Conference Proceedings -Innovations for the sheet metal industry 281 05 SPECIFIC PROCESS

Conference Proceedings - Innovations for the sheet metal industry 281

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* Corresponding author: postal address, phone, fax, email address

1,3Department of Metallurgical Engineering and Material Sciences

2Department of Mechanical Engineering Indian Institute of Technology Bombay

Single point incremental forming (SPIF) has gained considerable importance and attention in the sheet metal forming sector, particularly because of the enhanced forming limits achieved and its cost effective rapid prototyping capabilities. But certain intrinsic aspects like poor surface quality, dimensional inaccuracies and moreover the prohibitively high production times required, has prevented its large scale industrial application. High feed rates are possible but at the expense of surface quality, dimensional accuracy and formability. The present study investigates the effect of various process parameters (namely step size, tool diameter, feed rate, ans tool material) on surface quality and dimensional accuracy. Experiments are performed on drawing quality (DQ) steel sheets to make truncated cones. Design of experiments (DOE) based on the Taguchi method is used to optimize the process parameters keeping minimum dimensional errors and minimum roughness as the objective functions. ANOVA is performed to obtain the percentage effect of these parameters on the objective functions. The study reveals the effectiveness of DOE using Taguchi method in the optimization of the SPIF process, which allows finding the best machine and process setting with minimum number of experiments. Single point incremental forming, Taguchi method, ANOVA. Single point incremental forming has gained considerable importance in the metal forming sector due to enhanced forming limits and rapid prototyping capabilities. The process has the advantage of enhanced forming limits due to extremely localised plastic deformation. The history, advance and the detailed process mechanics of this technology are described in [1, 2, 3]. These references are comprehensive enough and the detailed process description is avoided in this paper for this reason. Due to the continuous contact with the forming tool, friction plays a key role not only in the deformation mechanics but also on the surface quality of the formed components [4]. The process is prone to various undesired surface and structural consequences if the key process parameters like the step size (z), feedrate (f), tool diameter (dt) and tool material are not judiciously chosen. Effect of z and spindle speeds on the surface roughness was studied by Hagan et al.[5]. Effect of coated and

uncoated tools on the same was studied by Cavalier et al. [6]. Studies by Ham et al. [7] show that the step size and tool radius affects the internal surface roughness largely. The geometric inaccuracies of the process and methods to quantify and reduce these errors are discussed in [8, 9]. A parametric study on the dimensional errors was done by Ham et al. [10] using a BoxBehnken design of experiments. The present study aims at minimising the dimensional errors and roughness, which could help the process to evolve as a more industrially suitable process by the combined use of Taguchi method and Analysis of variance (ANOVA). This method helps to make the process robust to inherent noise factors like initial thickness and roughness of the blank [11]. Dimensional accuracy of the formed components was checked using a coordinate measuring machine (CMM). Roughness measurements were done using White light interferometer as it is a noncontact type technique.

282 Conference Proceedings - Innovations for the sheet metal industry

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Experimental setup consists of sheet metal blanks, vertical CNC machining center HARDINGE VMC 600, fixture to hold the blank, forming tools and NX8 software to generate the CNC code. The fixture was designed with a backing plate so as to keep the distance between the clamped area and the actual forming area to a minimum. This avoids unnecessary bending effects leading to dimensional errors [8].

Cold rolled and annealed drawing quality grade steel sheets of dimensions 220x220 mm and 1mm thickness were used. Mechanical properties of the material and its chemical composition are given in tables 1 and 2. Mechanical properties of blank material

Property 0° 45° 90°

YS(MPa) 220.5 214.2 208.8 UTS(MPa) 366.49 365.1 364.2

Elongation(mm) 27.1 25.8 26 K value(MPa) 544 545 570

nvalue 0.1683 0.1712 0.1901 Microhardness(HV) 124.8 122.1 122.9 Chemical composition of blank material

