An improved cutting power-based model for evaluating total energy consumption in general end milling process

Journal of Cleaner Production 231 (2019) 1330e1341

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An improved cutting power-based model for evaluating total energy consumption in general end milling process K.N. Shi a, 1, J.X. Ren a, S.B. Wang b, 1, N. Liu c, *, 1, Z.M. Liu b, D.H. Zhang a, W.F. Lu c a The Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University (NPU), Xi'an, Shaanxi, 710072, People's Republic of China b State Key Laboratory of Mechanical Transmission, College of Mechanical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China c Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, 117576, Singapore

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 November 2017 Received in revised form 17 April 2019 Accepted 27 May 2019 Available online 28 May 2019

Modern manufacturing enterprises are consuming a considerable amount of energy every year. Improving energy efficiency will not only benefit the enterprises economically, but also help the world to overcome various problems such as energy crisis and air pollution. To achieve this, an accurate energy consumption model is essential. The main objective of this paper is to develop an improved cutting power-based energy consumption model for general end milling process. The proposed model consists of an idle part due to auxiliary components and spindle rotation, and an additional part due to cutting workpiece materials. The first part is modelled as a function of spindle rotation speed, and the other part is considered proportional to the cutting power. Experiments under various milling conditions have demonstrated the effectiveness and efficacy of the proposed model. Comparative studies show that the proposed model is more accurate than other models. Although calibrated from slotting experiments when cutting aluminium alloy, the proposed model is applicable for general milling process. Partialimmersion milling experiments show that the prediction error of the proposed model is as low as 1.74%. When workpiece material changes to titanium alloy, its performance remains decent, with low prediction error of 2.81%. This reveals its capability to provide reliable estimation for different workpiece materials. As such, it could help avoid tedious model calibration, thus saving time, material, and energy. Finally, the energy efficiency of general end milling process is investigated through numerical experiments with the proposed model. By revealing the relationship between energy consumption and various cutting parameters, the proposed model could serve as an excellent platform towards energy-efficient manufacturing/cleaner production. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Energy efficiency Energy consumption modelling Cutting power Slot milling Partial-immersion milling

1. Introduction and literature review In the past decades, environmental concerns of the world have been drastically increasing due to various problems such as air pollution and climate change. Driven by exhausting energy resources and stringent environmental legislations, modern enterprises are under great pressure to reduce their carbon footprint. It has been widely accepted that manufacturing industries are among the largest energy consumers, but with relatively low energy

* Corresponding author. E-mail addresses: [email protected] (K.N. Shi), [email protected]. cn (S.B. Wang), [email protected] (N. Liu). 1 Contribute equally to this paper. https://doi.org/10.1016/j.jclepro.2019.05.323 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

efficiency (Liu et al., 2019). Reported works have shown that there exists substantial energy saving potential in manufacturing sectors, especially in discrete parts manufacturing (Cai et al., 2016; Hu et al., 2012). As a consequence, energy efficiency-related issues have been attracting extensive attention from both industry and academia (Bunse et al., 2011). Over the years, many works have been reported to improve the energy efficiency in machining process by parameter study. For example, Oda et al. (2012) optimized the tool angles and cutting speed to reduce energy consumption in a 5-axis milling process. With Taguchi method, Hanafi et al. (2012) and Camposeco-Negrete (2013) improved energy efficiency by optimizing turning parameters. Using response surface method (RSM), Campatelli et al. (2014) minimized power consumption in a milling process by optimizing

K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341

related process parameters. Similarly, by integrating Taguchi, RSM, and multi-objective particle swarm optimization algorithm, the energy efficiency of CNC milling process was improved by Li et al. (2016). More recently, the energy efficiency of milling machining was investigated by Shin et al. (2017) through component modelling and online optimization of cutting parameters. To improve the energy efficiency and reduce the total production cost, Li et al. (2017) optimized the cutting parameters in multi-pass face milling. These works have provided valuable information regarding the influence of cutting parameters on energy efficiency of specific machining process. However, most works in this area employ design-of-experiment (DoE) methods, whose mechanism is like a black box. This may restrict their application in real production. As such, more insightful study is needed in order to better understand the energy consumption manner of machining processes/machine tools. To achieve better energy efficiency control, an important prerequisite is to accurately estimate energy consumption in discrete parts manufacturing. It is therefore necessary to develop energy consumption models for various machining processes on different machine tools. So far, the reported energy consumption models can be approximately categorized into two major types: machine statebased (MS) models and process parameter-based (PP) models. MS models estimate the total energy consumption of a machine tool as a sum of different machine states. Avram and Xirouchakis (2011) considered the total energy consumption of a machine tool as a sum of feeding energy requirements and spindle energy requirements such as acceleration/deceleration, rotating with/ without cutting, etc. Similarly, Mori et al. (2011) combined the energy requirements for basic operation, cutting, and accelerating/ decelerating the spindle to estimate the energy consumption of a machine tool. Further, the total energy consumption for NC machining was decomposed by He et al. (2012) into energy consumed by spindle, feed, tool, coolant pump, and components in fixed state, respectively. Later, Balogun and Mativenga (2013) presented an improved model for direct electrical energy requirements in machining by considering basic state power, ready state power, tool change power, air cutting power, spindle power, etc. By modelling key energy states of a milling machine tool, Aramcharoen and Mativenga (2014) linked the energy requirements of a milling process to toolpaths. Similarly, effects of toolpath machining strategies on energy consumption were investigated by Balogun et al. (2016), Xu et al. (2016), and Edem et al. (2017). Recently, Sealy et al. (2016, 2015) estimated the total energy consumption of a milling machine tool by considering various machine states including spindle, air cutting and net cutting. Edem and Mativenga (2017) modelled the energy requirements for CNC milling toolpath by considering energy requirements for baseline, tool change, feed, spindle, cutting, coolant, etc. Specifically, Lv et al. (2017) modelled the spindle acceleration energy consumption of machine tools and proposed related strategies to reduce this energy consumption. These models have demonstrated decent accuracy in energy consumption prediction. However, due to a larger number of machine states to identify and model, their implementation is neither easy nor straightforward. Besides, these models aim to estimate the energy consumption of a machine tool at any given machine state. In fact, process parameter optimization only makes sense when the machine tool is cutting. Thus, process parameters-based energy consumption models for machining processes are badly needed. PP models directly model the energy consumption of a machine tool as a function of certain process parameters. Due to their good accuracy and easy implementation, these models have great potential in application and thus have attracted extensive attention over the years. A summary of major models in this category is given

