A sustainability comparison between conventional and high-speed machining

Accepted Manuscript A Sustainability Comparison between Conventional and High-Speed Machining Khalid A. Al-Ghamdi, Asif Iqbal PII:

S0959-6526(15)00716-7

DOI:

10.1016/j.jclepro.2015.05.132

Reference:

JCLP 5651

To appear in:

Journal of Cleaner Production

Received Date: 26 December 2014 Revised Date:

22 May 2015

Accepted Date: 27 May 2015

Please cite this article as: Al-Ghamdi KA, Iqbal A, A Sustainability Comparison between Conventional and High-Speed Machining, Journal of Cleaner Production (2015), doi: 10.1016/j.jclepro.2015.05.132. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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A Sustainability Comparison between Conventional and High-Speed Machining

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Khalid A. Al-Ghamdi1, Asif Iqbal2

Department of Industrial Engineering, King Abdulaziz University, Jeddah, KSA; [email protected]

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Department of Mechanical & Aerospace Engineering, Institute of Avionics & Aeronautics, Air University,

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Islamabad, Pakistan; [email protected]

Manuscript’s Word Count: 10,723

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Abstract

In the 1990s, the industrial application of high-speed machining achieved enormous success because of its favorable characteristics such as high productivity, better work quality, and ease of machining thin-walled structures. With fast changing emphasis of the world’s manufacturing sector towards environmental benignity, the issue of sustainability with regard to application of high-speed machining becomes pivotal. The article presents an experimental

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investigation regarding comparison of conventional machining and high-speed machining with respect to sustainability measures. A set of 64 grooving experiments was performed on two tempers each of a high-strength low-alloy steel and a heat treatable titanium alloy. The experimental design focused on studying the effects of

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cutting mode (conventional/ high-speed machining), cutting speed levels for each of the two modes, feed rate, and minimum quantity lubrication on tool life, specific cutting energy, productivity, process cost, and machining forces.

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It was found that the choice between the two machining modes is highly sensitive with respect to manufacturing sustainability. The conventional machining mode was found to be comparatively economical, while the high-speed machining mode significantly outperformed the other in terms of low specific energy consumption and high productivity. The article asserts that high speed machining can completely surpass conventional machining as the sustainable way of metal cutting if the ways could be found to curb excessive tool damage observable at high cutting speeds.

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Keywords

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Specific cutting energy; MQL; Tool life; AISI 4340; Ti-6Al-4V

1. Introduction

High-speed machining (HSM) is characterized by cutting metals and alloys at very high cutting speeds and feeds.

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For a given work material, the machining is said to be in the high-speed range, if the cutting speed lies between 5 to 10 times of its conventional cutting speed (Schulz, 2004). The most significant difference between conventional

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machining (CM) and HSM is that localized overheating at primary shear zone, in the latter, leads to thermal softening of the work material. The resulting thermal softening causes substantial reduction in flow stress (Ning et al, 2001). The reported benefits of HSM include high material removal rates, improved work surface integrity (Chaplin et al, 1981), absolute elimination of built-up-edge, burr free machining (Nakayama and Ogawa, 1987), and better dimensional accuracy (Schulz and Moriwaki, 1992), Despite the overwhelming attention received from the researchers, the HSM domain has not yet been evaluated comprehensively and explicitly against the sustainability

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measures, especially in context of environmental impact or production cost.

Over the past two decades, the manufacturing processes have been readjusted in accordance with the environmental

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benignity requirements. For instance, it was recently reported that the “Use” phase of a milling machine causes 60% to 90% of CO2-equivalent emissions during its entire life-cycle (Diaz et al, 2010). The aim of the ongoing research

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activities is to cut harmful impacts by finding ways to enhance production and reduce demand of energy and other resources. Consequentially, the machining processes, like other manufacturing processes, are under intense scrutiny for having specific energy consumptions and process costs reduced. In this context, it becomes important to comparatively analyze various machining processes and their numerous approaches for sustainability.

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1.1 Literature Review

An analytical method for minimizing energy consumption and process cost for turning was reported (Rajemi et al,

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2010). The method searches for a cutting speed value that would ensure an optimal tool life and minimum energy cost for the process. Pusavec et al (2010) gave an idea of sustainable production with relevance to metal cutting. Based on an experimental work, the authors have claimed that cryogenic and high pressure jet assisted machining can slash process costs and improve effectiveness by reducing resource consumption. Shao et al (2010) have

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demonstrated a method that imparts capability of quantitatively analyzing the environmental impact of machining system. The method is based on Life Cycle Assessment and is applied to a traditional virtual Numerical Control

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(NC) machining model. A search method was proposed for finding ideal values of feed rate and depth of cut, in addition to cutting speed, for minimizing energy consumption in turning process (Mativenga and Rajemi, 2011). It was claimed that such a method could cause up to 64% reduction in energy footprint of a manufactured part. In another article, an experimental investigation for energy cost modeling of high-speed turning was reported (AlHazza et al, 2011). The researchers have used a response surface method and artificial neural networks for the modeling purpose. Fernandez-Abia et al (2012) have reported high-speed turning of austenitic stainless steel. The

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authors have developed a cutting force prediction model with consideration of the effect of edge force due to a rounded cutting edge. He et al (2012) have put forward an event-graph method to model energy consumption demanded by various machining processes of a manufacturing system. Diaz et al (2011) have compared power

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consumption demand in micro-machining of low carbon steel with those of aluminum and polycarbonate at various material removal rates. Iqbal et al (2013) have presented an experimental investigation and an application of a rule-

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based system to a grooving process for having trade-off among energy consumption, productivity, and tool life. It was found that the process must be operated at increased feed rates in order to significantly slash specific energy consumption. In a recent work, a model relating uncut chip thickness to specific energy consumption was developed and applied to machining of three work materials: steel, aluminum, and titanium alloy (Balogun and Mativenga, 2014).

Many articles have also reported minimization of machining process cost, another sustainability measure, alongside energy consumption. Anderberg et al (2011) have put forward a relationship between energy consumption and process cost. The machining cost model was developed from experimental data regarding tool wear and energy

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consumption. In micro-machining, the direct energy required for material removal is minute as compared to the energy demanded by the equipment modules. In this regard, Yoon et al (2013) have developed process cost and energy consumption models in terms of the micro-drilling process parameters. Bhushan (2013) has investigated the

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effects of cutting speed, feed rate, depth of cut and nose radius in turning of an Al-SiC metal matrix composite. The author has claimed 13.5% reduction in energy consumption and 22% increase in tool life after optimizing the parameters. Arif et al (2014) have developed a model that optimizes cutting parameters for maximization of profit per unit energy consumption in a single pass turning process. Wang et al (2014) have presented a multi-objective

optimization of cutting speed, feed rate, and depth of cut.

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analysis of energy consumption, process cost, and work surface roughness of a machining process based on

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High-pressure cooling has been predominantly used in machining of difficult-to-cut materials. Ezugwu et al (2005) have reported application of various levels of coolant pressure in machining of Inconel 718. It was found that maximum tool life was obtained at a pressure of 15 MPa because of effective suppression of notch wear. In another work, high-pressure jets were directed into the secondary shear zone to effectively penetrate and change the frictional and thermal characteristics there (Nandy et al, 2009). Significant improvements in tool life were reported

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when moderate levels of pressure were used.

