An Experimental Investigation towards Multi Objective

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machining The results show that Vegetable oils have potential to replace the mineral oils Ujjwal Kumar etal 6 focuses on an experimental investigation into the role of green machining on surface Roughness Ra in the machining of aluminium AA1050 A comparative study of turning experiments between VBCFs

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International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. characteristics of S45C steel bars using Tungsten carbide of similarity to the negative ideal solution The overall. cutting tools Through this study not only optimal cutting performance index of each alternative with in all criteria is. parameters for turning operations obtained but also the main determined based on the concept of degree of similarity. cutting parameters that affect the cutting performance in between each alternative and the ideal solution using. turning operations are found alternative gradient and magnitude. Shankar Chakraborty et al 2 presented the the applicability Madic M et al 8 focused on multi criteria economic analysis. of weighted aggregated sum product assessment WASPAS of various machining processes by applying recently. method is explored for parametric optimization of five non developed MCDM method i e weighted aggregated sum. traditional machining processes It is concluded that product assessment WASPAS method By using available. WASPAS method can be deployed as an effective tool for data from literature MCDM model consisting of eight. both single response and multi response optimization of the different machining processes and five economical criteria. NTM processes It is also observed that this method is quite was defined In order to determine relative significance of. robust considered criteria a pairwise comparison matrix was applied. Shankar Chakraborty and Edmundas Kazimieras Zavadskas Dinesh kumar kasdekar and Vishal parashar 9 carried out. 3 depicted the the applicability of weighted aggregated sum experimentation on EDM using En 353 steel which highlights. product assessment WASPAS method is explored as an the application of technique for order preference by similarity. effective MCDM tool while solving eight manufacturing to an ideal solution In this TOPSIS SAW based MCDM. decision making problems such as selection of cutting fluid methods are used and conducted study through computational. electroplating system forging condition arc welding process experiments. industrial robot milling condition machinability of materials Thaman Balgassim etal 10 conducted experimentation on. and electro discharge micro machining process parameters It EDM machine using AISI D3 tool steel An L9 orthogonal. is observed that this method has the capability of accurately array based on Taguchi method is used to conduct a series of. ranking the alternatives in all the considered selection experiments to optimize the EDM parameters Experimental. problems data were evaluated statistically by analysis of Variance. Madic milos et al 4 focused on multi criteria analysis of ANOVA The experimental results have given optimal. VCGT cutting inserts for aluminum alloys turning by combination of input parameters which give the optimum. applying recently developed MCDM method i e weighted surface finish of machined surface. aggregated sum product assessment WASPAS method The J S Dureja etal 11 investigated tool wear flank wear and. MCDM model was defined using the available catalogue data surface roughness during finish hard turning of AISI D3 steel. from cutting tool manufacturers 58HRC with coated carbide TiSiN TiAlN coated cutting. Papiya Bhowmik etal 5 focused on an experimental tool Taguchi L9 3 3 orthogonal array has been applied for. investigation into the role of green machining on surface experimental design S N ratio and ANOVA analyses were. Roughness Ra in the machining of aluminium AA1050 A performed to identify significant parameters influencing tool. comparative study of turning experiments between VBCFs wear and surface roughness The cutting speed and feed were. and MBCFs under various cutting conditions using neat or the most significant factors influencing tool wear flank wear. straight Sunflower oil and Coconut oil was conducted using and feed is the most significant factor influencing surface. the same machining parameter set up Vegetable oils used on roughness Ra Mathematical models for both response. the principle of Minimum Quantity parameters i e tool wear and surface roughness were obtained. Lubrication MQL that is oil dropped between the cutting through regression analysis The confirmation experiments. tool and workpiece interface directly The results show that carried out at optimal combination of parameters given by. vegetable oil performance is comparable to that of mineral oil Taguchi s analysis predicted the response factors with less. machining The results show that Vegetable oils have than 5 error In addition Desirability function module in. potential to replace the mineral