Research Papers

Iterative Learning Method for Drilling Depth Optimization in Peck Deep-Hole Drilling

[+] Author and Article Information
Ce Han

Key Laboratory of Contemporary Design and
Integrated Manufacturing Technology,
Ministry of Education,
Northwestern Polytechnical University,
Xi'an 710072, China
e-mail: hance@mail.nwpu.edu.cn

Ming Luo

Key Laboratory of Contemporary Design and
Integrated Manufacturing Technology,
Ministry of Education,
Northwestern Polytechnical University,
Xi'an 710072, China
e-mail: luoming@nwpu.edu.cn

Dinghua Zhang

Key Laboratory of Contemporary Design and
Integrated Manufacturing Technology,
Ministry of Education,
Northwestern Polytechnical University,
Xi'an 710072, China
e-mail: dhzhang@nwpu.edu.cn

Baohai Wu

Key Laboratory of Contemporary Design and
Integrated Manufacturing Technology,
Ministry of Education,
Northwestern Polytechnical University,
Xi'an 710072, China
e-mail: wubaohai@nwpu.edu.cn

1Corresponding author.

Manuscript received February 27, 2018; final manuscript received September 5, 2018; published online October 5, 2018. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 140(12), 121009 (Oct 05, 2018) (12 pages) Paper No: MANU-18-1122; doi: 10.1115/1.4041420 History: Received February 27, 2018; Revised September 05, 2018

