Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools

Journal of Cleaner Production 33 (2012) 1e9

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Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools Issam Hanafi a, *, Abdellatif Khamlichi a,1, Francisco Mata Cabrera b, 2, Emiliano Almansa b, 2, Abdallah Jabbouri c, 3 a b c

Analysis and Modelling of Systems Laboratory, Faculty of Sciences at Tetouan, BP. 2121 M’hannech, 93002 Tetouan, Morocco Polytechnic School of Almaden, 1 Plaza Manuel Meca, 13400 Almaden, Ciudad Real, Spain Materials and Mechanics of Structures Laboratory, Faculty of Sciences and Technology at Tangier, BP 416, Tangier, Morocco

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 May 2011 Received in revised form 31 March 2012 Accepted 6 May 2012 Available online 17 May 2012

Cleaner production and sustainability are of crucial importance in the field of machining processes where great amount of energy is being consumed. This paper outlines the application of grey relational theory and Taguchi optimization methodology in order to optimize the cutting parameters for PolyEtherEtherKeytone reinforced with 30% of carbon fibers. The material is turned by using TiN coated tools under dry conditions. The objective of optimization is to achieve simultaneously the minimum power consumption and the best surface quality. This involves in practice reducing the environmental footprint related to such manufacturing process while providing enhanced functional performance in terms of surface integrity of machined parts. The obtained results have indicated that cutting speed and depth of cut are the most influential parameters. The optimal setting of machining parameters achieving sustainability target in terms of minimum surface roughness and minimum cutting power was determined. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: PEEK-CF30 Turning TiN coated cutting tool Grey relational analysis Surface roughness Cutting power Power consumption Multi-criteria optimization

1. Introduction Sustainable production as a global concept encompasses important elements belonging to many engineering fields and applies, in particular, for machining processes. Adopting sustainable manufacturing practices offer the possibility for composite materials machining companies to improve their economic and environmental performance. Advanced composite materials have been used to produce structural parts in many industrial fields. These include electrical and electronic components, aircraft and automotive components, medical equipment, petroleum valves and gaskets, nuclear

* Corresponding author. Tel.: þ212 539 972 423; fax: þ212 539 994 500. E-mail addresses: hanafi[email protected] (I. Hanafi), [email protected] (A. Khamlichi), [email protected] (F.M. Cabrera), emiliano.almansa@ uclm.es (E. Almansa), [email protected] (A. Jabbouri). 1 Tel.: þ212 539 972 423; fax: þ212 539 994 500. 2 Tel.: þ34 926 264 007; fax: þ34 926 264 401. 3 Tel.: þ212 539 393 954/55; fax: þ212 539 393 953. 0959-6526/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2012.05.005

components, chemical processing industry and field drilling (bushings, bearings, seals, pump wear rings.). Composite materials such as those made from PolyEtherEtherKeytone (PEEK) have many attractive characteristics such as light weight, excellent chemical and stream resistance, superior mechanical and bearing properties, high stiffness, good fatigue resistance and good corrosion resistance. They provide also other special features such as flame retardation with low smoke generation and resistance to gamma radiation. The addition of short fibers to thermoplastic composites enhances their mechanical properties and increases the service temperature in comparison with nonreinforced thermoplastics (Davim et al., 2003; Davim and Reis, 2004). For PEEK-based composites, the most common fillers that are added are glass and carbon. The addition of short carbon fibers reduces the coefficient of friction and wear and decreases the thermal expansion coefficient (Bhuhan, 1999). The carbon fiber reinforcement provides also maximum rigidity and load-bearing capacity (Lee et al., 2006) as well as high temperature service (Harsha and Tewari, 2003). Composite materials offer the possibility to manufacture parts with complicated geometry using fewer components. This enables

