Laboratory for Manufacturing and Sustainability UC Berkeley
Title:
Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use Author:
Diaz, Nancy, University of California, Berkeley Dornfeld, David, UC Berkeley Publication Date: 05-04-2011
Series:
Green Manufacturing and Sustainable Manufacturing Partnership Publication Info:
Green Manufacturing and Sustainable Manufacturing Partnership, Laboratory for Manufacturing and Sustainability, UC Berkeley Permalink:
http://escholarship.org/uc/item/40g995w6 Keywords:
Green Machine Tools; Energy Consumption Reduction; Specific Energy Characterization Abstract:
Since machine tools are used extensively throughout their functional life and consequently consuming valuable natural resources and emitting harmful pollutants during this time, this study reviews strategies for characterizing and reducing the energy consumption of milling machine tools during their use. The power demanded by a micro-machining center while cutting low carbon steel under varied material removal rates was measured to model the specific energy of the machine tool. Thereafter the power demanded was studied for cutting aluminum and polycarbonate work pieces for the purpose of comparing the difference in cutting power demand relative to that of steel.
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Energy Consumption Characterization and Reduction Strategies
for Milling Machine Tool Use
Nancy Diaz，Elena Redelsheimer，David Dornfeld，
Laboratory for Manufacturing and Sustainability，University of California at Berkeley, USA
Abstract
Since machine tools are used extensively throughout their functional life and consequently consuming valuable natural resources and emitting harmful pollutants during this time, this study reviews strategies for characterizing and reducing the energy consumption of milling machine tools during t heir use. The power demanded by a micromachining center while cutting low carbon steel under varied material removal rates was measured to model the specific energy of the machine tool. Thereafter the power demanded was studied for cutting aluminum and polycarb onate work pieces for the purpose of comparing the difference in cutting power demand relative to that of steel.
Keywords:
Green Machine Tools; Energy Consumption Reduction;Specific Energy Characterization
1 INTRODUCTION
A product undergoes three life-cycle stages: manufacturing, use and end-of-life. Consumer products whose environmental impact is dominated by the use phase include light fixtures, computers, refrigerators, and vehicles, in general products that are used extensively during their functional life. All the while these products consume resources, in particular energy in the form of electricity or fuel. The machine tool is one such product. The use phase of milling machine tools has been found to comprise between 60 and 90% of CO2-equivalent emissions during its life cycle [1]. This study presents a method for predicting the electrical energy consumed in manufacturing a product for the purpose of reducing its environmental impact.
In conducting a life cycle assessment, product designers may choose to opt for a process, economic input-output (EIO), or hybrid approach. The drawback of the process LCA, though, is that because this method entails acquiring process-specific data it is time consuming and therefore resource intensive. An alternative to measuring the machine tool’s electrical energy consumption directly, for example, is to use aggregate data as is done with EIO-LCA [2]. An EIO-LCA, therefore, is not specific to the design of a particular product. The strategies presented herein provide a method for more quickly generating manufacturing energy consumption estimates for a particular product.
1.1 Cutting load profile
As described by Diaz et al. in [3] the power demand of a machine tool is comprised of cutting,
variable, and constant power components. The cutting power is the additional power drawn for the removal of material. The machine tool used in this analysis, the
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Mori Seiki NV1500 DCG, is a micro-machining center with a relatively low standby power demand when compared to large machining centers. Therefore, the cutting power can comprise a large portion of the machine tool’s total power demand.
Energy consumption for high tare machine tools was found to be primarily dependent on the processing time of the part, which is dictated by the part geometry, tool path, and material removal rate. One such method for optimizing the tool path for minimum cycle time was presented in [4]. This paper is concerned with the effect of the material removal rate on energy consumption. The material removal rate for a 3-axis machining center can be varied by changing the feed rate, width of cut, or depth of cut. Since increasing the feed rate was found to have dire consequences on the cutting tool life [5], the experiments conducted herein varied material removal rate through width of cut and depth of cut experiments for the purpose of analyzing the material removal rate’s effect on cutting power and more importantly, energy consumption. Although increases in the material removal rate translate to faster machining times, the loads on the spindle motor and axis drives increase as well, resulting in higher power demand. Since our main interest is energy consumed in product manufacture, the trade-off between power demand and machining time was analyzed to confirm that the increased loads due to faster material removal was not increasing the total energy consumed.