The requirement of using less of costly and harmful emulsion based flood coolants has motivated researchers to explore the sustainability of using minimum quantity lubrication (MQL) in machining processes. MQL is the name

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given to a process in which very small amount of oil (less than 30 ml/h) is pulverized into a flow of compressed air (Braga et al, 2002). It offers primary benefit of increased tool life (Iqbal et al, 2009). Moreover, the use of MQL also

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offers secondary benefits such as better safety characteristics, biodegradability, storage stability, and lesser pollution (Boubekri and Shaikh, 2012). Astakhov (2008) has reported benefits of near-dry (MQL) machining over dry machining in terms of increased tool life and improved work surface quality. The same researcher has also put forward the need of proper understanding of metal cutting theory under near-dry conditions for effective implementation of the cost-effective technology (Astakhov, 2010). Boyer et al (2011) have presented a comparison between MQL and wet machining regarding energy and environmental cost in machining of an aluminum clutch case. Very recently, Al-Ghamdi et al (2014) have experimentally investigated the effects of cutting parameters and various cooling combinations of CO2 snow and MQL on specific cutting energy consumption in machining of AISI

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4340 and Ti-6Al-4V. It was observed that simultaneous application of CO2 snow on tool’s flank face and MQL on rake face caused significant reduction in consumption of specific cutting energy.

Based on the perspective of the literature survey, the following points provided the motivation to undertake the

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current research work:

1.

A comparative analysis on HSM and CM regarding energy and process cost modeling is not available.

2.

The aspects of process cost and energy consumption in MQL machining have not been properly

3.

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investigated and quantified.

A comparative analysis of sustainability in machining tempered states of ferrous and non-ferrous alloys is

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not available.

The current work, under direction of the aforementioned motivational points, presents an experimental investigation to compare, with respect to the sustainability measures, the domains of conventional and high-speed machining in cutting two tempered states each of AISI 4340 and Ti-6Al-4V under dry and MQL environments at various levels of cutting speed and feed rate. The sustainability measures include energy consumption, process cost, tool life,

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productivity, cutting forces, and cutting power share.

It is worthwhile to discuss here that there are further vital sustainability factors with respect to machining processes such as swarf (chip) and coolant recycling, reduction of waste generation, and equipment acquisition cost. In the

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perspective of comparison between CM and HSM, the aspect of chip disposal and recycling does not bear any difference. The chips produced by the two processes are different in terms of geometrical shape only; CM produces

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continuous chips while HSM produces serrated ones. This dissimilarity is too trivial to justify for separate disposal and recycling procedures. Furthermore, the issue of separating coolant from the chips is not relevant to the current work as all the experiments were performed in dry or near dry conditions. The CM/HSM choice does not affect the amount of waste generation either. It can rather be minimized by wisely selecting the starting work dimensions in accordance with design requirements of the product. Lastly, the equipment related factor in respect of CM/HSM technologies has also gradually lost significance. Due to rapid and massive advancements in machine tools technology, nearly all the currently marketed CNC lathes and machining centers are capable of providing speeds and feeds up to the HSM ranges of most of the work materials. Obviously, these machine tools are capable of machining

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in CM ranges as well. The discussion, thus, implies that evaluation of the aforementioned three parameters, in the context of CM/HSM comparison, is not required.

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1.2 Distinction between CM and HSM

High-speed machining is distinct from conventional machining not just because of high levels of cutting speed employed. It has been well established that beyond specific values of cutting speed, most of the work materials display a significant change in cutting mechanism. It has been reported that such a change is caused by onset of

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work’s thermal softening and its dominance over work’s strain hardening (Recht, 1964). Cottrell (1957) suggested that high temperature occurring at primary shear zone, because of high strain rate and lack of time for heat to be

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conducted away, softens the deforming material causing reduction in cutting forces. High-speed machining ranges for different materials are different and are based on material’s intrinsic properties, such as alloying composition, microstructure, material strength, thermal conductivity, and more. Even chip morphology in HSM is significantly different from that in conventional cutting. Conventional machining of ductile materials normally results in formation of continuous chip while HSM gives rise to saw-toothed chip, which is the result of a very distinct stick-

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slip phenomenon that occurs in primary and secondary shear zones (Komanduri, 1982; Komanduri et al, 1982). These distinctive features of HSM have motivated the authors to explicitly compare the domains of HSM and CM in

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the perspective of sustainability.

2. Experimental Work

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The section covers descriptions of the predictors controlled and the responses measured along with the details of the experimental setup and various measurements carried out.

2.1 The predictors

Cylindrical grooving was selected as the machining process for sustainability comparison between HSM and CM. This is the process that closely matches the orthogonal cutting model. The grooving experiments were performed under the framework of rough machining, which means productivity, rather than surface quality, was included in the list of the responses.

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Table 1. Two levels each of the six predictors were tested in the experiments. Unit

Level 1

Level 2

Work material

-

AISI 4340

Ti-6Al-4V

Rp

MPa

900

1120

Cutting mode

-

CM

HSM

Cutting speed

-

low

high

f

mm/rev

0.08

0.11

Cooling mode

-

dry

MQL

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Parameter

A total of 64 experimental runs were performed to collect the pertinent data. The following six predictor variables were controlled in the experiments:

1.

Work material: Two commonly used alloys, AISI 4340 and Ti-6Al-4V, were used as work materials in the form of cylindrical rods.

Yield strength of the work material, Rp (MPa).

3.

Cutting mode: Conventional machining (CM) and high-speed machining (HSM).

4.

Cutting speed: low and high. The low and high cutting speeds for CM and HSM were fixed as 30 and 60

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2.

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m/min and 200 and 280 m/min, respectively. Feed rate, f (mm/rev).

6.

Cooling mode: dry and minimum quantity lubrication (MQL).

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5.

A 6-variable, 2-level, full-factorial DoE method was used for the design of experiments, which consisted of a total of 64 (= 26) runs. This is to be noted that the predictors 2 and 5 are numeric while the others are categorical. Table 1 presents the two levels each of the six predictor variables tested in the experiments.

2.2 The setup

AISI 4340 is a commonly used high strength low alloy steel. It is a heat-treatable alloy which is usually quenched and tempered to obtain the required levels of yield strength and elongation. Typical yield strength ranges from

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470MPa (annealed) to 1725MPa (quenched) (Chi et al, 1989). Ti-6Al-4V is a fully heat-treatable alpha-beta alloy of titanium. Excellent corrosion resistance and high strength-to-weight ratio are two of its main characteristics. Ti-6Al4V is considered as a difficult-to-cut material because of its poor thermal conductivity, retention of strength at

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elevated cutting temperatures, and chemical affinity with most of the tool materials at medium-to-high cutting speeds.

