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This paper presents an adaptive approach to improve the process planning of Rapid Prototyping/Manufacturing (RP/M) for complex product models such as biomedical models. Non-Uniform Rational B-Spline (NURBS)-based curves were introduced to represent the boundary contours of the sliced layers in RP/M to maintain the geometrical accuracy of the original models. A mixed tool-path generation algorithm was then developed to generate contour tool-paths along the boundary and offset curves of each sliced layer to preserve geometrical accuracy, and zigzag tool-paths for the internal area of the layer to simplify computing processes and speed up fabrication. In addition, based on the developed build time and geometrical accuracy analysis models, adaptive algorithms were designed to generate an adaptive speed of the RP/M nozzle/print head for the contour tool-paths to address the geometrical characteristics of each layer, and to identify the best slope degree of the zigzag tool-paths towards achieving the minimum build time. Five case studies of complex biomedical models were used to verify and demonstrate the improved performance of the approach in terms of processing effectiveness and geometrical accuracy.  相似文献
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Waste Electrical and Electronic Equipments (WEEEs) are one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customisation and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritises operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this paper, a Particle Swarm Optimisation (PSO)-based selective disassembly planning method embedded with customisable decision making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimisation algorithms, the developed research is capable to process complex constraints and achieve optimised selective disassembly plans. Industrial cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.  相似文献
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