Experimental Investigations To Optimize Process Parameters For CO2 Laser Welded Alloy Steel Automotive Gears

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ScienceDirect Materials Today: Proceedings 5 (2018) 11636–11654

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ICMMM - 2017

Experimental Investigations To Optimize Process Parameters For CO2 Laser Welded Alloy Steel Automotive Gears Anurag Khajancheea*, Sharad K. Pradhanb, Prabhash Jainc a,c b

University Institute of Technology, Barkatullah University, Bhopal 462026 (M.P.), India

National Institute of Technical Teachers’ Training and Research, Bhopal 462 002(M.P.), India

Abstract In the present research, CO2 laser welding process is successfully applied and optimized for joining automotive gears of 16MnCr5 Alloy Steel. The combinations of laser power and speed of welding are optimized with the aim to produce welded joint in automotive gear-synchro assembly with required weld depth, weld width and weld strength. Taguchi method of “Design of Experiments (DOE)” and ANOVA have been used as statistical design tools and techniques for optimizing the selected welding process parameters. Further, variation transmission analysis technique has also been applied to determine the range of input variable that gives least variability of the output. The result of this research effort indicates that the developed models are capable of predicting the responses with negligible errors. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Keywords: ANOVA, DOE, Alloy Steel 16MnCr5, CO2 LASER, Taguchi Method, Variability Transmission.

1. Introduction The assembly of synchro and gear used in synchromesh transmissions of automotive gear boxes had conventionally been manufactured through four stage process viz. broaching, shaping, press fitting followed by caulking. Recently, after the advancement of laser technology and availability of commercial laser welding machines at affordable prices, this traditional process of making gear-synchro assembly has almost been replaced by LASER welding technology due to high reliability, accuracy, strength and speed of laser welding technology. The quality of laser weldment is dependent upon the physical, mechanical, chemical composition and thermal properties of the materials to be welded. It also includes weld depth, weld width, weld geometry and part geometry. * Corresponding author. Tel.: +91-0755-2661528; Mob: +91-896-408-1089. E-mail address: [email protected]

2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

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1.1

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Need for Experimentation and research work/analysis

The problem that operators generally face is to decide the values of input process parameters in order to get the required good quality of weld joint with minimum rejection, distortion or induction of undesirable residual stresses. Therefore, it becomes necessary to select these input parameters in a precise technical manner, to get the specified weldment profile. Whenever a new product is to be developed with entirely different design requirements, it calls for a skilled and knowledgeable operator or supervisor or may be an engineer with thorough knowledge and experience regarding laser welding processes. Even with experienced operator, setting values of laser welding input parameters for a given situation is extremely time consuming as it requires trial-and-error approach. The trial-anderror process not only consumes time but also involves costs of machine, material and subsequent inspection time in each trial. This problem can be handled and taken care off through the use of various known or new optimization methods or techniques and development of customized mathematical models. Such techniques are capable to develop a relationship between the input parameters and output responses for a given design, thereby are very effective. Commonly used optimization techniques to tackle such problems in various welding processes include multiple regression analysis, Taguchi method, response surface method and ANN modelling. The most critical and important parameters of interest in the laser welding process are the ‘weld depth’, ‘weld width’ and ‘strength of weld joint (separation force)’. In laser welding the quality of the weld is assessed by the narrow weld width and high depth of weld. The weld depth (D), weld width (W) and separation force (FS) are functions of laser power (P) and welding speed (S). Investigations to perform optimization of laser welding power and speed through ‘DOE’, ‘ANOVA’ and ‘Variation Transmission Analysis’ for industrial problem of welding of gear-synchro assembly for automotive gears are the focus of this research endeavour. 2. Existing research efforts Hugger et al. (2014) [1], studied the effect of laser power, weld speed & focal point on Fusion & Heat Zone during laser welding of Brass with 37m% zinc (CuZn37) on 1mm thick plates. Bhujbal et al. (2013) [2], performed optimization on depth of penetration, ultimate tensile strength, chemical, physical and mechanical properties using Taguchi & Fuzzy Logic Technique on Stainless Steel 304L and Inconel 625 plates. Tadamalle et al. (2013) [3], investigated the impact of speed and laser power on tensile strength of dissimilar metals. Eagler et al. (2011) [4], observed the effect of laser power, weld speed & focal point on fusion & heat zone on Copper with Brilliant Green. Kanujia et al. (2011) [5], applied Taguchi method using Nd-Yag laser to study the effect of laser power and speed of welding on laser weld joint strength with dissimilar metals (AISI304 Stainless Steel and Copper). Ruggiero et al. (2011) [6], performed regression analysis for weld bead profile and cost optimization using Low Carbon Steel and Austenitic Steel AISI316 with input parameters as laser power, welding speed (CO2 Laser). Balasubramanian et al. (2010) [7], analyzed depth of penetration and weld width on stainless steel butt joints using laser beam power and welding speed as process parameters. Anawa et al. (2008) [8], investigated CO2 laser welding of dissimilar metals viz. Stainless Steel AISI 316 and Low Carbon Steel AISI 1009 Plates to optimize area of fusion, weld width at surface and width at mid point. While, in the present study, authors have applied the Taguchi method, ANOVA and “Variation Transmission Analysis” i.e. exploiting non-linearity of the response variable with respect to input factors to develop mathematical models and optimize CO2 laser welding parameters for real world 16MnCr5 Automotive Gears assembly problem. Dey et al. (2009) [9], applied genetic algorithm on bead geometry, weld depth and weld strength with electron beam energy and welding speed to analyze the tensile strength of weld joint. Park et al. (2008) [10], used non conventional optimization techniques like neural network and genetic algorithms to optimize the tensile strength of Aluminium alloy welded joint. Balasubramanian et al. (2008) [11], applied numerical and experimental investigations to optimize depth of penetration and bead width with laser beam power, welding speed and beam angle on 1.6 mm thick sheet of Stainless Steel (AISI 304). Bag et al. (2008) [12], used multivariate optimization algorithm to study microstructure and the mechanical properties of the weld joint on Low Carbon Steel. Benyounis et al. (2008) [13], applied Artificial Neural Networks (ANN) for optimization of depth of penetration and weld

