Figure 3: Energy and power demand as a function of M.R.R. for
depth of cut experiments with cutter (2).
2.3 Trade-off Between Power Demand and Processing Time
The machine tool’s electrical energy consumption is dependent on the power demand, p avg, and processing time, ?t, as seen in Equation（1）. Since the power demand shows some variability due to the internal cooling unit of the machine tool, the average power demand, p avg , will be used. As was mentioned previously, the average power demand is composed of a cutting, p cut, and air cutting, pair, component; consequently the energy consumption can be expanded as follows:
e?pavg*?t?(p?cutpair)*?t (1)
Two scenarios will be compared. Scenario (1) is the base scenario, while scenario (2) will be the scenario in which the material removal rate is increased for the purpose of reducing processing time. The constants,αandβ, were created to represent the increase in p cut and decrease in ?t, respectively (see Equations 2 and 3). Note that both constants are less than unity.
??pcutpcut1 (2)
2???t2 (3)
?t1Equation 4 shows the relationship between p avg1 and Pavg2, which assumes that the air cutting power demand, pair, remains relatively constant for both scenarios.
pavg??*pavg?p12air*(1??) (4)
If the relative size of the air cutting power demand is denoted by:
?
i?pair (5)
pavgii- 5 -
where i is 1 or 2 for scenarios 1 and 2, respectively, then the inequality presented in Equation 6 shows the condition that must be met in order for the energy consumption of scenario (2) to be smaller than that of scenario (1).
?2???? (6) 1??2
So if β is less than α, then e2 will always be less than e1. Also, asη
increases (i.e. if
the air cutting power demand comprises a large portion of the total power demand) then the probability of e2 being less than e1 increases. This would be the case for machine tools with large work volumes which have a high standby power demand. Further work can be conducted in which the assumption that the air cutting power demand does not stay constant to expand the applicability of the power and processing time trade-off analysis. 3 CHARACTERIZING THE SPECIFIC ENERGY
The specific energy of various manufacturing processes was previously summarized by Gutowski et al. [7], but for any given manufacturing process the data was limited to only a sample of process rates. This study, though, will focus on milling machine tools and the operable range of the machining center when characterizing the specific energy.
In characterizing the energy consumption of a machine tool, as the M.R.R. approaches infinity the specific energy is expected to reach a steady state of zero. But, given the work volume, spindle speed, and table feed constraints of a machine tool as well as the maximum loads that can be applied without deforming the main body frame or breaking the spindle motor, the operator will never reach a M.R.R. anywhere near infinity. So under the constraints of the M.R.R. a curve of the following form:
ecut?k*1?b (7)
M.R.Rwas fit to the data from the width of cut and depth of cut experiments. Note that the constant, k, essentially has units of power and b represents the steady-state specific energy.
The total specific energy, which accounts for cutting and air cutting power demand, was indeed found to have an inverse relationship with the M.R.R. (see Figure 4). The air cutting power demand dominated the specific energy. The impact of the cutting power demand on the specific energy was minimal since at high loads (i.e. at high M.R.R.’s) the machining time decreased significantly.
The specific energy decreases rapidly until a M.R.R. of approximately 75 mm?3/s is reached. For M.R.R.’s lower than 75 mm3/s, a slight increase in the material removal rate causes a sharp drop in the specific energy because machining time improves dramatically. At M.R.R.’s greater than 100 cm?3/s, the gain from increasing the process rate is minimal since the specific energy begins approaching a steady-state value. This gain could be significant for work pieces requiring a substantial amount of material removal, but since the machine tool used in this study is a Micro-machining center a M.R.R. greater than 100 mm?3/s would show only a minor decrease in energy consumption given standard work piece sizes.
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Figure 4: Specific energy as a function of M.R.R.
The best fit model was found to be:
1?3.541 (9)
M.R.R1 ecut?1488*?3.853 (10)
M.R.R
ecut?1478*This specific energy model can be used to estimate the total energy consumed while cutting. The part features and tolerances would dictate the size and type of machine tool required for part manufacture. The optimal M.R.R. can be determined using standard process parameters based on the work piece material and the appropriate cutting tool for the feature creation. Therefore, the total energy consumption while cutting can be calculated by multiplying the specific energy estimate by the volume of material removed.
