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Abstract This paper presents the development of an Intelligent Cavity Layout Design System (ICLDS) for multiple cavityin jection moulds. The system is intended to assist mould designers in cavity layout design at concept design stage. Thecomp lexities and principles of cavity layout design as well as various dependencies in injection mould design are introduced. The knowledge in cavity layout design is summarized and classified. The functionality, the overall structure and general process of ICLDS are explained. The paper also discusses such issues as knowledge representation and case-based reasoning used in the development of the system. The functionality of the system is illustrated with an example of cavity layout design problem.21940
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Keywords: Intelligent design, cavity layout design, injection mould design, case-based reasoning, design support system
1. Introduction
In manufacturing, the injection moul ding is one of he most widely used production processes for producing plastic parts with high production rate and little or no finishing required on plastic products. The process consists of injecting molten plastic material from a hot chamber into a closed mould, allowing the plastic to cool and solidify and ejecting the finished product from the mould. For each new plastic product, the injection moul ding machine requires a new injection mould. Design and manufacture of injection mould is a time consuming and expensive process and traditionally requires highly skilled tool and mould makers. An injection mould consists of several components, which include mould base, cavities, guide pins, a sprue, runners, gates, cooling water channels, support plates, slides and ejector mechanism [1]. Design of mould is also affected by several other factors such as part geometry, mould material, parting line and number of cavities per mould.
With the advances in computer technology and artificial intelligence, efforts have been directed to reduce the cost and lead time in the design and manufacture of an injection mould. Injection mould design has been the main area of research since it is a complex processinvolving several sub-designs related tovari ous components of the mould, each requiring expert knowledge and experience. Mould design also affects the productivity ,mould maintenance cost, manufacturability of mould ,and the quality of the mould ed part. Most of the workin mould design has been directed to the application of expert systems, knowledge based systems and artificial intelligence to eliminate or supplement the vast amount of human expertise required in traditional design process. Kruth and Willems [2] developed an intelligent support system for the design of injection moulds integrating commercial CAD/CAM, a relational database and an expert system. Lee et. al. [3] proposed a systematic methodology and knowledge base for injection mould design in a concurrent engineering environment. Raviwongse and Allada [4] developed a neural networkbased design support tool to compute the mould complexity index to help mould designers to assess their proposed mould design on mould manufacturability. Kwong and Smith [5] developed a computational system for the process design of injection moulding based on the blackboard-based expert system and the case-based reasoning approach, which includes mould design, production scheduling, cost estimation and determination of injection moulding parameters. Britton et. al. [6]discussed the injection mould design from a functional perspective using functional design knowledge and a number of knowledge libraries. Mok et. al. [7] developed an interactive knowledge-based CAD system for injection mould design incorporating computational, knowledge
and graphic modules.
Several studies have also been made on improving the design of specific components of an injection mould. On get. al. [8] developed a knowledge-based and objectoriented
approach for the design of the feed system for injection moulds, which can efficiently design the type, location and size of a gating system in the mould. Iraniet. al. [9] also developed a software system for automatic design of gating and runner systems for injection moulds and provide evaluation of gating design based on specified performance parameters. Nee et. al. [10] proposed a methodology for determination of optimal parting directions in injection mould design based on automatic recognition and extraction of undercut features. Chen and Chou [11] developed algorithms for selecting a parting line in mould design by computing the undercut volumes and minimising the number of undercuts. Park and Kwon [12] worked on the design of cooling systems in injection moulds and proposed an optimal design based on thermal analysis and design sensitivity analysis of the cooling stage of the injection moulding process. Lin [13] worked on the use of gate size and gate position as the major parameters for simulated injection mould performance prediction.