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width to study mechanical properties of Austenitic Stainless Steel AISI 304. Kim et al. (2007) [14], applied Taguchi experimental method for laser welding of Ta sheath for a thermocouple wire of an instrumented fuel irradiation test on Dissimilar Binary Metal Combination among Ta/Ta, Mo/Ta, Ti/Ta, Zr-4/Ta. Lim et al. (2006) [15], used Taguchi method on two different thickness plates for study of surface roughness of the cutting face with different speed, power efficiency and gas pressure. Olabi et al. (2006) [16], applied ANN & Taguchi method for optimization of tensile strength, impact strength and joint operating cost. Anawa et al. (2006) [17], studied the physical and mechanical properties on Ferrite/Austenitic Steel using laser power, welding speed and focal position in a CO2 Laser welding. Hyoung et al. (2006) [18], used Nd-Yag laser welding and applied DOE & statistical techniques for study of microstructure and mechanical properties of the weld joint for sealing small Titanium tube ends. Anawa et al. (2006) [19], used Nd-Yag laser welding and applied Taguchi method for optimization of laser welding parameters on dissimilar laser welded components. Casalino et al. (2005) [20], used ANN, ANOVA and Taguchi approaches for investigation on Ti6Al4V Alloy Steel Sheet with CO2 laser welding. Lung et al. (2005) [21], applied Taguchi analysis using Nd-Yag laser welding on Magnesium Alloy to optimize bead geometry, weld depth and weld strength. The literature review reveals that, up-till now, a lot of research has been done on the laser technology for cutting and welding of various materials and their combinations using Nd-Yag, CO2, Diode and other types of lasers. However most of the researchers have applied optimization techniques to study the effect of process parameters in welding plates of different materials and thicknesses using LASER technology. To study the effect of process parameters and to optimize them in case of LASER welding of critical industrial component like gear-synchro assembly for automotive gears has yet not been attempted. 3. Methodology Following approach has been applied for optimization of CO2 laser welding of gear-synchro assembly for automotive gears.        

Deciding about the process and performance parameters related to LASER welding of gear-synchro assembly for automotive gears. Deciding the “Matrix-Experiment” with the use of orthogonal arrays. Conducting the “Experimentation” according to the decided array. Applying Taguchi method for determination of “Signal-to-Noise” (S/N) ratio for analysis of factor impacts. Applying “Analysis of Variance” (ANOVA) to investigate the significance of various input factors on responses. Development of the governing “Mathematical Equations” satisfying the experimental results and predicted out-come. Application of “Variation Transmission Analysis” i.e. exploiting non-linearity of the response variable with respect to input factors. Prediction of optimized values of input parameters.

4. Experimental setup The experiments have been conducted using CO2 laser for welding 16MnCr5 Alloy Steel automotive gearsynchro assemblies. Taguchi L9 array using two factors and three levels each has been used to perform the design of experiments. The two process parameters indentified for the present study are, laser power (P) and welding speed (S). The selection of process parameters are as per the machine’s capacity and available ranges. The other process parameters such as shielding gas composition, flow rate, nozzle position and angle are kept fixed. The weld depth (D), weld width (W) and separation force (FS) are considered as responses. The alloy steel 16MnCr5 selected for the experimentation is common material for automotive gears. The material has good core toughness and surface hardenability to have sufficient case depth for wear resistance. The photograph and the cut-section view drawings of the gear-synchro assembly are as presented in Fig.1.

