Effects of tool life criterion on sustainability of milling

Journal of Cleaner Production 139 (2016) 1105e1117

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Effects of tool life criterion on sustainability of milling Asif Iqbal a, *, Khalid A. Al-Ghamdi a, Ghulam Hussain b a b

Department of Industrial Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi, Pakistan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 February 2016 Received in revised form 29 August 2016 Accepted 30 August 2016 Available online 31 August 2016

For the last ten years, sustainability has been the most important factor in readjusting the goals of metal cutting domain. Tool life and its attainment criteria have played a highly significant role in controlling the sustainability measures such as process cost, energy consumption, and work quality. The article presents an experimental investigation to study the effects of setting different levels of flank wear as tool life criterion on the sustainability measures of milling process. The metrics used in this work for measurement of process sustainability are specific energy consumption, process cost (including tooling cost), work surface roughness, and material removal rate. A total of 48 experiments were performed on two tempered forms of a cold work tool steel in order to quantify the effects of cutting parameters, tool life criterion, work material's temper state, and lubrication mode. It was found that tool life criterion possesses a very strong influence on all the sustainability metrics. A criterion allowing a tool to run longer is favorable for tool life and process cost while the one calling for early tool replacement suits the metrics of work surface quality and specific energy consumption. The effects of the other predictors on the sustainability metrics were also quantified and analyzed. Micro-chipping and adhesion were found to be the dominant modes of tool damage in the experiments involving the high levels of the cutting parameters and work material hardness. Finally, a multi-criteria decision making approach was used to optimize the milling process with respect to various combinations of the sustainability related objectives. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Specific cutting energy MQL AISI D2 Tool wear Surface roughness

1. Introduction Within the perspective of sustainability, the primary performance measures of a manufacturing process are work quality, process cost, specific energy consumption and productivity. With fast developing emphasis of the manufacturing sector on ensuring sustainability, it becomes pertinent to assess manufacturing processes in terms of the aforementioned performance measures. Unfavorably, the situation seems to be slightly complex because of the observation that the requirements of one sustainability measure are, generally, in opposition to those of the others (Iqbal et al., 2013). Milling is one of the most common manufacturing processes used for part shaping by the means of material removal. Widely spread across the globe, it is considered as a part and parcel of any machine shop. The enormousness of its usage suggests that any modification in the process (or its parameters) causing even a

minute reduction in process cost or energy consumption has a farreaching contribution towards its global sustainability. The requirement of reduced specific energy consumption has recently been adopted so as to reduce harmful CO2 emissions for the sake of environmental benignity. One of the most influential issues in milling domain is to decide on the limiting condition of an in-service tool for its replacement. It is well established that state of tool damage has direct influence on work surface integrity and energy consumption. Therefore, tool life criterion is believed to have implications on most of the sustainability metrics of a milling process. It is also believed that the longer a tool goes the worse the work surface quality gets but also the cheaper the process becomes. The slash in the process cost comes from the fact that fewer cutting tools are required to cut a given volume of material. In this context, the current work focuses on studying and quantifying the effects of choosing various levels of tool wear as tool life criterion on the sustainability metrics. 1.1. Literature review

* Corresponding author. E-mail address: asif.asifi[email protected] (A. Iqbal). http://dx.doi.org/10.1016/j.jclepro.2016.08.162 0959-6526/© 2016 Elsevier Ltd. All rights reserved.

Sustainability, in general, stands on three pillars: economy,

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society, and environment. With regard to manufacturing processes, most of the researchers in the recent past have focused on economic and environmental aspects of sustainability and have further subdivided these aspects with respect to domain of applicability, that is, manufactured product and manufacturing process. Application of sustainability in manufacturing is considered as a twofaceted issue: (a) do the right things (i.e. manufacturing of “sustainable” products) and (b) do the things right (i.e. sustainable manufacturing of “all” products) (Jawahir and Dillon, 2007; Jawahir et al., 2010). The presented work is associated with the second facet, that is, to find sustainable ways of manufacturing products. This obligation calls for producing everything with processes which consume less material and energy and are hazardless and toxicfree. Furthermore, sustainable manufacturing should not be considered as a liability; it is an innovative domain that brings back profits to the practitioners (Jayal et al., 2010; Jawahir et al., 2010). The sub-section provides a brief review of the published work related to quantification of the sustainability metrics (economic and environmental) of machining, especially milling of hardened steels, with regard to the effects of cutting parameters, tool and work material state, and lubrication mode. Jayal et al. (2010) have shed light on past trends and recent concepts in the developing sustainable systems, products, and processes. In this work, the authors have specially focused on describing predictive models and optimization techniques for sustainable manufacturing processes. Koshy et al. (2002) performed high-speed milling experiments on hardened AISI D2 cold work tool steel using cermet and carbide end mills. They found that chipping, adhesion and attrition wear were the principal mechanisms of tool damage. Moreover, the carbide tooling outperformed the cermet tooling by providing cut length between 10 and 35 m against a tool life criterion of 0.3 mm maximum flank wear. Performances of using minimum quantity lubrication (MQL) and completely dry lubrication were compared in machining of AISI 1040 steel (Dhar et al., 2007). It was found that MQL led to longer tool life and lower surface roughness and cutting forces. In another experimental work, optimization of cutting parameters was performed for maximizing tool life and minimizing work surface roughness (in feed and pick-feed directions) in high-speed milling of a hardened tool steel, AISI D2, under MQL environment (Iqbal et al., 2009). It was found that the most influential effects on tool life and surface roughness were of cutting speed and feed rate, respectively. Rajemi et al. (2010) have presented an analytical approach for minimization of process cost and energy consumption in machining. The approach focuses on finding the value of cutting speed that would lead to an optimal tool life and minimum energy cost for the process. Rao et al. (2011) have presented quantification of specific cutting energy, tool life, and surface integrity in face milling of a titanium alloy. The favorable condition of low surface roughness and specific cutting energy was achieved when milling was done within a limited level of tool wear. Yan and Li (2013) have simultaneously maximized material removal rate and minimized surface roughness and cutting energy by optimizing spindle speed, feed rate, depth of cut, and width of cut related to a milling process by using weighted grey relational analysis and response surface method. In another work, a multi-objective analysis of process cost, work surface roughness, and specific energy consumption, based on optimization of the cutting parameters of a machining process, was presented (Wang et al., 2014). Iqbal et al. (2013) have experimentally investigated trade-off among tool life, productivity, and specific energy consumption in a machining process. They found that the requirements for long tool life are contrary to those of low energy consumption and high productivity. Another study has presented a machining cost model, developed from experimental data related to energy usage and tool damage, establishing a