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in Table 1 in both forms of power and specific energy consumption (SEC). SEC models are converted to power form using P ¼ SEC∙MRR, where MRR is the material removal rate. Model (1) was presented by Gutowski et al. (2006) through thermodynamic analysis. It contains a constant idle part (Pidle) and a variable part that is proportional to the process rate (i.e., MRR in machining). This model has a strong theoretical background from thermodynamics, but lacks experimental verification. For example, the idle power may change with spindle speed. Therefore, it should not remain constant. Using an empirical method, model (2) was originally obtained in turning process by Li and Kara (2011), and was subsequently validated in milling process (Diaz et al., 2011; Kara and Li, 2011) and grinding process (Li et al., 2012). Following a similar method, Li et al. (2013) extended this model and proposed an improved model as model (3). The rotation speed was taken into consideration for machine tools with relatively low standby power. Models (2) and (3) have been reported with decent accuracy level, revealing that the total energy consumption of a machine tool is closely related to process parameters. However, only MRR and rotation speed were taken into consideration by these models; effects of other process parameters are not considered. To solve this, Liu et al. (2015) proposed a hybrid energy consumption model. Starting from the machining theory, the cutting power at the tool tip was analytically calculated. Subsequently, the total power was empirically modelled as a linear function of cutting power. This model successfully integrates most of the process parameters into the energy consumption model. However, the cutting power is only derived for slot milling process. For other milling processes (e.g., partialimmersion/side milling) on different milling machines, it fails to work. Besides, the predicted power (P ¼ C0) is not equal to predicted power when cutting power is zero, which contradicts with experimental observations. As such, a more comprehensive model is needed to address the challenges mentioned above. The research motivation of this paper comes from several observations in production. Firstly, it is common for the same machine tool to process different workpiece materials, and the power consumption usually changes with the workpiece materials. However, models (1), (2) and (3) cannot deal with this phenomenon reliably. Therefore, a more comprehensive energy consumption model is needed. Secondly, different machine tools may have different levels of standby power, and both partialimmersion milling and slot milling are common in production. This indicates model (4) may not be comprehensive enough. Thirdly, the variable power consumption is believed to be proportional to MRR in model (1). However, different parameter combinations may require different power consumption even though the MRR remains the same (Liu et al., 2016, 2015; Shi et al., 2018). Since the material removal process is achieved by cutting, it may be more comprehensive to use cutting power to characterize the variable energy requirements. To perform cutting powerbased analysis, a good understanding of the state of the art regarding the estimation and optimization of cutting forces during milling processes is needed. The differential cutting forces on an infinitesimal cutting edge were systematically modelled and validated by Lee and Altintas (1996) and Budak et al. (1996). Many efforts have been made towards accurate prediction of milling
rif et al. (2004) established a force since then. For example, Che general model for estimating the milling force and simplifying the experiment testing for the calibration procedure. Recently, in order to predict the cutting force in corner milling, Han and Tang (2015) proposed an instantaneous engagement angle model considering the geometrical relations of the cutting tool and the part corner radius, and derived the instantaneous undeformed chip thickness by the iteration algorithm. To account for the actual

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Table 1 A summary of major PP models. Models

Power form

SEC form

Gutowski et al. (2006) Li and Kara (2011) Li et al. (2013) Liu et al. (2015)

