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Open Access Peer-reviewed

Grey Relational Analysis of Chemical Assisted USM of Polycarbonate Bullet Proof (UL-752) & Acrylic Heat Resistant (BS-476) Glass

Kanwal Jeet Singh , Inderpreet Singh Ahuja, Jatinder Kapoor
American Journal of Mechanical Engineering. 2017, 5(3), 94-109. DOI: 10.12691/ajme-5-3-5
Published online: May 16, 2017

Abstract

This paper is developed an innovative process of chemical assisted ultrasonic machining of polycarbonate bullet proof UL-752 and acrylic heat resistant BG-476 glass and conduct an investigation for optimize the machining parameters associated with multiple performance characteristics using Grey relational analysis. Machining of polycarbonate bullet proof UL-752 and acrylic heat resistant BS-476 glass are difficult process via conventional machining, however, it can be easily machined by Ultrasonic machining. Carefully selected parameters gives the optimum results. In this experimental work input parameters abrasive slurry concentration, type of abrasive, power rate, grit size of abrasive particles, hydro-fluoride acid concentration and tool material are selected. The effect of input parameters viz material removal rate, tool wear rate and surface roughness are investigate. Grey relational analysis and analysis of variance are performed to optimize the input parameters and better output results. In PBPG UL-752, increment in material removal rate by 75.58%, tool wear rate by 45.34% and surface roughness by 34.18%. In other hand, in AHRG BS-476, increment in material removal rate by 61.24%, tool wear rate by 31.46% and surface roughness by 23.85%. The surface topography is investigate through SEM images. It also observed that HF acid have the significant role in surface roughness. It also reduce the micro cracks on machined zone. Harder tool and harder abrasive slutty gives the higher material removal rate, but it also enhance the tool wear rate.

1. Introduction

Ultrasonic machining (USM) is generally preferred for amorphous, hard and brittle materials. Glass, ceramics, titanium, titanium alloys and many more materials are easily machined with USM 1, 2, 3. If the hardness of material is more than 40 HRC then USM is effectively performed. Micro holes as small as 76 µm diameter can be easily drilled by USM. The ratio of depth to diameter is limited to 3:1. During USM neither chemical nor thermal changes occur 5. Moreover, no any metallurgical variations arise on work surface 4. The history of USM started since 1927 with a research paper reported by A.L. Loomis and R.W. Wood 8. American engineer Lewis Balamuth in 1945 was granted first patent 9. USM process have been classified as ultrasonic drilling, ultrasonic cutting, ultrasonic abrasive and ultrasonic dimensional machining 10, 30. In early 1950’s ultrasonic grinding was modified into ultrasonic impact machining. It was significant machining process and capable to machine toughest materials 11.

In USM process, power supply have an important role. It convert low frequency electrical signals into high frequency electrical signals 11, 12. After that these signals are transmitted to transducer. Two type of transducer are generally used, magnetostatic and piezoelectric transducer 14, 31. The transducer converts high frequency electrical signals into mechanical vibrations. Through horn, the effect of vibrations are amplified and concentrate on tool assembly. USM tool vibrates along its longitudinal axis with ultrasonic frequency between 20 kHz to 40 kHz 3, 14, 15. The amplitude of vibrations are measured in few hundredth of millimeters along longitudinal axis of tool. Horn and tool must be designed with mass and shape considerations, so that the resonance effect can be achieved with in the frequency range 15, 16. Power rating of USM varies in between 50 W to 4000 W. Along longitudinal axis controlled static load is applied on the USM tool. Mixture of abrasive and carrier medium is known as slurry. The concentration of slurry varies from 30% to 50% by volume 17, 29. It is pumped in between the gap of tool and work surface. The optimum pumping speed of slurry is 30 litter/ min 15, 18. Water is commonly used as a carrier medium 13, 15, 18, 19. Because, it have low viscosity, low density, high thermal conductivity and high specific heat characteristics 5, 18, 20, 28. Most preferable abrasives are boron carbide, alumina, silicon carbide and diamond dust 21. Abrasive particles are forced by tool vibrations to strike on work surface. The impact of abrasive particles supports to erode the material in the form of micro-chips 22, 27. The shape of cavity is similar to the USM tool 23. Volumetric material removal rate of USM is relatively low. Figure 1 shows the schematic diagram of simple USM 24. The new approach of USM is CNC controlled path rotary USM 1, 19, 20, 26. SONEX300 extrude horn made in France, EROSONIC US400 and EROSONIC US800 manufactured by Sonic Mill and made in USA are commonly used on commercial level 25.

The USM machine is available in small tabletop sized units. The different type of accessories are used for suitability of operation. Appropriate type of USM is determined through capacity of power rating 2, 7, 10, 32, 33. Figure 2 shows the 500 W USM machine manufactured by Sonic Mill and made in USA, which is used for small operation 7, 12, 34. The horn transfer mechanical vibration energy from transducer to tool. It also amplify the mechanical vibration effect. The horn material must have high fatigue, toughness and elastic properties 1, 13, 35, 36, 37, 38, 39, 40. Generally preferred material for horn are silver steel, monal and tungsten carbide 2, 17, 41, 42, 43, 44, 45, 46. Copper washer is introduced in between the transducer-horn and horn-tool fastening, to prevent unnecessary ultrasonic welding 8, 12, 47, 48. Figure 3 Shows the cause and effect chat of USM and CUSM for glass material.