C Mn P S Si Al Ni Fe

0.06 0.3 0.02 0.02 0.03 0.07 0.007 rest

To obtain a full understanding of how the key process parameters like step size (z), feed rate (f), tool diameter (dt) and tool material affect the surface roughness and dimensional accuracy, a set of experiments were planned based on the concept of orthogonal arrays. Each factor has three levels and thus an L9 array was chosen for the experiments. All other factors, namely, sheet material and thickness, final shape, lubrication and fixture were kept constant. The spindle rotation speed was fixed as per the formula given by Jesweit et al. [1]. This helps attain rolling contact at the toolsheet interface in all the cases. Levels for z, f and dt were chosen so as to achieve the extreme working conditions without defeating the objective function. Three different tool materials OHNS (Oil hardened and nonshrinking steel: HRC 62), WPS (Water quenched shock resisting steel: HRC 52) and MS (Mild steel: HRC 42) were employed as the tool hardness was also expected to affect the objective functions. Truncated cones of wall angle 45°, depth 45mm

and top diameter 61mm were made in all the experiments. The control factors and their levels are given in Table 3 Control factors and their levels

While a full factorial experiment design in this case requires (34) number of experiments, the use of Taguchi method allows fewer number of experiments to obtain the optimal setting. The present study does not consider any interaction effects among the factors. The L9 orthogonal array of experiments is given in table 4. Table.4Orthogonal array of experiments

Exp no.

z (mm)

f (mm/s)

dt (mm)

Tool material

1a/1b 0.2 15 10 OHNS 2a/2b 0.2 20 12.5 MS 3a/3b 0.2 25 15 WPS 4a/4b 0.5 15 12.5 WPS 5a/5b 0.5 20 15 OHNS 6a/6b 0.5 25 10 MS 7a/7b 0.8 15 15 MS 8a/8b 0.8 20 10 WPS 9a/9b 0.8 25 12.5 OHNS

Each of the experiments had a unique CNC code, generated by using NX8 CAM software and a unique tool. A contour type tool path was used in all the cases. For each experiment two sets of samples were made to capture the effect of noise factors, viz: initial sheet thickness and roughness variations in the blank material.

Fig.1Experimental setup mounted on CNC Machine

The experimental setup and 9 different tools used for conducting the experiments are shown in Fig1 and Fig 2 respectively.

Control Factors

Levels 1 2 3

A. (z),(mm) 0.2 0.5 0.8 B. f ,(mm/s) 15 20 25 C. (dt), (mm) 10 12.5 15 D. Tool Material OHNS MS WPS


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Experimental setup consists of sheet metal blanks, vertical CNC machining center HARDINGE VMC 600, fixture to hold the blank, forming tools and NX8 software to generate the CNC code. The fixture was designed with a backing plate so as to keep the distance between the clamped area and the actual forming area to a minimum. This avoids unnecessary bending effects leading to dimensional errors [8].

Cold rolled and annealed drawing quality grade steel sheets of dimensions 220x220 mm and 1mm thickness were used. Mechanical properties of the material and its chemical composition are given in tables 1 and 2. Mechanical properties of blank material

Property 0° 45° 90°

YS(MPa) 220.5 214.2 208.8 UTS(MPa) 366.49 365.1 364.2

Elongation(mm) 27.1 25.8 26 K value(MPa) 544 545 570

nvalue 0.1683 0.1712 0.1901 Microhardness(HV) 124.8 122.1 122.9 Chemical composition of blank material

C Mn P S Si Al Ni Fe

0.06 0.3 0.02 0.02 0.03 0.07 0.007 rest

To obtain a full understanding of how the key process parameters like step size (z), feed rate (f), tool diameter (dt) and tool material affect the surface roughness and dimensional accuracy, a set of experiments were planned based on the concept of orthogonal arrays. Each factor has three levels and thus an L9 array was chosen for the experiments. All other factors, namely, sheet material and thickness, final shape, lubrication and fixture were kept constant. The spindle rotation speed was fixed as per the formula given by Jesweit et al. [1]. This helps attain rolling contact at the toolsheet interface in all the cases. Levels for z, f and dt were chosen so as to achieve the extreme working conditions without defeating the objective function. Three different tool materials OHNS (Oil hardened and nonshrinking steel: HRC 62), WPS (Water quenched shock resisting steel: HRC 52) and MS (Mild steel: HRC 42) were employed as the tool hardness was also expected to affect the objective functions. Truncated cones of wall angle 45°, depth 45mm

and top diameter 61mm were made in all the experiments. The control factors and their levels are given in Table 3 Control factors and their levels

