Online Dimensional Controlling System for Drilling

journal of Artificial Intelligence in Electrical Engineering

مقاله 1، دوره 5، شماره 17، شهریور 2016، صفحه 1-10   

نویسندگان

Reza Farshbaf Zinati^{* 1}؛ Ahmad Habibi Zad navin²؛ Mohammad Reza Razfar³

¹Department of Mechanical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

²Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

³Department of Mechanical Engineering, AmirKabir University of Technology, Tehran, Iran.

چکیده

The drilling is well known as one of the most common hole making processes in the industry.
Due to close tolerance requirement for drilled holes in the most of work pieces, online
controlling of the diameter of drilled holes seems to be necessary. In the current work, an online
dimensional controlling system was developed for drilling process. Doing this, drilling process
was executed in different cutting conditions (feed per tooth and cutting speed) and different flank
wear of cutting edges. In each drilling test, axial force and diameter of drilled hole was recorded.
According to the results obtained from analysis of variance (ANOVA), increase of flank wear in
cutting edges increases the axial force and hole-diameter. In this way, the axial cutting force, as
online measurable parameter, could be used for online estimation of the hole-diameter. Neural
network (NN) was used to model the correlation between axial force and the hole-diameter. In
this way, the obtained NN model estimates the maximum acceptable axial force by receiving
cutting conditions and maximum acceptable hole-diameter. The drilling process has to be
stopped as its axial force exceeds the estimated value for drill changing.

کلیدواژه‌ها

Drilling؛ axial cutting force؛ diameter tolerance؛ analysis of variance؛ Neural network

مراجع

[1] Ahn, D.K.; Choi, J.H.; Kweon, J.H. (2014).
Relationship between the drilling condition and
the damage (delamination) zone of glass-fiberreinforced
plastic composites. Advanced
Composite Materials, DOI:
10.1080/09243046.2014.926655.
[2] Aramesh, M.; Shaban, Y.; Balazinski, M.; Attia,
H.; Kishawy, H.; Yacout, S. (2014). Survival Life
Analysis of the Cutting Tools During Turning
Titanium Metal Matrix Composites (Ti-MMCs).
Procedia CIRP, 14, 605-609.
[3] Aziz, M.H.; Ayub, M.A.; Jaafar R. (2012). Realtime
Algorithm for Detection of Breakthrough
Bone Drilling. Procedia Engineering, 41, 352-359.
[4] Debnath, K.; Singh, I.; Dvivedi, A. (2014).
Drilling Characteristics of Sisal Fiber-Reinforced
Epoxy and Polypropylene Composites. Materials
and Manufacturing Processes, 29(11-12), 1401-
1409.
[5] Farshbaf Zinati, R.; Razfar M.R. (2012).
Constrained optimum surface roughness
prediction in turning of X20Cr13 by coupling
novel modified harmony search-based neural
network and modified harmony search algorithm.
International Journal of Advanced Manufacturing
Technology, 58, 93–107.
[6] Farshbaf Zinati, R.; Razfar, M.R.; Nazockdast, H.
(2015). Surface Integrity Investigation for Milling
PA6-MWCNT. Materials and Manufacturing
Processes, DOI: 10.1080/10426914.2014.961473.
[7] Farshbaf Zinati, R.; Razfar, M.R. (2013). An
investigation of the machinability of PA 6/nano-
CaCO3 composite. International journal of
advanced manufacturing technology, 68, 2489–
2497.
[8] Figueroa, M.; García, E.; Muhl, S.; Rodil, S.
(2014). Preliminary Tribological Study and Tool
Life of Four Commercial Drills. Tribology
Transactions, 57(4), 581-588.
[9] Gokulachandran, J.; Mohandas, K. (2012).
Predicting remaining useful life of cutting tools
with regression and ANN analysis. International
Journal of Productivity and Quality Management,
9, 502-518.
[10] Guhring Inc. (2014). Full-line drill catalogue,
press: Guhring, USA.
[11] MacAvelia, T.; Ghasempoora, A.; Janabi-
Sharifia, F. (2014). Force and torque modelling of
drilling simulation for orthopaedic surgery.
Computer Methods in Biomechanics and
Biomedical Engineering, 17(12), 1285-1294.
[12] Palanikumar, K.; Srinivasan, T.; Rajagopal, K.;
Latha, B. (2014). Thrust Force Analysis in
Drilling Glass Fiber Reinforced/Polypropylene
(GFR/PP) Composites. Materials and
Manufacturing Processes ,DOI:
10.1080/10426914.2014.961478.
[13] Samuel Raj, D.; Karunamoorthy, L. (2015).
Study of the Effect of Tool Wear on Hole Quality
in Drilling Cfrp to Select a Suitable Drill for
Multi-Criteria Hole Quality. Materials and
Manufacturing Processes, DOI:
10.1080/10426914.2015.1004713.
[14] Si, X.S.; Wang, W.; Hu, C.H.; Zhou, D.H.
(2011). Remaining useful life estimation–A
review on the statistical data driven approaches.
European journal of operational research, 213, 1-
14.
[15] Steinzig, M.; Upshaw, D.; Rasty, J. (2014).
Influence of Drilling Parameters on the Accuracy
of Hole-drilling Residual Stress Measurements.
Experimental Mechanics, 54 (9), 1537-1543.
[16] Wang, M.; Wang, J. (2012). CHMM for tool
condition monitoring and remaining useful life
prediction. The International Journal of Advanced
Manufacturing Technology, 59, 463-471.
[17] Wang, Y.; Bao, Y.; Zhang, H.; Qiu, Q.; Yan, Z.;
Guo, C. (2014). Evaluating equipment reliability
function and mean residual life based on
proportional hazard model and semi-Markov
process. Power System Technology
(POWERCON), 2014 International Conference
on, 1293-1299.