2. Mechanism of Material Removal in USM & CUSM

2.1. Traditional Ultrasonic Machining

Material removal primarily occurs due to the indentation of the hard abrasive grits on the brittle work material. As the tool vibrates, it leads to indentation of the abrasive grits 16, 49. During indentation, due to Hertzian contact stresses, cracks would develop just below the contact site, then as indentation progresses the cracks would propagate due to increase in stress and ultimately lead to brittle fracture of the work material under each individual interaction site between the abrasive grits and the work-piece 50, 51. The tool material should be such that indentation by the abrasive grits does not lead to brittle failure. Thus the tools are made of tough, strong and ductile materials like steel, stainless steel and other ductile metallic alloys 52.

Other than this brittle failure of the work material due to indentation some material removal may occur due to free flowing impact of the abrasives against the work material and related solid-solid impact erosion, but it is estimated to be rather insignificant 53, 54, 55, 56, 57, 58, 59, 60. Thus, in the current model, material removal would be assumed to take place only due to impact of abrasives between tool and workpiece, followed by indentation and brittle fracture of the workpiece. The model does consider the deformation of the tool.

In the current model, all the abrasives are considered to be identical in shape and size 61, 62. An abrasive particle is considered to be spherical but with local spherical bulges as shown in Figure 4. The abrasive particles are characterized by the average grit diameter 63, 64, 65. It is further assumed that the local spherical bulges have a uniform diameter, and which is related to the grit diameter by . Thus an abrasive is characterized by and .

During indentation by the abrasive grit onto the work-piece and the tool, the local spherical bulges contact the surfaces and the indentation process is characterized by rather than by 66, 67. Figure 5 shows the interaction between the abrasive grit and the work-piece and tool.

As the indentation proceeds, the contact zone between the abrasive grit and work-piece is established and the same grows 68, 69, 70. The contact zone is circular in nature and is characterized by its diameter ‘2x’. At full indentation, the indentation depth in the work material is characterized by . Due to the indentation, as the work material is brittle, brittle fracture takes place leading to hemi-spherical fracture of diameter ‘2x’ under the contact zone. Therefore material removal per abrasive grit is given as.

As the indentation proceeds, the contact zone between the abrasive grit and work-piece is established and the same grows 71, 72. The contact zone is circular in nature and is characterized by its diameter ‘2x’. At full indentation, the indentation depth in the work material is characterized by . Due to the indentation, as the work material is brittle, brittle fracture takes place leading to hemi-spherical fracture of diameter ‘2x’ under the contact zone. Therefore material removal per abrasive grit is given as equation 1 and 2

(1)

Now From Figure 4 AB2= AC2+BC2

(2)

If at any moment of time, there are an average ‘n’ of grits and the tool is vibrating at a frequency ‘f’ then material removal rate can be expressed as equation 3

(3)

Now as the tool and workpiece would be pressing against each other, contact being established via the abrasive grit, both of them would deform or wear out. As the tool vibrates, for some time, it vibrates freely; then it comes in contact with the abrasive, which is already in contact with the job, and then the indentation process starts and finally completes with an indentation of and on the work and tool respectively. Figure 6 schematically depicts the same assuming the work to be rigid for easy depiction. The tool vibrates in a harmonic motion 73. Thus only during its first quarter of its cycle it can derive an abrasive towards interaction with the tool and work-piece as shown in Figure 7 Out of this quarter cycle, some part is used to engage the tool with abrasive particle as shown in Figure 6. 74 Thus the time of indentation τ can be roughly estimated as

Now during machining, the impulse of force on the tool and work would be balanced. Thus total impulse on the tool can be expressed as equation 4

(4)

Where Fmax is the maximum indentation force per abrasive. Now in the USM, the tool is fed with an average force F

(5)

Again, if the flow strength of work material is taken as , then force is equation 6

(6)

If ‘A’ is total surface area of the tool facing the work-piece, then volume of abrasive slurry of one grit thickness is

If n is the number of grits then the total volume of n grits is

Thus the concentration of abrasive grits in the slurry is related as follows shown in equation 7.

(7)

Now it is expected that indentation would be inversely proportional to the flow strength then,

Again combining, ‘F’ can be written as equation 8

(8)
(9)
(10)

Mechanism of material removal or erosion is investigated by various researcher 8, 9, 38, 74, 75. Figure 8 shows basic mechanism of material removal process in USM. For brittle and hard material, fracture effect produced the erosion. Similarly, shearing effect is utilized for ductile materials. Erosion effect is produced through bombardment of abrasive particles directly against the work surface 76, 78, 79. Appropriate flow of slurry will enhanced the material removal rate 80. Cavitation effect is also formed by slurry, which reduce the material removal rate. Material is removed in the form of micro-chips and flush out with slurry 20, 22, 46, 65.

2.2. Chemical-assisted Ultrasonic Machining

In chemical-assisted ultrasonic machining (CUSM), low concentration of hydrofluoric acid (HF) is used in slurry. HF acid reaction between Si and F- ions and O and H+ occur simultaneously. When HF acid react with glass then the bonding forces between Si molecules on the surface area become weakened. This mechanism improve the efficiency of USM 7, 11, 12, 13, 14, 21. Figure 9 shows Basic mechanism of material removal process in chemical.