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(a)

Gear Photograph

(b) Sectional view of Gear assembly

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(c) DETAIL ‘A’

Fig. 1. Gear & Synchro Assembly (Photograph & Cut-Section View).

Fig. 2 shows the schematic view of experimental set up for CO2 laser beam welding process while Fig. 3 shows the photographs of Machine spindle part and Welding station of CINETIC, France make Laser Welding Machine DC025 - ML44205. The shielding gas (Helium-Argon) nozzle is positioned at 40o near the weld joint. The Gear & Synchro Assembly is mounted on a rotating spindle with servo controlled CNC motor and various interlocking of the machine DC025 - ML44205.

Fig. 2. Schematic view of experimental set up for CO2 laser welding.

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Fig. 3. Pictures of Machine spindle part and Welding station.

The laser gas used is ROFIN CINAR laser mixture (LASL 200) having composition of different gases as given in Table. 1. The laser gas chamber pressure is maintained at – 200 hPa. Table 1. Composition of laser gas (LASL 200) He

O2

Xe

CO2

CO

N2

65%

3%

3%

4%

6%

19%

The laser beam is reflected through mirrors and focused exactly above the gear-synchro circumferential joint by a concave mirror. All mirrors are cooled with Chilled water at 25o C. The focal point is located and kept fixed during the experiments within the work piece to a depth of 1mm below the surface for maximum penetration. As defined in Table. 4, total nine sets of input parameters are used in this research effort. One set of input parameters viz. laser power and weld speed values are than set on the mentioned machine before starting the welding. The composition of shielding gas used is Helium – 70% and Argon – 30% and the flow of gas is maintained at 250 liters per minutes. The flow rate of shielding gas does not have much impact on the output responses and the machine has interlocks to ensure that the flow of the shielding gas is maintained at 250 liters per minutes. In case the flow rate drops below the set value, the laser cannot be made ‘ON’. The main purpose of using shielding gas is to prevent the oxidation of components and to avoid distortion. The work-piece material employed in this investigation is Alloy Steel 16MnCr5. The standard chemical composition of the materials as per manufactory information is given in Table 2. Table 2. Chemical composition of ISO Grade: 16MnCr5 Alloy Steel

Carbon (C)

Silicon (Si)

Manganese(Mn)

Phosphorus(P)

Sulphur (S)

Chromium(Cr)

0.14% - 0.19%

max 0.4%

1% - 1.3%

max 0.025%

Max 0.035%

0.8% - 1.1%

5. Implementation The experimental data is used for calculation of signal-to-noise ratio ( using equations (2) and (3). The results are calculated with the help of EXCEL 2007 and MINITAB 17 statistical software. The results of Taguchi experiment are summarized in Table 4. The experiment has been carried out using the L9 orthogonal array as given in Table 2.

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Matrix experiment using orthogonal arrays

According to Phadke, M. S. (1989) [22], “A matrix experiment consists of a set of experiments where the settings of the various process parameters under study can be changed. After conducting the matrix experiment, the data from all experiments in the set taken together are analyzed to determine the effect of various parameters. Conducting matrix experiments using special matrices, called orthogonal arrays, allows the effects of several parameters to be determined efficiently and is an important technique in Robust Design.” In the present study, full factorial method of design of experiments is used. Full factorial examines every possible combination of factors at the level tested. Full factorial enables us to determine the main effects of the factors manipulated on response variables, determine the main effects of factor interaction on response variable and estimate levels to set factors for best results. Full Factorial notation =

(1)

Where, L = Number of levels for each factor, K = Number of factors to be investigated and N = Number of runs or experiments. 2-level designs are the most common because they provide lots of information but require the fewest tests. In present study, the L = 3 and K = 2, therefore, the number of experiments are equal to N = LK N = (32) = 9 experiments. Therefore, an L9 orthogonal array with three columns and 9 rows is used for each response variable. Now, Taguchi approach has been applied for design of experiments with L9 standard orthogonal array. Design of experiment has been performed using two-welding process parameters viz. laser power (kW) and weld speed (mm/s) with three levels for each parameter while weld depth, weld width and separation force are considered as the performance parameters for this study. Table 3 shows the input factors and design levels for the experiment. MINITAB 17- statistical software and EXCEL 2007 have been used for analysis of the experimental data through the application of Taguchi method. 5.2

Signal-to-Noise (S/N) Ratio Analysis

Taguchi method attempts to identify the factors that have maximum influence on the performance measures. It focuses on whether the variability is most influenced by the main effects or by interaction. According to Phadke, M. S. (1989) [22], in Taguchi method, the robustness is measured through Signal-to-Noise ratio. The S/N ratio takes both the mean and the variability into account and in simplest form it is the ratio of the mean (signal) to the standard deviation (noise). The S/N equation depends on the criterion for the quality characteristic to be optimized. While there are many different possible S/N ratios but three of them are considered standard and are generally applicable in the following situations:   

