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Identification of two aluminum alloys and springback behaviors in cold bending
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Procedia 15 (2018)Metal 701–708 17th International Conference on Manufacturing Metal Forming, Procedia Manufacturing 00 (2017) 000–000Forming 2018, 16-19 September 2018, Toyohashi, Japan www.elsevier.com/locate/procedia
17th International Conference on Metal Forming, Metal Forming 2018, 16-19 September 2018, Identification of two aluminum alloys and springback behaviors in Toyohashi, Japan cold bending Identification of two aluminum alloys and springback in Manufacturing Engineering Society International Conference 2017, MESIC behaviors 2017, 28-30 June Yong Liu, Liang 2017, Wang,Vigo Bin(Pontevedra), Zhu, Yilin Wang*, cold bending Spain Yisheng Zhang
State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science & Technology, Wuhan 430074, China
Costing models for capacity optimization in Industry 4.0: Trade-off Yong Liu, Liang Wang, Bin Zhu, Yilin Wang*, Yisheng Zhang between usedandcapacity and operational State Key Laboratory of Materials Processing Die & Mould Technology, Huazhong University ofefficiency Science & Technology, Wuhan 430074, Abstract
China
A. Santana , P. Afonsoa,*, A. Zaninb, R. Wernkeb a
Aluminum alloys have drawn more and more attention in automobile and electronics industry. However, springback is a big a University Minho, Guimarães, Portugalalloys and their heat treatment condition, challenge sheets.of In order4800-058 to identify two aluminum Abstract in cold forming of aluminum alloy b Unochapecó, 89809-000 Chapecó, Brazil Microscope), SEM (Scanning Electron chemical composition detections, microstructure observation by OM SC, (Optical Microscopy)alloys and TEM Electron were carriedand out.electronics With additional hardness tests,springback the results indicate Aluminum have (Transmission drawn more and more Microscope) attention in automobile industry. However, is a big that the two aluminum alloys should bealloy 6xxxsheets. aluminum alloy. went solution hot rolling and challenge in cold forming of aluminum In order to They identify twothrough aluminum alloysheat and treatment their heat after treatment condition, then underwent different detections, time of natural aging. For theobservation purpose of investigating springback behaviors of the two aluminum alloys chemical composition microstructure by OM (Optical Microscope), SEM (Scanning Electron Abstract sheets in coldand forming, hardeningElectron exponents (n), strength coefficient (K) With and plastic strainhardness ratios (r)tests, in three were Microscopy) TEM strain (Transmission Microscope) were carried out. additional the directions results indicate calculated byaluminum tensile tests. Then cold be bending experiments and They finite went element (FE) solution simulations with the implementation of and the that the two alloys should 6xxx aluminum alloy. through heat treatment after hot rolling Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, calculated n anddifferent r were carried out. The tensile results show thatoftheinvestigating formability springback of the two aluminum alloy sheets in cold forming then underwent time of natural aging. For the purpose behaviors of the two aluminum alloys information based on a realoftime basis and, necessarily, muchon more efficient. In this context, capacity optimization is poorinfor theforming, small values n and anisotropy found based the(K) r values. The strain cold bending thatwere the sheets cold strain hardening exponents was (n), strength coefficient and plastic ratios (r) results in threeindicate directions goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. springback decreased with the increasing bending angle and thicker sheets had smaller springback. Besides, the simulation and calculated by tensile tests. Then cold bending experiments and finite element (FE) simulations with the implementation of the Indeed, leanagreed management and other, continuous improvement approaches suggest capacity optimization instead of experiments wellcarried with each indicating thethat springback prediction the calculated andforming r were calculated n and r were out. The tensile resultsthat show the formability of theusing two aluminum alloyvalues sheets of in ncold maximization. The study of capacity optimization and costing models is an important research topic that deserves suitable. It will provide significant guide in in springback prediction of the aluminum alloys coldbending formingresults and help to design the is poor for the small values of n and anisotropy was found based on r values. The incold indicate that the contributions from both theoretical perspectives. This presents and discusses a mathematical tools. springback decreased with the the practical increasingand bending angle and thicker sheets hadpaper smaller springback. Besides, the simulation and model for capacity management basedindicating on different modelsprediction (ABC and TDABC). A generic hasr been experiments agreed well with each other, that costing the springback using the calculated valuesmodel of n and were © 2018 The Authors. by Elsevier developed and it wasPublished used to analyze capacity andprediction to design of strategies towards thecold maximization organization’s suitable. It will provide significant guideidle in B.V. in springback aluminum alloys in forming andof help to design the Peer-review responsibility the scientific committee of the 17th International Conferenceand on Metal Forming. value. The under trade-off capacity of maximization vs operational efficiency is highlighted it is shown that capacity tools.
optimization might hide operational inefficiency.