oils RSM was applied to arrive at the optimal setting of input. Ujjwal Kumar etal 6 focuses on an experimental parameters to minimize tool wear and surface roughness The. investigation into the role of green machining on surface optimal solution provided by desirability function. Roughness Ra in the machining of aluminium AA1050 A optimization was compared with the optimal setting of. comparative study of turning experiments between VBCFs parameters given by Taguchi analysis The optimization. and MBCFs under various cutting conditions using neat or results provided by both techniques are in close proximity. straight Coconut oil and Castor oil was conducted using the Varaprasad BH etal 12 developed a model and predict tool. same machining parameter set up Vegetable oils used on the flank wear of hard turned AISI D3 hardened steel using. principle of Minimum Quantity Lubrication MQL that is oil Response Surface Methodology RSM The combined effects. dropped between the cutting tool and workpiece interface of cutting speed feed rate and depth of cut are investigated. directly The that vegetable oil performance is comparable to using contour plots and surface plots RSM based Central. that of mineral oil machining The results show that Vegetable Composite Design CCD is applied as an experimental. oils have potential to replace the Mineral oils design Al2O3 TiC mixed ceramic tool with corner radius 0 8. Hossein safari and Ehsan Khanmohammadali 7 proposed a mm is employed to accomplish 20 tests with six centre points. new MADM method This similarity based method effectively The adequacy of the developed models is checked using. makes use of ideal solution concept in such a way that the Analysis of Variance ANOVA Main and interaction plots. most preferred alternative should have highest degree of are drawn to study the effect of process parameters on output. similarity to the positive ideal solution and the lowest degree responses. International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Akash saini et al 13 investigated the influence of approach Table 3 Experimental condition. angle feed rate cutting speed and depth of cut has been on. cutting forces and tool tip temperature during turning of. Machine used, AISI 4340 steel Before conducting experiments on the AISI. 4340 steel work piece the chemical composition test Turn master conventional lathe power 4 HP. microstructure test were performed and hardness of the. work piece was improved by heat treatment A total of 64 Work material. experiments each by two different coated carbide inserts Hardened AISI D3 steel. PVD and CVD coated were conducted on AISI 4340 steel. under different environmental conditions dry and MQL Size of work piece. machining It is observed that the main cutting force was Diameter 40 mm x 100 mm. largest among the three cutting force components in case of. AISI 4340 steel turning and MQL machining show Cutting length 70 mm. beneficial effects compared to dry machining Cutting tool holder. From the literature survey it is evident that little work has PDJNR 2020M15 WIDAX. been reported on hardened AISI D3 tool steel work with MTJNR 2020K16 WIDAX. combination of CVD coated tools with different styles Also. little work has been reported on novel MCDM technique PCLNR 2020K12 V tool. WASPAS approach which is a combination of aggregated Cutting insert. sum and product method Hence the experimentation is done. on above said combination of work piece and tool and multi DNMG 150608 EN TMR CTC 2135. objective optimization technique namely WASPAS method is TNMG 160408 EN TM CTC 2135. CNMG 120408 EN TMR CTC 2135,EXPERIMENTATION MQL supply. In the present study three turning parameters were selected. with three levels as shown in Table 1 The experimentation Castor oil Palm oil and ground nut oil 500 ml hour. was carried out using L27 orthogonal array based on Taguchi Cutting parameters. design of experiments The work material selected for this. experiment is AISI D3 steel of 40 mm diameter length 100 Insert style. mm The chemical composition of hardened AISI D3 steel DNMG TNMG and CNMG. has been done by chemical Analyzer and is reported as below. in Table1 Cutting fluid, The process parameters and their corresponding levels are. Castor oil CO Palm oil PO and Ground nut oil GO, depicted in Table 2 The experimental condition is presented. in Table 3 Cutting velocity 100 200 mm min,Feed 0 05 0 09 mm rev.
Table 1 Chemical Analysis report, Element C Si Mn P S Cr V W Depth of cut 1 0 2 0 mm. Specified 2 00 0 10 0 10 0 03 0 03 11 00 1 00 1 00 Response variables measured. values 2 35 0 60 0 60 max max 13 50 max max,Surface roughness SR m Material removal rate. Observed 2 07 0 406 0 457 0 02 0 029 11 28 0 037 0 003 MRR mm3 sec Interface temperature 0C Specific energy. values SE J mm3 and flank wear mm, Table 2 The different styles of CVD coated inserts and the. Turning Level 1 Level 2 Level 3 corresponding tool holders are shown in figures 1 4. parameters,Insert style S DNMG TNMG CNMG,Cutting fluid CF Castor oil Palm oil Ground nut. Cutting speed 100 150 200,Feed F mm rev 0 05 0 07 0 09.
