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Role of trochoidal machining process parameter and chip morphology studies during end milling of AISI D3 steel

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Abstract

This study aims at discovering the effect of the trochoidal loop spacing parameter on Surface Roughness (SR), Specific Cutting Energy (SCE) and Temperature (T) during flat end milling operations. Twenty experimental runs were conducted based on the face centered central composite design (CCD) of response surface methodology (RSM). Artificial Neural Network (ANN) prediction modelling was created using four learning algorithms such as Batch Back Propagation Algorithm (BBPA), Quick Propagation Algorithm (QPA), Incremental Back Propagation Algorithm (IBPA) and Legvenberg–Marquardt back propagation Algorithm (LMBPA). The results were compared based on the value of Root mean square (RMSE) obtained for each learning algorithm and it was identified that LMBPA model produced least RMSE value. The predictive LMBPA neural network model was found to be capable of better predictions of surface roughness, temperature and specific cutting energy within the trained range. The Genetic algorithm(GA) gives the optimum parameters for conformation test and they are cutting speed of 41 m/min, feed rate of 136 mm/min and trochoidal loop spacing of 1.3 mm and error percentage between experimental and GA predicted values is 3.60% for surface roughness, 3.15% for specific cutting energy and 3.89% for temperature was found to be minimal. Scratches and serrated boundaries at both side of the chips were observed and laces, chip adhesion and side flow marks were found on machined surface.

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Abbreviations

AISI:

American Iron & Steel Institute

HRC:

Hardness measured with the Rockwell test for hard materials

RSM:

Response surface methodology

BUE:

Built up edge

CCD:

Central composite design

MRR:

Material removal rate

VMS:

Vision measuring system

SR:

Surface roughness

SCE:

Specific cutting energy

T:

Temperature

BBPA:

Batch back propagation algorithm

QPA:

Quick propagation algorithm

IBPA:

Incremental back propagation algorithm

LMBPA:

Legvenberg–Marquardt back propagation algorithm

GA:

Genetic algorithm

RMSE:

Root mean square error

ANN:

Artificial neural network

Cs :

Cutting speed

fz :

Feed rate

Ls :

Loop spacing

Fx :

Normal force

Fy :

Feed force

Fz :

Axial cutting forces

ap :

Depth of cut

xi :

Input to node j

yi :

Total input to node j in hidden

wij :

Weight representing the strength of the connection between the ith node and jth node

bj :

Bias associated with node j

References

  • Anand, K., Barik, B. K., Tamilmannan, K., & Sathiya, P. (2015). Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Engineering Science and Technology, 18, 394–407.

    Google Scholar 

  • Babu, K. K., Panneerselvam, K., Sathiya, P., Noorul Haq, A., Sundarrajan, S., Mastanaiah, P., et al. (2018). Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. International Journal of Advances Manufacturing Technology, 94, 3117–3129.

    Article  Google Scholar 

  • Balogun, V. A., & Mativenga, P. T. (2017). Specific energy based characterization of surface integrity in mechanical machining. Procedia Manufacturing, 7, 290–296. International conference on sustainable materials processing and manufacturing, SMPM 2017 23–25 January 2017, Kruger national Park.

    Article  Google Scholar 

  • Edem, I. F., Balogun, V. A., & Mativenga, P. T. (2017). An investigation on the impact of toolpath strategies and machine tool axes configurations on electrical energy demand in mechanical machining. International Journal of Advances Manufacturing Technology, 92, 2503–2509.

    Article  Google Scholar 

  • Ferreira, J. C. E., & Ochoa, D. M. (2013). A method for generating trochoidal tool paths for 2½D pocket milling process planning with multiple tools. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227, 1287–1298.

    Article  Google Scholar 

  • Gologlu, C., & Sakarya, N. (2008). The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of Materials Processing Technology, 206, 7–15.

    Article  Google Scholar 

  • Ibaraki, S., Yamaji, I., & Matsubara, A. (2010). On the removal of critical cutting regions by trochoidal grooving. Precision Engineering, 34, 467–473.

    Article  Google Scholar 

  • Kannan, D. B. T., Ramesh, T., & Sathiya, P. (2017). Application of artificial neural network modelling for optimization of Yb: YAG laser welding of nitinol. Transaction of indian institute Metals, 70, 1763–1771.

    Article  Google Scholar 

  • Koshy, P., Dewes, R. C., & Aspinwall, D. K. (2002). High speed end milling of hardened AISI D2 tool steel (58 HRC). Journal of Materials Processing Technology, 127, 266–273.

    Article  Google Scholar 

  • Ktema, O. H., Erzurumlu, T., & Kurtaran, H. (2005). Application of response surface methodology in the optimization of cutting conditions for surface roughness. Journal of Materials Processing Technology, 170, 11–16.

    Article  Google Scholar 

  • Liu, Z. Y., Guo, Y. B., Sealy, M. P., & Liu, Z. Q. (2016). Energy consumption and process sustainability of hard milling with tool wear progression. Journal of Materials Processing Technology, 229, 305–312.

    Article  Google Scholar 

  • Liu, D., Zhang, Y., Luo, M., & Zhang, D. (2019). Investigation of tool wear and chip morphology in dry trochoidal milling of titanium alloy Ti–6Al–4V. Materials, 12, 1937.

    Article  Google Scholar 

  • Mia, M., Khan, M. A., & Dhar, N. R. (2017). Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti–6Al–4Vunder cryogenic jets applied at flank and rake faces of coated WC tool. International Journal of Advances Manufacturing Technology, 93, 975–991.