The work materials AISI 4340 and Ti-6Al-4V were arranged in form of solid rods having diameters 100 mm and 70 mm, respectively, and lengths 450 mm. The heat treatments of the two materials were controlled in a manner to

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obtain two distinct tempers of each with one temper of AISI 4340 having almost the same yield strength as of one of Ti-6Al-4V. The stronger and the weaker tempers acquired the yield strength values of 1120 MPa (hardness: 43 and

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41.5 HRc and elongation: 10.5% and 10% for AISI 4340 and Ti-6Al-4V, respectively) and 900 MPa (hardness: 37 and 36 HRc and elongation: 13% and 14.5% for AISI 4340 and Ti-6Al-4V, respectively), respectively. Moreover, the Youngs moduli of elasticity of the alloy steel and the titanium alloy were found to be 197 - 199 GPa and 114 114.5 GPa, respectively. The normalized AISI 4340 rods were heated from room temperature to 845° C and holding the temperature for 50 minutes followed by quenching in oil. The rods intended for 1120 MPa yield strength were

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then tempered at 555° C for about two hours and then cooled in open air. Likewise, the rods for 900 MPa yield strength were tempered at 635° C for two hours before air cooling. On the other hand, the wrought Ti-6Al-4V rods were first solution-treated by heating up to 950° C, holding the temperature for 1 hour and then quenching in water. The rods intended for 1120 MPa yield strength were then age-hardened by heating them at 595° C for 8 hours

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followed by air cooling. Likewise, the rods for 900 MPa yield strength were aged at 480° C for 4 hours followed by air cooling. The MQL was supplied to the secondary shear zone via rake face through a duct by pulverizing

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vegetable based oil (UNIST Coolube 2210) at a rate of 30 mL/hr into the flow of air compressed at 8 bars.

Schulz and Moriwaki (1992), Ekinovic et al (2002), and few others have tried to specify the applicable cutting speed ranges in HSM of various work materials including steel and titanium alloys. The literature suggests that the threshold cutting speed for HSM of Ti-6Al-4V is roughly 90 – 130 m/min and that for hardened AISI 4340 is 140 – 180 m/min. It is important to note that the suggested threshold values depend on various parameters such as yield strength, depth of cut, feed rate, and choice of coolant.

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Prior to conducting the actual experimental runs, it was ensured by performing screening runs and subsequent examinations of chip structure that the selected higher level of the cutting speed related to CM, i.e. 60 m/min, would affirm cutting of the two work materials in their conventional machining ranges. Likewise, the selected lower level

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related to HSM, i.e. 200 m/min, was experimentally ensured to hold the cutting of both the work materials in their high-speed machining ranges.

The experiments were performed on a CNC horizontal lathe having a maximum motor power of 11 kW and a maximum spindle speed of 3500 rpm. Before start of the actual experimental runs, square shoulders of 3 mm width

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and 6 mm depth were cut into the cylindrical surface of all the rods. The width of the shoulders corresponds to the width of the cutting inserts. The shoulders were produced to ensure dynamic stability of the cutting process and true

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sensing of the machining forces. TiN coated tungsten carbide inserts, N123G2-0300-0003, having rake angle 10º, clearance angle 7º, corner (left/right) radii 0.3 mm, and width of cutting edge 3 mm were used for the cutting. Ceramic coated carbide tooling is chosen because it is cost effective and performs equally well in cutting alloy steels as well as titanium alloys. New and sharp cutting edge was employed for each of the 64 experimental runs. Thus, 32 grooving inserts were used in total, as each cutting insert consists of 2 cutting edges. Fig. 1 shows the details of the

2.3 The responses

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experimental setup.

1.

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The following responses were measured in each experimental run: Tool life, TL (mm3). A cutting edge of an insert is said to have completed its life when its flank wear land

2.

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had attained a maximum width (VBmax) of 0.2 mm. Material removal rate, MRR (mm3/min). The average value of this response is calculated by using the following mathematical formula: MRR = (Vc × 1000) × {(D – 6)/D} × 3 × f

(1)

The number “3”, in Eq. 1, is the fixed value of width of cut; “6” is the required depth of groove; and D is the starting diameter of the work rod.

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(b)

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(a)

(c)

Fig. 1. Core components of the experimental setup are demonstrated. (a) Top left image shows a grooving insert and

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AISI 4340 workpiece with square shoulders cut into it. (b) Top right image shows a grooving insert, Ti-6Al-4V workpiece, and the MQL duct. (c) Bottom image displays the cutting edge of a grooving insert.

3.

Specific energy, SE (J/mm3), consumed by the CNC lathe. The SE for each run is obtained by dividing its

measured average power input to the CNC lathe by the corresponding MRR.

4.

Specific cutting energy, SCE (J/mm3), consumed in cutting work material. The SCE for each run is obtained by dividing its measured average cutting power by the corresponding MRR.

5.

Cutting power share, CPS (%). CPS, for each run, is calculated by dividing the average power, in Watts, utilized for cutting to the corresponding average power drawn by the CNC machine tool.

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6.

Total cost, TC (PKR/dm3). TC is total processing cost incurred to remove 1 dm3 volume of work material. TC is summation of respective tooling cost, power consumption cost, overhead cost, and MQL cost. The fourth constituent of TC holds zero value for the runs involving dry machining. Machining forces (N): (a) Average static cutting force, Fc_stat; (b) Average dynamic cutting force, Fc_dyn; (c)

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7.

Average static feed force, Ff_stat; and (d) Average dynamic feed force, Ff_dyn.

2.4 The measurements

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The VBmax of insert’s cutting edge was measured with a 10X toolmaker’s microscope. An experimental run was ended as soon as the value of VBmax reached 0.2 mm. The tool life at that point was determined by calculating the

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total volume of grooves removed by the cutting edge. If the final VBmax had gone beyond the limiting value of 0.2 mm, the actual tool life was then determined by interpolation. The average power input to the CNC lathe was measured by applying Fluke 345, a power clamp meter, onto the power supply bus. The non-cutting power consumed by the lathe for each of the eight (= 4 × 2) speed-feed combinations was determined by rotating the work and moving the tool radially inwards at a given speed-feed combination but without contacting the work. The actual cutting power was calculated by subtracting the average of the total power measured during the complete cutting

the calculated MRR.

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process from the relevant non-cutting power. The SCE was then determined by dividing the actual cutting power by

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The evaluation of response number 3, SE, is not very simple for the experimental runs involving MQL. As the MQL system in this work was run external to the CNC lathe, it became vital to include the power (and energy) required to

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drive the system in the calculations related to determine SE. The power required to drive the MQL system consists of the following two parts: (a) power required to pulverize oil into air stream; and (b) power required to maintain flow of compressed air. The former for the MQL system (UNIST 9570-7-5-12) used in the current work was 5.4 Watts while the latter was calculated as follows: Air pressure = P = 8 bars = 800,000 N/m2. Air flow rate (as determined from the air compressor rating) = Q = 0.5 L/sec = 0.0005 m3/sec Power required to maintain the air flow is calculated by using the following equation:
 =


×

(2)

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where, ρ is electric motor efficiency. By substituting ρ = 0.95 and the calculated values of P and Q in Eq. 2, the power is evaluated as 421 W. Thus, the total power required to drive the MQL system is 425.4 Watts (= 5.4 + 421) or 25,524 J/min. The extra value of the energy required per unit time was then added to the specific energy

magnitudes of the energy consumed for utilizing MQL.

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requirements of the experimental runs involving MQL. The low levels of cutting speed and/or feed rate caused high

Total Cost (TC) is the combination of (a) purchase cost of grooving insert; (b) direct electric power consumption

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Following are the computations regarding each of the four cost factors:

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cost; (c) overhead costs (machine operator, lighting/HVAC, and machine depreciation); and (d) MQL oil cost.

