Development of a software-automated intelligent sculptured surface machining optimization environment

To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major r...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2014-11, Vol.75 (5-8), p.909-931
Main Authors: Fountas, Nikolaos A., Vaxevanidis, Nikolaos M., Stergiou, Constantinos I., Benhadj-Djilali, Redha
Format: Article
Language:English
Subjects:
Automation
CAD
CAD/CAM
CAE) and Design
CAM
Computer aided design
Computer aided manufacturing
Computer-Aided Engineering (CAD
End users
Engineering
Genetic algorithms
Industrial and Production Engineering
Machine tools
Machining
Mechanical Engineering
Media Management
Minimum cost
Numerical controls
Optimization
Original Article
Process parameters
Software
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Summary:To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-014-6136-5