relationship between energy consumption and process cost (Anderberg et al., 2010). Zhang et al. (2012) have described an application of minimum quantity cooling lubrication (MQCL), a combination of cryogenic air and MQL, in improving machinability of Inconel 718 alloy. They have reported enhancement in tool life and reduction in cutting forces in milling of the difficult-to-cut material while applying MQCL with biodegradable vegetable oil. Another study has presented a sustainability evaluation of high pressure jet-assisted and cryogenic machining in cutting of Inconel 718 alloy (Pusavec et al., 2010). It was emphasized that tooling cost holds the largest share in overall production cost. Bhushan (2013) has studied the effects of the three cutting parameters and insert's nose radius in machining of a metal matrix composite. A 22% improvement in tool life and 13.5% reduction in consumption of energy were reported after optimizing the parameters. Kuram et al. (2013) have presented mathematical models and statistical analyses to find out the most suitable cutting fluid and appropriate levels of cutting parameters in milling of AISI 304 while considering the performance measures of tool life, surface roughness, and specific energy. Pusavec et al. (2014) have put forward a statistics based predictive model that quantifies the effect of machining parameters and cooling methods, such as dry, near-dry, cryogenic and cryo-lubrication (cryogenic þ near-dry) on cutting forces, work surface roughness and tool-wear in high-performance machining of Inconel 718. A vast majority of the published work reports different values of maximum or average width of flank wear land as a tool life criterion. Zhang and Li (2010), based on their end milling experiments and resulting relationship between tool wear propagation and surface roughness variation, have proposed a model for finding optimal flank wear criterion that would integrate the surface roughness and the tool life requirements for a finish milling process. Serra et al. (2012) have proposed a criterion for detecting life of an in-process cutting tool from cutting tool vibration signals. Kazinczy (1971) has reported benefits of using depth of crater as tool life criterion. Gadelmawla et al. (2014) have presented a realtime method for estimation of machining time sustained by a cutting tool. They have described that texture features of the graylevel co-occurrence matrix appearing on machined part are strongly related to the machining time of the cutting tool. AlGhamdi and Iqbal (2015) have put forward a comparison between the phenomena of conventional machining and high-speed machining in the perspective of sustainability metrics such as tool life, specific energy consumption, productivity, process cost, and machining forces. It was reported that high-speed machining outperformed conventional machining in respect of productivity and energy consumption but could not perform better with respect to the other metrics. MQL, also referred to as near-dry machining, is a microlubrication process in which a small amount of oil (usually less than 30 ml/h) is added into a stream of compressed air. Its primary benefits of enhanced tool life and improved work surface finish in machining have been widely reported. Among those, Astakhov (2008) has comprehensively investigated the benefits of near-dry machining over completely dry machining in terms of improved work surface quality and tool performance. In addition, near-dry machining also offers other benefits related to sustainability such as biodegradability, lesser pollution, storage stability, and better safety characteristics (Boubekri and Shaikh, 2012). A relevant and notable work has also presented a comparison between wet machining and near-dry machining with respect to energy consumption and environmental cost (Boyer et al., 2011). The literature review suggests that there is a clear gap available for further contribution by quantifying the effects of tool life criterion on sustainability of machining. In this context, the current

A. Iqbal et al. / Journal of Cleaner Production 139 (2016) 1105e1117

work aims to study the effects of using three levels of flank wear as tool life criterion in conjunction with those of cutting speed, feed rate, work material temper state, and MQL on the sustainability metrics, namely energy consumption, productivity, work surface finish, and process cost in milling of a cold work tool steel. The current work uses the same metrics, except machining forces, for evaluation of the process's sustainability as were used in the previous work (Al-Ghamdi and Iqbal, 2015). However, as the process investigated in this work is an intermittent cutting process, as compared to the continuous cutting process used in the previous work, the evaluation of the metrics is altogether different. Furthermore, the previous work focusses on sustainability comparison between two machining phenomena, which are distinctly different from each other in respect of the thermo-mechanics involved while the current work aims to quantify the effects of setting different levels of tool damage state as tool replacement criterion on milling's sustainability. In general, the work focuses on the economic and environmental aspects of sustainability with its applicability on a manufacturing process. Coverage of the sustainability issues regarding the product (as partially produced by milling process) is out of scope of this work. 2. Experimental work The section provides the details of the predictors governed, the responses measured, the experimental setup and various measurements performed. 2.1. The predictors A total of 48 side and end milling experiments were performed to generate pertinent data. The following 5 predictor variables were controlled in the experiments: 1. Maximum width of flank wear land formed on the 4 flank faces of a milling tool, VBmax (mm). It is also termed as tool life criterion. 2. Surface hardness of the work material as measured on the Rockwell hardness scale ‘C’, H (HRc). 3. Cutting speed, Vc (m/min). 4. Feed per tooth, fz (mm/z). 5. Lubrication mode: dry and minimum quantity lubrication (MQL). The three levels of VBmax and two levels each of the other four predictors led to development of a full-factorial design of experiments consisting of 48 (¼ 3  24) runs. Table 1 presents the levels of the five predictor variables tested in the experiments. 2.2. Experimental setup and the fixed parameters The experiments were performed on a CNC 3-axis milling machine, which possesses a maximum power, spindle speed, and feed rate of 16 kW, 16,000 rpm, and 18 m/min, respectively. The work material used in all the experimental runs is a cold work tool steel