P ¼ Pidle þ kMRR P ¼ C0 MRR þ C1 P ¼ K0 MRR þ k1 n þ k2

SEC ¼ k þ Pidle =MRR SEC ¼ C0 þ C1 =MRR SEC ¼ k0 þ k1 n=MRR þ k2 =MRR

P ¼ C0 þ C1 Pcutting

SEC ¼ C0 =MRR þ C1 Pcutting =MRR

curved tool path and cutter runout effects, Zhang et al. (2016) presented an available approach for improving the modelling of instantaneous uncut chip thickness during the entry and exit angle of tool/workpiece in 5-axis flank milling. Further, Wojciechowski and Mrozek (2017) investigated the influence of the inclination angle of tool's axis along the toolpath on the processing stability, cutting force, and surface quality during micro ball end milling, and successfully optimized the milling process of hardened steel. To reduce the vibrations and cutting forces for better machined surface roughness, Wojciechowski et al. (2018) ulitized the signal to noise ratio and grey relational analysis to optimize the surface inclination angle and tool's overhang. These works have made significant contributions in milling force prediction and control, thus providing solid preparations for cutting power analysis in this paper. Motivated by aforementioned challenges, the main objective of this paper is to develop an improved cutting power-based energy consumption model which could predict energy consumption for general end milling process (i.e., slot milling, partial-immersion milling, and different workpiece materials). Compared to other studies, this study may advance the state of the art regarding energy consumption modelling in the following manners: (a) Compared to empirical models, the proposed model is more versatile. Empirical models use regression techniques and consider only a few machining parameters such as MRR or rotation speed. By sharp contrast, the proposed model models the energy consumption from a more fundamental viewpoint of cutting power. Many more machining parameters (e.g., depth of cut, width of cut, rotation speed, toolworkpiece properties, etc.) are involved in the calculation of cutting power. In this way, the nature of the machining process can be better characterized, and the prediction accuracy can be significantly improved even when the workpiece changes. (b) Compared to our previous hybrid model (Liu et al., 2015), the proposed model has the following advantages. Firstly, the previous model assumed idle power as a constant, leading to unreliable prediction results. The proposed model successfully overcomes this inconsideration by dynamically modelling the idle power as a quadratic relationship with reference to rotation speed, thus prediction accuracy is much higher. In addition, the proposed model is more analytical than the previous model. The two coefficients (C0 and C1) in the previous model (see Eq. (4)) are purely obtained through regression, thus lacking theoretical definition. In contrast, the proposed model is established based on theoretical framework (Gutowski and Dahmus, 2007) and cutting power analysis. The only coefficient has a clear theoretical definition of cutting efficiency. Last but not least, the model by Liu et al. (2015) can only address slot milling process. In contrast, the proposed model can deal with general milling processes (partial milling and slot milling), making it more applicable for real production.

(1) (2) (3) (4)

2. Energy consumption modelling in 3-axis milling process The energy consumption in milling process consists of two parts, (1) idle power consumption due to auxiliary components and spindle rotation, and (2) additional power consumption caused by cutting workpiece materials. In this study, the first part is modelled as a function of rotation speed, and the other part is characterized as an additional part that is proportional to cutting power at the tooltip. 2.1. Idle power consumption In this paper, the definition of idle is consistent with Li et al. (2013), which includes a standby part and a spindle rotation part. The idle power consumption is the total power consumption of the ready-for-production machine tool with its spindle rotating but without feeding or cutting. Apparently, the idle power changes with rotation speed due to the spindle's mass moment of inertia (Avram and Xirouchakis, 2011). Based on this, the idle power consumption (Pidle) can be modelled as a function of rotation speed (n),

Pidle ¼ f ðnÞ

(5)

Due to different energy consumption manners of machine tools, reported function may vary from linear (Li et al., 2013; Mativenga and Rajemi, 2011) to quadratic (Ma et al., 2016). 2.2. Cutting power consumption at the tool tip For a general milling process with a flat-end mill (see Fig. 1), the differential cutting forces (N) on an infinitesimal cutting edge in the t-r-a frame are (Budak et al., 1996; Lee and Altintas, 1996):

dFt ðq; zÞ ¼ Kte dS þ Ktc st sin jdz

(6a)

dFr ðq; zÞ ¼ Kre dS þ Krc st sin jdz

(6b)

dFa ðq; zÞ ¼ Kae dS þ Kac st sin jdz

(6c)

where Ktc, Krc, Kac (N/mm2) and Kte, Kre, Kae (N/mm) are the specific cutting and edge force coefficients; st (mm/rev) is the feed per tooth; dS (mm) is the length of the current infinitesimal cutting edge; q (rad) and z (mm) are the angular position and axial position of the first infinitesimal cutting edge, respectively; dz (mm) is the differential length in axial direction; j (rad) is the angular position of current infinitesimal cutting edge calculated using Eq. (7):

j ¼ q þ ði  1Þfp  z tani0 =r

(7)

where fp (rad) is the pitch angle of the cutter calculated as fp ¼ 2p/ N with N denoting the number of flutes of the cutter; i0 (o) is the helix angle of the cutter; r (mm) is the cutter radius. The instantaneous differential cutting power (W) at the infinitesimal cutting edge can be calculated as a sum of the differential cutting powers due to cutter rotation and feeding:

K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341

z

Pcutting

r

1 ¼ 2p

q2 ¼2 ð p z2 ¼a ð p

O1

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dPcutting ðq; zÞ,GðjÞ

(11)

q1 ¼0 z1 ¼0

where q1, q2 (rad) are the lower and upper limits for the angular position of the first infinitesimal cutting edge; z1, z2 (mm) are the lower and upper limits for the axial position of current infinitesimal cutting. dFr dS

f

V

dFt dz

2.3. The proposed energy consumption model

dFa

The proposed model inherits the theoretical framework presented by Gutowski et al. (2006). The total energy consumption also consists of two parts: (1) an idle part, and (2) an additional part. Unlike their model where the idle part is constant, the proposed model considers this part as a function of spindle rotation speed. Another difference is the additional part is no longer proportional to MRR. Instead, the proposed model employs the cutting power analysis and the additional part is assumed proportional to the required cutting power. The assumption is supported by the following experimental observations:

z ap ae

O2 y

x

(1) When the machine tool is idle, the total power consumption is equal to the idle power consumption since Pcutting is zero. (2) Under the same cutting condition, different materials require different power consumption from the machine tool. This is possibly because harder materials usually require a larger cutting power due to larger cutting forces.