(a) A box containing 10 grooving inserts (N123G2-0300-0003 CR4125) incurred cost of 20,000 PKR (Pakistan rupees; 1 USD ~ 100 PKR). As each insert consists of two cutting edges, the tooling cost for each experimental run becomes 1,000 PKR. The tool life criterion of VBmax = 0.2 mm is a very tough ask with respect to industrial traditions. Such a tough criterion helped the authors to enormously cut short the time and cost of experiments. At the commercial levels, the tool life criterion of VBmax = 0.6 to 0.8 mm is not

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very uncommon for rough machining operations. This suggests that the used inserts from these experiments can be resold with a strong possibility of getting back half of the purchase cost. In view of the stated possibility, the tooling cost for each experimental run becomes 500 PKR. This is to be specified that the

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specific tooling cost for each run is represented in terms of PKR per dm3 (cubic decimeter) of work

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material removed, which indicates that longer tool life causes smaller specific tooling cost.

(b) The direct electric power consumption is quantified in terms of SE (Joules per mm3 of work material removed). The commercial tariff for electric power consumption is, approximately, 18 PKR/kWh (= 5 PKR/MJ). This rate is multiplied by SE to obtain direct electric power cost in PKR/dm3.

(c) The CNC lathe was purchased for 10M PKR. Considering the machine salvage value as 1M PKR and useful life of 10 years, the machine depreciation cost becomes 900,000 PKR per year or 2,500 PKR /day. Further considering that the machine is operated in 2 shifts of 8 hours each, the depreciation cost becomes

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155 PKR/hr. Moreover, the machine operator cost is 200 PKR/hr. The HVAC and the lighting loads in the machine area are 7.0 and 0.2 kW, respectively. In this case, the lighting /HVAC overhead cost becomes 130 PKR/hr (= 7.2 kW × 18 PKR/kW.hr). By summing up all the three sub-categories, the total overhead

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cost becomes 485 PKR/hr. This rate is divided by the relevant MRR of an experimental run to get the overhead cost in PKR/dm3.

(d) 208 liters of the MQL oil were purchased for 380,000 PKR, thus, the MQL oil purchase cost becomes 1.83

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PKR/mL (or 55 PKR per 30 mL). As the oil mixing rate for the MQL based experimental runs was 30 mL/hr, the MQL cost becomes 55 PKR/hr. Again, this rate is divided by the relevant MRR of an

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experimental run to get the MQL cost in PKR/dm3. This is to be noted that the associated electric power cost to compress air for the MQL system and to pulverize the oil into the air stream has already been accounted for in (b).

The machining forces (in the cutting and the feed directions: Fc and Ff) were measured by Kistler Piezoelectric

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dynamometer 9257B. A 3-channel amplifier was utilized to enhance the electric output of the dynamometer. The measurement signals were recorded through an analog/digital converter on a standard desktop computer equipped with a lab view based data acquisition and processing software. A sampling rate of 1 kHz was used for the measurements. Each of the two forces was subdivided into the static (Fc_stat and Ff_stat) and dynamic (Fc_dyn and

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Ff_dyn) components and averaged for the machining duration just prior to attainment of the tool life criterion. The dynamic components give an idea about the fluctuations in the forces and are a measure of machining instability,

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chatter, and vibrations. The higher the difference between the average maximum and the average minimum, the higher is the dynamic component.

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80,000

AISI 4340, CM AISI 4340, HSM Ti-6Al-4V, CM Ti-6Al-4V, HSM

Y.S. = 900MPa Dry

1,00,000

60,000

60,000

40,000

40,000

20,000

20,000

0

0 0.08

0.11

0.08

0.11 f (mm/rev)

Low

Low

High

High Cut. Speed

0.08

0.11

Low

Low

High

High

Y.S. = 1120MPa MQL

1,00,000

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80,000 60,000

60,000

40,000

40,000

20,000

20,000

0

0 0.11

0.08

0.11

Low

Low

High

High

(c)

f (mm/rev)

Cut. Speed

0.08

0.11

0.08

0.11

Low

Low

High

High

(d)

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0.08

Fig. 2. The four plots display the experimental results for tool life measured against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a) Rp = 900 MPa, cooling mode = dry; (b) Rp = 1120 MPa, cooling mode = dry; (c) Rp = 900 MPa, cooling mode =

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MQL; and (d) Rp = 11200 MPa, cooling mode = MQL.

Table 2. ANOVA applied on the tool life data shows significance of the effects of the six predictors and their

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Tool Life (mm3)

0.11

(b)

Y.S. = 900MPa MQL

80,000

0.08

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(a) 1,00,000

Y.S. = 1120MPa Dry

80,000

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Tool Life (mm3)

1,00,000

Source Work material Rp Cutting mode Cutting speed f Cooling mode Material × cutting mode Material × cutting speed Cutting mode × cutting speed Cutting mode × f

selected interactions. Sum of squares 1.82 6.46 13.52 5.51 0.72 0.08 0.26 0.24 0.5 0.062

Degree of freedom 1 1 1 1 1 1 1 1 1 1

Mean square 1.82 6.46 13.52 5.51 0.72 0.08 0.26 0.24 0.5 0.062

F-value 149.3 530.97 1111.3 453.3 59.23 6.61 21.5 19.6 40.93 5.08

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0137 <0.0001 <0.0001 <0.0001 0.0294

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3. Experimental Results and Analyses The section presents the experimental data related to the six responses measured. The subsequent analyses and

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discussions are grouped under the upcoming four sub-headings.

3.1 Tool life and tool damage

Fig. 2 presents the experimental results for tool life. The plots (a – d), arranged in four combinations of yield strength and cooling mode, display the measured tool life values for the 64 experimental runs performed under the

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exhaustive combinations of the six predictors. A succinct view of the plots leads to the following observations: (a) AISI 4340 possesses better machinability than an equally strong Ti-6Al-4V when cut under same conditions; (b) the

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CM mode, in general, enjoys almost double tool life as compared to the HSM mode provided the other conditions are kept same; (c) the higher levels of the cutting parameters cause reduction in tool life; (d) increase in yield strength of work material causes drastic reduction in tool life; and (e) application of MQL causes improvement in tool life in the CM mode but its effect remains insignificant in the HSM mode.

For an in-depth analysis, Analysis of Variance (ANOVA) was carried out on the tool life data. It was found that the

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effects of all the predictors on tool life were statistically significant (p-value < 0.05). Moreover, the effects of four interactions were also found significant. Table 2 presents the ANOVA details. The interactions without significant effects are not included in the table. The table suggests that the predictors and their interactions can be arranged in

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the following order of decreasing significance of their effects: cutting mode; Rp; cutting speed; f; cutting mode ×

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cutting speed; material × cutting mode; material × cutting speed; cooling mode; cutting mode × f.

