سیستم هوشمند مبتنی بر کامپیوتر برای انتخاب ابزار اتوماتیک

This paper presents a computer-based intelligent system for the automatic selection of cutting processes and tools within a concurrent engineering environment. The main objective of the research work was to develop a procedure for the selection of cutting tools, develop a dynamic programming-based system that utilises mathematical modules and heuristic data to determine and calculate cutting parameters and total component cost. This will help the designers and manufacturing planners to select an optimal set of cutting tools and cutting conditions for different material properties and to give users alternatives on how to reduce cost and time. The system comprised of several modules; the knowledge acquisition module, the knowledge base module, the inference engine, the user interface, and the database. The developed system, which was designed to cover different component shapes including cylindrical, prismatic, different types of machining techniques, conventional and less conventional, has the potential to deal with complex products that are made up of multiple components. The system is capable of selecting cutting tools. It calculates cutting conditions and estimates component cost, based on the properties of the work piece material and features attributes, which include surface finish and tolerances, as well as using a number of production criteria such as material removal rate, tool life, machining time, and cost.

Over the last few decades, the range of engineering materials encountered in machine shops has increased greatly, as has the variety of cutting tools that are capable of machining these materials. In the early 1980s research work was undertaken in the area of computer-aided manufacturing and process planning. A number of systems have been developed to select a tool or a set of tools for a specific operation or a set of operations. The automatic tool selection system (ATS) which selects the optimum tools for rough turning and finishing operations was used by Maropoulos and Hinduja [5], [6], [8] and [9]. A knowledge-based module for an intelligent tool selection system ITS–KBS [10] was used for turning and boring operations performed on a cylindrical component. An integrated computer-based system for the selection of tool management (TLPM) for cylindrical components, was devised by Maropoulos and Alamin [11] and [16]. This system was used in milling operations to select cutter size and cutter path for prismatic parts [7], [9], [13] and [17]. These systems contained a simplified technology module, which calculated cutting conditions, selected tools and estimated tool life in turning, boring, and milling operations. Abdalla et al. [1], [2], [3] and [4] introduced an approach for integration of product, processes and tooling design and a systematic method for acquiring and analysing information about the capabilities of the manufacturing process. The problem of determining the optimum machining conditions, for single pass and multi pass operation, was described by Agapiou [18]. It also took into account the production cost and time. Some systems dealt with the cost estimation at an early stage of design. Cakir and Gurarda [19] described a procedure for turning operations that would minimise production cost and time. Yang and Lin [14] developed a system to estimate the manufacturing cost of a design according to the shape and required precision for its features, together with estimation of the machining time. Baker and Maropoulos [12] adopted this to conduct such evaluation in the early stages of design process. Some systems dealt mainly with the geometrical problem occurring during turning or milling. These concentrated upon prismatic components or cylindrical components. Other systems took into account the component material and geometric features but ignored selection of the cutting tool material and tool specification. An expert system, which is presented in this paper, is designed to deal with cylindrical and prismatic components, and different types of machining techniques (traditional and non-traditional techniques). This system has the ability to deal with complex products that are made up of many components. It has the ability to select cutting tools, calculate cutting parameters and estimate costs [20] for various machining techniques. It takes into consideration mechanical and thermal properties of the component material in order to select a suitable tool material, which can machine the desired component. At the same time, it will ensure that the required attributes of a feature such as surface finishing and tolerance are obtained. The main aim of this research is to help designers and manufacturing planners to select optimal cutting tools and optimum cutting conditions to reduce cost and time.

An intelligent prototype system for automatic cutting tool selection, for different work material properties, has been developed in this research project. The developed system enabled users and manufacturing planners to select suitable cutting tools, that could machine the work piece material and generate the desired feature, determine the optimum cutting conditions that leads to short cutting time, and subsequently, to low cost. The developed system was an effective tool for automatic cutting tool selection. It provided users with rapid results via a user-friendly interface. The system comprised of several modules: the knowledge acquisition module; the knowledge base module; the inference engine; the user interface and the database. The prototype system was developed using Kappa-PC software, which supported object-oriented programming, rule-based reasoning and enabled the knowledge to be represented in a rule and frame format. The prototype system dealt with different classes of tool materials currently in use for machining operation; high-speed steel, cemented carbides, cermets, ceramics, cubic boron nitride, polycrystalline diamond. The system worked in a fully interactive mode and was operated by guiding users through several stages: tool material selection in order to select a suitable tool material that can machine the desired work piece material; feature specification including cylindrical and prismatic feature and design attributes; and machining technique selection in order to select a suitable machining process that can achieve the desired feature at low cost. For cutting tool selection and optimum cutting conditions, the system selected the best cutting tool and determined the optimum cutting conditions, based on high material removal rate, long tool life, less machining time and low cost. The system was tested with two case studies. The first test was on a 3D component, to illustrate the capability of the prototype system. The results were very encouraging. The second case study was a comparison between theoretical results and experimental work; the results were reasonably acceptable.