difficulties have been encountered in image and speech recognition where automated systems still fail to reproduce human levels of performance.Because of this Geometric Reasoning represents a major technological bottleneck requiring many relatively trivial tasks to be done manually by engineers, a process that can be both time-consuming and sub-optimal (eg. frequently it will be infeasible to exhaustively explore all the alternatives paths, sequences or plans). Consequently removal of this geometric comprehension bottleneck would result in significant productivity gains across a wide range of industries. This proposal seeks to investigate the potential of a distributed approach (know colloquially as CrowdSourcing or Micro-outsourcing ) that has already proved its ability to provide practical solutions to many classic AI problems, such as image and speech interpretation. Research will use two exemplar applications to support a systematic investigation of the research issues. The first study will focus on a well defined task with easily quantifiable results (part nesting), while the second study will focus on a problem (shape similarity) easily stated but difficult to quantified.The project will create an experimental software platform, using the API of a commercial Crowdsourcing platform (i.e. Amazon's mechanical turk), to support the systematic investigation of the system's performance for these two different types of HIT.

最近，使用众包来提供HIT已经证明了一种提供廉价、健壮、基于内容的图像分析的可行方法。该提案寻求资金，以调查是否可以使用类似的方法来解决机械CAD/CAM中的几何推理问题。微外包或众包是一个新词，用于传统上由员工或承包商执行的任务，并将其外包给未定义的，通常是一大群人，以公开电话的形式。例如，可以邀请公众开发新技术，执行设计任务，改进算法或帮助捕获，系统化或分析大量数据。人类智能任务（Human Intelligence Task，HIT）是一个人类觉得很简单，但计算机觉得非常困难的问题。例如，与照片相关的HIT可以是：这张照片中有狗吗？许多制造操作需要几何推理来排序或识别2D和3D形状中的各种图案或约束。找到这些问题的最佳解决方案将提高许多行业的生产力，并直接影响其利润。然而，这些类型的问题往往是有效的不可计算的（即NP完全），因此目前的做法是CAD/CAM软件生成良好的，而不是最佳的解决方案。如果众包方法被证明是有效的，那么它将证明制造业可以解决多少类似的模式识别和优化问题，并提供一个令人信服的数字经济如何分配工作和数据的演示。在CAD/CAM研究的大部分历史中，其动机是希望通过算法来增加系统的智能，这些算法可以计算人类很容易看到的形状属性（例如：薄切片或孔的位置）。然而，事实证明这是困难的，在取得进展的地方，它通常解决了特殊情况（如：2.5D几何），而不是提供通用的解决方案。经过几十年的学术努力，几何推理问题仍然在研究议程上的例子很多，例如：路径规划，零件包装，工艺规划，部分对称检测和形状特征识别。关键的困难在于赋予计算机对物体整体形状的欣赏能力，而人类却可以毫不费力地获得这种能力。有趣的是，在图像和语音识别中也遇到了类似的困难，自动化系统仍然无法再现人类的性能水平。因此，几何推理是一个主要的技术瓶颈，需要工程师手动完成许多相对琐碎的任务，这是一个既耗时又不理想的过程（例如，几何推理）。通常，穷尽地探索所有备选路径、顺序或计划是不可行的）。因此，消除这一几何理解瓶颈将导致广泛行业的生产率显著提高。该提案旨在研究分布式方法（俗称众包或微外包）的潜力，该方法已经证明能够为许多经典的人工智能问题提供实用的解决方案，例如图像和语音解释。研究将使用两个范例应用程序来支持研究问题的系统调查。第一项研究将集中在一个明确的任务，易于量化的结果（部分嵌套），而第二项研究将集中在一个问题上该项目将创建一个实验软件平台，使用商业众包平台的API（即Amazon的mechanical turk），以支持对这两种不同类型的HIT的系统性能的系统调查。

项目成果

Outsourcing labour to the cloud

将劳动力外包到云端

• DOI: 10.1504/ijisd.2009.033083
• 发表时间: 2009
• 期刊: International Journal of Innovation and Sustainable Development
• 影响因子: 0.7
• 作者: Corney J

Geometric Reasoning With a Virtual Workforce (Crowd-sourcing for CAD/CAM)

使用虚拟劳动力进行几何推理（CAD/CAM 众包）

• 发表时间: 2008
• 作者: P Jagadeesan

Geometric reasoning via internet CrowdSourcing

通过互联网众包进行几何推理

• DOI: 10.1145/1629255.1629296
• 发表时间: 2009
• 作者: Jagadeesan A

Validation of Purdue Engineering Shape Benchmark clusters by Crowd-sourcing

通过众包验证普渡工程形状基准集群

• 发表时间: 2009
• 作者: P Jagadeesan