The machine tool analyzed in this paper is a micro-machining center. Larger machine tools can process material at higher rates, therefore shifting the specific energy curve to the right. But these machine tools will also have higher standby power demand due to the peripheral equipment [8] causing an upward shift in the specific energy curve (see Figure 5 ).
Figure 5: Shift in specific energy plot for larger machine tools.
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4 EFFECT OF WORK PIECE MATERIAL ON POWER DEMAND
The aforementioned experiments were conducted with a low carbon steel work piece. The type of material being machined is also a factor in the cutting power demand of the machine tool, though. A plastic work piece, for example, is expected to generate a smaller load on the spindle motor than a metal work piece and therefore result in a lower cutting power demand.
Since the cutting load is expected to vary with the work piece material, the following experiments were conducted to measure the power demand of the Mori Seiki NV1500 DCG while machining peripheral cuts on 1018 steel, 6061 aluminum, and polycarbonate. A depth of cut and width of cut of 2 mm and 4 mm, respectively, was used. The chip load of 0.0254 mm/tooth was maintained constant across the experiments, to allow for the comparison of the Sustainability in Manufacturing - Energy Efficiency in Machine Tools results. The process parameters used in the experiment are outlined in Table 3.
Table 3: Process parameters for power demand experiments with
multiple work piece materials.
The recommended cutting speed varied with the work piece material. Aluminum was cut at the highest speed, followed by polycarbonate, then steel. The use of coolant while machining aluminum was recommended by the cutting tool manufacturer due to the material’s ductility and its tendency to build-up on the cutting tool. Coolant was also recommended for polycarbonate to prevent it from melting because of the high temperature at the cutting tool and work piece interface. Steel can be cut without coolant (which would greatly reduce the total power demand of the machine tool), but since cutting fluid aids with chip exit and this study is primarily concerned with the cutting power demand, coolant was used when cutting all material types.
The power demand of the NV1500 DCG is shown in Figure 6 , and is broken down into cutting and air cutting power demand. The air cutting power demand is approximately the same across the three processing conditions. The difference is due primarily to the change in spindle speed, the highest of which was used while cutting aluminum. The difference in the power demanded by the axis drives was found to be negligible even though the feed rate for aluminum is more than two times that of steel.
The cutting power demand shows greater variability for the three work piece materials. The cutting power was the greatest while machining the steel work piece. In fact, it was approximately 7% of the total power demand. This may be due to the fact that it has the highest tensile strength, followed by aluminum, then polycarbonate. The cutting power while machining the polycarbonate work piece was the smallest and almost negligible, only 1% of the total power demand.
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Figure 6: Power demand of NV1500 DCG for steel, aluminum, and
polycarbonate work pieces.
Sustainability in Manufacturing-Energy Efficiency in Machine Tools A particular work piece material can be machined at a range of process parameters while maintaining minimal tool wear and good surface finish. So future experiments should be conducted in which the material removal rates overlap as much as possible across the work piece materials under study when calculating the cutting power demand for the purpose of comparison. Also, the power demand of the spindle motor and the axis feed drives should be measured directly since presently the cutting power demand is obtained by subtracting the air cutting power demand from the total power demand of the machine tool. 5 CONCLUSIONS
This study has shown that the machining time dominates energy demand for high tare machine tools. Additionally, it has provided a method for characterizing the specific energy of a machine tool as a function of process rate, which can be extended to other types of manufacturing processes.
The specific energy model allows a product designer to estimate the manufacturing energy consumption of their part’s production without needing to measure power demand directly at the machine tool during their part’s production. Since the specific energy as a function of M.R.R. for the micro-machining center presented herein varied by as much as an order of magnitude, it is important to use process parameters and machine tool-specific data to determine accurate electrical energy consumption. This model could therefore be used in place of aggregate embodied energy values for manufacturing processes as provided by [9] or to replace process estimates with great uncertainty when conducting hybrid life cycle assessments. 6 ACKNOWLEDGMENTS
This work was supported in part by Mori Seiki, the Digital Technology Laboratory (DTL), the Machine Tool Technologies Research Foundation (MTTRF), Kennametal, and other industrial partners of the Laboratory for Manufacturing and Sustainability (LMAS). The authors would like to thank the UC Berkeley Mechanical Engineering Department’s Student Machine Shop for providing valuable insight and advice . For more information, please visit lmas.berkeley.edu.
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