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manufacturers to save cost as compared with the same parts made from conventional metallic materials. Machining is the most widely used process in manufacturing. Reducing the environmental footprint of machining is required in order to attain sustainable and cleaner production objectives. As developing alternative manufacturing process technologies for replacing machining is still a prohibitive task, preventing the negative environmental impact of machining can be achieved essentially by operating modification of existing processes. An integrated approach of the problem should better be adopted in order to fully evaluate the trade-offs in part design, process planning and process operation. The important issues to address include analysis of process time, energy utilization, flow of workpiece material and flows of process catalysts. The interdependencies existing between these lasts must also be examined. Park et al. (2009) indicated that manufacturing and processing consumes large amounts of energy, so their impact on environment should be considered as a major priority. He et al. (2012) have reported that the energy efficiency of machine tools is generally less than 30%. Munoz and Sheng (1995) presented a model for the environmental impact of machining processes. In their analysis, they combined the effects of chip-formation mechanics, tool-life and the generation of cutting fluid waste streams in both liquid and vapor forms. They were able to estimate the effects of changes in operating parameters (speed, feed, depth of cut, tool angle) on process energy consumption, process rate, and mass flows of waste streams. Valuable works related to the optimization of energy consumption of machine tools have been carried out. Sarwar et al. (2009) have chosen Specific Cutting Energy (SCE) as the pertinent parameter for measuring the efficiency of the metal cutting process. They stated that the variation of SCE as function of different workpiece materials can provide useful information that enables estimating machinability characteristics for selected workpieces. Other recent works have focused on machining technologies in order to achieve sustainable development objectives by reducing energy footprint (Rajemi and Mativenga, 2008). Modeling the total energy of machining was achieved in Rajemi et al. (2010) where tool-life optimisation was realized in order to satisfy the minimum energy requirement. The work has pointed out the conflict existing between economical and environmental considerations since the optimum condition for minimum costs was found to be in disagreement with the minimum energy criterion. The important issue that the authors have emphasized is the necessity for reorganizing the whole machining activity through conciliating multiple objectives that emerge in this fabrication process. He et al. (2012) have considered the energy efficiency monitoring of machine tools through a model based on energy consumption. They observed that total energy consumption can be divided into two parts: constant energy consumption corresponding to idling regime and variable energy consumption related to cutting power. The constant part depends on machine tool technology and requires additional investment cost to be reduced. The variable part is affected by machining parameters and could be optimized through finding the optimal tuning of operative conditions. In machining, surface topography of machined parts is very important for many applications that involve friction, lubrication and wear. Surface roughness constitutes a key factor in evaluating machining accuracy. Reduced surface roughness or equally higher machining accuracy will guarantee enhanced performance during service as well as longer life of machined parts. Many factors affect the surface condition. It is known that the mechanism of cutting in fiber reinforced plastics composites consists of a complex combination of plastic deformation, shearing and bending rupture. These

lasts depend on the flexibility, orientation and toughness of the fibers, which together define the surface texture of the workpiece. Surface roughness varies with respect to the workpiece fiber orientation. Surface quality is better at low fiber orientation angles and decreases at larger fiber angles, as the compressive strain within the work material increases in this last case. The cutting speed plays an important role with regard to surface roughness. At lower cutting speed, large material flow with the cut fibers occurs. This produces high surface roughness. Whereas at high velocity, the chips mainly consist of less deformed matrix material and cut fibers. The increase in depth of cut can result in high normal pressure and seizure. The increase in feed rate increases the heat generation and hence tool wear which can result in higher surface roughness. The increase in feed rate can increase also chatter which leads in its turn to higher surface roughness. The cutting power represents the energy which is consumed by the machine tool in order to achieve the desired surface finish of a machined part. In this work, environmental footprint associated to cutting power is considered in view of its minimization. This implies controlling the cutting forces and speeds. As, this should be performed without deteriorating surface roughness of turned parts, the aim is to find machining parameters that enable satisfying both of these criteria. The problem is stated then within the general framework of sustainable and cleaner production. Analysis of single performance characteristics of machining process has been carried out by many researchers. But, monoobjective approaches constitute only adequate simplifications of the real problem. Machining processes are complex in nature and require often optimizing various different and conflicting objectives. Multiple objective optimization of surface roughness and cutting power is considered in the following within the framework of grey relational theory and Taguchi method. Taguchi method coupled to grey relational analysis has been satisfactorily used in dealing with optimization of multi-objective criteria (Park, 1996; Deng, 1989). Taguchi’s parameter design is widely employed in conducting and analyzing experiments (Phadke, 1989; Aggarwal et al., 2008). It offers a simple and systematic approach to optimizing design for performance, quality and cost of production (Ghani et al., 2004). Taguchi’s approach uses extensively statistical design of experiments which enables significant reduction of the number of experiments and hence time required for experimentation effort (Ross, 1996). The grey theory can provide a solution for a system in which the model is affected by uncertainties or when the available information about it is incomplete. It also provides an efficient solution when multiple random inputs and discrete data are present in the problem. Such situations are current in the field of machining (Narender et al., 2004). The grey relational analysis has been successfully applied in the past for this manufacturing process. Tosun (2006) used grey analysis in optimizing the drilling parameters. Huang and Lin (2002) applied the grey relational analysis to design the die-sinking electric discharge machining parameters. Chiang and Chang (2006) used the grey relational analysis to optimize the wire electric discharge machining process of particlereinforced material with multiple performance characteristics. Wang and Lan (2008) have used Taguchi optimization with grey relational analysis to achieve the optimum process parameters of precision CNC turning under the consideration of multiple objectives. A confirmation experiment within the optimum parameters was conducted and showed effectiveness of the proposed optimization method. Recently Chorng-Jyh et al. (2009) used Taguchi method with grey relational analysis for the optimization of turning operations with multiple performance characteristics. Krishnamoorthy et al. (2012) developed a model for optimization of drilling parameters at the aim of minimizing the damage caused