© 2018 The Authors. Published by Elsevier B.V. © 2017 2018 The The Authors. Authors. Published Published by by Elsevier B.V. B.V. © Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. Peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the Manufacturing 17th International Conference on Metal Forming. Conference Peer-review Engineering Society International 2017. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
* Corresponding author. Tel.: +86-27-87547935; fax: +86-27-87547935. E-mail address: [email protected]
1. Introduction
2351-9789 © 2018 author. The Authors. Published by Elsevier * Corresponding Tel.: +86-27-87547935; fax: B.V. +86-27-87547935. Peer-review under responsibility the scientific information committee of for the companies 17th International Conference on Metal E-mail address: [email protected] The cost of idle capacity is aoffundamental and their management of Forming. extreme importance
in modern production systems. In general, it is defined as unused capacity or production potential and can be measured 2351-9789 2018 The Authors. Published by Elsevier B.V.hours of manufacturing, etc. The management of the idle capacity in several©ways: tons of production, available Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. 10.1016/j.promfg.2018.07.303
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
702 2
Keywords: Aluminum alloy; Springback; Heat treatment condition; Cold bending; Strain hardening exponent; Plastic strain ratio; Finite element simulation
1. Introduction Aluminum alloys have drawn more and more attention in automobile and electronics industry. The choice of forming process parameters are affected by the categories and heat treatment conditions of aluminum alloys. Springback is a big challenge in cold forming of aluminum alloy sheets. In order to reduce the springback and cost in industrial production, numerical simulation is often used to predict the springback and then the tools are designed. As a result, accurate simulation parameters and FE model are of high importance. Thus it means the parameters should be determined by experiments, especially for the unknown type of aluminum alloys. Besides, aluminum alloy is an anisotropic material and this also affects the springback of aluminum alloys [1]. Two unknown kinds of aluminum alloys were used in cold stamping for automobile parts in practice, however, springback arose as expected. In order to identify the two aluminum alloys and understand their springback behaviors, (1) chemical composition detections, microstructure observation by OM, SEM and TEM were carried out; (2) strain hardening exponents (n), strength coefficient (K) and plastic strain ratios (r) in three directions were calculated by tensile tests; (3) and then cold bending experiments and FE simulations with the implementation of the calculated n and r were carried out. The methods and obtained results in this paper will provide significant guide in springback prediction of aluminum alloys in cold forming and help to design the tools. Nomenclature σ ε K n r εb εt b0 b l0 l t0 t
true stress true strain strength coefficient strain hardening exponent plastic strain ratio true strain of sheet in width direction true strain of sheet in thickness direction width of original gauge length width of measuring point after plastic deformation length of original gauge length length of gauge length after plastic deformation thickness of original gauge length thickness of measuring point after plastic deformation
2. Experiments and FE simulation 2.1. Material and chemical composition detections Alloy A has a thickness of 1.1 mm and Alloy B has a thickness of 1.7 mm. The chemical composition detections were done by optical emission spectroscopy with PDA-7000. Then the precise contents of Mg and Si were further detected by inductively coupled plasma (Prodigy) atomic emission spectrometer based on GB/T 20975.25-2008 in The Test & Supervision Center for Moulding Materials & Casting for Mechanical Industry in China. 2.2. Microstructure detections Microstructures of the cross-section of the two aluminum alloy samples were characterized using an Olympus DSX-500 OM, SEM and TEM. The samples for OM were ground and polished and then etched using Keller’s
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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reagent (190 ml water, 2 ml hydrofluoric acid, 3 ml hydrochloric acid and 5 ml nitric acid). For SEM, the samples were just ground and polished. The energy dispersive spectrometer detections were performed on a Uitra Plus SEM. Thin foils for TEM observation, cut from the base metal using an electrical-discharge machine, were prepared by jet electro-polishing with a solution of 70% methanol and 30% nitric acid at −30 °C and 19 V. The TEM study was performed on a Tecnai G220 TEM. 2.3. Mechanics performance tests The Vickers hardness measurement was carried out under a load of 50 g for 10 s. For each sample, the hardness value was a result of statistics of 15 measurement points. In order to calculate the values of n, K and r of the two alloys, uniaxial tensions were carried out. Samples were cut in 0°, 45° and 90° against rolling direction and Fig. 1 shows the principle shape and dimensions of the samples. The tension speed was 2 mm/min.
Fig. 1. Principle shape and dimensions of the tensile specimen (unit: mm).
Fig. 2. Cold bending equipment period.
Strain hardening exponent (n) characterizes the hardenability of material in work hardening and it can be obtained from hardening exponent curve in Eq. (1). Taking the logarithm on both sides of Eq. (1), n and K can be calculated by linear fitting from Eq. (2).
σ = Kε n ,
(1)
= ln σ n ln ε + ln K .
(2)
Plastic strain ratio (r) is a measure of the ability of a sheet metal to resist thinning or thickening when subjected to a tensile or compressive force. As shown in Eq. (3), the r value is the ratio of the true strain in the width direction to the true strain in the thickness direction when a sheet material is pulled in uniaxial tension beyond its elastic limit. And the samples were just pulled to a strain less than maximum force. In fact, determining the r value is governed by ASTM E517. As a result, the r is calculated form Eq. (4).
r = εb / εt ,
(3)
r = ln(b / b0 ) / ln(b0 l0 / bl ) .
(4)
2.4. Cold bending experiments and FE simulation Sheets with a length of 180 mm and a width of 30 mm were used in the cold bending. And the sheets were in 0° against rolling direction. Fig. 2 shows the cold bending equipment. Bending angles of 60°, 90° and 120° were chosen in the cold bending experiments. The punch speeds were 0.04, 0.4 and 4 mm/s. The real bending angles after bending and unloading were measured by a digital display angle rule. As a result, the springback angle was the latter minus the former. Three tests were carried out for each bending angle at each punch speed and the mean values were used. The calculated values of n, K and r were used in the simulation. The Young modulus is 69 GPa and the poisson ratio is 0.33. The Barlat material model was used and the Barlat exponent was 8 (for face-centred cubic structure).