Larger-is-better quality characteristic Smaller-is-better quality characteristic Nominal-is-best quality characteristic

The expression for S /N ratio ( for the-larger-the-better target response (weld depth and separation force) is: = −10

(1⁄ )

(2)

The expression for S/N ratio for the-smaller-the-better target responses (weld width) is: = −10

( )

(3)

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Where, ‘y’ = The average measured response for number of measurements ‘n’ = Total number of repetitions, in this study n = 8 Table 3. Process Factors and designed levels

Factors A. Laser Power (kW) B. Welding Speed (mm/s)

Factor Levels 1

2

3

1.80

2.00

2.50

16.667

25.000

33.333

Specimens preparation for measurement of weld geometry i.e. weld depth and weld width is carried out by first parting the sample components on a parting-machine followed by rough polishing of the cut-section with 50,100 & 220 grit emery on a belt driven machine. The final polishing is done using a metallographic polishing machine with 500 & 800 grit emery and then with diamond slurry of 9, 6 & 3 micron. Lastly the components are subjected to etching with 3% - 4% Nital solution and immediately cleaned with sufficient amount of distilled water and then dried under hot air blower to prevent oxidation of parts. The components are then etched with 10% oxalic acid to get the weld geometry features clearly visible for further measurement on “Profile-Projector” or “Tool-makers Microscope”. For each measured output parameter, the average of measurement on eight welded components is taken. Fig. 4, Depicts welded gear and detailed view of the sample weld joint after performing above steps.

Fig. 4. Photographs of laser welded Gear-Synchro assembly.

The weld depth (D) & weld width (W) are measured using a ‘Profile Projector’ which has a least count of + 0.001 mm and the separation force (FS) is measured on ‘Universal Testing Machine (UTM)’. The experimental values of measured responses are presented in Table 4 and indicated in Fig. 1

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Table 4. The experimental values of measured responses  = S/N Ratio

 Exp. No.

Laser Power (kW)

Weld Speed (mm/s)

Weld Depth (mm)

Weld Width (mm)

1

1.80

16.667

3.756

2

1.80

25.000

3.275

3

1.80

33.333

4

2.00

5



Separation Force (kN)

 for Depth - 10log10(1/y)2

 for Width -10log10(y2)

 for Separation Force - 10log10(1/y)2

2.401

203.00

11.4922

-7.6547

46.1499

1.781

178.44

10.3042

-5.0133

45.0299

2.820

1.661

151.70

9.0050

-4.4074

43.6199

16.667

4.153

2.521

223.73

12.3672

-8.0315

46.9947

2.00

25.000

3.594

1.871

194.27

11.1116

-5.4415

45.7680

6

2.00

33.333

3.165

1.761

169.17

10.0075

-4.9152

44.5662

7

2.50

16.667

4.934

3.024

267.39

13.8640

-9.6116

48.5429

8

2.50

25.000

4.380

2.414

237.38

12.8295

-7.6547

47.5088

9

2.50

33.333

3.935

2.131

212.82

11.8989

-6.5717

46.5603

6. Analysis and validation of results Table 5 depicts the optimum behavior of the input process variables on the of weld depth, weld width and separation force respectively. Delta (Max-Min) values show the difference of maximum and minimum values of input parameters levels, while the rank indicates the level of dominating behavior of selected input parameters and the percentage contribution indicates the degree of dominance of the factors. Table 5. Average  for each response of independent variables for weld depth, weld width and separation force Weld depth

Levels

Weld Width

Separation Force

Laser Power

Weld Speed

Laser Power

Weld Speed

Laser Power

Weld Speed

1

10.2679

12.5752

-5.6762

-8.4170

44.9332

47.2291

2

11.1621

11.4151

-6.1294

-6.0365

45.7763

46.1022

3

12.8641

10.3038

-7.9460

-5.2981

47.5373

44.9155

max-min)

2.5962

2.2715

2.2698

3.1189

2.6041

2.3137

%

57.162%

42.394%

34.979 %

64.397%

56.579 %

42.896%

Rank

1

2

2

1

1

2

Table 5 indicates that laser power is more dominating parameter for weld depth and separation force and it can be observed that speed of welding is more dominating factor for weld width. The result obtained, depicts that the developed models are statistically significant in predicting the output responses. Hence the developed models can applied for further study. The relations between signals-to-noise ratio () of weld depth, weld width versus each level of input factors, laser power and weld speed are indicated in Figure 5, 6 and 7.

