AI Search5-axis positioning sequence optimization using genetic algorithms
Call Number: AIT Thesis no. ISE-97-03 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ISE-97-03Publication details: Bangkok : Asian Institute of Technology, 1997Description: 41, [30] pSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology,1997 Summary: The flexibility of tool orientation in space is extremely high in five-axis machining unlike in three-axis machining. The tool vector can be positioned in any direction in space. A number of operations are performed in one set up of the work piece to obtain full advantage of the five-axis machining concept. The control of machining (actual and positioning) time in such cases can be very crucial. Planning the optimal path manually can be very cumbersome and unfortunately most of the CAM systems supp01iing five axis machining available in the market today do not provide an efficient and effective approach to optimal tool path generation. At the most they have an interactive approach with the user. In this report, an algorithm coupled with a programming language which is one of the best methods to ovei'come the above drawbacks is presented. A genetic approach (GA) was applied for solution of the model. The GA first minimizes the distance traveled by the tool (positioning) and then the positioning time is calculated subject to the number of tool changes The algorithm is tested for its performance with respect to population size and different genetic operators.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies
Thesis (M.Eng.) - Asian Institute of Technology,1997
The flexibility of tool orientation in space is extremely high in five-axis machining unlike in three-axis machining. The tool vector can be positioned in any direction in space. A number of operations are performed in one set up of the work piece to obtain full advantage of the five-axis machining concept. The control of machining (actual and positioning) time in such cases can be very crucial. Planning the optimal path manually can be very cumbersome and unfortunately most of the CAM systems supp01iing five axis machining available in the market today do not provide an efficient and effective approach to optimal tool path generation. At the most they have an interactive approach with the user. In this report, an algorithm coupled with a programming language which is one of the best methods to ovei'come the above drawbacks is presented. A genetic approach (GA) was applied for solution of the model. The GA first minimizes the distance traveled by the tool (positioning) and then the positioning time is calculated subject to the number of tool changes The algorithm is tested for its performance with respect to population size and different genetic operators.
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