Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the ene...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced manufacturing technology 2014-07, Vol.73 (5-8), p.1201-1212
Main Authors: Alrashdan, Abdalla, Bataineh, Omar, Shbool, Mohammad
Format: Article
Language:English
Subjects:
CAE) and Design
Chromium molybdenum vanadium steels
Comminution
Computer-Aided Engineering (CAD
Cost analysis
Cost function
End milling
Energy consumption
Energy costs
Engineering
Genetic algorithms
Graphs
Industrial and Production Engineering
Mechanical Engineering
Media Management
Milling (machining)
Multiple criterion
Operators (mathematics)
Optimization
Original Article
Process parameters
Production planning
Regression analysis
Surface finish
Surface roughness
Tool steels
Turning (machining)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-014-5921-5