3. Type of Glass and Application

Glass is non-crystalline or amorphous material. It is transparent by appearance and used in many appliances such as window panels, optoelectronics, technological equipment, optical etc. 65, 66, 67. The main ingredient of glass is silica (sand) 68. Many silica based glasses are exist such as container glass and ordinary glass, which are manufactured by specific type of soda-lime glass. The main composition of soda-lime glass is approximately 75% SiO2 (silicon dioxide), Na2CO3 (sodium carbonate), Na2O (Sodium oxide), CaO (calcium oxide or lime) and some minor other additives 67, 68, 69. Window panels are generally manufactured by silicate glasses. Glass reflect as well as transfer light from own self. These reflection and transformation quality is utilized to make prisms, optical fiber, lenses and fine glasses. Optical fiber are used for high speed data transmission. The color of the glass is changed by adding some ingredient like metallic salt, zinc etc. 68, 70, 71. These type of colored glass is used in manufacturing the art object, stained glass window, color glass window and many more applications. Glass is easily formed or molded into any required shape, so it is traditionally used in the manufacturing of bowls, jars, vases, bottles and drinking jars. The most hard and solid form of simple silicate glass used for marbles, beads and paperweights 71, 72, 74, 75. The other modern example of the glass is glass fiber, the glass is extrude under the high temperature and converted into the fiber glass or glass wool. Glass fiber have property of data transferred at the speed of light. Glass wool is used as the thermal insulating material 73, 76, 77, 78, 79. The other application of glass fiber, it is used as the reinforcement material in the composite material fiber glass. Many thermoplastic and porcelains material are the closer familiar to the glasses. The addition of these closer familiar material improve the properties of the silicate glass. Acrylic glass, polyethylene, terephthalate and polycarbonate these are the lighter alternative of the simple silicate glasses 67, 71, 77, 79.

3.1. Polycarbonate Bullet Proof & Acrylic Heat Resistant Glass

Polycarbonate bullet proof glass, acrylic heat resistant glass and glass-clad polycarbonate bullet proof glass are the advanced types of glass 78. These glasses are manufactured by affixing two different materials through epoxy resin liquid 59. In polycarbonate bullet proof glass, layers of glass and polycarbonate materials are affixed with each other. Number of layers are defined according to the bare load [85]. Elastic effect is produced by polycarbonate material during impact load 78. Thickness of polycarbonate bullet proof glass is varies from 10 mm to 76 mm 75. Similarly in acrylic heat resistant glass, layers of glass and acrylic material are affixed with each other through epoxy resin 56. Acrylic glass is also known as poly (methyl methacrylate) or PMMA material. It is a transparent thermoplastic often used in sheet form as a lightweight, shatter resistant and heat resistant material 71. Acrylic (PMMA) material of 3 mm thick sheet can transmits up to 92% of visible light. It have poor thermal conductivity 72, 76. Thickness of acrylic heat resistant glass is varies from 85 mm to 150 mm. Table 1 shows thickness and density of advanced glass material 45.

Glass have poor elasticity property, mean it can’t deformed when force applied on it 72. Figure 10 shows plain glass under impacted by bullet 71. In which impact load of bullet break the plain glass. Figure 11 shows polycarbonate bullet proof glass. First layer of glass may shatter when the bullet hits it, however the next layer of polycarbonate is more elastic so it moves when the bullet strike. Impact energy of bullet is dissipates vertically 56, 69. This takes the energy away from the bullet and it slowing down. If the enough energy is taken from the bullet, it will eventually stop it from passing through 69. Some important properties of these material make them so famous and the utility of these materials are increased in present era 69, 70, 71. Table 2 shows some important properties of polycarbonate bullet proof glass and acrylic heat resistant glass 54, 55, 76. Figure 12 shows the acrylic heat resistant glass.

4. Experimentation and Data Collection

The experiments were performed on 500 W USM machine manufactured by Sonic mill and made in USA, which is used for small operation. The different components of USM machine and enlarged view of cutting zone are shown in Figure 13. Input parameters and fixed parameters for investigation are shown in Table 4. MRR was calculated by measuring the weight loss after drilling throughout the work material. Similarly, TWR computed by weight loss during each experiment. Time duration of experiment was record by stop watch.

(11)
(12)

Surface roughness is measured is Ra, it is the universally recognised and most used international parameter of roughness. It is the arithmetic mean of the absolute departure of the roughness profile from the mean line.

After machining the MRR and TWR are calculated and SR is checked, machining data is shown in Table 5. In which MMR and TWR is calculated in mm3/min and surface roughness in Ra.

5. Grey Relational Analysis

In grey relation analysis, data pre-processing is necessary to sequence scatter range. In data pre-processing original sequence is transferred into comparable sequence. The experiment results are normalized in the range 0 and 1. Depending on output parameters, data pre-processing methodologies are adopted 35, 39. For example, MRR is the governing output parameter in USM, which decided the machinability of work material under deliberation. “Larger-the-better” characteristics was used for MRR to normalize the original sequence by equation 13.

(13)

Where, is the sequence after the data processing, is the comparability sequence, N=1 and N=4 for MRR; r= 1,2,3………27 for experiment number 1 to 27.