While a full factorial experiment design in this case requires (34) number of experiments, the use of Taguchi method allows fewer number of experiments to obtain the optimal setting. The present study does not consider any interaction effects among the factors. The L9 orthogonal array of experiments is given in table 4. Table.4Orthogonal array of experiments

Exp no.

z (mm)

f (mm/s)

dt (mm)

Tool material

1a/1b 0.2 15 10 OHNS 2a/2b 0.2 20 12.5 MS 3a/3b 0.2 25 15 WPS 4a/4b 0.5 15 12.5 WPS 5a/5b 0.5 20 15 OHNS 6a/6b 0.5 25 10 MS 7a/7b 0.8 15 15 MS 8a/8b 0.8 20 10 WPS 9a/9b 0.8 25 12.5 OHNS

Each of the experiments had a unique CNC code, generated by using NX8 CAM software and a unique tool. A contour type tool path was used in all the cases. For each experiment two sets of samples were made to capture the effect of noise factors, viz: initial sheet thickness and roughness variations in the blank material.

Fig.1Experimental setup mounted on CNC Machine

The experimental setup and 9 different tools used for conducting the experiments are shown in Fig1 and Fig 2 respectively.

Control Factors

Levels 1 2 3

A. (z),(mm) 0.2 0.5 0.8 B. f ,(mm/s) 15 20 25 C. (dt), (mm) 10 12.5 15 D. Tool Material OHNS MS WPS

Fig.2 Forming tools used for the experiments The experiments were conducted (see Table 5), and the resulting samples are shown in Fig 3.

Fig.3 CNC incrementally formed samples All the samples were then inspected on a CMM for dimensional accuracy.

The diameter of the circle on inner cone surface corresponding to different heights from the cone base, (Fig 4), was measured on a CMM. These diameter readings are compared with those of the designed component using a Matlab code. The diameter difference between the desired value and that from actual components at different heights Table 5: Experimental settings and results

Fig.4 Diameter measurement strategy from the cone base were also calculated and the plots for the same are shown in Fig 5.The plots are shown only for the first set of experiments, though the readings from the second set of samples were also used in further calculations. The diameter variation shows a peak towards the cone top, which is because of the sheet bending due to the clamping, though a backing plate was used to minimise this effect [8]. The overforming of the cone wall due to the extended deformation of the sheet outside the tool contact zone was also observed in the studies by Amirahmad et al. [9]. The average diameter difference (D), which is the arithmetic mean of all the diameter differences, from 10mm to 35mm from the cone base, is the objective function we intend to minimise for obtaining minimum dimensional errors. For this the S/N ratio for smaller the better criteria is used [11], which is given by,

η = 10 log10 (9

1

1n

i=∑ yi

2 ) (1)

Where, n, is the number of samples per experiment. y, is the observation in each sample The inherent nature of this formula is such that, minimum the y value larger is the S/N ratio. So intention of the paper is to find the factor level setting that maximises this ratio.

Exp. No

z (mm)

F (mm/s)

dt (mm)

Tool material

Time (min)

S/N ratio D Ra Rq Rz Rt

1a/1b 0.2 15 10 OHNS 60 3.69 8.69 8.91 23.01 24.19 2a/2b 0.2 20 12.5 MS 45 5.15 5.90 8.30 25.51 27.35 3a/3b 0.2 25 15 WPS 36 8.27 5.53 7.35 22.48 24.59 4a/4b 0.5 15 12.5 WPS 23 8.37 3.63 5.50 22.33 25.07 5a/5b 0.5 20 15 OHNS 18 10.76 1.53 3.11 18.58 21.09 6a/6b 0.5 25 10 MS 14 7.57 11.25 13.51 30.51 31.30 7a/7b 0.8 15 15 MS 13 12.26 2.35 4.08 20.92 25.69 8a/8b 0.8 20 10 WPS 11 9.77 13.02 14.48 28.38 29.33 9a/9b 0.8 25 12.5 OHNS 9 10.21 6.57 8.18 23.02 24.77


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Fig.5 (a, b) Plots showing dimensional variation in samples from 1a to 9a

The S/N ratios of average diameter difference for each of the samples are given in table 5. The corresponding factor effect plots are shown in Fig 6. The factor effect plot clearly shows the variation of D with various parameters and is selfexplanatory. The results obtained clearly shows that, factors A and C have the greatest effects on dimensional accuracy.

