Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1494
Title: Modelling and Optimization of Energy Consumption for Feature Based Milling
Authors: Altıntas, Resul Sercan
Kahya, Müge
Ünver, Hakkı Özgür
Keywords: Energy efficiency
Feature based milling
STEP AP224
Green manufacturing
Response surface methodology
Face centered composite design
Publisher: Springer London Ltd
Source: Altıntaş, R. S., Kahya, M., & Ünver, H. Ö. (2016). Modelling and optimization of energy consumption for feature based milling. The International Journal of Advanced Manufacturing Technology, 86(9-12), 3345-3363.
Abstract: Energy consumption is increasing along with the world's population and industrialization level; thus, energy and resource efficiency in manufacturing is of vital importance. In order to increase energy and resource efficiency, the amount of consumed energy must first be accurately quantified for each manufacturing process. Milling is one of the most common machining operations. In this study, a prediction model for estimating theoretical energy consumption involved in milling of prismatic parts is presented. The prediction model relies on the STEP Application Protocol 224 features for volumetric information and material properties of prismatic parts. Verification tests exemplify how engineers can utilize the presented prediction model and approach to measuring machine tool energy consumption. Test results show that the prediction model runs with 5 % accuracy. Also, effect of cutter path for prismatic milling is investigated for certain features. Furthermore, response surface methodology is utilized in order to determine optimal milling parameters of slot feature in order to minimize energy consumption when machining AISI 304 stainless steel.
URI: https://link.springer.com/article/10.1007/s00170-016-8441-7
https://hdl.handle.net/20.500.11851/1494
ISSN: 0268-3768
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record



CORE Recommender

SCOPUSTM   
Citations

43
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

64
checked on Dec 21, 2024

Page view(s)

150
checked on Dec 23, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.