TWR and SR are the important measure of USM, these output parameters are represent the machining accuracy under selected input parameters 32, 38. To get the optimum performance the “Smaller-the-better” characteristic has been preferred to normalize the original sequence date by equation 14.

(14)

Where, is the sequence after the data processing, is the comparability sequence, N=2, N=5 for TWR and N=3, N=6 for SR; i= 1,2,3………27 for experiment number 1 to 27. is the value after grey relational generation, Min and Max are the smallest and largest value of . After normalized MRR, TWR and SR of PBPG UL-752 and AHRG BS-476 comparable sequence is shown in the Table 6.

Now is the deviation sequence between reference sequence and the comparability sequence (Ahmad et al. 2016) 81. Deviation sequence is calculate and maximum and minimum difference is found, N=1, 2 and 3 and r= 1, 2, 3…27 by equation 15

(15)

The deviation sequence table is shown in the Table 6, Maximum () and Minimum are obtained and shown below.

After per-processing data, the next step in calculate the Grey relational coefficient and Grey relation grade with the pre-processed data 38, 47. It define the relationship between ideal and actual normalized results. Grey relational coefficient can be expressed by equation 16 82.

(16)

Where, is the deviation sequence of the reference sequence and the comparability sequence, is distinguishing or identification coefficient. In this calculation , because all parameters are given equal preference 42, 59. The Grey relation coefficient for each experiment of the L27 orthogonal array is calculated and shown in Table 8.

After obtaining the Grey relation coefficient, the Grey relation grade is obtained by averaging the Grey relation coefficient corresponding to each performance characteristic and represent by , , , , and by equation 17, the general formula of Grey relation grade and for three output parameters, shown in Table 8.

(17)

The higher value of Grey relation grade is represent that the corresponding experiment result is much closer to the ideally normalized value. Experiment number 25 get the best multiple performance characteristics among the 27 experiment because it have the highest value of grey relation grade. Now the experimental design is orthogonal, it is possible to separate out the effect of each parameters on the basis of Grey relation grade. Mean of Grey relation grade is calculated for level 1, 2 and 3 by averaging the Grey relation grade of the experiment 1to 9, 10 to18 and 19 to 27 are shown in Table 9. The mean of Grey relation grade for abrasive, power rate, grit size, HF acid and tool material are calculated in same manner. The total mean of Grey relation grade for 27 experiment is also shown in the Table 9. Level for optimum grey relational grade. Optimum level parameters are find out from response table and shown in the Figure 14. Larger value of Grey relation grade is closer to the ideal value. Therefore, the optimum parameters setting for higher MRR and lower TWR and SR are A3B3C2D3E1F3.

Furthermore, analysis of variance (ANOVA) is performed on Grey relation grade to achieve contribution of each input parameter affecting the output parameters. ANOVA for Grey relational grade is shown in Table 10. In addition, F-test is also used to find out the percentage contribution of each parameters. From Table 10 it is clear that material of tool have the significant role in the machining which have 30% contribution, 25% contribution of concentration, 21% contribution of grit size, 9% contribution of abrasive, 4% contribution of HF acid and 3% contribution of power rate in the machining of PBPG UL-752 and AHRG BS-476.

After getting the optimum parameters, the experiment was performed by GRA input setting (A3B3C2D3E1F3). Figure 15 show the Scanning electron microscope (SEM) images of PBPG UL-752 machining setting A1B1C1D1E1F1, In which, some crack are also found on the work surface. In other hand in Figure 16 the USM machining of PBPG UL-752 is performed by optimum parameters which are found by Grey relational analysis A3B3C2D3E1F3, there is smoother and crack free surface. Similarly, in Figure 17 show the Scanning electron microscope (SEM) images of AHRG BS-476 machining setting A1B1C1D1E1F1, in which machining by USM is performed and some crack are also found on the work surface. In other hand in Figure 18 the USM machining AHRG BS-476 is performed by optimum parameters which are found by Grey relational analysis A3B3C2D3E1F3, there is smoother and crack free surface.

MRR and TWR are also compared between optimum Grey relational analysis A3B3C2D3E1F3 and A1B1C1D1E1F1 input parameters. It observed that optimum parameters (A3B3C3D3E1F3) gives 73.02% improved MRR with comparison of A1B1C1D1E1F1 USM experiment setting. TWR is decreased by 37.25%. It is evident from SEM image, optimum parameters setting also give the better surface roughness which is 43.33% improved. Fig 19 shown the percentage contribution of optimum Grey relational analysis parameters.

Confirmation test is carried out to verify the improvement of performance characteristics in machining of PBPG UL-752 and AHRG BS-476 by USM. The optimum parameters are shown in the Table 11. The estimated Grey relational grade using the optimal level of machining parameters can be calculated by equation 18 15, 19, 43, 59

(18)

Where, is the total mean of Grey relational grade, is mean of the Grey relational grade at optimum level and n is the number of parameters that significantly affect multiple-performance characteristics. It is clearly show that the multiple-performance characteristics in USM process is greatly improved through this study.

6. Conclusion

The optimum machining parameters are identify by Grey relational grade for multiple performance characteristics that is MRR, TWR and SR. This experimental research paper presented the multi-objective optimization of USM machining parameters of polycarbonate bullet proof UL-752 and acrylic heat resistant BS-476 glass for drilling application by Grey relational analysis method. Following conclusion are conclude from the experimentation analysis.