Due to the enclosed chip evacuation space in deep hole drilling process, chips are accumulated in drill flutes as drilling depth increases, resulting in the increase of drilling torque and lead to drill breakage. Peck drilling is a widely used method to periodically alleviate the drilling torque caused by chip evacuation; the drilling depth in each step directly determines both drill life and machining efficiency. The existing drilling depth optimization methods face problems including low accuracy of the prediction model, the hysteresis of signal diagnosis, and onerous experiments. To overcome these problems, a novel drilling depth optimization method for peck drilling based on the iterative learning optimization is proposed. First, the chip evacuation torque coefficients (CETCs) are introduced into the chip evacuation torque model to simplify the model for learning. Then, the effect of chip removal process in peck drilling on drilling depth is analyzed. The extended depth coefficient by chip removal (EDCbCR) is introduced to develop the relationship between the extended depth in each drilling step and drilling depth. On the foundation of the modeling above, an iterative learning method for drilling depth optimization in peck drilling is developed, in which a modified Newton's method is proposed to maximize machining efficiency and avoid drill breakage. In experiments with different cutting parameters, the effectiveness of the proposed method is validated by comparing the optimized and measured results. The results show that the presented learning method is able to obtain the maximum drilling depth accurately with the error less than 10%.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Kim, H. Y. , and Ahn, J. H. , 2002, “ Chip Disposal State Monitoring in Drilling Using Neural Network Based Spindle Motor Power Sensing,” Int. J. Mach. Tool. Manuf., 42(10), pp. 1113–1119. [CrossRef]
Khan, S. A. , Nazir, A. , Mughal, M. P. , Saleem, M. Q. , Hussain, A. , and Ghulam, Z. , 2017, “ Deep Hole Drilling of AISI 1045 Via High-Speed Steel Twist Drills: Evaluation of Tool Wear and Hole Quality,” Int. J. Adv. Manuf. Technol., 93(1–4), pp. 1115–1125. [CrossRef]
Fu, L. , Ling, S. F. , and Tseng, C. H. , 2007, “ On-Line Breakage Monitoring of Small Drills With Input Impedance of Driving Motor,” Mech. Syst. Signal Process., 21(1), pp. 457–465. [CrossRef]
Ding, W. , Dai, C. , Yu, T. , Xu, J. , and Fu, Y. , 2017, “ Grinding Performance of Textured Monolayer CBN Wheels: Undeformed Chip Thickness Nonuniformity Modeling and Ground Surface Topography Prediction,” Int. J. Mach. Tool. Manuf., 122, pp. 66–80. [CrossRef]
Liu, C. , Ding, W. , Yu, T. , and Yang, C. , 2018, “ Materials Removal Mechanism in High-Speed Grinding of Particulate Reinforced Titanium Matrix Composites,” Precis. Eng., 51, pp. 68–77. [CrossRef]
Mellinger, J. C. , Ozdoganlar, O. B. , Devor, R. E. , and Kapoor, S. G. , 2002, “ Modeling Chip-Evacuation Forces and Prediction of Chip-Clogging in Drilling,” ASME J. Manuf. Sci. Eng., 124(3), pp. 605–614. [CrossRef]
Lacalle, L. N. L. D. , Fernández, A. , Olvera, D. , Lamikiz, A. , Olvera, D. , Rodríguez, C. , and Elias, A. , 2011, “ Monitoring Deep Twist Drilling for a Rapid Manufacturing of Light High-Strength Parts,” Mech. Syst. Signal Process., 25(7), pp. 2745–2752. [CrossRef]
Kim, D. W. , Lee, Y. S. , Min, S. P. , and Chong, N. C. , 2009, “ Tool Life Improvement by Peck Drilling and Thrust Force Monitoring During Deep-Micro-Hole Drilling of Steel,” Int. J. Mach. Tool. Manuf., 49(3–4), pp. 246–255. [CrossRef]
Mellinger, J. C. , Ozdoganlar, O. B. , Devor, R. E. , and Kapoor, S. G. , 2003, “ Modeling Chip-Evacuation Forces in Drilling for Various Flute Geometries,” ASME J. Manuf. Sci. Eng., 125(3), pp. 405–415. [CrossRef]
Kalita, B. , 2015, “ A Review on Optimization of Cutting Parameters in Drilling Using Taguchi Method,” Int. J. Eng. Trends Technol., 29(2), pp. 82–86. [CrossRef]
Kannan, T. D. B. , Kannan, G. R. , Kumar, B. S. N. , and Baskar, D. , 2014, “ Application of Artificial Neural Network Modeling for Machining Parameters Optimization in Drilling Operation,” Procedia Mater. Sci., 5, pp. 2242–2249. [CrossRef]
Jantunen, E. , 2002, “ A Summary of Methods Applied to Tool Condition Monitoring in Drilling,” Int. J. Mach. Tool. Manuf., 42(9), pp. 997–1010. [CrossRef]
Luo, M. , Luo, H. , Axinte, D. , Liu, D. , Mei, J. , and Liao, Z. , 2018, “ A Wireless Instrumented Milling Cutter System With Embedded PVDF Sensors,” Mech. Syst. Signal Process., 110, pp. 556–568. [CrossRef]
Kavaratzis, Y. , and Maiden, J. D. , 1990, “ Real Time Process Monitoring and Adaptive Control During CNC Deep Hole Drilling,” Int. J. Prod. Res., 28(12), pp. 2201–2218. [CrossRef]
Tarng, Y. S. , and Lee, B. Y. , 1999, “ Amplitude Demodulation of the Induction Motor Current for the Tool Breakage Detection in Drilling Operations,” Rob. Comput-Integr. Manuf., 15(4), pp. 313–318. [CrossRef]
Devries, M. F. , and Wu, S. M. , 1970, “ Evaluation of the Effects of Design Variables on Drill Temperature Responses,” J. Eng. Ind., 92(3), pp. 699–705. [CrossRef]
Zhang, J. , Starly, B. , Cai, Y. , Cohen, P. H. , and Lee, Y. S. , 2017, “ Particle Learning in Online Tool Wear Diagnosis and Prognosis,” J. Manuf. Process., 28, pp. 457–463. [CrossRef]
Wu, D. , Jennings, C. , Terpenny, J. , Gao, R. X. , and Kumara, S. , 2017, “ A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests,” ASME J. Manuf. Sci. Eng., 139(7), p. 071018. [CrossRef]
Sörnmo, O. , Olofsson, B. , Robertsson, A. , and Johansson, R. , 2015, “ Learning Approach to Cycle-Time-Minimization of Wood Milling Using Adaptive Force Control,” ASME J. Manuf. Sci. Eng., 138(1), p. 011013. [CrossRef]
Shao, C. , Ren, J. , Wang, H. , Jin, J. , and Hu, S. , 2016, “ Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling,” ASME J. Manuf. Sci. Eng., 139(1), p. 011014. [CrossRef]
Lu, J. S. , Cheng, M. Y. , Su, K. H. , and Tsai, M. C. , 2017, “ Wire Tension Control of an Automatic Motor Winding Machine—An Iterative Learning Sliding Mode Control Approach,” Rob. Comput-Integr. Manuf., 50, pp. 50–62. [CrossRef]
Wang, C. , Zhao, Y. , Chen, Y. , and Tomizuka, M. , 2015, “ Nonparametric Statistical Learning Control of Robot Manipulators for Trajectory or Contour Tracking,” Robot Comput. Integr. Manuf., 35, pp. 96–103. [CrossRef]
Bristow, D. A. , Tharayil, M. , and Alleyne, A. G. , 2006, “ A Survey of Iterative Learning Control,” Control Syst. IEEE., 26(3), pp. 96–114. [CrossRef]
Mansour, S. , and Seethaler, R. , 2016, “ Feedrate Optimization for Computer Numerically Controlled Machine Tools Using Modeled and Measured Process Constraints,” ASME J. Manuf. Sci. Eng., 139(1), p. 011012. [CrossRef]
Kondo, E. , and Shimana, K. , 2012, “ Monitoring of Prefailure Phase and Detection of Tool Breakage in Micro-Drilling Operations,” Procedia CIRP, 1, pp. 581–586. [CrossRef]
Wan, M. , Ma, Y. C. , Feng, J. , and Zhang, W. H. , 2016, “ Study of Static and Dynamic Ploughing Mechanisms by Establishing Generalized Model With Static Milling Forces,” Int. J. Mech. Sci., 114, pp. 120–131. [CrossRef]
Luo, M. , Luo, H. , Zhang, D. , and Tang, K. , 2018, “ Improving Tool Life in Multi-Axis Milling of Ni-Based Superalloy With Ball-End Cutter Based on the Active Cutting Edge Shift Strategy,” J. Mater. Process. Technol., 252, pp. 105–115. [CrossRef]
Yao, Q. , Luo, M. , Zhang, D. , and Wu, B. , 2018, “ Identification of Cutting Force Coefficients in Machining Process Considering Cutter Vibration,” Mech. Syst. Signal Process., 103, pp. 39–59. [CrossRef]
Heinemann, R. , and Hinduja, S. , 2012, “ A New Strategy for Tool Condition Monitoring of Small Diameter Twist Drills in Deep-Hole Drilling,” Int. J. Mach. Tool. Manuf., 52(1), pp. 69–76. [CrossRef]
Aized, T. , and Amjad, M. , 2013, “ Quality Improvement of Deep-Hole Drilling Process of AISI D2,” Int. J. Adv. Manuf. Technol., 69(9–12), pp. 2493–2503. [CrossRef]
Ke, F. , Ni, J. , and Stephenson, D. A. , 2006, “ Chip Thickening in Deep-Hole Drilling,” Int. J. Mach. Tool. Manuf., 46(12–13), pp. 1500–1507. [CrossRef]
Heinemann, R. , Hinduja, S. , Barrow, G. , and Petuelli, G. , 2006, “ The Performance of Small Diameter Twist Drills in Deep-Hole Drilling,” ASME J. Manuf. Sci. Eng., 128(4), pp. 884–892. [CrossRef]
Cruz, C. E. D. , Aguiar, P. R. D. , Á. R., Machado , Bianchi, E. C. , Contrucci, J. G. , and Neto, F. C. , 2013, “ Monitoring in Precision Metal Drilling Process Using Multi-Sensors and Neural Network,” Int. J. Adv. Manuf. Technol., 66(1–4), pp. 151–158. [CrossRef]
Battiti, R. , 1992, “ First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method,” Neural Comput., 4(2), pp. 141–166. [CrossRef]
Glasse, B. , and Fritsching, U. , 2016, “ Continuous Monitoring of Metal Working Fluid Quality in Machining Processes,” ASME J. Manuf. Sci. Eng., 139(4), p. 044501. [CrossRef]