Table 1 Levels and units of the predictor variables controlled in the experiments. Predictor

Unit

Level 1

Level 2

Level 3

VBmax H Vc fz Lubrication mode

mm HRc m/min mm/z e

0.2 55 90 0.08 dry

0.3 61 150 0.11 MQL

0.4 e e e e

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AISI D2. It is a heat-treatable alloy that can achieve a maximum hardness of 62 HRc. AISI D2 is considered a difficult-to-cut material in its hardened state because of high shear yield strength and presence of ultra-hard particles of chromium carbide. In this work, the prismatic bars of AISI D2 having dimensions 155 mm  65 mm  50 mm were heated to a temperature of 770  C and allowed to soak for 55 min. The bars were then rapidly heated to 1010  C followed by immediate air cooling to room temperature. Thereafter, the bars intended for the hardness values of 61 HRc and 55 HRc were tempered at 180  C and 400  C, respectively, with a soaking time of 2 h each. TiAlN coated tungsten carbide (grade K30) bull-nose end mill cutters having diameter (D) 10 mm, number of cutting teeth (z) 4, corner radius (R) 2 mm, cutting length (L) 25 mm, helix angle (l) 45 and flank angles (a) 6 (primary) and 10 (secondary) were used as the cutting tools. A tool hangover of 33 mm was maintained in all the runs. A new cutter was used for each experimental run involving tool life criterion VBmax ¼ 0.2 mm. For a run involving criterion VBmax ¼ 0.3 mm, the same cutter was utilized which was used in the run involving VBmax ¼ 0.2 mm and same levels of the other four predictors. The same tactic was used for the 16 runs involving criterion VBmax ¼ 0.4 mm. It means that each run involving VBmax ¼ 0.2 mm was followed by the one involving VBmax ¼ 0.3 mm and with the same levels of the other four predictors and then finally by the one involving VBmax ¼ 0.4 mm before replacing the cutter. This approach allowed saving a significant amount of experimental cost and time without compromising accuracies. Radial (ae) and axial (ap) depths of cut were fixed as 0.4 mm and 5 mm, respectively. The MQL, for the runs involving near-dry machining, was supplied to the cutting area in a direction perpendicular to the cutter axis via a single duct. The equipment used in this regard was UNIST 9570-7-5-12. The supplied aerosol consisted of a vegetable based oil (UNIST Coolube 2210) pulverized at a rate of 30 mL/h into a stream of air compressed at 8 bars. Fig. 1 presents the experimental setup. 2.3. The responses Following response variables were measured in each experimental run: 1. Average arithmetic roughness of machined surface, Ra (mm). Ra is average of surface roughness measurements taken in feed and pick-feed directions at five locations of end surface only. 2. Tool life, TL (mm3). An end mill is said to have completed its life when the flank wear land on any of the four cutting teeth had attained a maximum width (VBmax) equal to the one specified in the tool life criterion of the given experimental run. 3. Material removal rate, MRR (mm3/min). MRR is a productivity measure which is calculated, rather than measured, by the following mathematical relationship:


MRR ¼ ae  ap  fz  z 

Vc  1000 pD

 (1)

4. Specific energy, SE (J/mm3), consumed by the CNC milling machine. The SE for each run is obtained by dividing its measured average power input to the milling machine by the corresponding MRR. 5. Cutting power share, CPS (%). CPS, for each run, is calculated by dividing the average power utilized for removing work material only to the corresponding average power drawn by the CNC milling machine.

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Fig. 1. (a) The experimental setup; (b) Front view of a new bull-nose end mill cutter used in the experiments.

6. Total cost, TC (PKR/dm3). TC is total cost incurred to remove 1 dm3 of work material. TC is summation of power consumption cost, tooling cost, overhead cost, and MQL cost. MQL cost holds zero value for the runs involving dry milling. This is to be noted that 1 PKR roughly equals 0.01 USD.

2.4. Measurements and evaluations of the responses Work surface roughness was measured with Mitutoyo portable surface roughness tester, Surftest SJ-210. For each experimental run, roughness measurements were taken at five points with each point located at an equal distance from the preceding one. Flank wear of cutting teeth was measured using a 10X toolmaker's

microscope. After completion of predetermined lengths of cut, the process was required to be stopped, tool assembly dismantled and put under the microscope for capturing images of the flank faces. An image processing software was used to worked out maximum length of flank wear land on each of the four cutting teeth. VBmax was taken as the average of the four values. The resulting VBmax value also governed the value of next length of cut before stopping the process again. An experimental run was ended as soon as the value of VBmax reached 0.2 mm, 0.3 mm, or 0.4 mm as specified by the VBmax value. The tool life at that stage was determined by calculating the total volume of work material removed, which is equal to product of the two depths of cut and total length of cut accomplished by the cutter. Joel JSM 5610LV scanning electron microscope (SEM) was used to analyze tool damage mechanisms of

Table 2 A framework for evaluating the four components of total processing cost is presented. Cost type and description

Breakdown

Formulation

A. Cost of using direct electric power (PKR/dm ): It is the SE drawn in by the CNC milling machine and the MQL system

Commercial tariff for electric power Direct electric power cost

22 PKR/kWh 22 (PKR/kWh)  SE (J/dm3) A ¼ 22  SE (PKR/dm3)

B. Overhead cost (PKR/hr): ¼ B1 þ B2 þ B3 B1 ¼ Operator cost B2 ¼ Cost of lighting and HVAC B3 ¼ Machine depreciation cost

B1. Cost of employing a machine operator B2. Lighting and HVAC loads are 0.3 and 8.0 kW, respectively B3. Machine purchase cost ¼ 15M PKR; Salvage ¼ 1M PKR; Life ¼ 10 years; Number of working days in a year ¼ 250 B ¼ B1 þ B2 þ B3

200 PKR/hr (8.0 þ 0.3) kW  22 PKR/ kWh ¼ 183 PKR/hr 1.4M PKR/year ¼ 5600 PKR/ day ¼ 700 PKR/hr 200 þ 183 þ 700 ¼ 1083 B ¼ 1083 PKR/hr

C. Cost of acquiring tools (PKR/tool): The cutters, after attaining the tool life criterion of VBmax ¼ 0.4 mm, can be completely reground and recoated for the purpose of resale.

