A Generalized Analysis of Energy Saving Strategies Through Experiment for CNC Milling Machine Tools

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This study developed an energy consumption model for CNC milling machines considering cutting direction and non-load states, improving prediction accuracy through experimental validation and piecewise linear representation of spindle power.

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This paper develops an energy requirement prediction model for CNC milling that accounts for standby power and power contributions from spindle rotation during non-load, feeding, and rapid moves along the X, Y, and Z axes, as well as specific energy consumption measured in the X and Y cutting directions. The authors obtain component energy relationships between energy use and toolpath/cutting parameters using small experiments, then validate the model with 27 trial cutting experiments on a VMC850E machine in X and Y directions. They find that SEC differs between X and Y cutting directions and that spindle power is better represented with piecewise linear functions based on spindle speed characteristics, improving correlation from 25.45% (without segmentation) to over 99.98% within segments. A key limitation stated is that the HTML full-text could not be converted, requiring access via the PDF rather than complete HTML review. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding and rapid movement in X, Y, Z+ and Z- axially, and specific energy consumption (SEC) in the X and Y cutting directions respectively, which could not be considered completely in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in VMC850E machine, the results show that the SEC in the X and Y cutting directions are exactly different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of improved SEC model could reach to more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.
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A Generalized Analysis of Energy Saving Strategies Through Experiment for CNC Milling Machine Tools | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Generalized Analysis of Energy Saving Strategies Through Experiment for CNC Milling Machine Tools Chunhua Feng, Xiang Chen, Jingyang Zhang, Yugui Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-380607/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jul, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding and rapid movement in X, Y, Z+ and Z- axially, and specific energy consumption (SEC) in the X and Y cutting directions respectively, which could not be considered completely in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in VMC850E machine, the results show that the SEC in the X and Y cutting directions are exactly different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of improved SEC model could reach to more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors. Mechanical Engineering energy predict model energy-saving strategy CNC milling process tool path Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Published Journal Publication published 31 Jul, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 24 May, 2021 Reviews received at journal 04 Apr, 2021 Reviewers invited by journal 03 Apr, 2021 Editor assigned by journal 01 Apr, 2021 First submitted to journal 30 Mar, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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