A flexible energy behaviors modeling method for machining the workpiece based on feature technology

Yan He, Xiaocheng Tian, Yufeng Li, Yulin Wang, Yan Wang, Shilong Wang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Characterizing and analyzing energy behaviors are significantly essential to improve energy efficiency for machining the workpieces in modern manufacturing enterprises. This paper proposes a flexible energy behaviors modeling method for machining the workpiece based on feature technology. Energy behaviors for machining the features of the workpiece are flexible, which are affected by the design parameters and the production scenarios of the workpiece. In the method, the multiple factors influencing the energy behaviors are decomposed into Design-related factors, Process-related factors and Machine-related factors. A timed hierarchical object-oriented Petri net (TOPN) methodology is exploited to model the flexible energy behaviors for machining the workpieces. Energy behaviors affected by Design-related factors and Process-related factors are modeled by the data dictionaries and the associated attributes, while energy behaviors related to Machine-related factors are simulated by the structure of the TOPN. The case studies show that the quantitative insight of flexible energy behaviors can be provided efficiently, which can benefit to forecast energy consumption and find some potentials for saving energy during machining the workpiece.

    Original languageEnglish
    Pages (from-to)2849-2863
    Number of pages15
    JournalInternational Journal of Advanced Manufacturing Technology
    Volume113
    Issue number9-10
    DOIs
    Publication statusPublished - 26 Feb 2021

    Bibliographical note

    Funding Information:
    This research is supported by the National Key R&D Program of China (Grant No. 2018YFB2002100), National Natural Science Foundation of China (Grant No. 52075267), Chongqing General Program of Natural Science Foundation (Grant No.cstc2020jcyj-msxm2526) and the Science Fund for Distinguished Young Scholars of Chongqing (Grant No. cstc2020jcyj-jqX0011).

    Keywords

    • Energy behaviors
    • Energy consumption
    • Machining feature
    • Petri net

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