-5.0

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Laser Power (kW)

Weld Speed (m/s)

Mean of S/N Ratio (dB) Weld Depth

Mean of S/N Ratio (dB) Weld Width

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-5.5 -6.0 -6.5 -7.0 -7.5 -8.0 -8.5 1.8

2.0

2.5

16.6667

25.0000

33.3333

Laser Power (kW)

13.0 12.5 12.0 11.5 11.0 10.5 10.0

1.8

Signal-to Noise Ratio Smaller the better

Weld Speed (m/s)

2.0

2.5

16.6667

25.0000

33.3333

Signal-to-Noise Ratio Larger the better

Mean of S/N Ratio (dB) Sep. Force

Fig. 5. Main Effect plot  S/N (Weld Depth)

Fig. 6. Main Effect plot  S/N (Weld Width)

Laser Power (kW)

Weld Speed (m/s)

47.5 47.0 46.5 46.0 45.5 45.0 1.8

2.0

2.5

16.6667

25.0000

33.3333

Signal-to-Noise Ratio Larger the better

Fig. 7. Main Effect plot  S/N (Separation Force)

6.1

Analysis of variance (ANOVA)

Analysis of variance (ANOVA) is a statistical method used for investigation of significance levels of process input factors that affect the output responses or the quality characteristics. In the present work ANOVA has been applied to study the significance of input factors viz. laser power and welding speed on the output responses viz. weld depth and weld width and comparing the outputs of ANOVA with Taguchi S/N ratios indicate the rank of the dominating factor on responses. Tables 8 and 9 summarize the analysis of variance for weld depth and weld width respectively. The analysis has been performed using EXCEL 2007 and MINITAB 17 statistical software. Tables 6, 7 and 8 summarize the analysis of variance performed with MINITAB 17 for weld depth, weld width and weld pool area.

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Table 6. ANOVA for weld depth (mm) Adjusted SS

Adjusted MS

F -Value

P – Value

Model

Source

DOF 4

16.2956

4.0739

5819.8571

0.0000

Power (KW)

1

1.0691

1.0691

1527.2857

0.0000

0.0306 0.1173 0.0035 0.0028 1.2233

0.0306 0.1173 0.0035 0.0007 0.1529

43.714286 167.57143 5.0000

0.0000 0.0000 0.0530

Speed (m/min) Power Square (P2) Speed Square S2 Error Total

1 1 1 4 8 S = 0.0234709 R - Square (Adjusted) = 99.97%

R -Square = 99.98% R - Square (Predicted) = 95.53%

Table 7. ANOVA for weld width (mm) Source

Adjusted SS

Adjusted MS

F -Value

P – Value

Model Power (KW) Speed (m/min)

2 1 1

DOF

5.4572 4.6001 0.6701

2.7286 4.6001 0.6701

38.0823 64.2023 9.3524

0.0000 0.0000 0.0130

Error

6

0.4299

0.0716

Total

8

5.7001

0.7125

S = 0.247810

R -Square = 92.70%

R - Square (Adjusted) = 90.61%

R - Square (Predicted) = 45.91%

Table 8. ANOVA for separation force (kN) Source

DOF

Adjusted SS

Adjusted MS

F -Value

P – Value

Model Power (KW)

4 1

47814.10 18803.00

11953.53 18803.00

3297.52 5187.03

0.000 0.000

Speed (m/min)

1

412.10

412.10

113.68

0.000

Power Square (P2)

1

89.00

89.00

24.55

0.003

Speed Square S2

1

3.70

3.70

1.02

0.312

Error

4

14.50

3.63

8

19322.30

2415.29

Total

S = 1.70094 R - Square (Adjusted) = 99.95 %

6.2

R -Square = 99.97 % R - Square (Predicted) = 90.16 %

ANOVA Outputs

With ANOVA approach, Taguchi based optimum results have been validated and the information is presented in tables 6, 7 and 8 for all three response variables, weld depth, weld width and separation force respectively. The analysis has been done for a significance of 5% i.e. the confidence level is 95%. The F-test indicates that larger have more significant influence on responses. From ANOVA Tables it can be observed that for weld depth, the main significant factor is laser power. The second level significant factor is welding speed and the square terms for laser power & weld speed are relatively less significant. For weld speed the main significant factor is welding speed, the second level significant factor is laser power. The ANOVA tables 6, 7 and 8 also show the other measures such as R2, R2 (Adjusted) and R2 (Predicted) for each of the three responses. There measures are close to 1, which indicates the adequacy of the models. Small ‘S – value’ means the data points fall closer to the fitted curved line.

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6.3 Development of the governing “Mathematical Equations” The final governing mathematical equations in terms of factors and responses that predict the results with reasonable accuracy have been obtained through MINITAB 17 software. Various combinations of equations like, (Linear), (Linear + Interaction), (Linear + Squares), (Full Quadratic) have been tried out to validate the effectiveness of these mathematical models so as to select the model which closely depicts the behavior of the process parameters on performance responses. The study concludes that the following two equations in terms of responses and corresponding factors give best fit. = 0.0007

3.681

− 0.0880 − 0.4776

= 0.193

1.365

− 0.0363

= 0.06 6.4

174.21

− 2.558 − 18.14

0.000593

(4) (5)

− 0.308

(6)

Model validation

The final step is the prediction and verification of the improvement in the response with the optimum level of the welding process parameters. Figs. 8, 9 and 10 show the comparison between the actual and predicted values of weld depth and weld width respectively. The figures depict that the techniques adopted and the models developed are quite accurate in making predictions with a very small error for each response.