The machining of the titanium alloy resulted in shorter tool life values, in both the modes, in comparison to that of equally strong AISI 4340. A further analysis on the interaction “material × cutting mode” suggested that moving from CM mode to HSM mode caused more detrimental effect on tool life in machining of Ti-6Al-4V than of AISI 4340. In comparison to machining of alloy steels, Ti-6Al-4V is cut with shorter rake-chip contact length (Kirk, 1976), reduced levels of thermal softening, higher level of chemical affinity, and lower level of heat dissipation rate (Machai et al, 2013). These distinctive features make the titanium alloy less machinable than the alloy steel. Moreover, it was observed that chip serration in machining of Ti-6Al-4V, in comparison with that of AISI 4340, was

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initiated at lower cutting speed and became more intense as the speed was increased. In addition, it was also observed that machining at elevated speeds caused thicker tool adhesions in cutting of the titanium alloy. The aforementioned two observations explain the extraordinary detrimental effect of the HSM mode on tool life in

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machining Ti-6Al-4V. Nevertheless, in general, the HSM mode has proved to be highly disadvantageous with regard to tool life, cutting down the values to half of those experienced in the conventional mode. This observation is attributed to generation of extraordinary amount of heat because of very high work deformation rates. The heat, thereby, accelerates the temperature dependent modes of tool damage such as adhesion and chemical wear.

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forces, thereby speeding up the mechanical damage modes of the tool.

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Moreover, the HSM mode is also characterized by chip serrations. These serrations cause fluctuation in machining

MQL is a technology developed for penetrating droplets of lubricating oil in the secondary shear zone of cutting, which alters frictional properties at the chip-rake interface. This favorable alteration causes increase in tool life which is possible only at low cutting speeds. As MQL stream does not contain water or appreciable amount of any other cooling liquid, its cooling capacity is very limited. As the HSM mode is characterized by ultra-high temperature, the MQL technology does not have anything positive to offer in this case, rather its mist tends to

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partially block heat dissipation that normally happens through outward moving chip. A statistical analysis on the interaction “cutting mode × cooling mode” suggests that the effect of MQL on tool life is significant only when the

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cutting mode is CM.

Fig. 3 presents optical micrographs of the cutting edges of the selected inserts taken at the end of the respective

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experimental runs. The relevant settings of the predictors are labeled on the images (a – f). It is evident from the figure that apart from image (a), all the other images show signs of adhesion as well as clear indications of abrasive wear. It is also observable that machining of the titanium alloy, in comparison to that of the alloy steel, causes thicker adhesion on the cutting edge. The intensity of adhesion increases significantly in the HSM mode, especially for the titanium alloy. Furthermore, a comparison of images (c) and (d) with (e) and (f) suggests that higher yield strength of work material instigates more intense abrasive wear on the cutting edge and the adjacent flank face. Moreover, the application of MQL does not seem to have any significant effect on the magnitude of abrasion or adhesion.

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Fig. 3. Optical micrographs of the inserts used in the selected runs show the magnitudes of damage incurred by the cutting edges. The relevant details of the experimental runs are provided on the respective images (a – f) in the

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following format: work material, Rp, cutting mode, cutting speed, f, and cooling mode.

3.2 Specific energy, specific cutting energy, and cutting power share As described earlier, the response MRR was not measured; its calculated values for all the combinations of cutting mode, cutting speed, and feed rate are shown in Table 3. Since the tabulated values are obtained by using a formula, analysis of variance is not applicable.

Fig. 4, Fig. 5, and Fig. 6 present the experimental results regarding SE, SCE, and CPS, respectively. The plots (a – d) in each figure, arranged in four combinations of yield strength and cooling mode, display the measured values of the three responses for the 64 experimental runs performed under the exhaustive combinations of the six predictors.

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Table 3. The calculated values of MRR are presented for the various combinations of cutting mode, cutting speed, and feed rate used in the experiments.

AISI 4340, CM AISI 4340, HSM Ti-6Al-4V, CM Ti-6Al-4V, HSM

Y.S. = 900MPa Dry

40 30

30

Y.S. = 1120MPa Dry

10

10

0

0 0.08

0.11

0.08

Low

Low

High

0.11

f (mm/rev) High Cut. Speed

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(a)

Y.S. = 900MPa MQL

40 30

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20 10 0

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SE (J/mm3)

40

20

20

MRR (mm3/min) 6,768 9,306 13,536 18,612 45,120 62,040 63,168 86,856

f (mm/rev) 0.08 0.11 0.08 0.11 0.08 0.11 0.08 0.11

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SE (J/mm3)

Cutting speed low (30 m/min) low high (60 m/min) high low (200 m/min) low high (280 m/min) high

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Cutting mode CM CM CM CM HSM HSM HSM HSM

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S/No. 1 2 3 4 5 6 7 8

0.08

0.11

0.08

0.11

Low

Low

High

High

(b)

Y.S. = 1120MPa MQL

40 30 20 10 0

0.08

0.11

0.08

0.11

f (mm/rev)

0.08

0.11

0.08

0.11

Low

Low

High

High Cut. Speed

Low

Low

High

High

(c)

(d)

Fig. 4. The four plots display the experimental results for specific energy measured against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a) Rp = 900 MPa, cooling mode = dry; (b) Rp = 1120 MPa, cooling mode = dry; (c) Rp = 900 MPa, cooling mode = MQL; and (d) Rp = 11200 MPa, cooling mode = MQL.

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With regard to SE and SCE, the most prominent observation is that the HSM mode is at least 2.5 times superior to the CM mode. The difference peaks up to approximately 5 times for the runs involving the lowest levels of the cutting parameters. Likewise, Fig. 6 suggests that the share of cutting power in the total power (CPS) is significantly

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higher for the HSM mode. This is so because any increase in MRR, as caused by increasing cutting speed or feed rate, is accompanied by an increase in cutting power only, while the corresponding rise in non-cutting power is insignificant. Moreover, even the resulting increase in cutting power is not of the same proportion as that of MRR.

Y.S. = 900MPa Dry

SCE (J/mm3)

8 6

AISI 4340, CM AISI 4340, HSM Ti-6Al-4V, CM Ti-6Al-4V, HSM

2

0

0

0.08 Low

0.11 Low

0.08

0.11 f (mm/rev)

0.08

0.11

0.08

0.11

High

High Cut. Speed

Low

Low

High

High

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(a)

10

Y.S. = 900MPa MQL

8 6

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4 2

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SCE (J/mm3)

6

Y.S. = 1120MPa Dry

4

2

0

8

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4

10

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10

(b)

10

Y.S. = 1120MPa MQL

8 6 4 2 0

0.08

0.11

0.08

0.11 f (mm/rev)

0.08

0.11

0.08

0.11

Low

Low

High

High Cut. Speed

Low

Low

High

High

(c)

(d)

Fig. 5. The four plots display the experimental results for specific cutting energy measured against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a) Rp = 900 MPa, cooling mode = dry; (b) Rp = 1120 MPa, cooling mode = dry; (c) Rp = 900 MPa, cooling mode = MQL; and (d) Rp = 11200 MPa, cooling mode = MQL.

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This observation is attributed to the phenomenon of work material thermal softening in which yield strength of the material is considerably lowered because of increase in cutting temperature caused by increase in cutting speed and/or feed rate. For same work material, yield strength, and cooling mode, the highest ratio of the maximum to the

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minimum SE is 8.46 (Fig. 4 (d); for AISI 4340) while that of SCE is 4.66 (Fig. 5 (b); for Ti-6Al-4V). Moreover, the SCE consumed in machining of Ti-6Al-4V in each of the 32 runs remains significantly higher than the

CPS (%)

30

Y.S. = 900MPa Dry

Y.S. = 1120MPa Dry

40 30

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40

AISI 4340, CM AISI 4340, HSM Ti-6Al-4V, CM Ti-6Al-4V, HSM

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corresponding SCE of AISI 4340. Such a difference in the case of SE does not seem to be significant.

