Research Papers

Energy Consumption Prediction System of Mechanical Processes Based on Empirical Models and Computer-Aided Manufacturing

[+] Author and Article Information
Keyan He

Industrial Engineering Center,
Zhejiang University,
Hangzhou, Zhejiang Province 310027, China
e-mail: 11325019@zju.edu.cn

Renzhong Tang

Industrial Engineering Center,
Zhejiang University,
Hangzhou, Zhejiang Province 310027, China
e-mail: tangrz@zju.edu.cn

Zhongwei Zhang

Industrial Engineering Center,
Zhejiang University,
Hangzhou, Zhejiang Province 310027, China
e-mail: 11125041@zju.edu.cn

Wenjun Sun

Industrial Engineering Center,
Zhejiang University,
Hangzhou, Zhejiang Province 310027, China
e-mail: swj0611@163.com

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 14, 2016; final manuscript received June 11, 2016; published online November 7, 2016. Assoc. Editor: Giorgio Colombo.

J. Comput. Inf. Sci. Eng 16(4), 041008 (Nov 07, 2016) (9 pages) Paper No: JCISE-16-1021; doi: 10.1115/1.4033921 History: Received January 14, 2016; Revised June 11, 2016

Energy consumption prediction at the process planning stage is the basis of mechanical process optimization aiming at saving energy and reducing carbon emission. The accuracy and efficiency of the prediction method will be the most concerning issues. This paper presents an energy consumption prediction system of mechanical processes based on empirical models and computer-aided manufacturing (CAM). The system was developed based on analysis of energy-related data and data acquiring methods. The energy consumption sources of mechanical processes are divided into two parts: energy of auxiliary machine movements and intrinsic process movements. Considering data sources, there are two kinds of data acquiring methods: acquiring data from database or from CAM files. Process energy state is introduced to support calculation of energy consumption and presentation of calculation results. Example of the system was developed based on Microsoft SQL Server 2008 and ugs nx 7.0, and several examples of energy prediction of mechanical processes were also presented. The results demonstrate that the proposed system developing method is effective in predicting energy consumption of mechanical processes with high accuracy and high efficiency.

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Fig. 5

Database structure and relations between the tables

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Fig. 4

Examples of energy-related data acquisition methods

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Fig. 3

Relation between energy consumption sources

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Fig. 8

Energy prediction of a turning process (case no. 1)

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Fig. 9

Energy prediction of a face milling process (case no. 2)

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Fig. 2

Power profile of a milling process

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Fig. 1

Flowchart of the system functions

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Fig. 6

User interface of the energy prediction system

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Fig. 7

Process of acquiring process energy states

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Fig. 10

Prediction results, validation experiments, and comparison



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