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during drilling. Thy used grey relational analysis and Fuzzy logic. The input parameters for the proposed model were spindle speed, point angle and feed rate. The authors concluded that the optimization procedure improved quality of drilled holes in carbon fiber reinforced plastic composites. In this work, Taguchi’s L27 orthogonal array and S/N ratio as well as grey relational analysis are applied to achieve multi-objective optimization with regard to the two targets of surface roughness quality and cutting power diminution. The considered work material is assumed to be made of reinforced PEEK with 30% of carbon fibers (PEEK-CF30). It is assumed to be turned by using TiN coated cutting tools under dry conditions. These tools were chosen because of their low price and the objective is to investigate how, in addition to quality and cost effectiveness, the machining operation could perform well by encompassing sustainable production concerns. The control factors that are considered include cutting speed, feed rate and depth of cut. 2. Experimental work The work material used in the present investigation is PEEKCF30 (supplied by ERTAÒ). It consists of cylindrical work pieces having 50 mm diameter and length of 100 mm. The main mechanical and thermal properties of this work material are summarized in Table 1. Dry turning experiments were carried out on a GORATU G CRONO 4S CNC using TiN coated cutting tools (WNMG080408-TF). An SDJCL 2020 K11 tool holder was used. The surface roughness was evaluated according to ISO 4287-1:1997 with a Hommeltester T500 profilometer, Fig. 1. Considering the high number of palpations to be carried out, a programmable technique was used. The cut-off was fixed at 0.8 mm and the roughness evaluator parameters were according to ISO 4287-2:1997. The measured surface roughness amplitude parameter is the arithmetic average height (Ra). For each run, the profilometer probe swept the part according to five contours selected over the machined part. Three component turning forces (radial force eFp, cutting force e Fc and feed force e Fa) were recorded with a Kistler piezoelectric dynamometer model 9121 connected to a load amplifier and data acquisition board, Fig. 2. Inserts were examined periodically in between cutting passes according to the experimental plan and finally at the end of cutting. This was done using Mitutoyo optical microscope. The experiments were conducted according to a full factorial Design of Experiment (DOE) table. This particular case of Taguchi method organizes at best level variations of parameters that are expected to affect the process while taking into account all their possible mutual interactions. Upon performing analysis of variance of the obtained results, this method allows determination of factors which most affect the considered response by using a minimum amount of experiments, thus saving substantial time and resources.

Fig. 1. Hommeltester T500 profilometer used to measure surface roughness.

