A flexible tool selection decision support system for milling operations

Competitive cutting tool manufacturers are now facing increasing demands to supply a comprehensive advice service with relation to selection of approp riate tools and cutting data for a wide variety of workpiece materials and component geometries. This paper describes the development of methods and a computer based system for automated machinability assessment and tool sele ction for milling. The system is called OPTIMUM (Optimised Planning of Tool ing and Intelligent Machinability evalUation for Milling) and is designed t o provide reliable tool selection and cutting data for a range of milling o perations. The machinability assessment method employs rule based decision logic and multiple regression techniques to produce feasible initial cuttin g conditions for a wide range of workpiece materials. A novel feature is th at a wide variety of input data is permitted, including imprecise or incomp lete workpiece descriptions. The tool selection process features the select ion of tools based upon optimised machining performance. A new optimisation criterion related to initial average chip thickness, called harshness, is proposed. Unlike most CAPP systems, a large variety of workpiece materials (more than 750 ferrous alloys) and a comprehensive selection of tools (pote ntially 35 988 cutter/insert combinations) are considered. A tool variety r eduction post processor facilitates the rationalisation of sets of selected tools to produce optimised tool sets for a limited number of available too l positions on a machining centre. The combination of knowledge based logic and statistical methods provide a powerful and flexible support tool for t he process planning of milling operations. (C) 2000 Elsevier Science B.V. A ll rights reserved.