Workpiece

Fig. 1. General end milling process.

    dPcutting ðq; zÞ ¼ jdPn j þ dPf  ¼ jðKte dS þ Ktc st sin jdzÞ,2pnr=60000j þ jðdFt sin j þ dFr cos jÞ,f =60000j

where dPcutting, dPn, dPf (W) are the total differential cutting power, rotation cutting power, and feeding cutting power, respectively; n (rpm) is the rotation speed; f (mm/min) is the feedrate. To extend Eq. (8) for general end milling process with any cutter immersion, a binary engagement status function is introduced as

GðjÞ ¼




1; j 2 U 0; j ; U

(9)

where U is the angular engagement zone calculated as where ae (mm) is the width of cut.

U¼


½0; arccosð1  ae = rÞ;

With the assumption mentioned above, the proposed energy consumption model in power form and SEC form can be expressed using Eq. (12a) and Eq. (12b), respectively.

. P * ¼ Pidle þ Pcutting h

SEC * ¼

Pidle Pcutting þ MRR hMRR

(12a)

(12b)

where P* (W), SEC* (J/mm3) are the predicted total power consumption and specific energy consumption of the machine tool,

up milling½p  arccosð1  ae = rÞ; p; down milling

With Eq. (9) and Eq. (10), the engagement status of each infinitesimal cutting edge can be determined. Cutting edges outside the engagement zone are not engaged in cutting, thus not contributing to the cutting power. Therefore, the average cutting power (Pcutting ) for any general end milling process can be obtained by averaging the instantaneous cutting power over one cutter rotation:

(8)

(10)

respectively; h is the cutting efficiency coefficient. Pidle can be determined by experimental calibration of Eq. (5), and h can be calibrated through milling experiments under different cutting conditions.

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K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341 Table 2 Physical and mechanical properties of the used aluminium alloy and titanium alloy. Major Properties

Aluminium alloy Al-7050-T7451

Titanium alloy TC4 (Ti6Al4V)

Density [g$cm3] Thermal conductivity [W$m1 K1] Melting temperature [ C] Specific heat [J$kg1 K1] Elasticity modulus [GPa] Yield strength [MPa] Tensile strength [MPa]

2.83 157 560 860 70.3 455 510

4.42 7.2 1649 560 114 880 950

3. Experiment details for model calibration and validation In this paper, three sets of milling experiments were carried out. The first set was used for calibration of the proposed energy consumption model, while the other sets were used to demonstrate the effectiveness of the proposed model in Section 4. 3.1. Experiment details In order to calibrate and validate the proposed model, a set of experiments were carried out on LEADWELL MCV-1500iþ, a 3-axis CNC vertical milling centre with a 15 kW spindle motor. Two kinds of workpiece materials are used in the cutting experiments: (1) aluminium alloy Al-7050-T7451, and (2) titanium alloy TC4 (Ti6Al4V). Both alloys are extensively used in aerospace industry, and their major properties are shown in Table 2. The dimensions of both workpieces are 165 mm  60 mm42 mm. There are three sets of cutting experiments in total. In Experiment-I, 27 runs of slotting experiments were carried out

with depth of cut (ap), feedrate (f), and spindle speed (n) varying in three levels, respectively. In Experiment-II, 36 runs of partialimmersion milling experiments were performed. The width of cut (ae) was varied from 1 mm to 9 mm. For each ae, the other three parameters (i.e., ap, f, and n) were selected according to Taguchi L4 orthogonal array (3 factors/2 levels). Experiment-III is similar to Experiment-I, but the workpiece material changed from Al-7050T7451 alloy to TC4 alloy. Three 2-flute solid carbide flat-end mills were used for each set of milling experiments under dry cutting condition. Each cutter has a helix angle of 40 and a radius of 5 mm. The primary and secondary flank/relief angles of the cutters are 12 and 24 , respectively; the rake angle is 10 . They were manufactured in the same batch and thus can be viewed as identical cutters. In this study, vibration/chatter of the cutter are not considered since the resultant cutting force is small and the 3-axis milling machine tool has a relatively large stiffness (Wang et al., 2015). During the experiments, the force profiles of the workpiece and the power profiles of the machine tool were collected. The experimental setup is shown in Fig. 2. In this experiment, all force

(a)

(b)

(c)

(d)

Fig. 2. The experiment setup. (a): fixture of the workpiece and dynamometer table. (b): force signal processing and collection. (c) and (d): installation of power meter.

K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341

profiles were measured with a 3-channel dynamometer (Kistler 9255B) at a sampling frequency of 20 kHz. The fixture for workpiece and dynamometer together with the force registration is shown in Fig. 2 (a). As shown in Fig. 2 (b), the instantaneous signals of cutting forces were firstly amplified by a Kistler 5080 charge amplifier and then collected by a DEWE3010 digital collector. For power measurement, a Panasonic Eco-Power Meter KW9M was used. The installation of KW9M is briefly shown in Fig. 2 (c) and (d). Detailed information regarding the installation block diagram can be found in Liu et al. (2015). The power profile of the machine tool is measured at a sampling frequency of 10 Hz.