A Response Surface Model (RSM) which interpolates locally the response function can be derived by using the discrete results. The RSM is often obtained by means of polynomial regression, so the nonlinearities are of a pre-specified type and may not be those inherent to the physical process. This is known to have the following limitation: the output may not be predicted accurately. In our case coefficient of determination for surface roughness as predicted by a quadratic polynomial RSM was less than 90%. The three cutting parameters selected for the present investigation are: cutting speed (v), feed rate (f) and depth of cut (d). Since the considered variables are multi-level variables and their outcome effects are not necessarily linearly related, it has been decided to use three level tests for each factor. The machining parameters used and their levels are given in Table 2. As there are three factors and three levels for each factor, twenty-seven experiments were performed according to the standard L27 Taguchi orthogonal array. This array specifies 27 experimental runs and has 13 columns that can be used to determine relative influence of factors and of their interactions (Appendix). The full factorial L27 orthogonal array is defined by the second, third and fourth columns of Table 3, where the numbers 1, 2 and 3 stand for the levels of the factors as defined in Table 2. Table 3 gives also the measured surface roughness and cutting power as function of the trial combination. The last two columns of Table 3 give the calculated Signal to Noise ratio (S/N) associated respectively to surface roughness and cutting power. It should be mentioned that each run was repeated 4 times and the obtained results have indicated no significant variations of the responses, in terms of cutting power and surface roughness, from one run to the other. This allows us to believe that variations of the responses should only be attributed to those of the cutting parameters. No extra noise that could prejudice the results was detected.

Table 1 Mechanical and thermal properties of PEEK-CF30 composite. Mechanical and thermal properties

PEEK-CF30

Unit

Tensile modulus Rockwell hardness Charpy impact resistance Tensile strength Melting temperature Density Coefficient of thermal expansion at (<150  C) Coefficient of thermal expansion at (>150  C)

7700 M102 35 130 340 1.41 25  106 55  106

MPa e kJ/m2 MPa  C g/cm3 m/m K m/m K

3

Fig. 2. Kistler piezoelectric dynamometer used to measure cutting forces.

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Table 2 Machining parameters and their levels.

Cutting speed (m/min)

Feed rate (mm/rev)

Depth of cut (mm)

Symbol

Level

Code

A1 A2 A3 B1 B2 B3 C1 C2 C3

300 200 100 0.20 0.15 0.05 1.5 0.75 0.25

1 2 3 1 2 3 1 2 3

3. Sustainability in relation with the actual machining experiment Alting and Jøgensen (1993) defined sustainable production as the management of the whole product life cycle starting from designing, production, distribution to the disposal stage. This involves minimizing material and energy resources. In the past, metal cutting operations have been mainly optimized based on economical and technological considerations without taking into account the environmental dimension. As nowadays mature insight about ecological and sustainability problems has grown, machining processes are to be reconsidered by encompassing more deeply this crucial issue. In the field of machining processes, the application of sustainability principles requires some important measures that have to be resolved in order to assess the relative sustainability level that can be achieved. According to Pusavec et al. (2010), machining parameter operational ranges which are implicated by sustainability requirements should not give way to deteriorations affecting quality of machined parts, cutting tool-life and material removal rate as these could lower productivity. Other important stage of sustainable machining is connected with economy in resource utilization and raw materials extraction. It is necessary to improve

the proportion between incoming and outgoing raw materials in the production phase. This means basically reducing wastes and eliminating mechanical and chemical degradation of machined surface. In the actual work, improvement of sustainability is apparent through using economic new high performance coated TiN cutting tools. Comparing the surface roughness obtained by means of TiN coated tools with that obtained using special cutting tools such as Polycrystalline Diamond (PCD) and Chemical Vapour Deposition (CVD) diamond tools (Davim and Mata, 2008) has shown no significant differences. When considering the extension of machining parameters operational ranges to reach process productivity target which is compatible with sustainability principles, one of the most fundamental concerns is the usage of minimal quantity or even the complete omission of cutting fluids. These have in fact a huge negative impact on the environment. According to preliminary tests that have been performed in this work, the reached temperature has produced no significant changes in the surface texture and tool wear, Fig. 3. This was observed even while using, during dry turning of PEEK-CF30, the highest cutting speed and specific cutting pressure ranges. The maximum reached temperature was well below the heat resistance temperature which is for PEEK about 250  C. This experimental evidence proved that, for the specific tested range of parameters, there would be no need to use a coolant. Moreover, using a coolant during machining of PEEK is inadvisable as the risk of its absorption exists at the cost of increased dimensional instability. One of the most important items related to cleaner production consists in reducing energy consumption in order to cut down carbon emissions associated to energy generation. He et al. (2012) have presented a modeling method of task-oriented energy consumption for machining manufacturing system. They showed that a valuable insight of energy consumption in machining manufacturing system can be gained, so as to make robust decisions on the potential for improving energy efficiency.