1. It conclude that, harder material like HSS gives the better material removal rate and lower tool wear rate. In USM, concentration of abrasive slurry, concentration and grit size of abrasive play the significant role for optimum output performance parameters.

2. Harder abrasive particles increase the material removal rate, but it also enhanced the tool rear rate.

3. HF acid have the significant role play in the material removal rate as well as surface roughness. High concentration of HF acid will damage the work piece as well USM apparatus. It is also harm full for the operator.

4. Through mathematically modelling it conclude that shape of abrasive particles have the major role in material removal and tool wear rate. MRR affect is further encourage by the concentration of slurry.

5. Higher concentration of abrasive slurry gives higher material removal rate, but it decrease the flow rate of slurry.

6. ANOVA of Grey relational grade for multiple performance characteristics reveals that the concentration have the significant role in the MRR.

7. Based on SEM images, it is evident that optimum parameter improve the surface roughness and give better smooth surface and it also reduce the cracks formation.

8. PBPG UL-752 have improvement in MRR, TWR and SR is 75.58%, 45.34% and 34.18% respectively, based on confirmation test.

9. AHRG BS-476 have improvement in MRR, TWR and SR is 61.24%, 31.46% and 23.85% respectively, based on confirmation test.

10. It prove that, the performance characteristic of USM process like MRR, TWR and SR are improved together by using the Grey relational study and the effectiveness of this method is effectively recognised by authentication experiment.

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[40]  Singh, K.; Ahuja, I.P.S. (2014). Ultrasonic machining processes- review paper, International Journal for multi-disciplinary Engineering and Business Management, 2 (3) 57-66.
In article      View Article
 
[41]  Singh, K.; Kumar, V. (2014). A Study on the Tool Geometry and Stresses Induced in Tool in Ultrasonic Machining Process Applied for the Tough and Brittle Materials, International Journal for multi-disciplinary Engineering and Business Management, 2014, 2 (3), 67-71.
In article      View Article
 
[42]  Singh, K.; Kumar, V.S. (2014) “Finite Element Analysis of Ultrasonic Machine Tool”, International journal of engineering research and technology, 3 (7), 1647-1650.
In article      
 
[43]  Singh, K., Ahuja, I.P.S.; Kapoor. J. Study the effect of abrasive and hydrofluoric acid in ultrasonic machining of plain glass material, In proceeding, National Conference Latest Development in Materials, Manufacturing and Quality Control, 19th-20th February, 2015, GZSCCET, BTI, India.
In article      
 
[44]  Singh, K., Ahuja, I.P.S. and Kapoor. J. Ultrasonic machining of glass brittle material, In proceeding, National Conference Latest Development in Materials, Manufacturing and Quality Control, 19th-20th February, 2015, GZSCCET, BTI, India.
In article      
 
[45]  Singh, K., Ahuja, I.P.S. and Kapoor. J. Comparative study between conventional machining, chemical ultrasonic machining (CUSM) and ultrasonic machining (USM) of plain glass, polycarbonate, acrylic, bullet proof and heat resistant glass, In proceeding, International conference in latest development in materials, manufacturing and quality control, 12th -13th Feb-2016, GZSCCET BTI India.
In article      
 
[46]  Adithan, M. Tool wear characteristics in ultrasonic drilling, Tribology International, 1981, 14 (6), 351-356.
In article      View Article
 
[47]  Goetze, D. (1956). Effect of vibration amplitude, frequency and composition of the abrasive slurry on the rate of ultrasonic machining in Ketos tool steel, Journal of acoustical society of America, 28 (6), 1033-1037.
In article      View Article
 
[48]  Adithan, M. (1983). Abrasive wear in ultrasonic drilling, Tribology International, 16 (5), 253-255.
In article      View Article
 
[49]  Adithan, M.; Venkatesh, V.C. (1974). Parameter influence on tool wear in ultra-sonic drilling, Tribolology International, 7 (6), 260-264.
In article      View Article
 
[50]  Babitsky, V.I.; Astashev, V.K. (2007) Ultrasonic processes and machine, Springer Berlin Heidelberg New York.
In article      
 
[51]  Jain, N.K.; Jain, V.K. (2001) Modeling of material removal in mechanical type advanced machining processes: a state of art review, International journal of machine tools & manufacture, 41 (11) 1573-1635.
In article      View Article
 
[52]  Jain, V., Sharma, A.K.; Kumar, P. (2012). Investigations on tool wear in micro Ultrasonic machining, Applied Mechanics and Material, Tranc Tech Publication Switzerland, pp.1561-1566.
In article      View Article
 
[53]  I. Kaczmarek, (1976). Impact Grinding (Ultrasonic machining)--Book Chapter: 21 Principles of Machining by Cutting Abrasion and Erosion, Peter Peregrinus Ltd, Stevenage, pp. 448-462.
In article      
 
[54]  Dharmadhikari, S.W.; Sharma, C.S. (1985). Optimization of abrasive life in Ultrasonic Machining, Journal of Manufacturing Science & Engineering, 107 (4), 361-364.
In article      View Article
 
[55]  Bekrenev, N.V.; Muldasheve, G.K.; Petrovskii, A.P. Tsvetkova, O.A. (2015). Influence of the thermal effect on the cutting forces in the ultrasonic machining of high strength material, Russian Engineering Research, 35 (10) pp. 758-759.
In article      View Article
 