3.2. Calibration of proposed energy consumption model The proposed model consists of two parts: (1) an idle power consumption due to auxiliary components and spindle rotation, and (2) an additional power consumption caused by cutting workpiece material. The idle power requirements at different rotation speeds were measured according to standards recommended by Behrendt et al. (2012). The results are shown in Fig. 3. It can be seen that a significant quadratic relationship is observed for the used machine tool. This is also consistent with the findings presented by Ma et al. (2016). Using standard linear least squares (LLS) method, the idle power requirements of the machine tool can be determined as

Pidle ¼ 0:00009n2  0:1404n þ 512:45

(13)

As is mentioned above, the additional power requirements are caused by cutting workpiece materials. Instead of MRR (Gutowski et al., 2006), the proposed model uses cutting power to characterize the dynamic relationship between the total energy consumption and process parameters. The cutting force coefficients of the used tool-workpiece couple can be used directly if they are already in the database of the workshop. Otherwise, a calibration run should be performed to obtain these coefficients (Shi et al., 2018). In this paper, the cutting force coefficients are calculated using the measured cutting force profiles since they are not previously calibrated. The results are:

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K ¼ [Kte Ktc Kre Krc]T ¼ [ 3.8 -1383.1e46.1868.2]T Using the obtained cutting force coefficients, force profiles under new cutting conditions can be predicted (Liu et al., 2016; Wang et al., 2015). Fig. 4 shows some of the prediction results in a half rotation period. It can be seen the prediction results are within acceptable limits, indicating further cutting power calculation should also be reliable. Table 3 shows the results in Experiment-I. Based on these results, the relationship between the measured additional power (i.e., Paddtional ¼ P-Pidle) and the calculated cutting power is visualized in Fig. 5. As can be seen, the results clearly support the assumption that the additional power consumption is proportional to the cutting power with a constant proportionality coefficient (1/h). Finally, the proposed model for the used machine tool can be established as

P * ¼ Pidle þ

1 P 0:5442 cutting

(14)

For the purpose of comparison, the major models in Table 1 are slightly modified using the following adjustments: (1) In model-1, Pidle is no longer constant. Instead, it is calculated using Eq. (13). (2) In model-4, Pcutting is calculated using the latest version as shown in Eq. (11). With the above modifications, these models can be calibrated using the obtained data in Table 3. They are all calibrated using standard LLS regression and the results are listed in Table 4. 4. Results and discussions In this section, the effectiveness of the proposed model is tested in scenarios of partial-immersion milling and new workpiece material, respectively. In order to further demonstrate the accuracy of the proposed energy consumption model, calibrated major models in the literature are used for comparative studies. Finally, numerical experiments based on the proposed model are carried out to reveal

Fig. 3. The idle power requirements at different rotation speeds.

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(a)

(b)

Fig. 4. Cutting force prediction under new cutting conditions. (a): slotting experiment with ap ¼ 1 mm, n ¼ 1000 rpm, f ¼ 100 mm/min (b) partial-immersion milling experiment with ae ¼ 2 mm, ap ¼ 1 mm, n ¼ 1500 rpm, f ¼ 100 mm/min.

Table 3 Measured power (P) and calculated cutting power (Pcutting ) in Experiment-I. No.

ap [mm]

n [rpm]

f [mm/min]

P [W]

Pcutting [W]

No.

ap [mm]

n [rpm]

f [mm/min]

P [W]

Pcutting [W]

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 1 1 1 1 1 1 1 2 2 2 2 2

1000 1000 1000 1500 1500 1500 2000 2000 2000 1000 1000 1000 1500 1500

100 200 300 100 200 300 100 200 300 100 200 300 100 200

539.8 587.2 614.7 583.2 621.8 658.3 659.1 695.2 727.9 589.9 648.9 725.0 629.7 708.1

25.7 48.7 71.9 27.0 50.0 73.1 28.3 51.3 74.4 51.3 97.4 143.8 54.0 100.1

15 16 17 18 19 20 21 22 23 24 25 26 27

2 2 2 2 3 3 3 3 3 3 3 3 3

1500 2000 2000 2000 1000 1000 1000 1500 1500 1500 2000 2000 2000

300 100 200 300 100 200 300 100 200 300 100 200 300

766.2 720.1 800.3 879.7 640.3 720.1 854.7 680.1 793.1 894.9 785.1 895.9 1022.7

146.1 56.6 102.7 148.7 77.0 146.1 215.6 81.0 150.1 219.2 84.9 154.0 223.1

Fig. 5. The relationship between the additional power and the calculated cutting power.

K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341 Table 4 Calibrated models. No.

Expression in power form

1

P1

Reference

2

P 2 ¼ 2:4173MRR þ 558:9202

Li and Kara (2011)

3

P 3 ¼ 2:4173MRR þ 0:1406n þ 348:0896

Li et al. (2013)

4

P 4 ¼ 1:7207Pcutting þ 547:8630

Liu et al. (2015)

Gutowski et al. (2006)

¼ Pidle þ 2:7246MRR

the dynamic relationship between energy efficiency and cutting parameters in end milling process. 4.1. Model validation and comparison in partial-immersion milling In Section 3, the proposed model is calibrated from slotting experiments. To demonstrate its effectiveness for partialimmersion milling, Experiment-II was carried out. Different width of cuts have been applied to evaluate the performance of the proposed model. The 36 parameter settings together with the measured and calculated results are shown in Table 5. Predicted results from calibrated models (see Table 4) are also presented for comparison. As can be seen from Table 5, the proposed model has the smallest prediction error (1.74%). For each cutting condition, the proposed model gives an accurate estimation of the total power required by the machine tool. This clearly shows the effectiveness of the proposed model. It is also noticed that compared with other models, model-2 and model-4 have relatively large prediction