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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The strain-stress curves obtained from the tensile tests were loaded to the FE software. Full integration shell element was used and the punch speed was the same. 3. Results and discussion 3.1. Identification of two aluminum alloys The results of optical emission spectroscopy and inductively coupled plasma atomic emission spectrometer detections are given in Table 1 and Table 2, respectively. The nominal composition of 6451 is also listed in Table 1. Taking the contents of Si, Mg, Cu, Zn and Mn into consideration, the two alloys should be 6xxx aluminum alloys. It preliminarily indicates that the two aluminum alloys might be considered to be 6451 according to [2], but not sure. Table 1. Main chemical compositions of the as-received two aluminum alloys and 6061 [wt. %]. Chemical elements
Si
Mg
Cu
Zn
Mn
Fe
Sn
Cr
Al
Alloy A
0.757
0.771
0.2717
0.137
0.1187
0.0735
0.0029
0.0048
Balance
Alloy B
0.758
0.708
0.2645
0.141
0.1392
0.0792
0.0031
0.0061
Balance
6451
0.6-1.0
0.4-0.8
≤0.4
≤ 0.15
0.05-0.4
≤ 0.4
/
≤ 0.1
96.5-98.95
Table 2. Precise contents of Mg and Si detected by inductively coupled plasma atomic emission spectrometer [wt. %]. Chemical elements
Si
Mg
Alloy A
0.74
0.77
Alloy B
0.73
0.70
Fig. 3 shows the OM overview of the two aluminum alloys. It can be seen that there are two structures: fibrous tissues and dynamic recrystallized isoaxial tissues. The sheets were expected to go through a rolling process for the existence of fibrous tissue. It seems that the sheets were hot rolled for the existence of dynamic recrystallization structures. At a high temperature, hot rolling is good for dynamic recrystallized. Besides, heat treatment at high temperature after deformation also benefits the recrystallization (static). As a result, it is initially thought that both the two alloys have gone through hot rolling process. It also could be seen that the difference in the grain size is not obvious, indicating that the both two alloys have a very similar processing technology.
Fig. 3. OM overview for (a) Alloy A and (b) Alloy B.
Fig. 4 gives the SEM overview of the second phases in the two aluminum alloys and energy dispersive spectrometer detections. Coarse second phases can be seen in both alloys and the size was about 10 µm. No larger size of second phases under casting conditions was found, and this further proves that the materials have been rolled because the phases with larger size were crushed and became small during deformation. The second phases have two different appearances: one is rod-like structure and the other is widmanstatten-like structure. Energy dispersive spectrometer detections found that though they have different appearances, the chemical compositions are almost
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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the same, and the results show that Fe, Mn and Si are rich elements in these second phases, so the second phases in the two alloys are Al(Fe-Mn-Si) particles. They have very limited strengthening effect on the alloys. As a consequence, the difference of the second phases in the appearance, size and chemical compositions were very small. Thus, the two alloys are expected to be or close to be the same kind of aluminum alloy. Hardness tests show that the hardness of Alloy A is 90.16±3 HV while the hardness of Alloy B is 103.97±3 HV. Therefore they may have different heat treatment conditions.
Fig. 4. SEM overview of second phases in the two aluminum alloys; (a) Alloy A and (b) Alloy B; and energy dispersive spectrometer detections; (c) Alloy A and (d) Alloy B (experiment parameters in SEM photos: AccV - 15.0 kV; Probe - 7.0; Mag - ×1000; Det - SBSE).
Fig. 5. Dislocations for (a) Alloy A and(b) Alloy B.
Fig. 6. Precipitates for (a) Alloy A and (b) Alloy B.
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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Fig. 5 shows the dislocations in the two aluminum alloys. It is believed that the dislocations increase after hot rolling [3]. So the very limited amount of dislocations suggests that high temperature annealing or solution took place after hot rolling. For heat treatable wrought alloys, the precipitates dissolve in solution heat treatment and thus supersaturated solid solution forms. Besides, the dislocations decrease in solution heat treatment. The strength reduces after solution heat treatment and the difficulties in cold stamping are reduced as well. The strength improves after stamping and aging because the formation of precipitates from the supersaturated solid solution. As a consequence, the two alloys are expected to have undergone solution heat treatment after hot rolling. For further confirmation of solution heat treatment, observation of precipitates was carried out and it is shown in Fig. 6. As shown, no needle-like β″, rod-shaped β′ and the cube β (Mg2Si) could be observed, indicating that artificial aging was not carried out. However, GP Zone was observed. And Alloy B has more GP Zones. This provides more evidence that the two alloys have undergone solution heat treatment and then natural aging was implemented, but Alloy B had a longer natural aging time (longer storage time). Longer natural aging time means larger hardness and the hardness testing results also prove this. Finally, there are reasonable grounds to believe that the two alloys are 6xxx aluminum alloys and they underwent solution heat treatment after hot rolling. And then they were storage at room temperature or lower than artificial aging temperature for different time: Alloy B has a longer storage time. 3.2. Evaluation of anisotropy
Fig. 7. True stress – true strain curves for (a) Alloy A and (b) Alloy B at room temperature. Table 3. Mechanical properties of two aluminum alloys and anisotropy parameters. Alloy
Direction
n
Mean n
K
Mean K
r
Mean r
Strength
Mean Strength
Elongation
Mean Elongation
A
0°
0.1264
0.1201
424.4
415.1
0.624
0.527
348 MPa
352 MPa
17.7%
22.2%
45°
0.1132
407.7
0.393
357 MPa
24.7%
90°
0.1275
420.6
0.699
344 MPa
21.8%
0°
0.1294
45°
0.1122
419.0
0.383
371 MPa
25.3%
90°
0.1291
440.3
0.604
363 MPa
22.5%
B
0.1207
443.2
430.4
0.645
0.504
360 MPa
366 MPa
21.8%
23.7%