[56]  Chang, S.; Bone, G.M. (2005). Burr size reduction in drilling by ultrasonic assistance. Robotics and Computer-Integrated Manufacturing, 120, 442-450.
In article      View Article
 
[57]  Fan, W.H.; Chao, C.L.; Chou, W.C.; Chen, T.T; Chao, C.W. (2009). Study on the Surface Integrity of Micro-Ultrasonic Machined Glass-Ceramic Material, Key Engineering Materials, 407-408, 731-734.
In article      View Article
 
[58]  Kumar, J.; Khamba, J.S. (2009) An Investigation into the effect of work material properties, tool geometry and abrasive properties on performance indices of ultrasonic machining. International Journal of Machining and Machinability of Materials, 5(2/3): 347-365.
In article      View Article
 
[59]  Schorderet,A. ; Deghilage, E.; Agbeviade, K. (2013) tool type and hole diameters influence in deep ultrasonic drilling of micro holes in glass, Procedia CIRP, 565-570.
In article      View Article
 
[60]  Scholze, H. (1991) Glass – Nature, Structure, and Properties. Springer, Verlag, New York.
In article      
 
[61]  Phillips, J.C. (1979). Topology of covalent non-crystalline solids I: Short-range order in chalcogenide alloys, Journal of Non-Crystalline Solids 34 (2), pp.153-181.
In article      View Article
 
[62]  Folmer , J. C. W., Franzen, S. (2003) Study of polymer glasses by modulated differential scanning calorimetry in the undergraduate physical chemistry laboratory, Journal of Chemical Education , 80 (7), 813-818.
In article      View Article
 
[63]  Phillips D.C, Sambell R.A.J, Bowen D.H. (1972) The mechanical properties of carbon fiber reinforced pyrex glass, Journal of Material Science, 7 (12), 1454-1464.
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[64]  Dr Karl's Homework: Glass Flows Australia: ABC. 26 January 2000. Retrieved 24 October 2009.
In article      
 
[65]  Vogel, W.; Kreidl, N. (1985) Chemistry of Glass, Wiley.
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[66]  Stookey, S.D.; Beall, G.H. (2000). Explorations in Glass: An Autobiography, Wiley.
In article      PubMed
 
[67]  Noel C. (1998). Stokes, The Glass and Glazing Handbook, Standards Association of Australia.
In article      
 
[68]  Kuo, K.L. (2007). Experimental investigation of brittle material milling using rotary ultrasonic machining. Proceedings of the 35th International MATADOR Conference, Springer: London, 195-198.
In article      View Article
 
[69]  Hasani, H., Tabatabaei., S.A. & Amiri, G. (2012). Grey relational analysis to determine the optimum process parameters for open end sprnning yarns. Journal of engineering fibers and fabrics. 7 (2), 81-86.
In article      View Article
 
[70]  Hasiao, Y.F., Tarng, Y.S. & Huang, W.J. (2008) Optimization of plasma are welding parameters by using the Taguchi method with the Grey relational analysis. Materials and manufacturing processes, 23, 51-58.
In article      View Article
 
[71]  Lin, C.L., Lin, J.L. & Ko, T.C. (2002) Optimisation of EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. International journal of advanced manufacturing technology, 19, 271-277.
In article      View Article
 
[72]  You, M.L., Shu, C.M., Chen, W.T., Shyu, M.L. (2017) Analysis of cardinal grey relational grade and grey entropy on achievement of air pollution reduction by evaluating air quality trend in Japan. Journal of cleaner production, 142 (4), 3883-3889.
In article      View Article
 
[73]  Patil, P.J., Patil, C.R. (2016) Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade. Perspective in Science, 8, 367-369.
In article      View Article
 
[74]  Ahmad, N., Kamal, S., Raza, Z.A. Hussain, T. (2016). Multi-response optimization in the development of oleo-hydrophobic cotton fabric using Taguchi based grey relational analysis. Applied surface science, 367, 370-381.
In article      View Article
 
[75]  Lin, Y.H., Lee, P.C. & Chang, T.P. (2009) Practical expert diagnosis modal based on the grey relational analysis technique. Expert system with applications, 36, 1523-1528.
In article      View Article
 
[76]  Lin, H.L. (2012) The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process. Journal of intelligent manufacturing, 23(5), 1671-1680.
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[77]  Manivannan, S., Prasanna, S. & Aramugam, R. (2011) Multi-objective optimization of flat plate heat sink using Taguchi based grey relational analysis. International journal of advanced manufacturing technology, 52, 739-749.
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[78]  Meena, V.K., Azad, M.S. (2012). Grey relational analysis of micro EDM machining of Ti-6Al-4V alloy. Material and manufacturing processes 2012, 27, 973-977.
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[79]  Singh, P.N., Raghukandan, K., Pai, B.C. (2004) Optimization by grey relational analysis of EDM parameters on machining Al-10%SiCp composites. Journal of material processing technology, 155-156, 1658-1661.
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[80]  Sreenivasulu, R., Srinivasarao, C. (2012). Application of Grey relational analysis for surface roughness and roundness error in drilling of AL 6061 alloy. International journal of lean thinking, 3(2), 68-78.
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[81]  Köklü, U. (2013). Optimisation of machining parameters in interrupted cylindrical grinding using the Grey-based Taguchi method. International Journal of Computer Integrated Manufacturing, 26(8), 696-702.
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[82]  Karabatak Mustafa, Kara Fuat (2016). Experimental optimization of surface roughness in hard turning of AISI D2 cold work tool steel. Journal of Polytechnic, 19(3), 349-355.
In article      
 