1337

errors (6.05% and 5.75%). This is possibly because the idle power takes up a large portion of the total power, but these two models fail to take into the effects of idle power consumption at different rotation speeds. Model-1 and model-3 also give acceptable predictions, but not as accurate as the proposed model. This reveals MRR and rotation speed are not enough to fully characterize the total energy consumption. In contrast, the proposed model considers more parameters when calculating the cutting power, thus giving more accurate prediction results. 4.2. Model validation and comparison with new workpiece material The workpiece material in Experiment-I and Experiment-II is aluminium alloy. Based on the prediction results, the proposed model has been validated to a good accuracy. However, it seems no significant improvement has been made compared to model-1 and mode-3. To further demonstrate the advantage of the proposed model over other models, the workpiece has been changed to titanium alloy TC4 in Experiment-III. Similar to Experiment-I, 27 runs of slotting experiments were carried out. The measured and predicted results are shown in Table 6. Results in Table 6 are more insightful than Table 5. Prediction accuracy of model-1 and model-3 are no longer acceptable. In sharp contrast, the proposed model remains a low prediction error of 2.81%, much more accurate than any other model. Based on this observation, it can be safely concluded that the proposed model still works with decent accuracy even when the workpiece material changes. This is because the change of workpiece material can be

Table 5 Prediction results for partial-immersion milling in Experiment-II. No.

ae [mm]

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 1 20 2 21 3 22 4 23 5 24 6 25 7 26 8 27 9 28 1 29 2 30 3 31 4 32 5 33 6 34 7 35 8 36 9 Average prediction error

ap [mm]

n [rpm]

f [mm/min]

P [W]

P* [W]

P1 [W]

P2 [W]

P3 [W]

P4 [W]

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

1500 1500 1500 1500 1500 1500 1500 1500 1500 2000 2000 2000 2000 2000 2000 2000 2000 2000 1500 1500 1500 1500 1500 1500 1500 1500 1500 2000 2000 2000 2000 2000 2000 2000 2000 2000

100 100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 100 100 100 100 100 100 100 100 100

530.3 538.2 550.3 549.9 555.1 559.9 563.2 564.7 567.7 615.2 619.8 637.8 641.9 646.9 654.8 659.8 678.3 693.3 549.8 574.8 592.8 606.7 623.1 638.1 654.2 668.3 683.2 623.1 635.2 638.8 641.8 658.3 661.8 669.8 685.3 698.2

520.9 525.8 530.6 535.4 540.1 544.8 549.5 554.2 559.1 621.5 630.8 640.0 649.2 658.3 667.4 676.5 685.6 695 535.2 553.5 571.6 589.5 607.4 625.3 643.2 661.1 679.3 623.4 633.6 643.5 653.3 663.0 672.8 682.5 692.4 702.6 1.74%

514.5 519.1 523.6 528.2 532.7 537.2 541.8 546.3 550.9 614.1 623.2 632.2 641.3 650.4 659.5 668.6 677.7 686.7 528.2 546.3 564.5 582.7 600.8 619.0 637.1 655.3 673.5 614.1 623.2 632.2 641.3 650.4 659.5 668.6 677.7 686.7 2.16%

562.9 567.0 571.0 575.0 579.1 583.1 587.1 591.2 595.2 567.0 575.0 583.1 591.2 599.2 607.3 615.3 623.4 631.4 575.0 591.2 607.3 623.4 639.5 655.6 671.7 687.8 704.0 567.0 575.0 583.1 591.2 599.2 607.3 615.3 623.4 631.4 6.05%

563.0 567.0 571.1 575.1 579.1 583.2 587.2 591.2 595.2 637.3 645.4 653.5 661.5 669.6 677.6 685.7 693.7 701.8 575.1 591.2 607.3 623.4 639.6 655.7 671.8 687.9 704.0 637.3 645.4 653.5 661.5 669.6 677.6 685.7 693.7 701.8 3.17%

558.6 562.5 566.5 570.5 574.5 578.4 582.4 586.4 590.3 564.8 572.7 580.7 588.6 596.6 604.5 612.4 620.4 628.3 577.2 593.2 609.1 625.0 640.8 656.7 672.5 688.4 704.2 573.7 581.7 589.6 597.6 605.5 613.5 621.4 629.3 637.3 5.75%

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Table 6 Prediction results for Experiment-III. No.

ap [mm]

1 0.3 2 0.3 3 0.3 4 0.3 5 0.3 6 0.3 7 0.3 8 0.3 9 0.3 10 0.6 11 0.6 12 0.6 13 0.6 14 0.6 15 0.6 16 0.6 17 0.6 18 0.6 19 0.9 20 0.9 21 0.9 22 0.9 23 0.9 24 0.9 25 0.9 26 0.9 27 0.9 Average prediction error

n [rpm]

f [mm/min]

P [W]

P* [W]

P1 [W]

P2 [W]

P3 [W]

P4 [W]

1000 1000 1000 1500 1500 1500 2000 2000 2000 1000 1000 1000 1500 1500 1500 2000 2000 2000 1000 1000 1000 1500 1500 1500 2000 2000 2000

100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300

525.5 540.4 566.8 546.7 570.9 599.7 646.3 677.0 701.1 546.0 593.0 644.0 606.0 653.0 700.0 677.0 738.0 789.5 584.0 643.0 696.0 639.0 712.0 762.0 724.0 796.6 872.0

497.4 517.0 536.7 551.2 570.8 590.5 652.3 672.0 691.7 527.9 567.2 606.5 587.1 626.4 665.7 693.7 733.0 772.3 558.4 617.4 676.3 623.0 682.0 741.0 735.1 794.0 853.0 2.81%

491.6 505.2 518.9 523.6 537.2 550.9 618.6 632.2 645.9 505.2 532.5 559.7 537.2 564.5 591.7 632.2 659.5 686.7 518.9 559.7 600.6 550.9 591.7 632.6 645.9 686.7 727.6 10.61%