Purchase cost of a new cutter Resale price Net cost of using tools

2500 PKR/tool 1500 PKR/tool 2500e1500 ¼ 1000 C ¼ 1000 PKR/tool

D. Cost of acquiring oil for the MQL system (PKR/dm3)

Purchase cost of MQL oil Oil mixing rate Cost of using MQL oil

1.83 PKR/mL ¼ 55 PKR/30 mL 30 mL/h D ¼ 55 PKR/hr

3

 .  TC PKR dm3 ¼ A þ fðB þ DÞ=MRRÞg þ ðC=TLÞ

(3)

A. Iqbal et al. / Journal of Cleaner Production 139 (2016) 1105e1117

0.7 0.6

Ra (microns)

0.5

55 HRc, 90 m/min 55 HRc, 150 m/min 61 HRc, 90 m/min 61 HRc, 150 m/min

VBmax = 0.2 mm

0.4 0.3 0.2 0.1 0 Dry

MQL

Dry

MQL

0.08

0.08 (a)

0.11

0.11 mm/z

VBmax = 0.3 mm

0.7 0.6

Ra (microns)

0.5 0.4

(90 m/min & 0.08 mm/z; 90 m/min & 0.11 mm/z; 150 m/min & 0.08 mm/z; and 150 m/min & 0.11 mm/z) used in the experiments was determined by rotating as well as linearly moving a tool at a given speed-feed combination but without making any physical contact with the work. The actual cutting power was evaluated by deducting the relevant non-cutting power from the average of the total power measured during the tool's life in the cutting process. The cutting power share (CPS) was then determined by dividing the difference between the total power (consumed by the milling machine) and the corresponding actual cutting power by the total power. As the MQL system was operated through a separate power source, it became essential to include the power and energy requirements of the system in the calculations related to evaluation of specific energy (SE). The power required to run the MQL system consists of the following two parts: (1) power required for feeding compressed air from compressor to the delivery duct; and (2) power required for atomizing and spraying lubricating oil into the air stream. The first part is calculated as follows: Volumetric air flow rate (as determined from the air compressor rating) ¼ Q ¼ 30 L/min ¼ 0.0005 m3/sec Air pressure ¼ P ¼ 8 bars ¼ 800,000 N/m2.

0.3 0.2 0.1

Power required for feeding the compressed air is calculated by using the following formula:

0.0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

Power ¼

(b)

VBmax = 0.4 mm

0.7 0.6 0.5

Ra (microns)

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0.4 0.3 0.2 0.1 0.0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(c) Fig. 2. The three plots display the experimental results regarding work surface roughness.

Q P

(2)

r

where, r is efficiency of the electric motor driving the compressor. By substituting r ¼ 0.95 and the values of Q and P and in Eq. (2), the value of power becomes 421 W. For the second part, the power required for spraying lubricating oil is 5 Watts. Thus, the total power required to run the MQL system becomes 425 Watts (¼ 5 þ 421) or 25,500 J/min. This extra value of the energy required per unit time was then added to the SE requirements of the experimental runs utilizing MQL. As the volumetric air flow rate and oil mixing rate were kept same for all the corresponding 24 runs, the low levels of the two cutting parameters caused higher magnitudes of energy consumption in using MQL. Total cost (TC) is summation of the following four components: (1) cost of using direct electric power; (2) overhead cost; (3) cost of purchasing end mill cutters; and (4) cost of acquiring lubricating oil for the MQL system. The aforementioned four components are evaluated as shown in Table 2.

Table 3 ANOVA applied on the surface roughness data shows significance of the effects of the five predictors and their selected interactions. Source

Sum of squares

Degree of freedom

Mean square

F-value

p-value

VBmax H Vc fz Lubrication mode VBmax  H H  Vc

0.196 0.089 0.18 0.031 0.00263 0.003 0.018

2 1 1 1 1 1 1

0.098 0.089 0.18 0.031 0.00263 0.003 0.018

178.51 162.74 320.05 55.85 4.79 5.48 32.05

<0.0001 <0.0001 <0.0001 <0.0001 0.0438 0.0326 <0.0001

the used tools. The electric power drawn by the CNC milling machine was measured by applying a power clamp meter, FLIR CM83, onto the main supply bus. The non-cutting power consumed by the milling machine for each of the four speed-feed combinations

3. Experimental results The experimental results related to the six responses and their pertinent analyses are grouped into the following sub-sections.

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80,000 70,000

VB_max = 0.4 mm VB_max = 0.3 mm

60,000

TL (mm3)

VB_max = 0.2 mm 50,000 40,000 30,000 20,000 10,000

MQL

Dry

MQL

Dry

MQL

Dry

MQL

Dry

MQL

Dry

MQL

Dry

MQL

Dry

MQL

Dry

0

0.08 0.08 0.11 0.11 0.08 0.08 0.11 0.11 0.08 0.08 0.11 0.11 0.08 0.08 0.11 0.11 mm/z 90

90

90

90 150 150 150 150 90

90

90

90 150 150 150 150 m/min

55

55

55

55

61

61

61

55

55

55

55

61

61

61

61

61 HRc

Fig. 3. Tool life data for the 48 experimental runs are embodied by the 16 bars stacked with respect to VBmax.