The true stress–true strain curves of the two aluminum alloys at room temperature are demonstrated in Fig. 7. Table 3 shows the mechanical properties of the two aluminum alloys and anisotropy parameters. As seen, the two alloys have similar strength and elongation. But just like the hardness testing results, Alloy B has relatively larger strength. Obviously, Alloy B has more GP Zone as shown in Fig. 6, leading to a larger strength. Meanwhile, Alloy B also has relative larger elongation. Therefore, the two alloys were considered to have different brands. Besides, the
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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strength and elongation in 45° against rolling direction is the largest whereas the other two directions have very similar values in both of the two alloys. The values of n, K and r also reveal the same situation. The small values of n indicate that the formability of the two aluminum alloy sheets in cold forming is poor and anisotropy was found based on the r values. 3.3. Springback behaviors in cold bending and simulation results Fig. 8 shows the springback of the two aluminum alloys at different bending angles and punch speeds. The springback angles in Fig. 8 are given in Fig. 9. It could be seen that the both two alloys had springback more than 10° and Alloy A (1.1 mm) had larger springback than Alloy B (1.7 mm) under same conditions. Fig. 9(a) reveals that the springback angles decreased with the increase of bending angle. Mehmet Alper Sofuoğlu et al. [4] presents the same results in three-point bending of AA6082T6 tubes with different wall thickness. With the decreasing of the bending angle, the length of the deformation zone increases. As a result, more regions make contributions to the springback, leading to the increase of springback. Though the two alloys have differences, they have very similar mechanical properties and anisotropy parameters as shown in Table 3. The different thickness of the sheets was expected to the reason why they have very different springback behaviors. During bending, the bottom layer of the sheet in outer region is in tension stress state, while the top layer in inner region is in compressive stress state. With thicker thickness at a same bending angle, more material in outer and inner region deforms plastically, and thus springback decreases. Fig. 9b indicates that the punch speed had a very limited effect on the springback. This may be resulted from the small speeds. Thus, as mentioned before, the same punch speed was used in the simulation.
Fig. 8. Springback of two aluminum alloys for (a) and (c) different bending angles and for (b) and (d) different punch speeds.
Fig. 9. Springback angles of two aluminum alloys at different bending angles and punch speeds: (a) different bending angles at a punch speed of 0.4 mm/s; (b) with a bending angle of 90° at different punch speeds.
The comparison of springback angles in experiments and simulations is shown in Fig. 10. The simulation results and experiment results agreed well with each other, indicating that the springback prediction using the calculated values of n and r were suitable. And the Barlat material model can be used to predict the springback behaviors of the two aluminum alloys. As the demonstrated by the experiments and simulations results, springback behaviors of
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Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
aluminum alloys are affected by many factors, such as the kind, thickness, heat treatment condition, stamping speed and so on. What is more, thickness is an important factor which may be ignored. Some kinds of aluminum alloys, for example, the Al-Mg-Si alloys (6xxx series) have serious negative natural aging effect, which means different storage time before aging will bring different mechanics properties. And some kinds of aluminum alloys, for example, the 7xxx series, have very long time natural aging. As a result, the mechanics properties of the aluminum alloys are uncertain if their kinds, heat treatment condition and so on are unknown. So springback in cold forming of aluminum becomes more complicate. Therefore, fundamental work must be done to obtain accurate simulation results to predict the springback behaviors better for aluminum alloys in cold forming.
Fig. 10. Springback angles in experiments and simulations.
4. Conclusions (1) The two alloys are 6xxx aluminum alloys and they underwent solution heat treatment after hot rolling. And then they were storage at room temperature or lower than artificial aging temperature for different time: Alloy B has a longer storage time. (2) The two alloys have similar strength and elongation whereas Alloy B has relatively larger strength and elongation. Besides, the strength and elongation in 45° against rolling direction is the largest whereas the other two directions have very similar values in both of the two alloys. The values of n, K and r also reveal the same situation. (3) The cold bending results indicate that the springback decreased with the increasing bending angle and thicker sheets had smaller springback. Besides, the simulation and experiments agreed well with each other, indicating that the springback prediction using the calculated values of n and r were suitable. (4) Fundamental work must be done to obtain accurate simulation results to predict the springback behaviors better for aluminum alloys in cold forming. Acknowledgements This research work was financially supported by the National Natural Science Foundation of China (grant No. U1760205 and U1564203). References [1] V.T. Nguyen, Z. Chen, P.F. Thomson, Prediction of spring-back in anisotropic sheet metals, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science advances, 218 (2004) 651–661. [2] The Aluminum Association, International alloy designations and chemical composition limits for wrought aluminum and wrought aluminum alloys, http://www.aluminum.org/sites/default/files/TEAL_1_OL_2015.pdf. [3] C.Q. Huang, J.P. Diao, H. Deng, B.J. Li, X.H. Hu, Microstructure evolution of 6016 aluminum alloy during compression at elevated temperatures by hot rolling emulation, Transactions Nonferrous Metals Society of China, 23 (2013) 1576−1582. [4] M.A. Sofuoğlu, S. Gürgen, F.H. Çakır, S. Orak, Springback behavior of AA6082T6 tubes in three-point bending operation, Procedia Engineering, 182 (2017) 658−664.