Table 3 Experimental results for surface roughness, cutting power and their corresponding S/N ratios. Trial no

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

Factors

Measured parameters

Calculated S/N ratio

A

B

C

Surface roughness Ra (mm)

Cutting power (kW)

S/N ratio for surface roughness Ra

S/N ratio for cutting power

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

1.855 1.72 1.03 1.315 1.305 1.13 1.47 1.14 0.985 1.255 1.07 0.945 1.275 1.055 1.095 1.43 1.265 1.2 1.26 0.995 0.89 1.485 1.23 1.065 1.285 1.225 1.14

49.7 43.1 29.5 33.5 35.7 24.4 24.4 23.6 16.5 40.8 37.3 25.9 30.3 27.2 18.5 19.2 17.5 12.5 22.3 20.4 14.0 13.1 13.6 13.1 10.3 9.62 7.30

5.37 4.71 0.26 2.38 2.31 1.06 3.35 1.14 0.13 1.97 0.59 0.49 2.11 0.47 0.79 3.11 2.04 1.58 2.01 0.04 1.01 3.43 1.80 0.55 2.18 1.76 1.14

33.93 32.69 29.40 30.50 31.05 27.75 27.75 27.46 24.35 32.21 31.43 28.27 29.63 28.69 25.34 25.67 24.86 21.94 26.97 26.19 22.92 22.35 22.67 22.35 20.26 19.66 17.27

I. Hanafi et al. / Journal of Cleaner Production 33 (2012) 1e9

5

Fig. 3. Optical microscopy image of cutting tool after the cutting operation.

In machining, the energy consumed for non-cutting operations dominates the total energy consumption (Rajemi and Mativenga, 2008) and (He et al., 2012). Machine selection plays an important role in reducing the energy footprint of a machined product (Liow, 2009). As machine tools are not so cheap to replace because of the extra investment cost that can not be afforded at the present times, improvement of energy efficiency has to be reached firstly at the level of existing machines. This important issue of energy consumption is considered in the following through a multi-objective optimization procedure where minimum surface roughness and cutting power are tracked simultaneously. Grey relational theory and Taguchi method are used for that. 4. Optimization of cutting parameters by using grey relational analysis The grey relational theory provides an efficient management upon the uncertainty that could affect a system having multiple inputs and supporting discrete data. It enables to integrate the system uncertainty in order to perform relation analysis, modeling, decision and control. The grey relational analysis (GRA) is used to find the most significant relations existing in the system in order to assess the alternatives before decision making in a complicated context characterized by multiple attributes. GRA considers simultaneously the overall relational rating regarding each alternative and enables selecting the best alternative as being that one for which the highest degree is attained. The main advantage of GRA technique is that it enables converting the multiple performance characteristics problem into a single performance characteristic and hence simplifies the optimization procedure. This is especially interesting when dealing with optimization problems including multiple objectives, such as those arising in cleaner production where a conflict often exists between production cost and environmental footprint. To perform optimization of process parameters for the machining experiment presented in Section 2 according to Taguchi S/N ratio and GRA, the following five steps are followed. 4.1. Calculation of S/N ratio for the responses

observed data, sy the standard deviation, n the number of observations and log10 the decimal logarithm, the S/N ratio characteristics are given in Table 4 as function of the considered category of quality. Since the responses considered in the present experiment work are surface roughness and cutting power which are both to be minimized, they are assumed having the smaller-the-better characteristics. The S/N ratio of the obtained responses has been calculated and the results were presented in the two last columns of Table 3. 4.2. Normalization of experimental results Depending on the characteristics of a data sequence, there are various methodologies for data pre-processing that are available for GRA (Deng, 1989). In the present experiment, the-lower-the-better is the chosen characteristic and accordingly the original sequence is normalized as follows