Table 4 ANOVA applied on the tool life data shows significance of the effects of the predictors and selected interactions. Source

Sum of squares

Degree of freedom

Mean square

F-value

p-value

VBmax H Vc fz Lubrication mode VBmax  H VBmax  Vc VBmax  fz Vc  fz Vc  Lubrication mode

3.3eþ9 3.77eþ9 1.33eþ9 1.53eþ8 5.41eþ7 4.59eþ8 1.1eþ8 1.2eþ7 1.39eþ7 2.39eþ7

2 1 1 1 1 1 1 1 1 1

1.65eþ9 3.77eþ9 1.33eþ9 1.53eþ8 5.41eþ7 4.59eþ8 1.1eþ8 1.2eþ7 1.39eþ7 2.39eþ7

865.74 1976.61 697.28 80.28 28.39 240.94 57.87 6.31 7.27 12.55

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0231 0.0159 0.0027

3.1. Work surface roughness Fig. 2 presents the experimental results of Ra for the 48 experimental runs. Few observations can be directly made from the graphs. Firstly, the longer a cutter operates, the rougher the machined surface gets. It means that average surface roughness generated by the criterion VBmax ¼ 0.4 mm is the highest and that by VBmax ¼ 0.2 mm is the lowest. As a tool proceeds in cutting, its cutting edges get blunt due to wear, which in turn develop gradually increasing work surface roughness. Secondly, the combination of the softer temper of the work material and the low level of cutting speed gives worst surface quality. On the other hand, the best surface quality is generally provided by the high level of cutting speed especially in combination with the harder temper of the work material. Harder materials tend to produce better surface finish because of lesser involvement of material plowing and galling in cutting process. On the other hand, high cutting speed contributes towards better surface finish due to more mechanical and less thermal effects imparted to the work surface. For a deeper investigation, analysis of variance (ANOVA) was applied on the Ra data. Table 3 presents the ANOVA details. This is to be noted that the table includes only those interactions whose effects on Ra are statistically significant (p-value < 0.05). The table suggests that the predictors and the two influential

interactions can be arranged in the following order of descending significance: Vc; VBmax; H; fz; H  Vc; VBmax  H; and lubrication mode. Unexpectedly, the effect of Vc (F-value ¼ 320) on Ra is about six times stronger than that of fz (F-value ¼ 55.8), which means that the effect of improving work surface finish by increasing cutting speed is dominant over the effect of degrading it by increasing feed rate within the tested ranges of the cutting parameters. The option of using MQL proved to be beneficial with respect to surface finish but the ANOVA suggests that the effect is marginally significant as its p-value (0.0438) is close to 5%. A further analysis on the influential interaction VBmax  H reveals that the effect of material hardness on Ra is more pronounced when VBmax is high, which means that the difference between surface finish of the two tempers of the work material is higher for VBmax ¼ 0.4 mm than for VBmax ¼ 0.2 mm. It suggests if a tool goes longer, the softer temper of the work material would develop increasingly rough surface at a rate higher than the harder temper would do. The other influential interaction, H  Vc, possesses a very strong effect (F-value ¼ 32) on Ra. 3.2. Tool life Fig. 3 presents the experimental results for tool life. The results of the runs involving different values of tool life criterion (VBmax)

A. Iqbal et al. / Journal of Cleaner Production 139 (2016) 1105e1117

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Fig. 4. SEM images of the worn-out (VBmax ¼ 0.4 mm) end mills used in the following six experimental runs: (a) 55 HRc, 90 m/min, 0.08mm/z, Dry; (b) 55 HRc, 90 m/min, 0.11mm/z, MQL; (c) 55 HRc, 150 m/min, 0.11 mm/z, Dry; (d) 61 HRc, 90 m/min, 0.08mm/z, Dry; (e) 61 HRc, 150 m/min, 0.08mm/z, MQL; and (f) 61 HRc, 150 m/min, 0.11mm/z, Dry.

Table 5 The MRR values are shown against the four combinations of Vc and fz. S/No.

Vc (m/min)

fz (mm/tooth)

MRR (mm3/min)

1 2 3 4

90 90 150 150

0.08 0.11 0.08 0.11

1833.5 2521.0 3055.8 4201.7

and same values of the other predictors are stacked together in a single bar. Few observations can be extracted directly from the graph. Increasing levels of work material hardness, cutting speed, and feed rate have a negative impact on tool life whereas the option of using MQL has a favorable effect. Generally, tool life in the range from VBmax ¼ 0.2 mme0.3 mm is longer than in the one from 0.3 mm to 0.4 mm. This difference (in percentage) rises as work material hardness and levels of the cutting parameters are increased. Table 4 presents the details of ANOVA applied on the tool life data. The table suggests that the predictors and the significant interactions can be arranged in the following order of decreasing significance of their effects: H; VBmax; Vc; VBmax  H; fz; VBmax  Vc;

lubrication mode; Vc  lubrication mode; Vc  fz; and VBmax  fz. The harder temper causes abrupt shortage in tool life because it requires higher cutting energy to plastically deform the material and convert it into form of a chip. The increased level of cutting energy leads to higher temperatures at the cutting zones which, in turn, accelerate the temperature dependent modes of tool damage. The high levels of the cutting parameters (Vc and fz) also cause increase in cutting temperature because of increase in amount of energy per unit time delivered to the cutting region. The consequence is again the shortening of tool life. On the other hand, the application of MQL led to increase in tool life. It has been reported that lubricating oil in mist form penetrates deep into secondary shear zone and causes reduction in tool-chip contact length which, in turn, causes drop in cutting temperature and subsequent tool life enhancement (Iqbal et al., 2009). It is trivial to study the effect of VBmax alone on tool life because, quite simply, a slack tool life criterion (the one allowing tool to run longer) would always lead to increased tool life values. An analysis on the most significant interaction (VBmax  H) revealed that the positive effect of relaxing tool life criterion from VBmax ¼ 0.2 mme0.3 mm and from 0.3 mm to 0.4 mm is much more significant for the softer temper of the work material.