Factor effect plot for ∆D

Analysis of variance (ANOVA) was performed and the results are given in table 6. Table 6: ANOVA for dimensional variation

Factor Degree of freedom

Sum of squares

Mean square

A 2 39.03 19.51 B 2 0.55 0.28 C 2 18.80 9.40 D 2 0.57 0.29

Error 0 Total 58.95 The percentage factor effects of various factors are summarized in Table7. Table 7: Percentage factor effect

Factor Percentage Effect

A 66.20 B 0.94 C 31.89 D 0.97

It can be seen that the step size has the largest effect followed by tool diameter. Smaller the z better is the dimensional accuracy and same is the trend seen with dt.. As expected, tool material seems to have negligible effect. The negligible effect of feed rate suggests that higher feed rates can be used, thereby reducing the process time.

Surface roughness is an important feature in the sheet metal forming industry. So the forming process that is chosen needs to impart fairly good surface finish to the components. In SPIF, due to the continuous contact of the workpiece with the tool, blank surface quality changes significantly. Strips of 12 mm width running from top to bottom of the curved surface of the cone were cut using water jet machining and the surface roughness measurements were made using white light interferometry. The cut strips which were used for roughness measurements are shown together with the tool travel direction in Fig 7.

Fig.7 Strips cut out from different samples for roughness measurements

The surface roughness scan gives an output for 1.253x0.97mm area. 20 readings on the inner sur


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Fig.5 (a, b) Plots showing dimensional variation in samples from 1a to 9a

The S/N ratios of average diameter difference for each of the samples are given in table 5. The corresponding factor effect plots are shown in Fig 6. The factor effect plot clearly shows the variation of D with various parameters and is selfexplanatory. The results obtained clearly shows that, factors A and C have the greatest effects on dimensional accuracy.

Factor effect plot for ∆D

Analysis of variance (ANOVA) was performed and the results are given in table 6. Table 6: ANOVA for dimensional variation


Sum of squares

Mean square

A 2 39.03 19.51 B 2 0.55 0.28 C 2 18.80 9.40 D 2 0.57 0.29

Error 0 Total 58.95 The percentage factor effects of various factors are summarized in Table7. Table 7: Percentage factor effect

Factor Percentage Effect

A 66.20 B 0.94 C 31.89 D 0.97

It can be seen that the step size has the largest effect followed by tool diameter. Smaller the z better is the dimensional accuracy and same is the trend seen with dt.. As expected, tool material seems to have negligible effect. The negligible effect of feed rate suggests that higher feed rates can be used, thereby reducing the process time.

Surface roughness is an important feature in the sheet metal forming industry. So the forming process that is chosen needs to impart fairly good surface finish to the components. In SPIF, due to the continuous contact of the workpiece with the tool, blank surface quality changes significantly. Strips of 12 mm width running from top to bottom of the curved surface of the cone were cut using water jet machining and the surface roughness measurements were made using white light interferometry. The cut strips which were used for roughness measurements are shown together with the tool travel direction in Fig 7.

Fig.7 Strips cut out from different samples for roughness measurements

The surface roughness scan gives an output for 1.253x0.97mm area. 20 readings on the inner sur

face of the strip at a spacing of 2mm were taken along the length. The values of Rz obtained in the experiments performed are in the range 8.526.5 µm. This is in good agreement with the range of values obtained in the literature [6]. The range of roughness values obtained in different forming processes is shown in table 8 for sake of comparison. Table 8: Typical roughness values obtained [6]

Metal forming process

Average value of Rz [µm]

Hot rolling 18090 Forging 9023.04 Extruding 23.045.76 Cold rolling 23.045.76 Finish rolling 2.881.44

The roughness values obtained for Average roughness(Ra), Root mean square roughness (Rt), Mean peak to valley height (Rz) and Maximum peak to valley height (Rt) obtained from the white light interferometer are used to calculate the S/N ratio for smaller the better criteria according to (1). The results are summarized in table 5. Factor effect plots for the roughness parameters are shown in Fig.8.