x*i ðkÞ


max x0i ðkÞ  x0i ðkÞ i h h i i ¼ max x0i ðkÞ  min x0i ðkÞ i

i

where i ¼ 1,...,n in which n is the number of experiments, k ¼ 1,...,m in which m is the number of experiments data items (observational data), x0i (k) denotes the original sequence, x*i (k) the sequence after the data pre-processing, max½x0i ðkÞ the largest value of x0i (k) and i min½x0i ðkÞ the smallest value of x0i (k). i In the present experiment, 27 tests were performed (n ¼ 27) and two responses are considered (m ¼ 2). 4.3. Calculation of the deviation sequence In GRA, the measure of the relevancy between two systems or two sequences is defined as the Grey relational grade (GRG). Let x0(k) be the ideal sequence for surface roughness or cutting power. The definition of the GRG in GRA emphasizes the relational degree Table 4 The S/N ratio characteristics as function of the considered category of quality. Category of quality

The S/N ratio is an effective representation that enables to find the degree of significance of parameters which intervene in a process through evaluating their variance. Usually, there are three categories of quality characteristics in the analysis of the S/N ratio, i.e. the-lower-the-better, the-higher-the-better, and the-nominalthe-better. Denoting y the observed data, y the average of

(1)

Nominal-is-best Lower-the-better Higher-the-better

S/N ratio characteristics   y S=N ¼ 10log10 sy  X  1 S=N ¼ 10log10 y2 n  X  1 1 S=N ¼ 10log10 n y2

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Table 5 The calculated grey relational coefficient and grey relational grade. Trial no (i)

Grey relational coefficient xi(k)

Factors

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

A

B

C

Surface roughness Ra (mm)

Cutting power (kW)

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

0.333 0.358 0.715 0.485 0.490 0.606 0.423 0.597 0.784 0.517 0.666 0.860 0.506 0.683 0.639 0.436 0.511 0.552 0.514 0.767 1.000 0.418 0.532 0.672 0.500 0.535 0.597

0.333 0.351 0.407 0.386 0.377 0.443 0.443 0.450 0.541 0.358 0.370 0.431 0.403 0.422 0.508 0.498 0.523 0.641 0.462 0.483 0.596 0.621 0.607 0.621 0.736 0.777 1.000

that exists between the sequences xO(k) and xi(k). The grey relational coefficient (GRC) xi(k) can be calculated as follows

xi ðkÞ ¼

Dmin þ zDmax DOi ðkÞ þ zDmax

(2)

where DOi(k) denotes the absolute value of the difference between xO(k) and xi(k), it is also known as the deviation sequence, finally z is the distinguishing coefficient. A value of the z parameter is the smaller and the distinguishing ability is the larger, z ¼ 0.5 is generally used. The deviation sequence DOi(k), Dmin and Dmax are defined as

  DOi ðkÞ ¼ x*O ðkÞ  x*i ðkÞ

Dmin

(3)

i h   ¼ min min x*O ðkÞ  x*j ðkÞ j

(4)

k



h i Dmax ¼ max max x*O ðkÞ  x*j ðkÞ

(5)

k

Rank

0.333 0.354 0.561 0.436 0.433 0.525 0.433 0.524 0.662 0.437 0.518 0.645 0.454 0.552 0.574 0.467 0.517 0.596 0.488 0.625 0.798 0.520 0.569 0.646 0.618 0.656 0.799

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

Using Table 3, Dmin and Dmax can be calculated. One can see that the actual values are Dmin ¼ 0 and Dmax ¼ 1. Calculation of the GRC is performed by using Eq. (2). The obtained results are given in the fifth and sixth columns of Table 5.

4.4. Calculation of the grey relational coefficient After deriving the GRC, it is usual to take the average value of the GRC as to be the GRG. The GRG is defined as follows

gi ¼

n 1 X xi ðkÞ n

The higher value of GRG corresponds to intense relational degree between the reference sequence xO(k) and the given sequence xi(k). The reference sequence xO(k) represents the best process sequence. Therefore, higher GRG means that the corresponding parameter combination is closer to the optimal one. Here, it is assumed that the two response features are equally important, so no different weightings have been assigned to the considered responses.

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1

2

3

4

5

6

7

8

(6)

k¼1

1 0.9

Grey relational grade

j

Grey relational grade gi

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Experiment number Fig. 4. Grey relational grade graph.