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120

VBmax = 0.2 mm

100

18% 16% 14% 12%

CPS

SE (J/mm3)

80

55 HRc, 90 m/min 55 HRc, 150 m/min 61 HRc, 90 m/min 61 HRc, 150 m/min

60

10% 8% 4% 2% 0%

0 Dry 0.08

120

MQL 0.08 (a)

Dry 0.11

MQL 0.11 mm/z

Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(a)

VBmax = 0.3 mm

18%

100

16%

80

12%

VBmax = 0.3 mm

CPS

14%

60 40

10% 8% 6% 4%

20

2%

0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

0% Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(b) 120

(b)

VBmax = 0.4 mm

18%

VBmax = 0.4 mm

16%

100

14%

80

12%

60

CPS

SE (J/mm3)

VBmax = 0.2 mm

6%

40 20

SE (J/mm3)

55 HRc, 90 m/min 55 HRc, 150 m/min 61 HRc, 90 m/min 61 HRc, 150 m/min

40

10% 8% 6% 4%

20

2%

0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(c) Fig. 5. The three plots display the experimental results regarding specific energy.

Likewise, the other two significant interactions involving VBmax suggest that the positive effect of relaxing tool life criterion on tool life is stronger when the milling is done at the lower levels of the two cutting parameters. An analysis on the interaction ‘Vc  lubrication mode’ suggested that the option of using MQL holds a positive effect on tool life only when milling is done at the low level of cutting speed. At high cutting speeds, a milling cutter is damaged because of excessive cutting temperature. As MQL has limited cooling capacity, it cannot contribute effectively towards enhancing tool life at high cutting speeds. 3.3. Tool damage Fig. 4 presents SEM images of cutting edges of the selected worn-out cutters (having achieved VBmax ¼ 0.4 mm criterion). It is evident that the main tool damage modes are adhesion and microchipping besides gradual mechanical wear. Images (a) and (b) show

0% Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(c) Fig. 6. The three plots display the experimental results regarding cutting power share.

no signs of adhesion while image (d) gives a weak indication of it. All the cutters employed in these three runs were used with the low level of cutting speed, that is, 90 m/min. On the other hand, the images (e) and (f) show thick adhesions near the cutting edges. The common parameters in these two runs were the high levels of cutting speed and material hardness. This observation suggests that adhesion wear occurs at high cutting temperature, which is caused by machining at high cutting speed or cutting a work material of high yield strength (or hardness). A small, medium, and high level of chipping is apparent in the images (b), (c), and (f), respectively. A feed rate of 0.11 mm/z is common in the runs corresponding to these images. Images (c) and (f) correspond to the runs involving high levels of cutting speed and the image (f) corresponds to the high level of material hardness as well. This suggests that high levels of feed rate and material hardness have strong effects in causing chipping.

A. Iqbal et al. / Journal of Cleaner Production 139 (2016) 1105e1117

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Table 6 ANOVA applied on the SE and CPS data shows significance of the effects of the five predictors and their selected interactions. S/No.

1 2 3 4 5 6 7 8 9 10 11

SE

CPS

Source

F-value

p-value

Source

F-value

p-value

VBmax H Vc fz Lubrication mode VBmax  H VBmax  Vc VBmax  fz Vc  fz Vc  Lub. mode fz  Lub. mode

1866.51 214.06 1.02eþ5 42132.68 7918.81 13.56 55.86 49.63 3134.02 385.53 168.87

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0018 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

VBmax H Vc fz Lubrication mode VBmax  H H  Vc Vc  fz Vc  Lub. mode

2098.65 253.15 783.25 209.28 24.67 9.41 8.58 93.59 9.70

<0.0001 <0.0001 <0.0001 <0.0001 0.0001 0.0074 0.0098 <0.0001 0.0067

Fig. 7. Effect plots show the significance of the effects of the statistically influential predictors on specific energy: (a) cutting speed; (b) feed rate; and (c) lubrication mode and (d) interaction between hardness and tool life criterion.

3.4. Productivity and the energy related responses The productivity measure, MRR, was calculated using Equation (1) for the different speed feed combinations used in the 48 experimental runs. Table 5 presents the details of the calculated MRR values. Cutting speed and feed rate are the only influential parameters. The higher the cutting speed and/or feed rate, the higher is the productivity. Figs. 5 and 6 present the experimental results for SE and CPS, respectively. Quite clearly, the higher levels of cutting speed and/or feed rate cause reduction in specific energy and increase in cutting power share. An increase in MRR causes a rise in power required to cut the increased volume of work material per unit time but does not cause any significant increase in consumption of non-cutting power. Thus, a rise in MRR leads to reduction in SE and increase in CPS values. Furthermore, the figure also illustrates that a lenient tool life criterion (high value of VBmax) leads to high values of SE and

CPS. A blunt edge needs higher cutting forces and, thus, higher cutting energy to cut a given volume of work material, which causes a rise in SE. As this rise in specific energy comes without any corresponding increase in non-cutting power (or energy), CPS also experiences upsurge in its values. Table 6 presents the details of ANOVA applied on the specific energy and cutting power share data. For brevity, only F- and pvalues and the details of significant interactions are included. For SE, the strongest effects are of the two cutting parameters followed by those of lubrication mode, tool life criterion, and material hardness. The striking outcome is the highly significant effect of lubrication mode as the application of MQL caused an increase in specific energy consumption. This is because of additional power (and energy) required to drive the MQL system. The application of the aerosol might have reduced the specific cutting energy, especially for the low level of cutting speed, by penetrating into the secondary shear zone and reducing chip-tool