Fig. 8. Comparison of Results for Weld Depth at Laser Power 1.8, 2.0 & 2.5 (kW)

Fig. 9. Comparison of Results for Weld Width at Laser Power 1.8, 2.0 & 2.5 (kW)

Fig. 10. Comparison of Results for Separation Force at Laser Power 1.8, 2.0 & 2.5 (kW)

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Table 9 provides the summary of the experimental values, predicted values and % of error. The comparative results conclude that the adopted techniques and the developed mathematical models are quite accurate in making predictions with a very small error. Table 9. Comparison of actual (experimental) values of D, W and FS v/s predicted values

Laser Power

Weld Speed

(Kw)

(mm/s)

Weld Depth (mm) Actual

Pred.

Weld Width (mm)

│%Error│

Actual

Pred.

│% Error│

Separation Force (kN) Actual

Pred.

│% Error│

1.80

16.667

3.756

3.775

0.51

2.401

2.045

14.83

203.00

202.99

0.00

1.80

25.000

3.275

3.247

0.85

1.781

1.743

2.16

178.44

177.05

0.78

1.80

33.333

2.820

2.801

0.66

1.661

1.440

13.31

151.70

151.12

0.39

2.00

16.667

4.153

4.148

0.11

2.521

2.318

8.05

223.73

223.02

0.32

2.00

25.000

3.594

3.621

0.74

1.871

2.016

7.72

194.27

196.57

1.19

2.00

33.333

3.165

3.175

0.31

1.761

1.713

2.73

169.17

170.12

0.56

2.50

16.667

4.934

4.914

0.40

3.024

3.001

0.78

267.39

266.74

0.24

2.50

25.000

4.380

4.387

0.15

2.414

2.698

11.76

237.38

239.01

0.69

2.50

33.333

3.935

3.941

0.15

2.131

2.396

12.41

212.82

211.28

0.73

7. Effect of the process parameters on responses This section presents range of output parameter with respect to a combination of input parameters for different cases. In Fig. 11, the contour plot depicts the effect of laser power and weld speed on weld depth. Similarly, in Fig. 13 and 15 the contour plots depict the effect of laser power and weld speed on the weld width and separation force respectively.

Weld Depth (mm) < -1.6 -1.6 – -0.8 -0.8 – 0.0 0.0 – 0.8 0.8 – 1.6 1.6 – 2.4 2.4 – 3.2 3.2 – 4.0 4.0 – 4.8 4.8 – 5.6 > 5.6

Weld Speed (mm/s)

30

25

20

15

10

D E P T H

5

0

0.0

0.5

1.0

1.5

2.0

2.5

Laser Power (kW)

Weld Speed mm/s

Laser Power (kW) Fig. 11. Contour plot for weld depth (mm)

Fig. 12. 3-D Surface plot for weld depth (mm)

7.1 Weld depth (D) As a result of the study the predicted weld depth has been represented by 3-D Surface Plot as shown in Fig. 12. The Figure indicates that laser power has a higher significance on weld depth i.e., by increasing the laser-power, weld depth increases. The same is represented in Table 5, wherein the laser-power has greater rank (rank = 1) in SN ratio and Table 6 ANOVA (refer F-value). The increase in laser power increases the weld depth. When laser power

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is highest (2.5kW) and speed is minimum (8.700 mm/s), maximum weld depth of 5.5 mm has been predicted as presented in Table 11. Weld depth is directly proportional to the laser-power and inversely proportional to speed.

Weld Width (mm) < -1.0 -1.0 – -0.5 -0.5 – 0.0 0.0 – 0.5 0.5 – 1.0 1.0 – 1.5 1.5 – 2.0 2.0 – 2.5 2.5 – 3.0 3.0 – 3.5 > 3.5

Weld Speed (mm/s)

30 25 20 15 10 5 0 0.0

0.5

1.0

1.5

2.0

2.5

Laser Power (kW)

Figure 13. Contour plot for weld width (mm)

Figure 14. 3-D Surface plot weld width (mm)

7.2 Weld width (W) As a result of the study the predicted weld width, has been represented by 3-D Surface Plot as shown in Fig. 14. The Figure indicates that weld speed has a higher significance on weld-width i.e., by increasing the weld speed, weldwidth decreases. The same is represented in Table 5 wherein the weld speed has greater rank (rank = 1) in SN ratio and Table 7 ANOVA (refer F-value). The increase in weld speed decreases the weld width. When laser power is lowest (1.1731 kW) and speed is higher (42.30 mm/s), minimum weld width of 0.258mm has been predicted as presented in Table 11 Weld width is inversely proportional to the weld-speed.

