Use of Digital Manufacturing to Improve Management Learning in Aerospace Assembly

The primary goal of this work is to quantify any bene?ts that the use of digital manufacturing methods can offer when used upstream from production, for manufacturing process design, and tool development. Learning at this stage of product development is referred to as management learning. Animated build simulations have been used to develop build procedures and tooling for a panel assembly for the new Bombardier CRJ1000 (Canadair Regional Jet, 100 seat). When the jig format was developed, its simulated performance was compared to that of current CRJ700/900 panel builds to identify and quantify any improvements in terms of tooling cost and panel build time. When comparing like-for-like functions between existing CRJ700/900 (Canadair Regional Jet, 70/90 seat) and the
CRJ1000 tooling, it was predicted that the digitally assisted improvements had brought about a 4.9% reduction in jig cost. An evaluation of the build process for the CRJ1000 uplock panel predicted a 5.2% reduction in the assembly time. In addition to the improvement of existing tooling functions, new jig functionality was added so that both the drilling and riveting functions could be carried out in a single jig for the new RJ1000 panel.

Teaching and learning technologies in Higher Education: Applied behaviour analysis and autism.

Professionals on both international and national levels who work with children with autism are expressing the need for graduate-level training in applied behaviour analysis. The implementation of effective instruction in higher education for professionals working with children with autism and their families is a complex undertaking: the learner needs to acquire an understanding of the principles and procedures of applied behaviour analysis and also adapt this knowledge to the learning prerequisites of individuals with autism. In this paper we outline some current thinking about adult education and blended learning technologies and then describe and illustrate with examples emerging possibilities of multimedia technology in the development of teaching materials. We conclude that synergies between graduate-level curriculum requirements, knowledge of adult learning, and communication technology are necessary to establish comprehensive learning environments for professionals who specialize in autism intervention.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.