2 POWER DEMAND FOR VARIED M.R.R.’S
Since machine tool programmers and operators have an array of options when defining the process plan for part production, this analysis strives to reduce energy consumption by process parameter selection of a machine tool. Specifically, the parameters concerning material removal rate (M.R.R.) were varied on a Mori Seiki NV1500 DCG while selecting appropriate tooling. The power demand was measured with a Wattnode MODBUS wattmeter.
In previous work, experiments we re conducted in which spindle speed, feed rate, feed per tooth, and cutter type were varied to analyze the change in energy consumption while milling a low carbon steel, AISI 1018 steel [5]. Also, [6] conducted experiments on face milling, end milling, and drilling operations in which the energy consumption, machining cost, and tool wear were compared for increased cutting speeds. Tool wear and, consequently, cutting tool cost increased significantly when the process parameters veered away from the recommended cutting conditions. So in the following experiments the cutting tool type was changed to maintain the recommended process parameters, but reduce energy consumption while machining, nonetheless. 2.1 Width of Cut Experiments
Given the energy savings from changing the cutter type this project focused on varying material removal rate. First the width of cut was increased while machining with a: 1. 2 flute uncoated carbide end mill,
2. 2 flute TiN coated carbide end mill, and 3. 4 flute TiN coated carbide end mill.
Peripheral cuts were made along the y-axis at a depth of cut of 2 mm with an 8 mm diameter end mill over a length of 101 mm in a 1018 steel work piece. The width of cut was varied by 1 mm increments between 1 mm and 7 mm, in addition to a 7.5 mm width of cut. Table 1 summarizes the cutting conditions used. The chip load was maintained at approximately 0.03 mm/tooth to avoid excessive tool wear and breakage.
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Table 1: Process parameters for width of cut experiments.
Once the power was measured for each width of cut experiment,the power demand was measured for the machine tool while air cutting, that is, while running the tool path without material removal. This way the power associated with the material removal process could be extracted, known hereafter as the cutting power demand. The average air cutting power demand was found to be 1510 W for the cutter (2) process parameters, so it was subtracted from the average total power demand. Figure 1 shows the cutting power demand as a function of the M.R.R. for cutter (2). This plot has a slightly parabolic trend with a point of inflection at approximately 75 mm3/s.
The cutting power demand for the 7.5 mm width of cut was almost nine times greater than the 1 mm width of cut. Since the total air cutting power demand was only 1510 W, though, the resulting increase in total power demand of the machine tool was only 28%. Thus in terms of energy consumption, the operator still experiences energy savings with the increase in M.R.R.
Figure 1: Cutting power demand using cutter (2) while cutting 1018
steel.
Figure 2 shows the average power demand of the NV1500 DCG for cutters (1)–(3). The relationship between power and M.R.R. shifts from parabolic to linear in moving from the conditions imposed on cutter (1) to cutter (3). The increase in power demand is the greatest for cutter (3), but the load on the spindle motor and axis drives is also much greater than that of the 2 flute cutting tools since the feed rate is twice as large or greater.
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Figure 2: Average total power demand as a function of M.R.R.
2.2 Depth of Cut Experiments
Depth of cut experiments were also conducted on a 1018 steel work piece 101 mm in length. Cuts were made along the y-axis using 8 mm diameter, 2 flute uncoated and TiN coated carbide end mills under near slotting conditions (a width of cut of 7.5 mm). The power demand was measured at depths of cut of 1, 2, 4, and 8 mm. The chip load was maintained constant across the various cutters at 0.051 mm/tooth. The spindle speed and feed rate were varied, though, to account for higher loads on the machine tool during the depth of cut experiments (see Table 2 for a summary of the processing conditions).
Table 2: Process parameter ranges for depth of cut experiments.
Figure 3 summarizes the power demanded by the NV1500 DCG for the 2 flute TiN coated end mill (cutter (2)) and the energy consumed as a function of material removal rate. Although the power demand increases with load the energy consumption still drops drastically with the increase in material removal rate. The machine tool experiences a power demand increase of approximately two-thirds, whereas the energy consumption reduces to less than one-third of its original value. This shows that the decrease in processing time effectively dominates over the increase in power demand due to increased loads.
Since the power demand was shown to increase with load, and experimentally this increase in load was not enough to increase the overall energy consumption, the trade-off between power demand and processing time will be analyzed.
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