Depth of cut 1 0 1 5 2 0 Figure 1 CVD coated DNMG TNMG and CNMG cutting. International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Surface roughness is measured using SJ 201 P surface. roughness measuring instrument, The material removal rate mm3 sec is calculated using. MRR 4 D12 D22 L t mm3 sec, Figure 2 PDJNR2020M15WIDAX D1 Diameter of the work piece before turning mm. D2 Diameter of the work piece after turning mm,L Length of turning mm. t Machining time sec, Specific energy is obtained by considering the ratio between. Power consumed and material removal rate Power consumed. is measured by using Watt meter fitted to lathe machine. Figure 3 MTJNR 2020K16 WIDAX METHODOLOGY,A Entropy approach for weight determination.
Entropy method is one of the well known and widely used. methods to calculate the criteria of decision weights Ding S. and Shi Z 14 Decision weights increases the importance of. criteria and is usually categorized into two types One is. subjective weight which is determined by the knowledge and. experience of experts or individuals and the other is objective. weight which is determined mathematically by analyzing the. Figure 4 PCLNR 2020K12 V tool collected data Here it is an objective weighting method. are the weights assigned to the,Ra MRR Temp SE and FW 0 191 0 308. The turning tests were carried out on Kirloskar model centre. 0 017 0 189 and 0 295, lathe machine presented in Figures 5 6 to determine the. responses characteristics for various runs of experiment. B WASPAS method,Weighted aggregated sum product assessment WASPAS. method for solving MCDM problems was proposed by, Zavadskas et al 15 The procedural steps being involved in. solving multi objective optimization problems is presented. Step 1 Set the initial decision matrix, Step 2 Normalization of the decision matrix by using the.
following equations, Figure 5 Kirloskar model Turn master 35 centre lathe. Where xij is the assessment value of the i th alternative with. respect to the j th criterion and eqs 1 and 2 are used for. maximization and minimization criteria respectively. Step 3 The total relative importance of the i th alternative. based on weighted sum method WSM is calculated as, Step 4 The total relative importance of the i th alternative. based on weighted product method WPM is calculated as. Figure 6 Lubricant dropping on cutting zone 2, International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Step 5 In order to have increased ranking accuracy and RESULTS. effectiveness of the decision making process in the WASPAS. A series of turning tests were conducted to assess the effect of. method a more generalized equation for determining the total. turning parameters on surface roughness and material removal. relative importance of alternatives is developed as below. rate and the results of experimentation are shown in table 3. Where 0 0 1 1, Table 3 Experimental data and results for 5 parameters corresponding SR MRR TEMP Specific energy and flank wear for. SNo Insert style Cutting Cutting Feed Depth of SR MRR TEMP Specific Flank. fluid speed mm rev cut mm m mm3 sec OC Energy Wear. m min J mm3 mm, 1 DNMG CO 100 0 05 1 0 0 268 55 190 35 67 36 445 0 094.
