This paper presents novel tools to assist manual assembly in an virtual environment. While assembling virtual components, the user's actions are logged and an assembly plan is produced. Prototyping is reduced and concurrency is enhanced using such tools. Successful pilot studies have now been completed.Index terms -virtual reality, assembly planning, manual assembly.
The ®rst part of this paper introduced a procedure for rapidly calculating optimized cutting data for all the feasible tools for a given milling operation. Having produced this list of tools with associated optimized cutting conditions, the preferred tool is selected by sorting the list by a composite objective function incorporating a combination of four desirable conditions: maximum metal removal rate, maximum tool life, minimum overall cost and minimum overall cutting time. These four criteria are normalized by a constant multiplier and prioritized by user-de®ned weighting coecients. The tool selection procedure is implemented in software with a graphical user interface. The system includes material data for more than 750 ferrous alloys and speci®cations for 35 988 possible holder/insert combinations. Several examples are presented to demonstrate the capability of the system and the subtle interplay of technological constraints that makes optimized tool selection a dicult process to perform manually. This automated procedure oers consistent selection of tools with ecient cutting data that can produce considerable reductions in machining cost when compared with non-optimal solutions. This tool selection procedure is designed to select tools and associated cutting conditions for single milling operations. As many machining centres have a limited number of tool positions available for automated tool changing, it is possible that the optimal set of tools for a given component is not the set of tools that are optimal for each operation considered singly. A post-processing method is presented which rationalizes a set of tools so as to reduce the number of unique tools with the minimal decrease in performance when compared with the set of individually optimized tools.NOTATION c average average total cost of machining operation for the list of feasible tools (£) c total total cost of machining operation (£) m metal removal rate (mm 3 /min) m average average metal removal rate for the list of feasible tools (mm 3 /min) rnc number of components that a tool can machine t 2 total cutting time (min) t average average total production time for the list of feasible tools (min) t total total production time for one operation (min) T tool life (min) T average average tool life for the tool list (min)
The selection of tools and cutting data is a central activity in process planning and is often liable to an element of subjectivity. It is further complicated by the wide range of choice presented by the various operation types and the huge portfolio of cutters and inserts available from many different tool manufacturers. This paper describes a procedure to select consistently and efficiently tools for rough and finish milling operations performed on a computer numerical controlled (CNC) machining centre. A wide range of milling operations is considered, including faces, square shoulders, slots, T-slots, pockets, holes and profiles. An initial set of feasible tools is generated that satisfy the constraints of the tool type, the operation geometry, the insert geometry and carbide grade, the workpiece material and the machine tool capacity. Each tool consists of a holder and one or more indexable carbide inserts. Aggressive cutting data are generated for each feasible tool using a rapid search procedure in the permissible depth/width/feed space for good chip control. The cutting data are further refined by a set of technological constraints, which include tool life, surface finish, machine power and available spindle speeds and feeds. The overall cutting data optimization criterion is selected by the user from minimum cost, maximum production rate or predefined tool life. A new optimization criterion, called ‘harshness’, allows the user to influence the chip thickness that is achieved for any given cutter. Any feasible tools that fail to satisfy all the constraints and optimization criteria are discarded.
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