Figure 15. Contour plot for separation force (kN)

Figure 16. 3-D Surface plot separation force (kN)

7.3 Separation force (FS) As a result of the study the predicted separation force has been represented by 3-D Surface Plot as shown in Fig. 16. The Figure indicates that laser power has a higher significance on separation force due to higher weld depth i.e., by increasing the laser-power, weld depth increases thereby increasing the separation force. The same is represented in Table 5, wherein the laser-power has greater rank (rank = 1) in SN ratio and Table 8 ANOVA (refer F-value). The

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increase in laser power increases the separation force. When laser power is highest (2.5kW) and speed is minimum (8.700 mm/s), maximum separation force of 293.26 kN has been predicted as presented in Table 11. Separation force is directly proportional to the laser-power and inversely proportional to speed. This section presents range of output parameter with respect to a combination of input parameters for different cases. In Fig. 17, 18 and 19, the 2-D plot depicts the effect of ‘P’ and ‘S’ on weld depth, weld width and separation force respectively. Depth at 8.333 mm/s 16.67 mm/s 25.00 mm/s 33.33 mm/s 41.67 mm/s 50.00 mm/s 58.33 mm/s

6.00 5.50 5.00

Weld Depth (mm)

4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

0.50

1.00

1.50

2.00

2.50

Laser Power (kW)

Figure 17. 2-D Plot weld depth (mm 6.00 5.50

Weld Width (mm)

5.00 4.50 4.00

Width at

3.50

8.333 mm/s 16.67 mm/s 25.00 mm/s 33.33 mm/s 41.67 mm/s 50.00 mm/s 58.33 mm/s

3.00 2.50 2.00 1.50 1.00 0.50 0.00

0.50

1.00

1.50

Laser Power (kW)

Figure 18. 2-D Plot weld width (mm)

2.00

2.50

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Separation

Separation Force (kN)

300

8.333 mm/s 16.67 mm/s

250

25.00 mm/s 33.33 mm/s

200

41.67 mm/s 50.00 mm/s

150

58.33 mm/s 100 50 0.00

0.50

1.00

1.50

Laser Power (kW)

2.00

2.50

Figure 19. 2-D Plot separation force (kN)

8. Variation transmission analysis The main objective of robust design is to reduce variability in the product or process thereby making the system less sensitive to noise. Phadke M. S. (1989) [22], Taguchi G. (1989) [23] and Taylor W. A. (1991) [24], the key approach is to reduce the variability of the output (dependent variable) with respect to the variability in the input (independent variable). Variation transmission analysis is a technique used to determine the range of input variable that gives least variability of the output. The variation transmission analysis approach is especially useful when the relation between input variable and output variable has nonlinearity (exploiting nonlinearity). The application of the variation transmission is a very effective tool for determining the optimum operating range for input variables so that the variation on the output responses is minimized. There are two different approaches to variation transmission: (i). Direct observation: The effect that variations of the inputs have on the variation of the output is observed directly from the graph. (ii). Variation transmission analysis: The variations transmitted by the inputs to the output can be calculated based on the relationship between the inputs and outputs and on estimates of the variations of the individual inputs. In the present study first approach of Variation Transmission Analysis i.e. Direct Observation has been selected. From graphs shown through Figures 20 and 21, the effect of variations of the input parameters on variation of the output parameters can directly be observed. Two variation transmission graphs for speed 8.333 mm/s and 58.333 mm/s (observed in Fig. 17 and reproduced here) have been selected and plotted as samples to depict the range of input parameters that has minimum variation on the output responses e.g. in Fig. 20, variability of 2 kW power gives less variability in output response (weld depth) as compared to 1.0 kW power. However, such plots can be drawn for analysis of any selected input parameters to take decision about selection of appropriate input parameters.

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5.00 OUTPUT - Weld Depth (mm)

10 4.00 7.5 3.00 5 2.00 2.5 1.00 0 0.00 0.00

0.50

1.00

1.50

2.00

2.50

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5.00Depth at 4.50(8.33 mm/s) 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

INPUT - Lase Power in (kW)

Figure 20. Variation transmission of laser power v/s weld depth at speed 8.33 mm/s

4.00 OUTPUT - Weld Depth (mm)

4.00 16

3.50

Depth at

3.00(58.33 mm/s) 3.00 12

2.50 2.00

2.00 8

1.50 1.00

4 1.00

0.50 0.00 0

0.00

0.50

1.00

1.50

2.00

2.50

0.00

INPUT - Lase Power in (kW)

Figure 21. Variation transmission of laser power v/s weld depth at speed 58.33 mm/s