    Article  Google Scholar 

  • Munoz-Escalona, P., & Maropoulos, P. G. (2010). Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351. Journal of Materials Engineering and Performance, 19, 185–193.

    Article  Google Scholar 

  • Niaki, F. A., Pleta, A., & Mears, L. (2018). Trochoidal milling: investigation of a new approach on uncut chip thickness modeling and cutting force simulation in an alternative path planning strategy. The International Journal of Advanced Manufacturing Technology, 97, 641–656.

    Article  Google Scholar 

  • Öktem, H. (2009). An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation. International Journal Advance Manufacturing Technology, 43, 852–861.

    Article  Google Scholar 

  • Otkur, M., & Lazoglu, I. (2007). Trochoidal milling. International Journal of Machine Tools and Manufacture, 47, 1324–1332.

    Article  Google Scholar 

  • Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2008). Prediction of tool wear using regression and ANN models in end-milling operation. International Journal of Advances Manufacturing Technology, 37, 29–41.

    Article  Google Scholar 

  • Pimenov, D., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing, 29, 1045–1061.

    Article  Google Scholar 

  • Pleta, A., Ulutan, D., & Mears, L. (2014). Investigation of trochoidal milling in nickel-based superalloy Inconel 738 and comparison with end milling. In ASME 2014 international manufacturing science and engineering conference collocated with the JSME 2014 international conference on materials and processing and the 42nd North American manufacturing research conference. American Society of Mechanical Engineers MSEC2014-4151 (pp. V002T02A058). https://doi.org/10.1115/msec2014-4151.

  • Pleta, A., Ulutan, D., & Mears, L. (2015). An investigation of alternative path planning strategies for machining of nickel-based superalloys. Procedia Manufacturing, 1, 556–566.

    Article  Google Scholar 

  • Polishetty, A., Goldberg, M., & Littlefair, N. (2014). Slot machining Of Ti6al4v with trochoidal milling technique. Journal of Machine Engineering, 14, 42–54.

    Google Scholar 

  • Polishettya, A., Goldberg, M., Littlefair, G., Puttaraju, M., Patil, P., & Kalra, A. (2014). A preliminary assessment of machinability of titanium alloy TI–6AL–4V during thin wall machining using trochoidal milling. Procedia Engineering, 97, 357–364. 12th global congress on manufacturing and management, GCMM 2014.

    Article  Google Scholar 

  • Quintana, G., Garcia-Romeu, M. L., & Ciurana, J. (2011). Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing, 22, 607–617.

    Article  Google Scholar 

  • Santhakumar, J., & Mohammed Iqbal, U. (2019). Parametric optimization of trochoidal step on surface roughness and dish angle in end milling of AISID3 steel using precise measurements. Materials, 12, 1335.

    Article  Google Scholar 

  • Shixiong, W., Wei, M., Bin, L., & Chengyong, W. (2016). Trochoidal machining for the high-speed milling of pockets. Journal of Material Processing and Technology, 233, 29–43.

    Article  Google Scholar 

  • Topal, E. S. (2009). The role of stepover ratio in prediction of surface roughness in flat end milling. International Journal of Mechanical Sciences, 51, 782–789.

    Article  Google Scholar 

  • Uhlmann, E., Fürstmann, P., & Rosenau, B. (2013). The potential of reducing the energy consumption for machining TiAl6V4 by using innovative metal cutting processes. In The 11th global conference on sustainable manufacturing (pp. 593–598). Berlin.

  • Wangn, C., Xie, Y., Zheng, L., Qin, Z., Tang, D., & Song, Y. (2014). Research on the chip formation mechanism during the high-speed milling of hardened steel. International Journal of Machine Tools and Manufacture, 79, 31–48.

    Article  Google Scholar 

  • Yan, R., Li, H., Peng, F., Tang, X., Jiawei, X., & Zeng, H. (2017). Stability prediction and step optimization of trochoidal milling. Journal of Manufacturing Science and Engineering, 139, 1–10.

    Google Scholar 

  • Zagórski, I., Kulisz, M., Kłonica, M., & Matuszak, J. (2019). Trochoidal milling and neural networks simulation of magnesium alloys. Materials, 12, 2070.

    Article  Google Scholar 

  • Zain, A. M., Haron, H., & Sharif, S. (2010). Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Systems with Applications, 37, 4650–4659.

    Article  Google Scholar 

  • Zain, A. Z., Haron, H., & Sharif, S. (2012). Integrated ANN–GA for estimating the minimum value for machining performance. International Journal of Production Research, 50, 191–213.

    Article  Google Scholar 

  • Zeroudi, N., Fontaine, M., & Necib, K. (2012). Prediction of cutting forces in 3-axes milling of sculptured surfaces directly from CAM tool path. Journal of Intelligent Manufacturing, 23, 1573–1587.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India

    J. Santhakumar & U. Mohammed Iqbal

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  1. J. Santhakumar
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  2. U. Mohammed Iqbal
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Correspondence to J. Santhakumar.

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Santhakumar, J., Iqbal, U.M. Role of trochoidal machining process parameter and chip morphology studies during end milling of AISI D3 steel. J Intell Manuf 32, 649–665 (2021). https://doi.org/10.1007/s10845-019-01517-5

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  • DOI: https://doi.org/10.1007/s10845-019-01517-5

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