I. Hanafi et al. / Journal of Cleaner Production 33 (2012) 1e9 Table 6 Mean table for grey relational grade.

A B C AB AC BC

Level 1

Level 2

Level 3

MaxeMin

Rank

0.47 0.53 0.47 0.51 0.54 0.52

0.53 0.52 0.53 0.56 0.55 0.55

0.64 0.59 0.65 0.57 0.55 0.56

0.16 0.06 0.18 0.06 0.01 0.04

2 4 1 3 6 5

The calculated GRG gi as function of the combination used of cutting parameters is given in the seventh column of Table 5. The last column of Table 5 gives the obtained rank of GRG. The obtained GRG graph as function of the experiment number is presented in Fig. 4. 4.5. Analysis of the experimental results and selection of the optimal levels for process parameters The grey relational coefficients and grey relational grades were calculated and presented in the previous section. From Table 5, it is found that the machining parameters which correspond to experiment number 27, having GRG ¼ 0.799, achieve the highest GRG. Therefore the machining parameters setting for experiment number 27 constitutes, among the 27 considered experiments, the optimal setting of operative conditions that optimize the tracked multiple objectives. However, the relative importance among the achieving parameters for the multiple performance characteristics needs to be further analyzed in the following, so that the optimal combinations of the machining parameter levels can be assessed more clearly. Table 5 indicates the response table for GRG. For analyzing the results, mean (average) analysis is used and the results are presented as the response Table 6. The procedure for deriving the response table consists in grouping GRG by factor levels before proceeding to average them. The maxemin column indicates that

7

depth of cut is the most significant factor among the three input variables. In order to produce the best output, the optimal combination of the parameters as determined from the response table shows that cutting speed, feed rate and depth of cut must be maintained at level 3. Fig. 5 shows the response graph plotted for the calculated grey relational grade. The graph illustrates that all the three input parameters and their interactions have a GRG above 0.45. In this graph, the symbols A1, A2, A3 in the x-axis correspond to the three levels of cutting speed. Similarly the symbols B1, B2, B3 and C1, C2, C3 correspond to the three levels of feed rate and depth of cut respectively. The optimal level of the machining parameters is the level with the greatest GRG value which is here the combination A3B3C3. The optimal parameters for achieving the best surface roughness and power consumption are then: cutting speed at level 3, feed rate at level 3 and depth of cut at level 3. These correspond to the cutting speed of 100 m/min, feed rate of 0.05 mm/rev and depth of cut of 0.25 mm. These values constitute the recommended levels of the controllable parameters during turning operations as they achieve simultaneous minimization of surface roughness and cutting power. They will enable reducing the environmental footprint by minimizing energy consumption associated to cutting operation while enhancing quality as surface roughness of machined parts is optimized. Based on the results shown in Fig. 5, the order of the importance for the controllable factors based on the GRG is depth of cut, then cutting speed. The interaction between the parameters AB (cutting speed * feed rate) has also a significant effect on the GRG. Statistical Pareto ANOVA analysis can also be used to analyze relative effects of cutting parameters. This method enables to evaluate more easily the significance of factors and interactions by applying Pareto type analysis. It allows getting more directly the optimal levels of factors. The habitual criterion to determine the significant factors is generally based upon the derived cumulative contribution percentage of about 90%.

0.65

Grey Relational Grade

0.63 0.61 0.59 0.57 0.55 0.53 0.51 0.49 0.47 0.45 A1

A2

A3

B1

B2

B3

C1

1

2 AB

3

1

2 AC

3

1

C2

C3

0.65 Grey relational Grade

0.63 0.61 0.59 0.57 0.55 0.53 0.51 0.49 0.47 0.45 2 BC

Fig. 5. Main effect on grey relational grade for each factor and factors mutual interactions.