20

20

10

10

0

0 0.08

0.11

0.08

0.11 f (mm/rev)

Low

Low

High

High Cut. Speed

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(a)

Y.S. = 900MPa MQL

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30 20 10 0

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CPS (%)

40

40

0.08

0.11

0.08

0.11

Low

Low

High

High

(b)

Y.S. = 1120MPa MQL

30 20 10 0

0.08

0.11

0.08

0.11

f (mm/rev)

0.08

0.11

0.08

0.11

Low

Low

High

High Cut. Speed

Low

Low

High

High

(c)

(d)

Fig. 6. The four plots display the experimental results for cutting power share measured against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a) Rp = 900 MPa, cooling mode = dry; (b) Rp = 1120 MPa, cooling mode = dry; (c) Rp = 900 MPa, cooling mode = MQL; and (d) Rp = 11200 MPa, cooling mode = MQL.

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Table 4. ANOVA applied on the SE, SCE, and CPS data shows significance of the effects of the six predictors and their selected interactions.

F-value 221.96 120.97 6786.4 778.3 188.9 26.43 102.46 25.1 92.37 604.29 107.55 23.94

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

CPS Source Work material Rp Cut. mode (Cu.M.) Cut. speed (Cu.S.) f Cool mode (Co.M.) Rp × Cu.M. Cu.M. × f Cu.M. × Co.M. Cu.S. × f

F-value 151.61 52.18 2054.2 347.23 247.7 148.03 17.77 52.79 4.29 4.57

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SCE Source Work material Rp Cut. mode (Cu.M.) Cut. speed (Cu.S.) f Cool mode (Co.M.) Material × Cu.M. Material × Cu.S. Rp × Cu.M. Cu.M.× Cu.S. Cu.M. × f Cu.M. × Co.M.

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 0.0446 0.0383

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p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0017 <0.0001 <0.0001 0.0168

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F-value 125.12 39.75 1.84×105 15455 6332.76 459.82 11.24 3860.8 60.8 6.18

Fig. 6 also suggests that CPS for machining Ti-6Al-4V is significantly higher than that of AISI 4340 and also it increases with increase in cutting speed and/or feed rate. This observation is self-explanatory by considering that SCE is higher for machining the titanium alloy and that an increase in MRR does not cause any appreciable increase in non-cutting power.

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Table 4 presents selected details of ANOVA applied on the experimental data related to SE, SCE, and CPS. This is to be noticed that the table includes statistically significant predictors only. The table suggests that the most influential parameter for all the three responses is cutting mode followed by cutting speed and feed rate. Thus, the higher levels of the cutting parameters are proven to reduce specific energy consumption and enhance cutting power share. The advantageous situation in favor of the HSM mode regarding energy related responses is not attributed to

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

SE Source Work material Rp Cut. mode (Cu.M.) Cut. speed (Cu.S.) f Cool mode (Co.M.) Rp × Cu.M. Cu.M.× Cu.S. Cu.M. × f Cu.S. × f

its specific thermo-mechanics of machining, rather it is due to extraordinarily high levels of cutting speed employed by the mode. Table 4 suggests that the effects of work material and material yield strength are also significant. The

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S/No.

titanium alloy, because of its inconvenient machining characteristics, demands higher energy for machining than does an equally strong AISI 4340. Moreover, the stronger temper of a material obviously consumes more specific energy than the softer one because of higher forces/stresses required to set the material in its plastic range.

This is to be noted from Table 4 that the interactions involving work material are significant for SCE only. An analysis performed on the interaction “work material × cutting mode” revealed that the effect of work material

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selection on SCE is significant only if the cutting mode is HSM. This is because that at ultra-high speeds, the SCE value is very close to its minima and, thus, a change in work material won’t make a meaningful difference.

9

9

Dry, 900 MPa

4 3

5 4 3 2

1

1

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2

0

0 0

100 200 Cutting speed (m/min)

(a)

0

300

100 200 Cutting speed (m/min)

300

(b)

9

9

Dry, 1120 MPa 8

8

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7 6 5 4 3

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SCE (J/mm3)

6 SCE (J/mm3)

5

7

2

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1

100 200 Cutting speed (m/min)

(c)

6 5 4 3 2 1

0

0

MQL, 1120 MPa

7

SCE (J/mm3)

SCE (J/mm3)

6

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AISI 4340, 0.08mm/r AISI 4340, 0.11mm/r Ti-6Al-4V, 0.08mm/r Ti-6Al-4V, 0.11mm/r

7

MQL, 900 MPa

8

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8

0 300

0

100 200 Cutting speed (m/min)

300

(d)

Fig. 7. The four plots demonstrate the observed relationship between SCE and cutting speed against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a) Rp = 900 MPa, cooling mode = dry; (b) Rp = 900 MPa, cooling mode = MQL; (c) Rp = 1120 MPa, cooling mode = Dry; and (d) Rp = 1120 MPa, cooling mode = MQL.

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Fig. 7 presents a graphical description of this observation. The plots (a – d), arranged in four combinations of yield strength and cooling mode, demonstrate the empirical relationship between SCE and cutting speed. It is observable that the SCE curves become nearly horizontal for the cutting speeds falling in the HSM range. On the other hand,

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any change in cutting speed within the CM range causes substantial change in SCE. Moreover, it is also evident that the SCE plots are pushed upwards if the work material is changed from AISI 4340 to Ti-6Al-4V or feed rate is increased.

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3.3 Total cost

Fig. 8 presents the experimental results regarding total processing cost. The plots (a – d), arranged in four

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combinations of yield strength and cooling mode, display the measured TC values for the 64 experimental runs performed under the exhaustive combinations of the six predictors. Clearly, the CM option appears to be the economical choice albeit it remains downgraded with respect to product lead time. For the HSM mode, the largest share of the exceedingly high costs comes from the rapid tool replacements caused by excessively high tool damage rates. The figure also indicates that although machining of the titanium alloy is costlier than that of the alloy steel for all the given combinations but the gap widens to exceptional levels in high-speed machining of the stronger tempers.

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Again, the reason behind this observation is unprecedented rate of tool damage in machining the stronger form of Ti-6Al-4V at high cutting speeds.

Table 5 presents the selected details of ANOVA carried out on TC. An increase in either of the two cutting

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parameters causes hike in total cost because of resulting intensification of various tool damage modes. On the other hand, the provision of MQL was found to slash TC. The cost of utilizing MQL, which includes oil consumption cost

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and cost of electric power required to compress air and pulverize oil, was compensated and even surpassed by cost saving caused by resulting increase in tool life. An analysis performed on the interaction “work material × cutting mode” suggested that the effect of changing material from AISI 4340 to Ti-6Al-4V on total cost is weightier in the HSM mode than in the CM mode. Table 5 also suggests that the effect of the interaction “cutting mode × cutting speed” is highly significant. Increase in cutting speed in the HSM mode causes a more pronounced hike in TC in comparison to that in the CM mode.

