A generic energy prediction model of machine tools using deep learning algorithms

He, Yan; Wu, Pengcheng; Li, Yufeng; Wang, Yulin; Tao, Fei; Wang, Yan

doi:10.1016/j.apenergy.2020.115402

Cited by 72 publications

(22 citation statements)

References 39 publications

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“…In terms of the Modelling Technique, the usage of Artificial Intelligence (AI) is a rather young but promising field of research, with Artifical Neural Network (ANN) being the most used technique. A Modelling Technique from the Artificial Intelligence (AI) sub-field Deep Learning (DL) was only used by one of the examined studies [25]. However, this modelling technique seems to be promising, especially in the field of Forecasting, as Deep Learning (DL) techniques show great results for related forecasting tasks such as renewable energies forecasting [95], energy demand forecasting from the supplier perspective [96,97], and building thermal load forecasting [98].…”

Section: Resultsmentioning

confidence: 99%

“…When using Artifical Neural Network (ANN), ten articles used a simple Multilayer Perceptron. Only one articles applied a modelling technique from the filed of Deep Learning (DL) with the development of a Convolutional Neural Network [25]. Three of the analysed articles compared several Artificial Intelligence (AI) techniques [25][26][27].…”

Section: Analysis and Synthesismentioning

confidence: 99%

“…Only one articles applied a modelling technique from the filed of Deep Learning (DL) with the development of a Convolutional Neural Network [25]. Three of the analysed articles compared several Artificial Intelligence (AI) techniques [25][26][27]. Considering the time trend, Artificial Intelligence (AI) methods show a strong increase since the year 2019 with a maximum value of seven.…”

Section: Analysis and Synthesismentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

Walther

Weigold

2021

Energies

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In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy. Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry. In order to quantify energy efficiency or flexibility potentials, it is often necessary to predict or forecast the energy consumption. This paper presents a systematic review of state-of-the-art of existing approaches to predict or forecast the energy consumption in the manufacturing industry. Seventy-two articles are classified according to the defined categories System Boundary, Modelling Technique, Modelling Focus, Modelling Horizon, Modelling Perspective, Modelling Purpose and Model Output. Based on the reviewed articles future research activities are derived.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis and Synthesismentioning

confidence: 99%

Section: Analysis and Synthesismentioning

confidence: 99%

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A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

Walther

Weigold

2021

Energies

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“…Chen et al [17] proposed the framework of Internet of Things to monitor and management energy contributing to identify the strategies of reducing energy. The deep learning algorithms are used to establish a generic energy prediction model in [18] for identifying energy consumption characteristics among different machine tools under the condition of big machinery data. Xu et al [19] proposed a novel intelligent reasoning system to assess energy consumption and optimize cutting parameters through black-box theory.…”

Section: Introductionmentioning

confidence: 99%

A generalized analysis of energy saving strategies through experiment for CNC milling machine tools

Feng

Chen

Zhang

et al. 2021

Int J Adv Manuf Technol

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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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“…• Energy [45,46]: the energy consumption prediction of a machine tool is becoming increasingly critical in terms of emission reduction and energy efficiency of the manufacturing processes. The application of ML techniques permits to predict the most suitable MT setting to save energy in machining, guaranteeing the required production performance.…”

mentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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A generic energy prediction model of machine tools using deep learning algorithms

Cited by 72 publications

References 39 publications

A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

A generalized analysis of energy saving strategies through experiment for CNC milling machine tools

Recent Advances on Machine Learning Applications in Machining Processes

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