Available online at www.sciencedirect.com Procedia Manufacturing 00 (2018) 000–000
ScienceDirect
Available online atatwww.sciencedirect.com Available www.sciencedirect.com Procedia online Manufacturing 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
www.elsevier.com/locate/procedia
Procedia 15 (2018)Metal 701–708 17th International Conference on Manufacturing Metal Forming, Procedia Manufacturing 00 (2017) 000–000Forming 2018, 16-19 September 2018, Toyohashi, Japan www.elsevier.com/locate/procedia
17th International Conference on Metal Forming, Metal Forming 2018, 16-19 September 2018, Identification of two aluminum alloys and springback behaviors in Toyohashi, Japan cold bending Identification of two aluminum alloys and springback in Manufacturing Engineering Society International Conference 2017, MESIC behaviors 2017, 28-30 June Yong Liu, Liang 2017, Wang,Vigo Bin(Pontevedra), Zhu, Yilin Wang*, cold bending Spain Yisheng Zhang
State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science & Technology, Wuhan 430074, China
Costing models for capacity optimization in Industry 4.0: Trade-off Yong Liu, Liang Wang, Bin Zhu, Yilin Wang*, Yisheng Zhang between usedandcapacity and operational State Key Laboratory of Materials Processing Die & Mould Technology, Huazhong University ofefficiency Science & Technology, Wuhan 430074, Abstract
China
A. Santana , P. Afonsoa,*, A. Zaninb, R. Wernkeb a
Aluminum alloys have drawn more and more attention in automobile and electronics industry. However, springback is a big a University Minho, Guimarães, Portugalalloys and their heat treatment condition, challenge sheets.of In order4800-058 to identify two aluminum Abstract in cold forming of aluminum alloy b Unochapecó, 89809-000 Chapecó, Brazil Microscope), SEM (Scanning Electron chemical composition detections, microstructure observation by OM SC, (Optical Microscopy)alloys and TEM Electron were carriedand out.electronics With additional hardness tests,springback the results indicate Aluminum have (Transmission drawn more and more Microscope) attention in automobile industry. However, is a big that the two aluminum alloys should bealloy 6xxxsheets. aluminum alloy. went solution hot rolling and challenge in cold forming of aluminum In order to They identify twothrough aluminum alloysheat and treatment their heat after treatment condition, then underwent different detections, time of natural aging. For theobservation purpose of investigating springback behaviors of the two aluminum alloys chemical composition microstructure by OM (Optical Microscope), SEM (Scanning Electron Abstract sheets in coldand forming, hardeningElectron exponents (n), strength coefficient (K) With and plastic strainhardness ratios (r)tests, in three were Microscopy) TEM strain (Transmission Microscope) were carried out. additional the directions results indicate calculated byaluminum tensile tests. Then cold be bending experiments and They finite went element (FE) solution simulations with the implementation of and the that the two alloys should 6xxx aluminum alloy. through heat treatment after hot rolling Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, calculated n anddifferent r were carried out. The tensile results show thatoftheinvestigating formability springback of the two aluminum alloy sheets in cold forming then underwent time of natural aging. For the purpose behaviors of the two aluminum alloys information based on a realoftime basis and, necessarily, muchon more efficient. In this context, capacity optimization is poorinfor theforming, small values n and anisotropy found based the(K) r values. The strain cold bending thatwere the sheets cold strain hardening exponents was (n), strength coefficient and plastic ratios (r) results in threeindicate directions goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. springback decreased with the increasing bending angle and thicker sheets had smaller springback. Besides, the simulation and calculated by tensile tests. Then cold bending experiments and finite element (FE) simulations with the implementation of the Indeed, leanagreed management and other, continuous improvement approaches suggest capacity optimization instead of experiments wellcarried with each indicating thethat springback prediction the calculated andforming r were calculated n and r were out. The tensile resultsthat show the formability of theusing two aluminum alloyvalues sheets of in ncold maximization. The study of capacity optimization and costing models is an important research topic that deserves suitable. It will provide significant guide in in springback prediction of the aluminum alloys coldbending formingresults and help to design the is poor for the small values of n and anisotropy was found based on r values. The incold indicate that the contributions from both theoretical perspectives. This presents and discusses a mathematical tools. springback decreased with the the practical increasingand bending angle and thicker sheets hadpaper smaller springback. Besides, the simulation and model for capacity management basedindicating on different modelsprediction (ABC and TDABC). A generic hasr been experiments agreed well with each other, that costing the springback using the calculated valuesmodel of n and were © 2018 The Authors. by Elsevier developed and it wasPublished used to analyze capacity andprediction to design of strategies towards thecold maximization organization’s suitable. It will provide significant guideidle in B.V. in springback aluminum alloys in forming andof help to design the Peer-review responsibility the scientific committee of the 17th International Conferenceand on Metal Forming. value. The under trade-off capacity of maximization vs operational efficiency is highlighted it is shown that capacity tools.
optimization might hide operational inefficiency.
