NDLI: Modelling and optimization of energy consumption for feature based milling

Content Provider	Springer Nature Link
Author	Altıntaş, Resul Sercan Kahya, Müge Ünver, Hakkı Özgür
Copyright Year	2016
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.
Starting Page	3345
Ending Page	3363
Page Count	19
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	86
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-02-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Energy efficiency Feature based milling STEP AP224 Green manufacturing Response surface methodology Face centered composite design Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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