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Fig. 8. Effect plots show the significance of the effects of the statistically influential predictors on cutting power share: (a) tool life criterion; (b) cutting speed; and (c) work material hardness and (d) interaction between cutting speed and feed rate.

contact length there. But this reduction in energy, if any, is enormously smaller than the additional energy required to run the MQL system. As expected, the harder temper of the work material causes increased specific energy consumption. The most influential interaction on SE is “Vc  fz”. It suggests that the favorable effect on SE obtained by increasing feed rate is much more significant at the lower level of cutting speed. For CPS, tool life criterion proved to be the most influential parameter followed by cutting speed, material hardness, feed rate, and lubrication mode. The application of MQL causes reduction in CPS by bringing an additional energy requirement to the system without significantly reducing specific cutting energy. A study on the interaction “Vc  fz” reveals that the favorable effect of increasing feed rate on CPS is significant only at the higher level of cutting speed. Figs. 7 and 8 present the effect plots showing significance of the effects of the most influential predictors and interactions on specific energy and cutting power share, respectively. 3.5. Process cost Fig. 9 presents the total cost results for the 48 experiments. The following two observations can be directly extracted from the plots: (1) the longer a tool goes, the cheaper the process gets; it means the process cost is slashed if the tool life criterion is set at high level of VBmax.; (2) increase in material hardness as well as increase in cutting speed for cutting the harder temper of the work material cause a substantial increase in process cost. The reason attributed to the former is reduction in tooling cost as the number of tools used to machine a given volume of work material is reduced with a slack criterion. The latter is also largely related to the tooling cost. Hard temper of work material and high cutting speed cause an extensive increase in heat generation and rise in temperature around the

cutting edge which lead to acceleration of temperature dependent damage modes, such as adhesion and diffusion wear, of coated carbide tools. The speedy deterioration of tools results in their quicker replacements leading to a surge in process cost. ANOVA was applied for a thorough analysis of the experimental results related to TC. Table 7 presents the details. The predictors and the significant interactions can be arranged in the following order of decreasing significance: H; VBmax; Vc; H  Vc; H  fz; lubrication mode; Vc  fz; and fz. The interaction “H  Vc” possesses such a strong effect on TC that it is directly observable from the data plots (Fig. 9). For the low level of material hardness, the change in cutting speed has almost no effect on the total cost, whereas it causes a substantial change in cost when the hard temper of the material is in use. A study on the interaction “H  fz” presents an interesting outcome. For the soft temper, an increase in feed rate results in a reduction in TC while an increase is observed for the hard temper. The attributed reason is that an increase in feed rate for cutting the soft temper does not cause a significant damage to the tool and, thus, any minor rise in tooling cost is outdone by a significant slash in direct power cost and overhead cost caused by a resulting growth in productivity. In contrast, the increase in feed rate for cutting the hard temper has a much weightier effect on tool damage and, consequentially, on tooling cost, whereas the savings in the other costs remain unchanged. The option of MQL has shown a significant effect of plummeting TC. Though the MQL system has added to the power consumption and caused an addition oil cost, it has also significantly slashed the tooling cost by substantially increasing tool life against all the three levels of the tool life criterion. It is important to present a brief statistical detail regarding the constituents of TC. Of the 48 experimental results, the minimum, the maximum, and the average values of tooling cost; direct electric cost; overhead cost; and MQL oil cost, in PKR, are 12,609, 50,327,

A. Iqbal et al. / Journal of Cleaner Production 139 (2016) 1105e1117

60,000

TC (PKR/dm3)

50,000

55 HRc, 90 m/min 55 HRc, 150 m/min 61 HRc, 90 m/min 61 HRc, 150 m/min

VBmax = 0.2 mm

40,000 30,000 20,000 10,000 0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

1115

tooling cost varies from a minimum of 73.5% (for the run involving MQL, VBmax ¼ 0.4 mm, and the low levels of H, Vc, and fz) to a maximum of 82% (for the run involving no lubrication, VBmax ¼ 0.2 mm, and the high levels of the other three predictors). The corresponding shares of the overhead cost for these two runs are 25% (the maximum) and 16% (the minimum), respectively. This is so because a slack tool life criterion and the low levels of hardness and the cutting parameters cause tool run longer and remove larger volume of work material, thus, cutting down the tooling cost per unit volume of material removed. At the same time, a low level of MRR causes higher consumption of the utilities per unit volume removed, thus, leading to an increase in overhead cost. 4. Optimization and response prediction

(a) 60,000

The section focuses on optimal selection of tool life criterion and optimization of the cutting parameters with respect to various objectives of a milling process. Keeping in view the real-world requirements, the following optimization objectives can be worked out:

VBmax = 0.3 mm

TC (PKR/dm3)

50,000 40,000

30,000 20,000 10,000 0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(b) 60,000

VBmax = 0.4 mm

TC (PKR/dm3)

50,000 40,000 30,000 20,000 10,000 0 Dry

MQL

Dry

MQL

0.08

0.08

0.11

0.11 mm/z

(c) Fig. 9. The three plots display the experimental results regarding total cost.

1. 2. 3. 4. 5. 6. 7.

Minimize Ra. Minimize SE. Minimize TC. Minimize Ra and minimize SE simultaneously. Minimize Ra and minimize TC simultaneously. Maximize TL and maximize MRR simultaneously. Minimize SE and minimize TC simultaneously.