2 DNMG CO 100 0 05 1 5 0 738 80 909 38 67 26 675 0 106. 3 DNMG CO 100 0 05 2 0 1 113 116 664 40 00 19 441 0 118. 4 DNMG PO 150 0 07 1 0 0 336 73 896 38 67 32 177 0 105. 5 DNMG PO 150 0 07 1 5 0 680 138 459 43 67 18 229 0 109. 6 DNMG PO 150 0 07 2 0 0 854 177 686 45 60 14 823 0 113. 7 DNMG GO 200 0 09 1 0 0 184 206 897 37 00 14 145 0 012. 8 DNMG GO 200 0 09 1 5 0 452 263 404 38 25 11 666 0 023. 9 DNMG GO 200 0 09 2 0 0 596 349 252 39 33 9 112 0 052. 10 TNMG CO 150 0 09 1 0 0 232 126 373 37 40 19 394 0 051. 11 TNMG CO 150 0 09 1 5 0 582 180 543 42 00 14 791 0 068. 12 TNMG CO 150 0 09 2 0 0 682 260 220 44 00 10 824 0 101. 13 TNMG PO 200 0 05 1 0 0 432 114 151 35 60 24 035 0 042. 14 TNMG PO 200 0 05 1 5 0 648 158 305 35 75 18 024 0 109. 15 TNMG PO 200 0 05 2 0 0 878 194 311 46 80 15 249 0 113. 16 TNMG GO 100 0 07 1 0 0 322 44 159 43 20 42 248 0 114. 17 TNMG GO 100 0 07 1 5 0 510 60 537 43 40 32 631 0 118. 18 TNMG GO 100 0 07 2 0 0 568 88 332 47 00 24 848 0 145. 19 CNMG CO 200 0 07 1 0 0 567 146 216 33 00 19 765 0 025. 20 CNMG CO 200 0 07 1 5 0 728 216 592 35 25 14 018 0 033. 21 CNMG CO 200 0 07 2 0 1 047 282 822 35 50 11 123 0 061. 22 CNMG PO 100 0 09 1 0 0 334 76 273 41 00 25 419 0 125. 23 CNMG PO 100 0 09 1 5 0 438 111 533 44 00 19 023 0 131. 24 CNMG PO 100 0 09 2 0 0 690 140 177 52 75 15 926 0 140. 25 CNMG GO 150 0 05 1 0 0 448 61 848 42 00 37 854 0 031. 26 CNMG GO 150 0 05 1 5 0 526 105 896 43 30 23 489 0 063. 27 CNMG GO 150 0 05 2 0 0 700 135 025 44 00 19 235 0 082. International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Tables 4 5 6 7 and 8 depicts the results related with WASPAS method. Table 4 Normalized decision matrix, SNo Insert Cutting Cutting Feed Depth of Normalized values fij. style fluid speed mm rev cut mm,m min SR m MRR TEMP OC Specific Flank Wear. mm sec Energy mm, 1 DNMG CO 100 0 05 1 0 0 686567 0 158023 0 925225 0 250021 0 12766. 2 DNMG CO 100 0 05 1 5 0 249322 0 231664 0 853441 0 341593 0 113208. 3 DNMG CO 100 0 05 2 0 0 165319 0 33404 0 825 0 4687 0 101695. 4 DNMG PO 150 0 07 1 0 0 547619 0 211584 0 853441 0 283184 0 114286. 5 DNMG PO 150 0 07 1 5 0 270588 0 396444 0 755719 0 499863 0 110092. 6 DNMG PO 150 0 07 2 0 0 215457 0 508762 0 723684 0 61472 0 106195. 7 DNMG GO 200 0 09 1 0 1 000000 0 5924 0 891892 0 644185 1 000000. 8 DNMG GO 200 0 09 1 5 0 40708 0 754195 0 862745 0 781073 0 521739. 9 DNMG GO 200 0 09 2 0 0 308725 1 000000 0 83899 1 000000 0 230769. 10 TNMG CO 150 0 09 1 0 0 793103 0 361839 0 882353 0 469836 0 235294. 11 TNMG CO 150 0 09 1 5 0 316151 0 516942 0 785714 0 61605 0 176471. 12 TNMG CO 150 0 09 2 0 0 292994 0 745078 0 75 0 841833 0 118812. 13 TNMG PO 200 0 05 1 0 0 425926 0 326844 0 926966 0 379114 0 285714. 14 TNMG PO 200 0 05 1 5 0 283951 0 453269 0 923077 0 505548 0 110092. 15 TNMG PO 200 0 05 2 0 0 209567 0 556363 0 705128 0 597547 0 106195. 16 TNMG GO 100 0 07 1 0 0 571429 0 126439 0 763889 0 215679 0 105263. 17 TNMG GO 100 0 07 1 5 0 360784 0 173333 0 760369 0 279244 0 101695. 18 TNMG GO 100 0 07 2 0 0 323944 0 252918 0 702128 0 36671 0 082759. 19 CNMG CO 200 0 07 1 0 0 324515 0 418655 1 000000 0 461017 0 48. 20 CNMG CO 200 0 07 1 5 0 252747 0 62016 0 93617 0 650021 0 363636. 21 CNMG CO 200 0 07 2 0 0 17574 0 809794 0 929577 0 819203 0 196721. 22 CNMG PO 100 0 09 1 0 0 550898 0 21839 0 804878 0 358472 0 096. 23 CNMG PO 100 0 09 1 5 0 420091 0 319348 0 743243 0 478999 0 091603. 24 CNMG PO 100 0 09 2 0 0 266667 0 401192 0 625592 0 572146 0 085714. 25 CNMG GO 150 0 05 1 0 0 410714 0 177087 0 785714 0 240714 0 387097. 26 CNMG GO 150 0 05 1 5 0 34981 0 303214 0 762125 0 387926 0 190476. 