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Cite this article:

Normal Style
Kanwal Jeet Singh, Inderpreet Singh Ahuja, Jatinder Kapoor. Grey Relational Analysis of Chemical Assisted USM of Polycarbonate Bullet Proof (UL-752) & Acrylic Heat Resistant (BS-476) Glass. American Journal of Mechanical Engineering. Vol. 5, No. 3, 2017, pp 94-109. http://pubs.sciepub.com/ajme/5/3/5
MLA Style
Singh, Kanwal Jeet, Inderpreet Singh Ahuja, and Jatinder Kapoor. "Grey Relational Analysis of Chemical Assisted USM of Polycarbonate Bullet Proof (UL-752) & Acrylic Heat Resistant (BS-476) Glass." American Journal of Mechanical Engineering 5.3 (2017): 94-109.
APA Style
Singh, K. J. , Ahuja, I. S. , & Kapoor, J. (2017). Grey Relational Analysis of Chemical Assisted USM of Polycarbonate Bullet Proof (UL-752) & Acrylic Heat Resistant (BS-476) Glass. American Journal of Mechanical Engineering, 5(3), 94-109.
Chicago Style
Singh, Kanwal Jeet, Inderpreet Singh Ahuja, and Jatinder Kapoor. "Grey Relational Analysis of Chemical Assisted USM of Polycarbonate Bullet Proof (UL-752) & Acrylic Heat Resistant (BS-476) Glass." American Journal of Mechanical Engineering 5, no. 3 (2017): 94-109.
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[34]  Kainth, G.S.; Nandy, A.; Singh, K. (1979). On the mechanics of material removal in ultrasonic machining, International Journal of Machine Toll Design And Research, 19 (1), 33-41.
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[35]  Miller, G.E. (1957). Special Theory of Ultrasonic Machining, Journal of applied physics, 28 (2) 149-156.
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[39]  Khairy, A.B.E. (1990). Assessment of some dynamic parameters for the ultra-sonic machining process, Wear, 137, 187-198.
In article      View Article
 
[40]  Singh, K.; Ahuja, I.P.S. (2014). Ultrasonic machining processes- review paper, International Journal for multi-disciplinary Engineering and Business Management, 2 (3) 57-66.
In article      View Article
 
[41]  Singh, K.; Kumar, V. (2014). A Study on the Tool Geometry and Stresses Induced in Tool in Ultrasonic Machining Process Applied for the Tough and Brittle Materials, International Journal for multi-disciplinary Engineering and Business Management, 2014, 2 (3), 67-71.
In article      View Article
 
[42]  Singh, K.; Kumar, V.S. (2014) “Finite Element Analysis of Ultrasonic Machine Tool”, International journal of engineering research and technology, 3 (7), 1647-1650.
In article      
 
[43]  Singh, K., Ahuja, I.P.S.; Kapoor. J. Study the effect of abrasive and hydrofluoric acid in ultrasonic machining of plain glass material, In proceeding, National Conference Latest Development in Materials, Manufacturing and Quality Control, 19th-20th February, 2015, GZSCCET, BTI, India.
In article      
 
[44]  Singh, K., Ahuja, I.P.S. and Kapoor. J. Ultrasonic machining of glass brittle material, In proceeding, National Conference Latest Development in Materials, Manufacturing and Quality Control, 19th-20th February, 2015, GZSCCET, BTI, India.
In article      
 
[45]  Singh, K., Ahuja, I.P.S. and Kapoor. J. Comparative study between conventional machining, chemical ultrasonic machining (CUSM) and ultrasonic machining (USM) of plain glass, polycarbonate, acrylic, bullet proof and heat resistant glass, In proceeding, International conference in latest development in materials, manufacturing and quality control, 12th -13th Feb-2016, GZSCCET BTI India.
In article      
 
[46]  Adithan, M. Tool wear characteristics in ultrasonic drilling, Tribology International, 1981, 14 (6), 351-356.
In article      View Article
 
[47]  Goetze, D. (1956). Effect of vibration amplitude, frequency and composition of the abrasive slurry on the rate of ultrasonic machining in Ketos tool steel, Journal of acoustical society of America, 28 (6), 1033-1037.
In article      View Article
 
[48]  Adithan, M. (1983). Abrasive wear in ultrasonic drilling, Tribology International, 16 (5), 253-255.
In article      View Article
 
[49]  Adithan, M.; Venkatesh, V.C. (1974). Parameter influence on tool wear in ultra-sonic drilling, Tribolology International, 7 (6), 260-264.
In article      View Article
 
[50]  Babitsky, V.I.; Astashev, V.K. (2007) Ultrasonic processes and machine, Springer Berlin Heidelberg New York.
In article      
 
[51]  Jain, N.K.; Jain, V.K. (2001) Modeling of material removal in mechanical type advanced machining processes: a state of art review, International journal of machine tools & manufacture, 41 (11) 1573-1635.
In article      View Article
 