A numerical optimization approach called Derringer-Suich multi-criteria decision making algorithm was used to accomplish the 7 optimization cases. The details of the algorithm can be studied from the paper (Derringer and Suich, 1980). The numerical optimization tool box of a statistical software package named DesignExpert by Stat-Ease®, which follows the aforementioned optimization algorithm, was used for the automated processing of optimization. A general constraint was enforced on all the optimization objectives that the predictor variables should not go beyond their tested ranges (see Table 1). In all the optimization cases involving two objectives, both the objectives were assigned equal weightage. Furthermore, for the cases related to multi-objective optimization (6e12), the two objectives were so grouped together that the requirements of the one were opposite to those of the other. Table 8 presents the optimization results. The predictors optimized are tool life criterion (VBmax), cutting speed (Vc), feed rate (fz), and lubrication mode. As work material hardness cannot be optimized, it was fixed as 58 HRc (the average of 55 and 61 HRc).

Table 7 ANOVA applied on the TC data shows significance of the effects of the five predictors and their selected interactions. Source

Sum of squares

Degree of freedom

Mean square

F-value

p-value

VBmax H Vc fz Lubrication mode H  Vc H  fz Vc  f z

1.002eþ9 1.720eþ9 2.699eþ8 3.462eþ6 1.221eþ7 1.823eþ8 1.389eþ7 5.724eþ6

2 1 1 1 1 1 1 1

5.011eþ8 1.720eþ9 2.699eþ8 3.462eþ6 1.221eþ7 1.823eþ8 1.389eþ7 5.724eþ6

956.12 3282.57 514.92 6.61 23.30 347.80 26.50 10.92

<0.0001 <0.0001 <0.0001 0.0199 0.0002 <0.0001 <0.0001 0.0042

and 27,379; 256, 675, and 431; 4,296, 9,845, and 6802; and 0, 500, and 173, respectively. The highest share is taken by the tooling cost (78.7% of the average TC) followed by the overhead cost (19.6%), direct electric cost (1.2%) and MQL oil cost (0.5%). The share of

The optimization results clearly suggest that apart from the requirements of minimizing work surface roughness and specific energy consumption, all the other objectives demand the slackest tool life criterion (VBmax ¼ 0.4 mm). With regard to cutting speed,

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Table 8 Optimization results and predicted values of the response variables are shown against the corresponding objectives. Work material hardness of 58 HRc was used in all the cases. S/No.

1 2 3 4 5 6 7

Objective(s)

Min. Ra Min. SE Min. TC Min. Ra & min. SE Min. Ra & min. TC Max. TL & max. MRR Min. SE & min. TC

Optimized predictors VBmax (mm)

Vc (m/min)

0.2 0.2 0.4 0.2 0.4 0.4 0.4

150 150 90 150 150 140 150

the cost related objective (TC) demand the process to be operated at its low level while the objectives related to energy consumption, work quality, and productivity call for the high level. Moreover, the low level of feed rate is suitable for the objectives related to surface quality while the high level is favorable for the other objectives. Lastly, MQL is not the choice of lubrication mode for the objectives involving specific energy. It is not the pick for minimizing surface roughness either but it should be regarded that the effect of lubrication mode on Ra is just marginally significant. Therefore, it can be safely stated that MQL has positives to offer to the sustainability metrics barring specific energy consumption.

fz (mm/z)

Lub. mode

Predicted value(s)

0.08 0.11 0.11 0.09 0.08 0.11 0.11

Dry Dry MQL Dry MQL MQL Dry

Ra ¼ 0.223 mm SE ¼ 42.5 J/mm3 TC ¼ 27,637 PKR Ra ¼ 0.256 mm; SE ¼ 50.57 J/mm3 Ra ¼ 0.32 mm; TC ¼ 33,147 PKR TL ¼ 40,942 mm3; MRR ¼ 3916 mm3/min SE ¼ 45.4 J/mm3; TC ¼ 34,892 PKR

accomplished by prolonging the use of cutting tools, employing low level of cutting speed, and using the option of micro-lubrication. As such, there is no single setting of the tested predictors that would ensure favorable outcomes with regard to all the measured responses. The milling process needs to be optimized separately for each combination of the sustainability objectives. Acknowledgement The authors of the article are thankful to the Deanship of Scientific Research at King Abdulaziz University, Jeddah for funding this project.

5. Conclusions References The article presents an experimental investigation to analyze the effects of choosing various levels of flank wear as tool life criterion on sustainability metrics of milling such as work surface quality, tool life, process cost, productivity, specific energy consumption, and cutting power share. The effects of the other important parameters namely work material hardness, cutting speed, feed rate, and choice of MQL on the aforementioned metrics are also studied. The study finds that tool life criterion possesses a strong influence on all the sustainability metrics. A slack criterion, with respect to level of flank wear attained, bears a favorable impact on process cost, a significant part of which comes from increased tool life. At the same time, it dents work surface quality and causes machine tool to draw more energy for the same volume of material removed. Of the other four input parameters, work material hardness and cutting speed possess stronger influences on the sustainability metrics. Hardness was found to be the most influential predictor with respect to tool life. As the largest share of total cost comes from tooling, hardness turns out to be the most significant predictor for process cost as well. Even a minute increase in work material hardness may lead to enormous cost hike in hard milling process. Likewise, cutting speed was found to be the most effective parameter in controlling work surface quality and specific energy consumption, with its high value imparting favorable impacts on both. Though, the effects of feed rate and lubrication mode on all the sustainability metrics were found to be significant e the effect of the latter is marginally significant on work surface quality e they were not as weighty as those of the other three predictors. High feed rate ensures low values of process cost and specific energy consumption and high level of productivity but also causes poor work surface quality. The choice of MQL proved to be beneficial for enhancing tool life and reducing process cost. In a nutshell, milling process, with regard to environmental benignity, should be operated at high levels of the cutting parameters aided with early replacements of tools so as to make the process less energy consumptive and, thus, more eco-friendly. On the other hand, the economic aspect of sustainability is

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