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Purpose The purpose of this paper is to design and develop a rotary three-dimensional (3D) printer for curved layer fused deposition modeling (CLFDM), and discuss some technical challenges in the development. Design/methodology/approach Some technical challenges include, but are not limited to, the machine design and control system, motion analysis and simulation, workspace and printing process analysis, curved layer slicing and tool path planning. Moreover, preliminary experiments are carried out to prove the feasibility of the design. Findings A rotary 3D printer for CLFDM has been designed and developed. Moreover, this printer can function as a polar 3D printer for flat layer additive manufacturing (AM). Compared with flat layer AM, CLFDM weakens the staircase effect and improves geometrical accuracy and mechanical properties. Hence, CLFDM is more suitable for parts with curved surfaces. Research limitations/implications Double extruders have brought improved build speed. However, this paper is restricted to complex process planning and mechanical structures, which may lead to collisions during printing. Meanwhile, the rotation range of the nozzle is limited by mechanical structures, affecting the manufacturing capability of complex curved surfaces. Originality/value A novel rotary 3D printer, which has four degrees of freedom and double extruders, has been designed and manufactured. The investigation on the prototype has proved its capability of CLFDM. Besides, this rotary 3D printer has two working modes, which brings the possibility of flat layer AM and CLFDM.

Abstract-Taxonomy is becoming indispensable to a growing number of applications in software engineering such as software repository mining and defect prediction. However, the existing related taxonomies are always manually constructed. The sizes of these taxonomies are small and their depths are limited. In order to show the full potential of taxonomies in software engineering applications, in this paper, we present the first large-scale software programming taxonomy which is more comprehensive than any existing ones. It contains 38,205 concepts and 68,098 subsumption relations. Instead of learning from a open domain, we focus on taxonomy construction from Stackoverflow which is one of the largest QA websites about software programming. We propose a machine learning based method with novel features to create a taxonomy that captures the hierarchical semantic structure of tags in Stackoverflow. This method executes iteratively to find as many relations as possible. Experimental results show that our approach achieves much better accuracy than baselines. Compared with taxonomies related to software programming which are extracted from the general-purpose taxonomies such as WikiTaxonomy, Yago Taxonomy and Schema.org, our taxonomy has the widest coverage of concepts, contains the largest number of subsumption relations, and runs up to the deepest semantic hierarchy.

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