571.0 583.1 595.2 571.0 583.1 595.2 571.0 583.1 595.2 583.1 607.3 631.4 583.1 607.3 631.4 583.1 607.3 631.4 595.2 631.4 667.7 595.2 631.4 667.7 595.2 631.4 667.7 9.38%

500.8 512.9 524.9 571.1 583.2 595.2 641.4 653.5 665.5 512.9 537.0 561.2 583.2 607.3 631.5 653.5 677.6 701.8 524.9 561.2 597.5 595.2 631.5 667.8 665.5 701.8 738.1 7.72%

576.4 594.7 613.1 581.5 599.8 618.2 586.5 604.9 623.2 605.0 641.7 678.5 615.1 651.9 688.7 625.3 662.0 698.8 633.5 688.7 743.9 648.8 704.0 759.1 664.0 719.2 774.4 6.86%

Experiment-III, the proposed model predict the energy consumption in a more comprehensive manner. Many more parameters are considered during the calculation of cutting power, including machining parameters (i.e., depth of cut, width of cut, rotation speed, and feedrate), tool geometrical parameters (i.e., helix angle, radius, and flute number), and tool-workpiece properties (i.e., cutting force coefficients). Compared to model-4, the proposed model employs theoretical framework, and takes the ready-forproduction power into consideration. As such, the proposed model could better characterize both the cutting nature and energy flow of the machining process. Therefore, the prediction accuracy can be significantly improved for general end milling process even when the workpiece changes. Fig. 6. Comparison of different models in Experiment-II and Experiment-III.

reflected when calculating the cutting force coefficients. For titanium alloy TC4, the obtained cutting force coefficient is K ¼ [-28.8 -2131.0 -96.3146.3]T, which is quite different from Al-7050-T7451 alloy. In this way, the change of material properties is automatically considered in the process of cutting power calculation. Thus, the results are of high prediction accuracy. This explanation is also supported by Fig. 6, where a comparison of prediction errors by different models is presented. Although model-1 and model-3 give accurate prediction results in Experiment-II, they fail to provide reliable prediction when the workpiece changes. In contrast, the prediction accuracy of model-4 shows little deviation in both sets of experiments. In ExperimentIII, it even outperforms models 1e3. This is possibly because model-4 also has a cutting power nature, and thus could handle different materials. In another sense, the results demonstrated the importance of cutting power analysis in energy consumption prediction. By calculating the cutting power for general end milling process, the proposed model provides promising results. Compared to previous models 1e3 in Experiment-II and

4.3. Energy efficiency analysis of the milling process As is validated in previous sections, the proposed model could provide accurate power prediction under new cutting conditions. In addition, this model considers most of the cutting parameters (depth of cut, width of cut, feedrate, rotation speed, etc.) during the calculation of cutting power. Without performing real cutting experiments, the energy efficiency of the milling process under different cutting conditions can be accurately predicted. Therefore, this model can be used as a reliable platform for process parameter optimization towards cleaner manufacturing. In this section, the effects of the four cutting parameters (ae, ap, n and f) on the energy efficiency during end milling of TC4 are thoroughly investigated through simulated experiments with the established model. The simulated cutter is identical with the cutter used in previous experiments. SEC is used to indicate the energy efficiency of the slotting process using Eq. (12b). A smaller SEC indicates a higher energy efficiency. Two sets of numerical experiments (NM), NM-I and NM-II, were performed. In NM-I, ae increases from 0.1 mm to 10 mm by a step of 0.1 mm. Other parameters remain constant with ap ¼ 0.6 mm, n ¼ 1000 rpm, and f ¼ 100 mm/min. The relationship of SEC vs. ae is shown in Fig. 7.

K.N. Shi et al. / Journal of Cleaner Production 231 (2019) 1330e1341

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(2) Under the same rotation speed, the energy efficiency gets higher with a larger feedrate and a larger depth of cut. The reason is similar to that in NM-I, i.e., a larger feedrate and a larger depth of cut bring a more significant increase of MRR than the total power increase.

Fig. 7. Relationship between SEC and ae with ap ¼ 0.6 mm, n ¼ 1000 rpm, f ¼ 100 mm/ min.

When ae gets larger, SEC decreases, indicating a higher energy efficiency. It can also been observed that slotting process (ae ¼ 10 mm) is the most energy-efficient milling process when cutting TC4. This is because a larger ae leads to a larger MRR, and MRR increases more significantly than the total power increase. To test the reliability of NM-I, ten actual cutting experiments are performed. The measured SEC are also shown in Fig. 7. As can be seen, the measured results agree well with the numerical results, with the optimal ae also to be 10 mm. This clearly shows the reliability of the numerical studies. To further investigate the effects of the other three cutting parameters (ap, n, and f) on the energy efficiency of slot milling process, NM-II is carried out. In NM-II, slotting process (ae ¼ 10 mm) is simulated with ap, n, and f varying in certain ranges. The results are shown in Fig. 8. A 3D surface is constructed by ap, f and SEC under each rotation speed. The following observations are worth noticing: (1) Under the same depth of cut and feedrate, the energy efficiency gets higher when rotation speed decreases. This is due to a considerable amount of unproductive energy consumption caused by spindle rotation.

Fig. 8. SEC map for slot milling process of TC4.

