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Research Papers

Toward a Generalized Energy Prediction Model for Machine Tools

[+] Author and Article Information
Raunak Bhinge

Laboratory for Manufacturing and Sustainability,
University of California,
Berkeley, CA 94720
e-mail: raunakbh@berkeley.edu

Jinkyoo Park

Engineering Informatics Group,
Stanford University,
Stanford, CA 94305
e-mail: jkpark11@stanford.edu

Kincho H. Law

Engineering Informatics Group,
Stanford University,
Stanford, CA 94305
e-mail: law@stanford.edu

David A. Dornfeld

Laboratory for Manufacturing and Sustainability,
University of California,
Berkeley, CA 94720
e-mail: dornfeld@berkeley.edu

Moneer Helu

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: moneer.helu@nist.gov

Sudarsan Rachuri

Advanced Manufacturing Office,
Office of Energy Efficiency
and Renewable Energy (EERE),
Department of Energy,
Washington, DC 20585
e-mail: sudarsan.rachuri@hq.doe.gov

Manuscript received September 29, 2015; final manuscript received July 21, 2016; published online November 9, 2016. Assoc. Editor: Jorge Arinez. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Manuf. Sci. Eng 139(4), 041013 (Nov 09, 2016) (12 pages) Paper No: MANU-15-1500; doi: 10.1115/1.4034933 History: Received September 29, 2015; Revised July 21, 2016

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

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References

Figures

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Fig. 1

Data acquisition system

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Fig. 2

Categorization of types of manufacturing data obtained using MTConnect

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Fig. 3

Test part design for experimentation

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Fig. 4

Machining process parameters: (a) process parameters of a milling process and (b) cutting strategy

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Fig. 5

Generic test part used to validate the energy prediction model

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Fig. 6

Prediction of energy density values for generic test parts (machined using face-milling, y-direction cut, conventional cutting strategy, and depth of cut = 1 mm). The band represents μ1(xi|D1)±1.96σ1(xi|D1). (a) Test part 1, (b) test part 2, and (c) test part 3.

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Fig. 7

Prediction of total mean energy consumptions including all operations: (a) test part 1, (b) test part 2, and (c) test part 3

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Fig. 8

Distributions of errors: (a) test part 1, (b) test part 2, and (c) test part 3

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Fig. 9

Toolpath comparison

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Fig. 10

Part geometry for ordering different toolpath strategies

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Fig. 11

Total energy consumption comparison for different toolpath strategies

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