3

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Table 7 Pareto ANOVA analysis for grey relational grade. Sum at factor level

1 2 3 Sum of square difference Contribution ratio (%)

Factor and interaction A

B

AB

AB

C

AC

AC

BC

BC

4.26 4.76 5.72 3.29 36.141

4.76 4.71 5.27 0.58 6.392

4.56 5.07 5.11 0.56 6.089

4.90 4.89 4.95 0.01 0.086

4.19 4.75 5.81 4.06 44.536

4.87 4.91 4.96 0.01 0.145

5.03 4.75 5.08 0.19 2.033

4.70 4.96 5.08 0.22 2.453

4.87 4.76 5.11 0.19 2.124

99.76

99.91

100

44.54 36.14

6.39

C

Cumulative contribution

44.54

A

80.68

B

6.09

AB

87.07

The Pareto ANOVA analysis has been conducted on the set of data given in Table 5. Matlab software package has been used to develop the statistical analysis script which enabled performing that. This environment offers many preprogrammed commands that facilitate the development task. By using the script, effects on grey relational grade resulting from the individual parameters: cutting speed, feed rate and depth of cut as well as their interactions have been determined. Table 7 gives the Pareto ANOVA optimization results for GRG. The optimal combination is found to be A3B3C3. From Table 7, it is clear that depth of cut, for which P ¼ 44.54%, is the main parameter that significantly affects the mean average of surface roughness. Among the interactions between factors only AB is found to have some effect. The optimal solution A3B3C3 found in this work is only an approximation of the real optimal solution. To increase accuracy, new experimental results are needed in the vicinity of the actual optimum. One could notice however that the higher bounds of machining parameters are already reached by the solution A3B3C3, as increasing them more would be accompanied by undesirable temperature effects. This indicates that the obtained solution constitutes the best feasible target optimum. 5. Conclusions Sustainability of CNC machining process related to turning of reinforced PEEK with 30% of carbon fibers was considered in this work. The PEEK-based material was machined by using low cost coated TiN cutting tools under dry conditions. Two target performances were tracked simultaneously. These include surface quality of machined parts expressed in terms of arithmetic roughness and cutting power consumption during machining. A campaign of experiments was conducted according to a full factorial design of experiment. The ranges of machining parameters were selected such that no coolant was needed as the reached temperature during cutting was kept sufficiently low. The coated TiN cutting tool was found to yield a huge process gain from the sustainability point of view. It enabled producing parts having excellent surface finish, while achieving low cost of production and reduced impact on environment. The optimal cutting parameters setting that enables to achieve simultaneous minimization of surface roughness and cutting power was determined by using grey relational theory and Taguchi

2.45

2.12

2.03

BC

BC

AC

93.16

0.15

0.09

AC

95.61

AB

97.73

optimization method. It was identified that cutting speed of 100 m/ min, feed rate of 0.05 mm/rev and depth of cut of 0.25 mm are the optimal combination of cutting parameters. Pareto analysis of variance statistics conducted on the obtained results revealed that depth of cut is the most influencing parameter. It is followed by cutting speed and feed rate. A further general conclusion can be drawn. It is related to the particular nature of sustainability problems where a conflict exists often between quality, cost and environmental footprint. This study emphasized the fact that multiple criteria needed within the context of cleaner production could be tackled straightforwardly by using greyeTaguchi method. Appendix

L27 (313) Orthogonal array with parameters and interactions assigned. Trial no

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

Factors A

B

A*B

A*B

C

A*C

A*C

B*C

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

1 1 1 2 2 2 3 3 3 2 2 2 3 3 3 1 1 1 3 3 3 1 1 1 2 2 2

1 1 1 2 2 2 3 3 3 3 3 3 1 1 1 2 2 2 2 2 2 3 3 3 1 1 1

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 2 3 1 2 3 1 2 3 2 3 1 2 3 1 2 3 1 3 1 2 3 1 2 3 1 2

1 2 3 1 2 3 1 2 3 3 1 2 3 1 2 3 1 2 2 3 1 2 3 1 2 3 1

1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2

B*C 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2 1 2 3 3 1 2 1 2 3 2 3 1

1 2 3 2 3 1 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1 3 1 2 1 2 3

1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1

1 2 3 3 1 2 2 3 1 2 3 1 1 2 3 3 1 2 3 1 2 2 3 1 1 2 3

1 2 3 3 1 2 2 3 1 3 1 2 2 3 1 1 2 3 2 3 1 1 2 3 3 1 2

I. Hanafi et al. / Journal of Cleaner Production 33 (2012) 1e9

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