© 2018 The Authors. Published by Elsevier B.V. © 2017 2018 The The Authors. Authors. Published Published by by Elsevier B.V. B.V. © Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. Peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the Manufacturing 17th International Conference on Metal Forming. Conference Peer-review Engineering Society International 2017. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
* Corresponding author. Tel.: +86-27-87547935; fax: +86-27-87547935. E-mail address: [email protected]
1. Introduction
2351-9789 © 2018 author. The Authors. Published by Elsevier * Corresponding Tel.: +86-27-87547935; fax: B.V. +86-27-87547935. Peer-review under responsibility the scientific information committee of for the companies 17th International Conference on Metal E-mail address: [email protected] The cost of idle capacity is aoffundamental and their management of Forming. extreme importance
in modern production systems. In general, it is defined as unused capacity or production potential and can be measured 2351-9789 2018 The Authors. Published by Elsevier B.V.hours of manufacturing, etc. The management of the idle capacity in several©ways: tons of production, available Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 17th International Conference on Metal Forming. 10.1016/j.promfg.2018.07.303
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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Keywords: Aluminum alloy; Springback; Heat treatment condition; Cold bending; Strain hardening exponent; Plastic strain ratio; Finite element simulation
1. Introduction Aluminum alloys have drawn more and more attention in automobile and electronics industry. The choice of forming process parameters are affected by the categories and heat treatment conditions of aluminum alloys. Springback is a big challenge in cold forming of aluminum alloy sheets. In order to reduce the springback and cost in industrial production, numerical simulation is often used to predict the springback and then the tools are designed. As a result, accurate simulation parameters and FE model are of high importance. Thus it means the parameters should be determined by experiments, especially for the unknown type of aluminum alloys. Besides, aluminum alloy is an anisotropic material and this also affects the springback of aluminum alloys [1]. Two unknown kinds of aluminum alloys were used in cold stamping for automobile parts in practice, however, springback arose as expected. In order to identify the two aluminum alloys and understand their springback behaviors, (1) chemical composition detections, microstructure observation by OM, SEM and TEM were carried out; (2) strain hardening exponents (n), strength coefficient (K) and plastic strain ratios (r) in three directions were calculated by tensile tests; (3) and then cold bending experiments and FE simulations with the implementation of the calculated n and r were carried out. The methods and obtained results in this paper will provide significant guide in springback prediction of aluminum alloys in cold forming and help to design the tools. Nomenclature σ ε K n r εb εt b0 b l0 l t0 t
true stress true strain strength coefficient strain hardening exponent plastic strain ratio true strain of sheet in width direction true strain of sheet in thickness direction width of original gauge length width of measuring point after plastic deformation length of original gauge length length of gauge length after plastic deformation thickness of original gauge length thickness of measuring point after plastic deformation
2. Experiments and FE simulation 2.1. Material and chemical composition detections Alloy A has a thickness of 1.1 mm and Alloy B has a thickness of 1.7 mm. The chemical composition detections were done by optical emission spectroscopy with PDA-7000. Then the precise contents of Mg and Si were further detected by inductively coupled plasma (Prodigy) atomic emission spectrometer based on GB/T 20975.25-2008 in The Test & Supervision Center for Moulding Materials & Casting for Mechanical Industry in China. 2.2. Microstructure detections Microstructures of the cross-section of the two aluminum alloy samples were characterized using an Olympus DSX-500 OM, SEM and TEM. The samples for OM were ground and polished and then etched using Keller’s
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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reagent (190 ml water, 2 ml hydrofluoric acid, 3 ml hydrochloric acid and 5 ml nitric acid). For SEM, the samples were just ground and polished. The energy dispersive spectrometer detections were performed on a Uitra Plus SEM. Thin foils for TEM observation, cut from the base metal using an electrical-discharge machine, were prepared by jet electro-polishing with a solution of 70% methanol and 30% nitric acid at −30 °C and 19 V. The TEM study was performed on a Tecnai G220 TEM. 2.3. Mechanics performance tests The Vickers hardness measurement was carried out under a load of 50 g for 10 s. For each sample, the hardness value was a result of statistics of 15 measurement points. In order to calculate the values of n, K and r of the two alloys, uniaxial tensions were carried out. Samples were cut in 0°, 45° and 90° against rolling direction and Fig. 1 shows the principle shape and dimensions of the samples. The tension speed was 2 mm/min.
Fig. 1. Principle shape and dimensions of the tensile specimen (unit: mm).
Fig. 2. Cold bending equipment period.
Strain hardening exponent (n) characterizes the hardenability of material in work hardening and it can be obtained from hardening exponent curve in Eq. (1). Taking the logarithm on both sides of Eq. (1), n and K can be calculated by linear fitting from Eq. (2).
σ = Kε n ,
(1)
= ln σ n ln ε + ln K .
(2)
Plastic strain ratio (r) is a measure of the ability of a sheet metal to resist thinning or thickening when subjected to a tensile or compressive force. As shown in Eq. (3), the r value is the ratio of the true strain in the width direction to the true strain in the thickness direction when a sheet material is pulled in uniaxial tension beyond its elastic limit. And the samples were just pulled to a strain less than maximum force. In fact, determining the r value is governed by ASTM E517. As a result, the r is calculated form Eq. (4).
r = εb / εt ,
(3)
r = ln(b / b0 ) / ln(b0 l0 / bl ) .
(4)
2.4. Cold bending experiments and FE simulation Sheets with a length of 180 mm and a width of 30 mm were used in the cold bending. And the sheets were in 0° against rolling direction. Fig. 2 shows the cold bending equipment. Bending angles of 60°, 90° and 120° were chosen in the cold bending experiments. The punch speeds were 0.04, 0.4 and 4 mm/s. The real bending angles after bending and unloading were measured by a digital display angle rule. As a result, the springback angle was the latter minus the former. Three tests were carried out for each bending angle at each punch speed and the mean values were used. The calculated values of n, K and r were used in the simulation. The Young modulus is 69 GPa and the poisson ratio is 0.33. The Barlat material model was used and the Barlat exponent was 8 (for face-centred cubic structure).
