Energy consumption and modeling in precision hard milling

doi:10.1016/j.jclepro.2015.10.094

Journal of Cleaner Production

Volume 135, 1 November 2016, Pages 1591-1601

https://doi.org/10.1016/j.jclepro.2015.10.094 Get rights and content

Highlights

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A set of parameters has been defined to characterize power and energy consumption.
•
Traditional model (U = C₁/MRR + C₀) is not accurate for net cutting specific energy.
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MRR is not a unique identifier of net cutting specific energy.
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Tool wear has the greatest influence on net cutting specific energy.
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Energy efficiency increases with MRR in hard milling.

Abstract

Energy consumption in precision cutting has a significant impact on manufacturing cost and environmental impact. However, the characteristics of energy consumption in hard milling at the machine, spindle, and the process level remain unclear. In particular net cutting specific energy consumed in surface generation is yet to understand. This study explores power and energy consumption characteristics in hard milling by examining the relationships between power, energy consumption, and process parameters including tool wear. A new set of parameters has been defined to characterize power and energy characteristics. Tool wear has the greatest influence on net cutting specific energy compared with feed and cutting speed. Traditional empirical models may predict specific energy at machine and spindle levels, but lack accuracy when describing net cutting specific energy. A power regression model was developed to predict net cutting specific energy with high accuracy. Material removal rate (MRR) was found not to be a unique identifier for net cutting specific energy. Energy efficiency increased with MRR. In addition, up milling consumed slight more energy than down milling.

Graphical abstract

Introduction

Manufacturing is energy intensive, and as a result, has a significant impact on sustainable production (Hauschild et al., 2005, Jovane et al., 2008). In fact, manufacturing consumes more than two-thirds of industrial energy use or approximately 22.2 quadrillion kJ in the U.S. alone (U.S. Energy Information Administration, 2012). Non-renewable energy sources, including petroleum, coal, and natural gas, account for the majority of energy consumption in the U.S. power grid. Extensive usage of these fossil fuels in industry, especially in manufacturing, presents both economic and environmental concerns. Exploiting new energy sources is one approach to shifting towards sustainable manufacturing, while minimizing energy consumption in manufacturing is quite another. Thus, it is necessary to conduct a thorough study on energy consumption in manufacturing to improve energy efficiency.

Machine drives, i.e. electric motors, pumps, and fans that are used in manufacturing processes, such as machining, account for over half of the manufacturing industry's electricity use (U.S. Energy Information Administration, 2012). Depending on the machine, actual energy used for material removal can range from 15% to 70% (Dahmus and Gutowski, 2004, Dietmair and Verl, 2009). The remainder is consumed by pumps, coolant, centrifuges, and other peripheral equipment.

There exists a need to improve the energy efficiency of machining hard and brittle materials, such as difficult-to-cut alloys and ceramics. Compared with abrasive machining processes, hard milling (milling of hardened steels) as a sustainable finishing process may produce a superior surface integrity in precision components such as bearings, dies, and molds (Klocke et al., 2005). However, poor machinability leads to lower material removal rates and thus lower energy efficiency. Improving energy efficiency by reducing energy consumption in cutting-based manufacturing processes will not only benefit manufacturers economically but will also improve their environmental performance (Neugebauer et al., 2011).

Several studies on specific energy and process parameters in machining have been conducted to characterize and reduce energy consumption (Avram and Xirouchakis, 2011, Balogun and Mativenga, 2013, Diaz et al., 2011, Draganescu et al., 2003, Gutowski et al., 2006, Li and Kara, 2011, Schlosser et al., 2011). However, previous studies focused on specific energy at the machine level or spindle level, which is not the actual specific energy (i.e., net cutting specific energy) consumed by the cutting process.

The relationship between net cutting specific energy and milling process parameters, i.e. depth of cut, cutting speed, and feed per tooth, is poorly understood. While material removal rate (MRR) is a convenient process parameter to predict total or spindle specific energy, the relationship between MRR and net cutting specific energy has not been investigated. Also, the inevitable tool wear during cutting not only consumes more energy but also deteriorates surface integrity and fatigue performance.

Therefore, the objectives of this study are to: (1) characterize the specific cutting energy at machine or process level in precision hard milling and its relationship with key process parameters in surface generation (e.g. depth of cut, cutting speed, feed per tooth, MRR, up vs. down milling, and tool wear); (2) determine if MRR is a unique indicator of power and energy consumption, i.e. whether varying process parameters under the same MRR produces a unique power and energy consumption; and (3) analyze the difference in power and energy consumption between up milling and down milling for which the cutting mechanics differ.

Section snippets

Energy consumption models

The development of analytical models of energy consumption is critical for reducing energy consumption in manufacturing (Vijayaraghavan and Dornfeld, 2010). Several researchers have modeled total energy consumption in machining (Aramcharoen and Mativenga, 2014, Gutowski et al., 2006, He et al., 2012, Mori et al., 2011, Yoon et al., 2014). Table 1 summarizes the major empirical models of specific energy consumption in cutting.

The model developed by Gutowski et al. (2006) is for direct energy

Experiment

Total and spindle rotation energy consumed by dry end milling of AISI H13 tool steel (50 ± 1 HRC) was measured using a Fluke Power Analyzer (Fig. 1). The machining center was a Cincinnati Arrow 500. The relationships between power and energy consumption with process parameters were analyzed.

The chemical composition of AISI H13 and its material properties are shown in Table 2, Table 3, respectively. Samples were sectioned into cuboids and then ground on the top and bottom surfaces to (1) remove

Specific energy vs. MRR

The total, spindle, and net cutting specific energy vs. MRR for sharp and worn tools are presented in Fig. 6. The traditional empirical model reported by Kara and Li (2011) closely approximates the measured total and spindle specific energies; for a sharp tool, R² was 99.7% and 98.9%, respectively. When analyzing the net cutting specific energy, the accuracy of this model significantly reduces; R² was only 41.7%. Similar results are shown for a worn tool. Therefore, the traditional model has

Summary and conclusions

The power and energy consumed by precision hard milling AISI H13 tool steel with sharp and worn tool conditions was measured and analyzed. The total and spindle energy were measured, and the net cutting energy was calculated based on spindle energy. The key conclusions are summarized as follows:

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A new set of parameters was defined to characterize power and energy consumption in cutting at the process level.
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It was confirmed that the traditional empirical models effectively described the

Acknowledgments

The corresponding author would like to thank Prof. John Sutherland at Purdue University for discussing the concept of specific cutting energy.

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View full text

Energy consumption and modeling in precision hard milling

Highlights

Abstract

Graphical abstract

Introduction

Section snippets

Energy consumption models

Experiment

Specific energy vs. MRR

Summary and conclusions

Acknowledgments

J. Clean. Prod.

J. Clean. Prod.

J. Clean. Prod.

J. Clean. Prod.

J. Mater. Process. Technol.

CIRP Ann. Manuf. Technol.

CIRP Ann. Manuf. Technol.

CIRP Ann. Manuf. Technol.

CIRP Ann. Manuf. Technol.

J. Clean. Prod.

J. Clean. Prod.

CIRP Ann. Manuf. Technol.