27 CNMG GO 150 0 05 2 0 0 262857 0 386612 0 75 0 47372 0 146341. International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Table 5 Ranking of alternatives using WASPAS method. SNo Insert style Cutting fluid Cutting speed Feed Depth of cut mm WSM values WPM values Q Rank. m min mm rev, 1 DNMG CO 100 0 05 1 0 0 280448 0 220771 0 250609 21. 2 DNMG CO 100 0 05 1 5 0 231439 0 209273 0 220356 25. 3 DNMG CO 100 0 05 2 0 0 267069 0 222619 0 244844 23. 4 DNMG PO 150 0 07 1 0 0 271507 0 228916 0 250212 22. 5 DNMG PO 150 0 07 1 5 0 313586 0 266774 0 29018 17. 6 DNMG PO 150 0 07 2 0 0 357663 0 283564 0 320613 12. 7 DNMG GO 200 0 09 1 0 0 805372 0 781671 0 793522 1. 8 DNMG GO 200 0 09 1 5 0 626247 0 606724 0 616485 2. 9 DNMG GO 200 0 09 2 0 0 638306 0 516832 0 577569 3. 10 TNMG CO 150 0 09 1 0 0 43614 0 394906 0 415523 8. 11 TNMG CO 150 0 09 1 5 0 401452 0 356817 0 379135 9. 12 TNMG CO 150 0 09 2 0 0 492352 0 371224 0 431788 7. 13 TNMG PO 200 0 05 1 0 0 353717 0 345897 0 349807 10. 14 TNMG PO 200 0 05 1 5 0 337559 0 28214 0 30985 13. 15 TNMG PO 200 0 05 2 0 0 367638 0 288269 0 327954 11. 16 TNMG GO 100 0 07 1 0 0 232888 0 182235 0 207561 26. 17 TNMG GO 100 0 07 1 5 0 218 0 191175 0 204587 27. 18 TNMG GO 100 0 07 2 0 0 24543 0 20817 0 2268 24, 19 CNMG CO 200 0 07 1 0 0 43666 0 429127 0 432894 6.
20 CNMG CO 200 0 07 1 5 0 485326 0 453479 0 469402 4. 21 CNMG CO 200 0 07 2 0 0 511648 0 400247 0 455947 5. 22 CNMG PO 100 0 09 1 0 0 28224 0 229608 0 255924 20. 23 CNMG PO 100 0 09 1 5 0 308786 0 254993 0 281889 19. 24 CNMG PO 100 0 09 2 0 0 318557 0 2536 0 286078 18. 25 CNMG GO 150 0 05 1 0 0 306035 0 284681 0 295358 16. 26 CNMG GO 150 0 05 1 5 0 302668 0 289116 0 295892 15. 27 CNMG GO 150 0 05 2 0 0 314736 0 283387 0 299062 14. Table 6 Response table for WASPAS method,Process parameters Average WASPAS index SPM. Level 1 Level 2 Level 3 Max Min Rank, Insert style 0 396043 0 317001 0 341383 0 079042 4. Cutting fluid 0 366722 0 296945 0 39076 0 093815 3. Cutting speed V 0 242072 0 330863 0 481492 0 23942 1. Feed F 0 288192 0 317577 0 448657 0 160465 2, Depth of cut D 0 361268 0 340864 0 352295 0 020404 5. Total mean value of the overall performance index 0 351476. Optimum levels, International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. Table 7 ANOVA based on WASPAS method, Source of Degrees of Sum of squares Mean sum of F ratio Percent contribution.
variation freedom squares,Insert style 2 0 02949 0 01475 7 51506 5 891. Cutting fluid 2 0 04274 0 02137 10 89241 8 539,Cutting speed 2 0 26369 0 13184 67 19595 52 676. Feed 2 0 13138 0 06569 33 48076 26 246,Depth of cut 2 0 00188 0 00094 0 479719 0 376. Error 16 0 03139 0 00196 6 273,26 100 0000, Prediction at optimum levels m 1 where m is total mean of the. The objective of the prediction at optimum levels is to required responses. validate the conclusions drawn during the analysis phase j is the mean of the required responses at optimum level. Once the optimal level of process parameters is selected the n is the number of process parameters that significantly. next step is to verify the improvement in response affects the multiple performance characteristics. characteristics using optimum level of parameters A. conformity test is conducted using the following equation. Table 8 Comparison of predicted and Experimental results using WASPAS method. Optimum process parameters, Initial process parameters Predicted values Experimental values.
