The additive manufacturing material extrusion surface finish is periodic in nature, which has been explored by researchers employing either rectangular or elliptical bead models. The shallow inclination angle configur...
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The additive manufacturing material extrusion surface finish is periodic in nature, which has been explored by researchers employing either rectangular or elliptical bead models. The shallow inclination angle configurations exhibit the greatest 'staircase impact', but in addition to this, the assumed bead geometry influences physical distribution of undercuts and voids. Depending on the initial assumptions, the predictive model may not reflect a valid build configuration. Consequently, graphical programming tools arc employed to develop rectangular, obround, and elliptical bead sets, which allows the bead shape and inclination angle to be altered dynamically for a wide range of configurations. The surface finish is predicted for selected bead geometry while considering the critical angle (multiple beads on the base layer) impact. (C) 2016 IFAC (International Federation or Automatic (Control) Hosting by Elsevier Ltd. All rights reserved.
This paper examines traits of graphical programming tools that are publicly available as freeware, downloadable from the Internet;and presents a common traits model of indicators that will assist in the study, selecti...
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ISBN:
(纸本)9781467348683
This paper examines traits of graphical programming tools that are publicly available as freeware, downloadable from the Internet;and presents a common traits model of indicators that will assist in the study, selection and development of graphical programming tools for children. The model is intended to supplement a contextualized study in developing graphical programming tools, and on teaching and learning approach to computer programming for children in the local community.
Machine Learning (ML) has gained prominence and has tremendous applications in fields like medicine, biology, geography and astrophysics, to name a few. Arguably, in such areas, it is used by domain experts, who are n...
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Machine Learning (ML) has gained prominence and has tremendous applications in fields like medicine, biology, geography and astrophysics, to name a few. Arguably, in such areas, it is used by domain experts, who are not necessarily skilled-programmers. Thus, it presents a steep learning curve for such domain experts in programming ML applications. To overcome this and foster widespread adoption of ML techniques, we propose to equip them with domain-specific graphicaltools. Such tools, based on the principles of flow-based programming paradigm, would support the graphical composition of ML applications at a higher level of abstraction and auto-generation of target code. Accordingly, (i) we have modelled ML algorithms as composable components;(ii) described an approach to parse a flow created by connecting several such composable components and use an API-based code generation technique to generate the ML application. To demonstrate the feasibility of our conceptual approach, we have modelled the APIs of Apache Spark ML as composable components and validated it in three use-cases. The use-cases are designed to capture the ease of program specification at a higher abstraction level, easy parametrisation of ML APIs, auto-generation of the ML application and auto-validation of the generated model for better prediction accuracy.
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