[52]  Jain, V., Sharma, A.K.; Kumar, P. (2012). Investigations on tool wear in micro Ultrasonic machining, Applied Mechanics and Material, Tranc Tech Publication Switzerland, pp.1561-1566.
In article      View Article
 
[53]  I. Kaczmarek, (1976). Impact Grinding (Ultrasonic machining)--Book Chapter: 21 Principles of Machining by Cutting Abrasion and Erosion, Peter Peregrinus Ltd, Stevenage, pp. 448-462.
In article      
 
[54]  Dharmadhikari, S.W.; Sharma, C.S. (1985). Optimization of abrasive life in Ultrasonic Machining, Journal of Manufacturing Science & Engineering, 107 (4), 361-364.
In article      View Article
 
[55]  Bekrenev, N.V.; Muldasheve, G.K.; Petrovskii, A.P. Tsvetkova, O.A. (2015). Influence of the thermal effect on the cutting forces in the ultrasonic machining of high strength material, Russian Engineering Research, 35 (10) pp. 758-759.
In article      View Article
 
[56]  Chang, S.; Bone, G.M. (2005). Burr size reduction in drilling by ultrasonic assistance. Robotics and Computer-Integrated Manufacturing, 120, 442-450.
In article      View Article
 
[57]  Fan, W.H.; Chao, C.L.; Chou, W.C.; Chen, T.T; Chao, C.W. (2009). Study on the Surface Integrity of Micro-Ultrasonic Machined Glass-Ceramic Material, Key Engineering Materials, 407-408, 731-734.
In article      View Article
 
[58]  Kumar, J.; Khamba, J.S. (2009) An Investigation into the effect of work material properties, tool geometry and abrasive properties on performance indices of ultrasonic machining. International Journal of Machining and Machinability of Materials, 5(2/3): 347-365.
In article      View Article
 
[59]  Schorderet,A. ; Deghilage, E.; Agbeviade, K. (2013) tool type and hole diameters influence in deep ultrasonic drilling of micro holes in glass, Procedia CIRP, 565-570.
In article      View Article
 
[60]  Scholze, H. (1991) Glass – Nature, Structure, and Properties. Springer, Verlag, New York.
In article      
 
[61]  Phillips, J.C. (1979). Topology of covalent non-crystalline solids I: Short-range order in chalcogenide alloys, Journal of Non-Crystalline Solids 34 (2), pp.153-181.
In article      View Article
 
[62]  Folmer , J. C. W., Franzen, S. (2003) Study of polymer glasses by modulated differential scanning calorimetry in the undergraduate physical chemistry laboratory, Journal of Chemical Education , 80 (7), 813-818.
In article      View Article
 
[63]  Phillips D.C, Sambell R.A.J, Bowen D.H. (1972) The mechanical properties of carbon fiber reinforced pyrex glass, Journal of Material Science, 7 (12), 1454-1464.
In article      View Article
 
[64]  Dr Karl's Homework: Glass Flows Australia: ABC. 26 January 2000. Retrieved 24 October 2009.
In article      
 
[65]  Vogel, W.; Kreidl, N. (1985) Chemistry of Glass, Wiley.
In article      View Article
 
[66]  Stookey, S.D.; Beall, G.H. (2000). Explorations in Glass: An Autobiography, Wiley.
In article      PubMed
 
[67]  Noel C. (1998). Stokes, The Glass and Glazing Handbook, Standards Association of Australia.
In article      
 
[68]  Kuo, K.L. (2007). Experimental investigation of brittle material milling using rotary ultrasonic machining. Proceedings of the 35th International MATADOR Conference, Springer: London, 195-198.
In article      View Article
 
[69]  Hasani, H., Tabatabaei., S.A. & Amiri, G. (2012). Grey relational analysis to determine the optimum process parameters for open end sprnning yarns. Journal of engineering fibers and fabrics. 7 (2), 81-86.
In article      View Article
 
[70]  Hasiao, Y.F., Tarng, Y.S. & Huang, W.J. (2008) Optimization of plasma are welding parameters by using the Taguchi method with the Grey relational analysis. Materials and manufacturing processes, 23, 51-58.
In article      View Article
 
[71]  Lin, C.L., Lin, J.L. & Ko, T.C. (2002) Optimisation of EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. International journal of advanced manufacturing technology, 19, 271-277.
In article      View Article
 
[72]  You, M.L., Shu, C.M., Chen, W.T., Shyu, M.L. (2017) Analysis of cardinal grey relational grade and grey entropy on achievement of air pollution reduction by evaluating air quality trend in Japan. Journal of cleaner production, 142 (4), 3883-3889.
In article      View Article
 
[73]  Patil, P.J., Patil, C.R. (2016) Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade. Perspective in Science, 8, 367-369.
In article      View Article
 
[74]  Ahmad, N., Kamal, S., Raza, Z.A. Hussain, T. (2016). Multi-response optimization in the development of oleo-hydrophobic cotton fabric using Taguchi based grey relational analysis. Applied surface science, 367, 370-381.
In article      View Article
 
[75]  Lin, Y.H., Lee, P.C. & Chang, T.P. (2009) Practical expert diagnosis modal based on the grey relational analysis technique. Expert system with applications, 36, 1523-1528.
In article      View Article
 
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