(3) The SEC will change even when the MRR remains the same. For example, the SEC value under ap ¼ 0.4 mm, f ¼ 150 mm/min is different from SEC value under ap ¼ 0.6 mm, f ¼ 100 mm/min. This is because different parameter combinations will affect the cutting power differently, which subsequently differentiates the required power consumption from each other. This also reveals the energy efficiency in milling process is not only related to MRR. Instead, it changes with the specific combination of cutting parameters. To test the effectiveness and reliability of the numerical results in Fig. 8, a set of actual cutting experiments were carried out for comparison. Taguchi L9 orthogonal array is used to design the experiment. The cutting conditions were shown in Table 7. It can be clearly observed that the measured SEC values agree very well with the values in NM-II. For validation of the effects of involved cutting parameters, the S/N ratios were plotted in Fig. 9. As can be seen, the trend is exactly the same as suggested by NM-II. In addition, the optimal parameter combination identified by both NM-II and Taguchi S/N ratio analysis is the same, i.e., ap ¼ 1 mm, f ¼ 300 mm/ min, n ¼ 1000 rpm. To validate this parameter combination, a confirmation test was also performed, as show in the last row of Table 7. The comparison between numerical studies and actual experiment results successfully demonstrates the effectiveness and reliability of the established model. Compared to DoE methods such as Taguchi methodology, numerical studies enabled by the proposed model are more efficient due to its high accuracy and reliability. No actual cutting experiment is needed when optimizing the energy efficiency of milling process, saving both materials and time. Therefore, it can contribute significantly to energy-efficient manufacturing and cleaner production. 5. Conclusions Milling process is extensively used in modern manufacturing enterprises and it consumes a significant amount of energy every year. To improve the energy efficiency in milling processes, an accurate and reliable energy consumption model is urgently needed. In this paper, an improved cutting power-based energy consumption model for milling process is proposed. It consists of an idle part that is a function of rotation speed, and an additional part proportional to the cutting power at the tool tip. The proposed model has been validated in various cutting experiments. Compared with major models in the literature, the proposed model is more accurate and more reliable for predicting energy consumption in milling experiments. This model has proved its capability to predict energy consumption in both slotting and partial-immersion milling. More importantly, it could still give reliable prediction even when the workpiece changes. Thus, it may have more application potential for improving energy efficiency in real production. Unlike other models, the proposed model only need one calibration for one machine tool. In addition, due to its analytical cutting power analysis and validated decent accuracy, it also offers an excellent platform for energy efficiency-oriented cutting parameter optimization. Energy efficiency could be optimized through numerical simulation instead of actual cutting. Therefore, it can help avoid waste of time, materials, and energy, which significantly contributes to cleaner production in discrete parts manufacturing systems.

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Table 7 Taguchi L9 orthogonal array for validation of numerical results in NM-II. ap [mm]

No.

1 0.2 2 0.2 3 0.2 4 0.6 5 0.6 6 0.6 7 1.0 8 1.0 9 1.0 Confirmation test

n [rpm]

f [mm/min]

P [W]

SEC [J/mm3]

SEC* [J/mm3]

Error

100 200 300 100 200 300 100 200 300

1000 2000 3000 2000 3000 1000 3000 1000 2000

525.5 540.4 566.8 546.7 570.9 599.7 646.3 677.0 701.1 649.7

142.2 90.2 89.1 62.3 52.5 19.7 64.4 18.0 16.6 13.4

131.1 90.2 95.5 64.3 51.9 18.5 64.1 17.5 16.5 13.0

7.81% 0.00% 7.18% 3.21% 1.14% 6.09% 0.47% 2.78% 0.60% 2.99%

Fig. 9. Plot of S/N ratios for Taguchi L9 orthogonal array.

In the next phase of our study, energy-efficient process planning based on the proposed energy consumption model will be carried out. Also, the effects of tool vibrations on the energy consumption behaviour of the machine tool will be investigated since they are inevitable in machining process. Last but not least, multi-criterion optimization will be carried out due to the fact that energy saving can only be meaningful when other rigid technical requirements (e.g., surface roughness, dimensional accuracy) can be satisfied. Acknowledgements This study was supported by the National Natural Science Foundation of China (grant number 51775444). The authors would like to acknowledge this financial support. References Aramcharoen, A., Mativenga, P.T., 2014. Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. J. Clean. Prod. 78, 63e74. https://doi.org/10.1016/j.jclepro.2014.04.065. Avram, O.I., Xirouchakis, P., 2011. Evaluating the use phase energy requirements of a machine tool system. J. Clean. Prod. 19, 699e711. https://doi.org/10.1016/ j.jclepro.2010.10.010. Balogun, V.A., Edem, I.F., Mativenga, P.T., 2016. E-smart toolpath machining strategy for process planning. Int. J. Adv. Manuf. Technol. 86, 1499e1508. https://doi.org/ 10.1007/s00170-015-8286-5. Balogun, V.A., Mativenga, P.T., 2013. Modelling of direct energy requirements in mechanical machining processes. J. Clean. Prod. 41, 179e186. https://doi.org/ 10.1016/j.jclepro.2012.10.015. Behrendt, T., Zein, A., Min, S., 2012. Development of an energy consumption monitoring procedure for machine tools. CIRP Ann. - Manuf. Technol. 61, 43e46. https://doi.org/10.1016/j.cirp.2012.03.103. Budak, E., Altintaş, Y., Armarego, E.J.A., 1996. Prediction of milling force coefficients from orthogonal cutting data. J. Manuf. Sci. Eng. 118, 216e224. https://doi.org/ 10.1115/1.2831014.

Average Error

3.25%

e

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