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The strain-stress curves obtained from the tensile tests were loaded to the FE software. Full integration shell element was used and the punch speed was the same. 3. Results and discussion 3.1. Identification of two aluminum alloys The results of optical emission spectroscopy and inductively coupled plasma atomic emission spectrometer detections are given in Table 1 and Table 2, respectively. The nominal composition of 6451 is also listed in Table 1. Taking the contents of Si, Mg, Cu, Zn and Mn into consideration, the two alloys should be 6xxx aluminum alloys. It preliminarily indicates that the two aluminum alloys might be considered to be 6451 according to [2], but not sure. Table 1. Main chemical compositions of the as-received two aluminum alloys and 6061 [wt. %]. Chemical elements
Si
Mg
Cu
Zn
Mn
Fe
Sn
Cr
Al
Alloy A
0.757
0.771
0.2717
0.137
0.1187
0.0735
0.0029
0.0048
Balance
Alloy B
0.758
0.708
0.2645
0.141
0.1392
0.0792
0.0031
0.0061
Balance
6451
0.6-1.0
0.4-0.8
≤0.4
≤ 0.15
0.05-0.4
≤ 0.4
/
≤ 0.1
96.5-98.95
Table 2. Precise contents of Mg and Si detected by inductively coupled plasma atomic emission spectrometer [wt. %]. Chemical elements
Si
Mg
Alloy A
0.74
0.77
Alloy B
0.73
0.70
Fig. 3 shows the OM overview of the two aluminum alloys. It can be seen that there are two structures: fibrous tissues and dynamic recrystallized isoaxial tissues. The sheets were expected to go through a rolling process for the existence of fibrous tissue. It seems that the sheets were hot rolled for the existence of dynamic recrystallization structures. At a high temperature, hot rolling is good for dynamic recrystallized. Besides, heat treatment at high temperature after deformation also benefits the recrystallization (static). As a result, it is initially thought that both the two alloys have gone through hot rolling process. It also could be seen that the difference in the grain size is not obvious, indicating that the both two alloys have a very similar processing technology.
Fig. 3. OM overview for (a) Alloy A and (b) Alloy B.
Fig. 4 gives the SEM overview of the second phases in the two aluminum alloys and energy dispersive spectrometer detections. Coarse second phases can be seen in both alloys and the size was about 10 µm. No larger size of second phases under casting conditions was found, and this further proves that the materials have been rolled because the phases with larger size were crushed and became small during deformation. The second phases have two different appearances: one is rod-like structure and the other is widmanstatten-like structure. Energy dispersive spectrometer detections found that though they have different appearances, the chemical compositions are almost
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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the same, and the results show that Fe, Mn and Si are rich elements in these second phases, so the second phases in the two alloys are Al(Fe-Mn-Si) particles. They have very limited strengthening effect on the alloys. As a consequence, the difference of the second phases in the appearance, size and chemical compositions were very small. Thus, the two alloys are expected to be or close to be the same kind of aluminum alloy. Hardness tests show that the hardness of Alloy A is 90.16±3 HV while the hardness of Alloy B is 103.97±3 HV. Therefore they may have different heat treatment conditions.
Fig. 4. SEM overview of second phases in the two aluminum alloys; (a) Alloy A and (b) Alloy B; and energy dispersive spectrometer detections; (c) Alloy A and (d) Alloy B (experiment parameters in SEM photos: AccV - 15.0 kV; Probe - 7.0; Mag - ×1000; Det - SBSE).
Fig. 5. Dislocations for (a) Alloy A and(b) Alloy B.
Fig. 6. Precipitates for (a) Alloy A and (b) Alloy B.
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Fig. 5 shows the dislocations in the two aluminum alloys. It is believed that the dislocations increase after hot rolling [3]. So the very limited amount of dislocations suggests that high temperature annealing or solution took place after hot rolling. For heat treatable wrought alloys, the precipitates dissolve in solution heat treatment and thus supersaturated solid solution forms. Besides, the dislocations decrease in solution heat treatment. The strength reduces after solution heat treatment and the difficulties in cold stamping are reduced as well. The strength improves after stamping and aging because the formation of precipitates from the supersaturated solid solution. As a consequence, the two alloys are expected to have undergone solution heat treatment after hot rolling. For further confirmation of solution heat treatment, observation of precipitates was carried out and it is shown in Fig. 6. As shown, no needle-like β″, rod-shaped β′ and the cube β (Mg2Si) could be observed, indicating that artificial aging was not carried out. However, GP Zone was observed. And Alloy B has more GP Zones. This provides more evidence that the two alloys have undergone solution heat treatment and then natural aging was implemented, but Alloy B had a longer natural aging time (longer storage time). Longer natural aging time means larger hardness and the hardness testing results also prove this. Finally, there are reasonable grounds to believe that the two alloys are 6xxx aluminum alloys and they underwent solution heat treatment after hot rolling. And then they were storage at room temperature or lower than artificial aging temperature for different time: Alloy B has a longer storage time. 3.2. Evaluation of anisotropy
Fig. 7. True stress – true strain curves for (a) Alloy A and (b) Alloy B at room temperature. Table 3. Mechanical properties of two aluminum alloys and anisotropy parameters. Alloy
Direction
n
Mean n
K
Mean K
r
Mean r
Strength
Mean Strength
Elongation
Mean Elongation
A
0°
0.1264
0.1201
424.4
415.1
0.624
0.527
348 MPa
352 MPa
17.7%
22.2%
45°
0.1132
407.7
0.393
357 MPa
24.7%
90°
0.1275
420.6
0.699
344 MPa
21.8%
0°
0.1294
45°
0.1122
419.0
0.383
371 MPa
25.3%
90°
0.1291
440.3
0.604
363 MPa
22.5%
B
0.1207
443.2
430.4
0.645
0.504
360 MPa
366 MPa
21.8%
23.7%