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80,000

Y.S. = 900MPa Dry

AISI 4340, CM AISI 4340, HSM Ti-6Al-4V, CM Ti-6Al-4V, HSM

80,000

60,000

60,000

40,000

40,000

20,000

20,000

0

0 0.08

0.11

0.08

0.11 f (mm/rev)

Low

Low

High

High Cut. Speed

Y.S. = 900MPa MQL

0.11

0.08

0.11

Low

Low

High

High

(b)

Y.S. = 1120MPa MQL

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1,00,000

80,000

80,000

60,000

60,000

40,000

40,000

20,000

20,000

0

0

0.11

0.08

0.11

f (mm/rev)

0.08

0.11

0.08

0.11

Low

Low

High

High Cut. Speed

Low

Low

High

High

(c)

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0.08

(d)

Fig. 8. The four plots display the experimental results for total cost evaluated against the 64 combinations of the six predictors. The results are grouped according to the following combinations of yield strength and cooling mode: (a)

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Rp = 900 MPa, cooling mode = dry; (b) Rp = 1120 MPa, cooling mode = dry; (c) Rp = 900 MPa, cooling mode = MQL; and (d) Rp = 11200 MPa, cooling mode = MQL.

Table 5. ANOVA carried out on the TC data presents significance of the effects of the six predictors and their

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TC (PKR/dm3)

0.08

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(a)

1,00,000

Y.S. = 1120MPa Dry

1,00,000

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TC (PKR/dm3)

1,00,000

S/No. 1 2 3 4 5 6 7 8 9 10 11

selected interactions. Source Work material Rp Cutting mode (Cu.M.) Cutting speed (Cu.S.) f Cooling mode (Co.M.) Material × Cu.M. Material × Cu.S. Rp × Cu.S. Cu.M.× Cu.S. Cu.M.× f

F-value 147.51 506.52 957.05 399.99 49.98 5.18 24.86 21.49 8.63 64.5 8.64

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.028 <0.0001 <0.0001 0.0054 <0.0001 0.0053

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The reason behind the observation is that the HSM mode, in its essence, is responsible for causing extraordinary tool damage and any increase in cutting speed in that mode further deteriorates tool performance with a magnitude much

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higher than that caused by a similar cutting speed hike in the CM mode.

3.4 Machining forces

As described in section 2.4, two components (static and dynamic) each of the cutting and feed forces were

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evaluated. The experimental results for the four responses are presented in a tabular form in Table 6. An inference can be extracted from the table that the dynamic components are highly sensitive to the choice of cutting mode. Their values in the HSM mode are at least twice as high as those in the CM mode. The reason is that the HSM mode

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is characterized by strong chip serrations (Kitagawa et al, 1997), which cause intense fluctuations in machining forces. These fluctuations are quantified as high magnitudes of the dynamic components of the forces. Table 7 presents the selected details of ANOVA carried out on the four components. Following points can be drawn from the table:

Of the four components, the effect of MQL is significant on static cutting force only. Its application caused

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1.

marginal reduction in its magnitude because of its ability at low cutting speeds to penetrate the chip-rake interface and alter frictional properties there. 2.

For all the four force components, the most influential parameter is cutting mode followed by yield

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strength, cutting speed, feed rate, and work material. Furthermore, cutting mode is more influential on the dynamic components as compared to the static ones. As expected, the machining forces increased in

3.

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magnitude with increase in values of the cutting parameters and/or yield strength. Unlike the other responses, the significant interactions for all the four force components include the response “work material”. An investigation carried out on the interaction “work material × cutting mode” suggested that the effect of changing work material from AISI 4340 to Ti-6Al-4V on all the four components is much more significant in the HSM mode than in the CM mode.

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Table 6. Experimental results for the four components of the machining forces are tabulated against the 64 combinations of the six predictors. Cooling mode dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry

Fc_stat (N) 513 466 578 541 565 559 713 695 597 617 701 683 771 752 859 883 728 660 793 748 805 785 1009 997 898 881 1041 1028 1136 1151 1289 1308 509 481 588 552 617 592 787 794 724 707 794 773 887 866 948 962 726 652 818

Fc_dyn (N) 88 79 105 111 81 96 125 116 239 251 292 276 313 288 346 333 91 101 138 119 98 105 148 142 261 254 303 287 277 249 355 341 68 85 106 94 119 116 133 140 271 238 319 330 287 285 354 367 162 148 208

Ff_stat (N) 372 341 412 406 398 410 457 464 363 412 511 498 675 711 798 815 439 424 505 501 488 511 571 594 517 583 622 641 722 708 837 812 303 288 411 389 377 363 477 491 514 456 608 586 725 741 854 827 404 385 528

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f (mm/rev) 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 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 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 0.08 0.08 0.11

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Cutting speed low low low low high high high high low low low low high high high high low low low low high high high high low low low low high high high high low low low low high high high high low low low low high high high high low low low

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Cutting mode CM CM CM CM CM CM CM CM HSM HSM HSM HSM HSM HSM HSM HSM CM CM CM CM CM CM CM CM HSM HSM HSM HSM HSM HSM HSM HSM CM CM CM CM CM CM CM CM HSM HSM HSM HSM HSM HSM HSM HSM CM CM CM

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Rp (MPa) 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900 1120 1120 1120

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Work material AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 AISI 4340 Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V

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S/No.

Ff_dyn (N) 310 272 423 396 372 333 591 603 1098 1132 1221 1197 1314 1288 1542 1577 354 306 410 404 418 378 611 592 1478 1443 1574 1589 1957 1922 2221 2306 194 168 318 286 294 273 624 579 1316 1287 1585 1617 1730 1708 2134 2178 442 406 664

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Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V Ti-6Al-4V

1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120 1120

CM CM CM CM CM HSM HSM HSM HSM HSM HSM HSM HSM

low high high high high low low low low high high high high

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

MQL dry MQL dry MQL dry MQL dry MQL dry MQL dry MQL

754 879 835 1086 1063 923 909 1016 996 1303 1328 1416 1428

201 159 170 218 227 336 321 425 444 404 392 508 503

482 472 475 617 641 588 601 717 682 873 891 1016 978

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52 53 54 55 56 57 58 59 60 61 62 63 64

638 616 599 883 851 1687 1701 2102 2069 2249 2228 2688 2633

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Table 7. ANOVA applied on the data related to the four components of the machining forces shows significance of the effects of the six predictors and their selected interactions. Fc_dyn Source Material Rp Cu.M. Cu.S. f Mat × Rp Mat × Cu.M. Mat × f Rp × f Cu.M.×Cu.S. Cu.M. × f

F-value 195.7 176.73 2638.3 71.27 185 125.25 16.87 8.01 10.25 14.2 19.92

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0002 0.007 0.0026 0.0005 <0.0001

Ff_stat Source Material Rp Cu.M. Cu.S. f Mat × Rp Mat × Cu.M. Mat × Cu.S. Mat × f Cu.M.×Cu.S.

F-value 30.83 227.2 1119.9 561.3 206.9 5.16 52.57 4.08 4.57 164.37

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p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0312 0.003 0.0002 <0.0001 <0.0001 <0.0001 0.0004

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0281 <0.0001 0.0494 0.0382 <0.0001

Ff_dyn Source Material Rp Cu.M. Cu.S. f Mat × Rp Mat × Cu.M Cu.M. × f

F-value 19.77 106.47 1851.6 97.56 89.06 18.17 5.87 18.9

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4.

