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Composite reinforcement manufacturing is gaining the attention of many researchers because it enhances the physical and chemical properties of the material that it is reinforced into. In th...

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon?s Mechanical Turk.

Low volume production associated with space systems manufacturing is inherently expensive, time-consuming, and risk-laden. At the root of this problem is an inability to adequately predict, monitor, and control the product development and sustainment process. This paper describes the ASSIST system, an intelligent knowledge management system designed to address inefficient information management processes and improve space system affordability. ASSIST is designed for collaborative engineering, manufacturing, and testing within a company, between companies, and between local and remote individuals. ASSIST uses web-based standards (including HTTP and XML) as the common message approach connecting its components. Automated process planning and production scheduling is a key component of ASSIST. This paper describes an integrated process planning and production scheduling problem and discusses the solution approach.