The true stress–true strain curves of the two aluminum alloys at room temperature are demonstrated in Fig. 7. Table 3 shows the mechanical properties of the two aluminum alloys and anisotropy parameters. As seen, the two alloys have similar strength and elongation. But just like the hardness testing results, Alloy B has relatively larger strength. Obviously, Alloy B has more GP Zone as shown in Fig. 6, leading to a larger strength. Meanwhile, Alloy B also has relative larger elongation. Therefore, the two alloys were considered to have different brands. Besides, the
Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
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strength and elongation in 45° against rolling direction is the largest whereas the other two directions have very similar values in both of the two alloys. The values of n, K and r also reveal the same situation. The small values of n indicate that the formability of the two aluminum alloy sheets in cold forming is poor and anisotropy was found based on the r values. 3.3. Springback behaviors in cold bending and simulation results Fig. 8 shows the springback of the two aluminum alloys at different bending angles and punch speeds. The springback angles in Fig. 8 are given in Fig. 9. It could be seen that the both two alloys had springback more than 10° and Alloy A (1.1 mm) had larger springback than Alloy B (1.7 mm) under same conditions. Fig. 9(a) reveals that the springback angles decreased with the increase of bending angle. Mehmet Alper Sofuoğlu et al. [4] presents the same results in three-point bending of AA6082T6 tubes with different wall thickness. With the decreasing of the bending angle, the length of the deformation zone increases. As a result, more regions make contributions to the springback, leading to the increase of springback. Though the two alloys have differences, they have very similar mechanical properties and anisotropy parameters as shown in Table 3. The different thickness of the sheets was expected to the reason why they have very different springback behaviors. During bending, the bottom layer of the sheet in outer region is in tension stress state, while the top layer in inner region is in compressive stress state. With thicker thickness at a same bending angle, more material in outer and inner region deforms plastically, and thus springback decreases. Fig. 9b indicates that the punch speed had a very limited effect on the springback. This may be resulted from the small speeds. Thus, as mentioned before, the same punch speed was used in the simulation.
Fig. 8. Springback of two aluminum alloys for (a) and (c) different bending angles and for (b) and (d) different punch speeds.
Fig. 9. Springback angles of two aluminum alloys at different bending angles and punch speeds: (a) different bending angles at a punch speed of 0.4 mm/s; (b) with a bending angle of 90° at different punch speeds.
The comparison of springback angles in experiments and simulations is shown in Fig. 10. The simulation results and experiment results agreed well with each other, indicating that the springback prediction using the calculated values of n and r were suitable. And the Barlat material model can be used to predict the springback behaviors of the two aluminum alloys. As the demonstrated by the experiments and simulations results, springback behaviors of
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Yong Liu et al. / Procedia Manufacturing 15 (2018) 701–708 Author name / Procedia Manufacturing 00 (2018) 000–000
aluminum alloys are affected by many factors, such as the kind, thickness, heat treatment condition, stamping speed and so on. What is more, thickness is an important factor which may be ignored. Some kinds of aluminum alloys, for example, the Al-Mg-Si alloys (6xxx series) have serious negative natural aging effect, which means different storage time before aging will bring different mechanics properties. And some kinds of aluminum alloys, for example, the 7xxx series, have very long time natural aging. As a result, the mechanics properties of the aluminum alloys are uncertain if their kinds, heat treatment condition and so on are unknown. So springback in cold forming of aluminum becomes more complicate. Therefore, fundamental work must be done to obtain accurate simulation results to predict the springback behaviors better for aluminum alloys in cold forming.
Fig. 10. Springback angles in experiments and simulations.
4. Conclusions (1) The two alloys are 6xxx aluminum alloys and they underwent solution heat treatment after hot rolling. And then they were storage at room temperature or lower than artificial aging temperature for different time: Alloy B has a longer storage time. (2) The two alloys have similar strength and elongation whereas Alloy B has relatively larger strength and elongation. Besides, the strength and elongation in 45° against rolling direction is the largest whereas the other two directions have very similar values in both of the two alloys. The values of n, K and r also reveal the same situation. (3) The cold bending results indicate that the springback decreased with the increasing bending angle and thicker sheets had smaller springback. Besides, the simulation and experiments agreed well with each other, indicating that the springback prediction using the calculated values of n and r were suitable. (4) Fundamental work must be done to obtain accurate simulation results to predict the springback behaviors better for aluminum alloys in cold forming. Acknowledgements This research work was financially supported by the National Natural Science Foundation of China (grant No. U1760205 and U1564203). References [1] V.T. Nguyen, Z. Chen, P.F. Thomson, Prediction of spring-back in anisotropic sheet metals, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science advances, 218 (2004) 651–661. [2] The Aluminum Association, International alloy designations and chemical composition limits for wrought aluminum and wrought aluminum alloys, http://www.aluminum.org/sites/default/files/TEAL_1_OL_2015.pdf. [3] C.Q. Huang, J.P. Diao, H. Deng, B.J. Li, X.H. Hu, Microstructure evolution of 6016 aluminum alloy during compression at elevated temperatures by hot rolling emulation, Transactions Nonferrous Metals Society of China, 23 (2013) 1576−1582. [4] M.A. Sofuoğlu, S. Gürgen, F.H. Çakır, S. Orak, Springback behavior of AA6082T6 tubes in three-point bending operation, Procedia Engineering, 182 (2017) 658−664.