Level of parameters setting S1 CF1 V1 F1 D1 S1 CF3 V3 F3 D1 S1 CF3 V3 F3 D1. Surface roughness m 0 268 0 183 0 179,MRR mm3 sec 55 190 226 866 233 788. Interface temperature 0C 35 67 35 505 34 249,Specific energy J mm3 36 445 18 449 17 890. Flank wear mm 0 094 0 0109 0 0105, Single performance measure 0 25060 0 67231 0 68198. CONCLUSIONS experimental specific energy 17 890 J mm3 are much. 1 The optimal parameters setting lies at DNMG insert style lower than initial setting which is highly expected for. Ground nut oil cutting fluid 200 m min cutting speed 0 09 reduced machine vibration as also reduced power. mm rev and 1 0 mm depth of cut The optimum predicted consumption Also predicted flank wear 0 0109 mm and. value for surface roughness is 0 183 m MRR 226 866 experimental flank wear 0 0105 mm are much lower than. mm3 sec interface temperature 35 505 0C specific energy initial setting which is commendable for increased tool life. 18 449 J mm3 flank wear 0 0109 mm and performance which is desirable It may be noted that there is a good. index is 0 67232 Also the experimental value for surface agreement between the predicted single performance. roughness is 0 179 m MRR is 233 788 mm3 sec measure 0 6723 and experimental single performance. interface temperature 34 249 0C specific energy 17 890 measure 0 6819 and therefore the condition S1 CF3 V3. J mm3 flank wear 0 105 mm and performance index is F3 D1 of process parameters combination was tested as. 0 68198 optimal Further significant improvement in machinability. 2 It is found that both predicted and experimental response is observed and measured that there is substantial. characteristics are significantly better as compared to improvement in MRR both Experimental value and. initial machining parameters To be specific predicted predicted and effective improvement in specific energy. MRR 226 866 mm3 sec and experimental MRR 233 788 Experimental value and predicted value as compared. mm3 sec are much higher as compared to MRR at initial with initial machining parameters This encourages. setting level which paves way for higher productivity applying WASPAS approach for optimizing multi. Also predicted specific energy 18 449 J mm3 and response problems. International Journal of Applied Engineering Research ISSN 0973 4562 Volume 12 Number 9 2017 pp 1899 1907. Research India Publications http www ripublication com. 3 Further from Analysis of variance ANOVA depicts that 10 Thaman Belgassim and Abdurrahman Abusada. cutting speed is the most significant parameter followed Optimization of the EDM parameters on the surface. by feed affecting multi response characteristics with roughness of AISI D3 steel Proceedings of the 2012. cutting speed 52 676 feed 26 246 cutting fluid International conference on Industrial Engineering and. 8 539 insert style 5 891 and depth of cut almost Operation Management Istanbul Turkey 2012. negligible,11 J S Dureja Rupinder Singh Manpreet S Bhatti. Optimizing flank wear and surface roughness during. hard turning of AISI D3 steel by Taguchi and RSM,ACKNOWLEDGEMENTS.
methods Production and Manufacturing Research 2 1, The authors would like to thank Machine tool laboratory. 2014 767 783, Department of Mechanical Engineering Srikalahasteeswara. Institute of Technology Srikalahasti for carrying out this 12 Varaprasad Bh Srinivasa Rao Ch and P V Vinay. experimental work The authors also thankful to the S V Effect of Machining Parameters on Tool Wear in Hard. University and Kosaka Laboratories Chennai for providing Turning of AISI D3 Steel 12th Global congress on. the necessary experimentation Manufacturing and Management 97 2014 338 345. 13 Akash saini Suresh Diman Rajesh Sharma and Sunil. REFERENCES,setia Experimental estimation and optimization of. process parameters under minimum quantity lubrication. 1 W H Yang Y S Tang Design optimization of, and dry turning of AISI 4340 with different carbide. cutting parameters based on Taguchi method Journal. inserts Journal of Mechanical science and Technology. of Materials processing Technology Vol 84 1998 pp,122 129 28 6 2014 pp 2307 2318.
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