Grahic Jump Location
Fig. 4

Peck drilling procedure

Grahic Jump Location
Fig. 3

Chip flow and force balance on a differential chip section in the drill flute in deep hole drilling

Grahic Jump Location
Fig. 2

Measured drilling torque in deep hole drilling

Grahic Jump Location
Fig. 1

Drill geometry and drilling process: (a) geometry of a typical twist drill, (b) drill-in process, and (c) drilling process after full immersion of the drill bit

Grahic Jump Location
Fig. 5

Residual chips in the flutes after chip removal by coolant

Grahic Jump Location
Fig. 6

Measured chip evacuation torque in peck drilling

Grahic Jump Location
Fig. 7

Iterative learning procedure for drilling depth optimization

Grahic Jump Location
Fig. 8

Experimental setup

Grahic Jump Location
Fig. 9

Iterative learning process in real peck drilling

Grahic Jump Location
Fig. 10

Fitted curve by measured chip evacuation torque in each drilling step

Grahic Jump Location
Fig. 11

Experimental data and fitted curve of chip evacuation torque in one-step drilling verification test

Grahic Jump Location
Fig. 12

Extended depth coefficient by chip removal in peck drilling tests

Grahic Jump Location
Fig. 13

Chips collected from different drilling depth

Grahic Jump Location
Fig. 14

Variation of EDbCR with drilling depth



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In