Fig.8 Factor effect plots for (a) Ra (b) Rq (c) Rz and (d) RtTable 9: ANOVA for Ra


Sum of squares

Mean square

A 2 5.31 2.65 B 2 13.03 6.51 C 2 98.22 49.11 D 2 2.84 1.42

Error 0 Total 119.40 Table 10: ANOVA for Rq


Sum of squares

Mean square

A 2 3.57 1.79 B 2 19.52 9.76 C 2 86.49 43.24 D 2 10.03 5.02

Error 0 Total 119.62 Table 11: ANOVA for Rz


Sum of squares

Mean square

A 2 0.30 0.15 B 2 16.21 8.10 C 2 66.37 33.19 D 2 26.65 13.33

Error 0 Total 109.53


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Table 12: ANOVA for Rt


Sum of squares

Mean square

A 2 2.27 1.14 B 2 5.44 2.72 C 2 30.32 15.16 D 2 34.78 17.39 Error 0

Total 72.81 The ANOVA tables for the roughness parameters are given in Tables 912. The percentage factor effects of various factors on the roughness parameters are summarised in table 13. Table 13: Percentage factor effect

Factor Percentage effect Ra Rq Rz Rt

A 4.4 3.0 0.3 3.1 B 10.9 16.3 14.8 7.5 C 82.3 72.3 60.6 41.6 D 2.4 8.4 24.3 47.8

It is evident from these calculations that tool diameter has the greatest effect on roughness. As the tool diameter increases, the roughness decreases, which is in good agreement with the findings of Cavalier et al. [6]. The effect of feed rate is similar for all the parameters which is, smaller the feed, better the surface finish. Though the effect of step size seems to be negligible, the trend shown in the factor effect plots for Ra and Rq suggests that a step size of 0.5 gives better results compared to that of 0.2 or 0.8. Rz and Rt seem to be negligibly affected by step size. The factor effect plots clearly reveal the effect of tool hardness, which is, higher the tool hardness, smaller the roughness, as expected.

The predicted best factor level settings for minimising the dimensional variations and surface roughness is given in table 13. Experiments using the predicted settings need to be performed so as to validate the obtained results. Table 13: Predicted best setting

Objective function Best setting

Dimensional accuracy A1(B1/B2/B3)C1D1

Surface roughness

Ra A2B1C3(D1/D3) Rq A2B1C3(D1/D3) Rz (A1A2A3)B1C3D1 Rt (A1A2)B1C3D1

The authors gratefully acknowledge the financial support from the Department of Mechanical Engineering and Department of Metallurgical Engineering and Material Science, IIT Bombay. [1] Jesweit J., Micari F., Hirt G., Bramely G.,

Duflou J., Allwood J.: Asymmetric single point incremental forming of sheet metal. CIRP AnnalsManufacturing Technology, 54 (2) : 88–114, 2005.

[2] Jackson K., Allwood J.: The mechanics of incremental sheet forming. Journal of materials processing technology, 209: 11581174, 2009.

[3] Emmens W.C., Sebastiani G., Van den Boogaard A.H.: The technology of incremental sheet forming a brief review of the history. Journal of materials processing technology, 210: 981997, 2010.

[4] Eyckens P., Duflou J., Van Bael A.: The significance of friction in the single point incremental forming process. International journal of material forming, 3: 947950,2010.

[5] Hagan E., Jesweit J.: Analysis of surface roughness of computer numerically controlled incremental forming. Journal of engineering manufacture, 218: 13071312,2004.

[6] Cavalier L.C.C., Schaeffer ., Rocha A.S.: Surface roughness in the incremental forming of AISI 304L stainless steel sheets. Far east journal of mechanical engineering and physics, 1(2):8798,2010.

[7] Hamilton K., Jesweit J.:Single point incremental forming at high feed rates: Surface and structural consequences.CIRP annals Manufacturing technology,59:311314,2010.

[8] Shape and dimensional accuracy in single point incremental formingState of the art and future trends. Journal of materials processing technology, 191:390395, 2007.

[9] Amirahmad M., Vanhove H., Van Bael A.:On the geometric accuracy in shallow sloped parts in single point incremental forming. Key Engineering Materials,554557:14431450,2013.

[10]Micari F., Ambrogio G.: Dimensional Accuracy of Single Point Incremental Forming. International journal of material forming, 1: 11711174,2008.

[11]Madhav S Phadke.: Quality engineering using robust design. Pearson education, Inc. & Dorling Kindersley publishing Ltd, 2012.

Conference Proceedings -Innovations for the sheet metal industry 281 05 SPECIFIC PROCESS

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