F-value 58.64 1320.5 899.27 677.7 221.96 4.96 9.91 17.26 46.02 50.47 26.72 15.01

The most influential interaction, according to Table 7, is “work material × yield strength on Fc_dyn”, having an F-value of 125. Altering the work material from AISI 4340 to Ti-6Al-4V, while keeping the yield

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

Fc_stat Source Material Rp Cu.M. Cu.S. f Co.M Mat × Cu.M. Mat × Cu.S. Rp × Cu.M. Rp × Cu.S. Cu.M.×Cu.S. Cu.S. × f

strength at the lower level, did not significantly affect the magnitude of Fc_dyn. On the other hand, the said change caused a significant hike in the force component when the yield strength of the work materials was

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S/No.

kept at the higher level.

4. Optimization

At completion of a comparative experimental analysis carried out under the umbrella of sustainability, it becomes vital to optimize the machining process with respect to various pragmatic requirements. The following optimization scenarios can be worked out in this regard:

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p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 0.0197 <0.0001

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Maximize TL.

2.

Minimize specific energy consumption in terms of SCE / SE.

3.

Maximize CPS.

4.

Minimize TC.

5.

Maximize TL and maximize MRR simultaneously.

6.

Maximize TL and minimize SE simultaneously.

7.

Maximize MRR and minimize TC simultaneously.

8.

Minimize SE and minimize TC simultaneously.

9.

Minimize Fc_stat, Fc_dyn, Ff_stat, and Ff_dyn simultaneously.

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1.

Table 8. Optimization results and predicted values of the responses are tabulated against the respective objectives. Work material yield strength of 1010 MPa was used in all the calculations.

1

Max. TL

2

Min. SCE

3

Max. CPS

4

Min. TC

6 7 8

9

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

Optimized predictors Cu.M. Cu.S. f (mm/r) CM low 0.08 CM low 0.08 HSM high 0.11 HSM low 0.11 HSM high 0.11 HSM high 0.11 CM low 0.08 CM low 0.08 HSM low 0.11 HSM low 0.08 HSM low 0.08 HSM low 0.08 HSM low 0.11 HSM low 0.08 HSM low 0.09 HSM low 0.08

Co.M. MQL MQL MQL Dry Dry Dry MQL MQL MQL MQL Dry Dry MQL MQL Dry Dry

AISI 4340

CM

low

0.08

MQL

Ti-6Al-4V

CM

low

0.08

MQL

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5

Work material AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V AISI 4340 Ti-6Al-4V

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Objective

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S/No.

Min. Fc_stat, Fc_dyn, Ff_stat, & Ff_dyn

Predicted value(s)

TL = 63,616 mm3 TL = 59,556 mm3 SCE = 1.565 J/mm3 SCE = 1.845 J/mm3 CPS = 35.8% CPS = 38.5% TC = 9,385 PKR TC = 9,818 PKR TL=27,405 mm3; MRR=62,040 mm3/min TL=25,650 mm3; MRR=45,121 mm3/min

TL=34,380 mm3; SE=7.02 J/mm3 TL=25,040 mm3; SE=7.27 J/mm3 MRR=62,040 mm3/min; TC=18,306 PKR MRR=45,121 mm3/min; TC=20,300 PKR

SE=6.76 J/mm3; TC=15,481 PKR SE=7.27 J/mm3; TC=20,272 PKR Fc_stat=561 N, Fc_dyn=91 N, Ff_stat=392 N, Ff_dyn=273 N Fc_stat=565 N, Fc_dyn=115 N, Ff_stat=340 N, Ff_dyn=264 N

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Derringer-Suich multi-criteria decision making algorithm was used to achieve the aforementioned optimization objectives. It is a numerical optimization method which utilizes ANOVA results of all the concerned responses. Its details can be studied from the article (Derringer and Suich, 1980). A constraint was applied on all the listed

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optimization cases that the controlled variables should not take values beyond their tested ranges, as detailed in Table 1. Moreover, in the cases of multi-objective optimization (5 – 8), two objectives were so grouped together that the requirements of the one were in opposition to those of the other. Table 8 presents the optimization results. The predictor variables to be optimized were cutting mode, cutting speed, feed rate, and cooling mode. The yield

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strength of the work materials (AISI 4340 and Ti-6Al-4V) was fixed as 1010 MPa (the mean of 900 and 1120 MPa). The table suggests that HSM is the obvious mode of choice for most of the sustainability related objectives (S/No. 1

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– 8). On the other hand, conventional machining is the mode of choice for the individual objectives involving tool life and total cost only. As total cost is itself dependent on tool life, it can be safely stated that high-speed machining is more sustainable mode of metal cutting than conventional machining. The table also suggests that machining of

5. Conclusions

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Ti-6Al-4V remains more difficult than an equally strong AISI 4340.

The article presents an experimental investigation and comparative analyses between conventional and high-speed machining in perspective of sustainability. It also covers machinability comparisons between different tempers of a

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high strength low alloy steel and an α+β titanium alloy at various levels of cutting speed and feed rate.

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The major finding of the presented work is that the choice of cutting mode (HSM / CM) is highly influential on sustainability of metal cutting process. Its effects on all the tested measures such as tool life, productivity, specific energy consumption, and process cost are highly significant. The analyses have clearly identified the HSM mode as the obvious choice for enhancement of productivity and reduction of specific energy consumption. On the other hand, the CM mode is comparatively more economical mainly because of associated longer tool life values. For most of the responses, the F-value for cutting mode was found to be more than double of that for cutting speed. This finding is an indication that the HSM mode possesses more than just the effect of high cutting speed that makes it extremely significant. After cutting mode, the most influential predictors on the sustainability measures are work

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material’s yield strength and cutting speed. A stronger temper acts unfavorably towards all the responses except cutting power share.

The article also reports that Ti-6Al-4V possesses poorer machinability characteristics than an equally strong AISI

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4340. Under same conditions, the titanium alloy is machined costlier with shorter tool life, higher levels of machining forces, and more consumption of specific energy. The high levels of cutting speed and feed rate proved to be favorable towards the requirement of less energy consumption but unfavorable towards processing cost. The application of MQL offers positive effects regarding tool life, specific energy consumption, and total cost in the CM

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mode. In the HSM mode, the application of MQL shows no significant effects on the sustainability measures.

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The article emphasizes that high speed machining possesses strengths to become the obvious choice for cutting metals in a sustainable way, provided intense tool damage observed at high cutting speeds is inhibited. The reported work is expected to find applicability in metal cutting industry under the principles of green and sustainable manufacturing paradigms. It will guide engineers and machinists to work out machining conditions for higher

Acknowledgement

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productivity and lesser production cost and environmental impact.

The authors of the article are thankful to the Deanship of Scientific Research at King Abdulaziz University, Jeddah,

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KSA for funding this project.

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Highlights 1. The choice of conventional or high-speed machining is influential on sustainability. 2. High-speed machining is extremely favorable with regard to specific energy consumption.

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3. Conventional machining outperforms high-speed machining in terms of process cost.

4. Ti-6Al-4V alloy possesses poorer machinability characteristics than an equally strong AISI 4340. 5. Minimum quantity lubrication has favorable sustainability effects for conventional machining

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only.