Any nonlinear effect can be used to reduce variation on the output response. The above examples illustrate the exploitation of nonlinearity for reducing the variation in the output response. It is evident from the above two graphs that we can achieve a large reduction in the variation of output response, the weld depth by simply changing the nominal setting values of input variable, the laser power. This change, however, does not change the manufacturing cost of welding. Thus, the variance of the output response, weld depth can be reduced by two distinct actions:

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a) Move the nominal value of laser power in the range so that output response is less sensitive to the tolerance on the variation of power. b) Reduce the tolerance i.e. variation in laser power. Phadke, M. S. (1989) [22], Taguchi, G. (1989) [23] refers to action (a) as parameter design and action (b) as tolerance design. Typically, no manufacturing cost increase is associated with changing the nominal values of input parameters (parameter design). However, reducing tolerances (tolerance design) leads to higher manufacturing costs. 9. Optimization of process parameters In case of welding of automotive gears the main criteria is to achieve required strength of the weld joint that can withstand the designed torsion and impact loads. The strength of the weld joint that can withstand the designed torsion and impact loads is directly dependent on the weld depth (D) with minimum weld width (W). The higher weld width and area of weld pool does not have appreciable contribution in increasing the weld joint strength. As a matter of fact they only contribute to larger heat affected zone (HAZ) which can lead to distortion of the gear & synchro assembly depending on the geometry, mass and material of both the parts. This is the reason that minimum of weld depth (D) has always been specified as the design requirement for a particular gear-synchro assembly. The objective of optimization, in this study is determination of optimum selection of input variables viz. laser-power (P) & weld-speed (S) that can achieve the specified weld depth (D) with minimum weld width (W). The optimization criterion is to determine the optimal input parameters for CO2 laser-welding process. There can be three criteria that can be implemented to achieve the weld depth (D) with optimum values of laser-power (P) and weld-speed (S). First is to achieve the weld depth without any limitations either on parameters of the process or the operating costs. Second criteria can be to apply the approach of variation reduction of the output parameters for the selected input parameters by ‘Variation Transmission Analysis’. Third criteria can be a goal to reach the designed weld depth at lowest operating costs using optimum values of power and speed. However, in this study the focus is on the first and second criteria as the approach for optimization. The third criteria can be considered for further research on optimization of the process parameters. Table 11 summarizes the optimum predicted values of input parameters i.e. laser-power (P), weld-speed (S) for selected specified of weld depth (D) and corresponding output parameter i.e. Weld width. These values are obtained through MINITAB 17 software. Table 11. Optimum Values of Laser Power and Weld Speed Weld Depth (mm)

Weld Width (mm)

1.000

0.258

1.1731

42.30

1.500

0.400

1.4915

50.40

2.000

0.558

1.8186

2.500

0.935

2.0946

3.000

1.448

3.500

2.014

4.000

Desirability for Depth

Composite Desirability

55.98

1.0000

1.0000

67.46

1.0000

1.0000

58.30

75.10

1.0000

1.0000

58.30

98.63

1.0000

0.9346

2.3636

54.30

132.05

0.9987

0.9994

2.5000

43.80

176.44

1.0000

0.9751

2.437

2.5000

32.20

215.05

1.0000

0.9972

4.500

2.766

2.5000

23.10

245.33

1.0000

0.9741

5.000

3.045

2.5000

15.50

270.63

0.9999

0.9836

5.500

3.291

2.5000

8.700

293.26

1.0000

0.9991

Popt

(kW)

Sopt

(mm/s)

Fsopt

(kN)

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10. Conclusions The investigations carried out in the research work lead to following concluding remarks: 1.

Taguchi method of DOE and Analysis of variance (ANOVA) are powerful techniques for evaluation of influence of selected input factors on the output responses in case of welding of automotive gear using Laser welding.

2.

Power of laser beam (P) has strong effect on weld depth (D) and separation force (FS). Small change in the value of ‘P’ has a large effect on the response variable i.e. weld depth and separation force. Therefore, the value of ‘P’ should be selected with care. Weld speed strongly affect the weld width (W). The response (W) is inversely proportional to the traverse speed.

3.

Results in the form of mathematical equations that have been optimized through MINITAB 17 software allow quick selection of the optimum settings of weld parameters viz., laser power and weld speed.

4.

The direct approach of variation transmission analysis allows the manufacturing engineer to take quick decision on the optimum operating range for the input parameters as indicated through sample figures 17 and 18.

5.

The adopted techniques and the developed models are quite adequate for the prediction of the response variables for the selected input factors.

Acknowledgements We thank to the management of AVTEC LIMITED, for the encouragement and support in carrying out the study. We would also like to express our sincere thanks to Vikas R. Manjarekar, Executive Vice President for his guidance and support in accomplishing the study. References [1]

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