Monitoring thrust during drilling operations is critical to optimizing drilling parameters and ensuring safe and efficient drilling. In this study, we will monitor variations in thrust force during the drilling process using a sensor coated with carbon black. These sensors measure changes in electrical resistance when the sensor is subjected to a mechanical load, such as drilling force. Carbon black at 5 wt% was used, which was embedded in a fiberglass composite. For this study, an orthogonal arrangement of Taguchi’s L27 sequence was used, with drill diameter D, feed rate F, and spindle speed S as machining parameters. The thrust force was measured with a force dynamometer and the resistance change was measured simultaneously with the developed new system. Analysis of variance was used to find the optimal parameters, and in this study, a mathematical model was proposed to measure the thrust force directly using the electrical resistance detected. NSGA-II algorithm was used to predict solutions for the given parameters for comparison. A comprehensive way of drilling process and data-driven decisions to improve drilling performance and safety was studied, and a correlation between actual thrust and calculated thrust of 0.91 was found. The proposed system was able to monitor and continuously record thrust force in real time during drilling, with electrical resistance as the most important factor with an error percentage of less than 8% compared to the NSGA-II predictions of less than 5%.
AliustaogluCErtuncHMOcakH. Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Process2009; 23(2): 539–546.
2.
ErtuncHMLoparoKAOzdemirE, et al. Real time monitoring of tool wear using multiple modeling method. In: IEMDC 2001 - IEEE international electric machines and drives conference, 2001, pp.687–691. New York, NY: IEEE.
3.
JantunenE. A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tools Manuf2002; 42: 997–1010.
4.
LinSCTingCJ. Tool wear monitoring in drilling using force signals. Wear1995; 180: 53–60.
5.
RameshMGopinathA. Measurement and analysis of thrust force in drilling sisal-glass fiber reinforced polymer composites. IOP Conf Ser Mater Sci Eng2017; 197(1): 012056.
6.
CaggianoANapolitanoFNeleL, et al. Study on thrust force and torque sensor signals in drilling of Al/CFRP stacks for aeronautical applications. Procedia CIRP2019; 79: 337–342.
7.
RajmohanTPalanikumarK. Experimental investigation and analysis of thrust force in drilling hybrid metal matrix composites by coated carbide drills. Mater Manuf Process2011; 26(8): 961–968.
8.
PalanikumarKMuniarajA. Experimental investigation and analysis of thrust force in drilling cast hybrid metal matrix (Al–15%SiC–4%graphite) composites%graphite) composites. Measurement2014; 53: 240–250.
9.
KondoEKamoRMurakamiH. Monitoring of burr and prefailure phase caused by tool wear in micro-drilling operations using thrust force signals. J Adv Mech Des Syst Manuf2012; 6(6): 885–897.
10.
LathaBSenthilkumarVSPalanikumarK. Influence of drill geometry on thrust force in drilling GFRP composites. J Reinforced Plast Compos2011; 30(6): 463–472.
11.
OgawaKAoyamaEInoueH, et al. Investigation on cutting mechanism in small diameter drilling for GFRP (thrust force and surface roughness at drilled hole wall). Compos Struct1997; 38(1-4): 343–350.
12.
ArulSVijayaraghavanLMalhotraSK, et al. The effect of vibratory drilling on hole quality in polymeric composites. Int J Mach Tools Manuf2006; 46(3-4): 252–259.
13.
ArulSVijayaraghavanLMalhotraSK. Online monitoring of acoustic emission for quality control in drilling of polymeric composites. J Mater Process Technol2007; 185(1-3): 184–190.
14.
PatraKJhaAKSzalayT, et al. Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng2017; 48: 279–291.
15.
LathaBSenthilkumarVS. Analysis of thrust force in drilling glass fiber-reinforced plastic composites using fuzzy logic. Mater Manuf Process2009; 24(4): 509–516.
16.
VimalRSinghSLathaB, et al. Modeling and analysis of thrust force and torque in drilling GFRP composites by multi-facet drill using fuzzy logic. Int J Recent Trends Eng2009; 1(5): 66
17.
ValarmathiTNPalanikumarKSekarS. Modeling of thrust force in drilling of plain medium density fiberboard (MDF) composite panels using RSM. Procedia Eng2012; 38: 1828–1835.
18.
DiazMBKimKYKangTH, et al. Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting improvement. Geothermics2018; 72: 348–357.
19.
PrasopchaichanaKKwonO-Y. Sensor Fusion by neural network and wavelet analysis for drill-wear monitoring. J Solid Mech Mater Eng2010; 4(6): 749–760.
20.
Zarif KarimiNHeidaryHMinakG. Critical thrust and feed prediction models in drilling of composite laminates. Compos Struct2016; 148: 19–26.
21.
TsaoCC. Prediction of thrust force of step drill in drilling composite material by Taguchi method and radial basis function network. Int J Adv Manuf Technol2008; 36(1-2): 11–18.
22.
DongSLiaoWZhengK, et al. Investigation on thrust force in rotary ultrasonic drilling of CFRP/aluminum stacks. Proc IMechE, Part C: J Mechanical Engineering Science2020; 234(2): 394–404.
23.
PashmforoushFFarshbafZinatiRMalekiD. Investigation of ultrasonic vibration on thrust force, surface integrity, and geometrical tolerances during drilling of natural filler reinforced composites. Proc IMechE, Part C: J Mechanical Engineering Science2020; 234(20): 4041–4055.
24.
EkiciEMotorcuAUzunG. Multi-objective optimization of process parameters for drilling fiber-metal laminate using a hybrid GRAPCA approach. FME Trans2021; 49(2): 356–366.
25.
AravindSShunmugeshKBijuJ, et al. Optimization of micro-drilling parameters by Taguchi Grey Relational Analysis. Mater Today Proc2017; 4(2): 4188–4195.
26.
ZhangPChenYLiY, et al. A flexible strain sensor based on the porous structure of a carbon black/carbon nanotube conducting network for human motion detection. Sensors2020; 20(4): 1154.
27.
ArduiniFCintiSMazzaracchioV, et al. Carbon black as an outstanding and affordable nanomaterial for electrochemical (bio)sensor design. Biosens Bioelectron2020; 156: 112033
28.
AndreghettoDHFuzariGDC. Piezoresistive epoxy resin films with carbon black particles for small-strain sensors. Polym Bull2020; 77(7): 3725–3734.
29.
TsaoCCChiuYC. Evaluation of drilling parameters on thrust force in drilling carbon fiber reinforced plastic (CFRP) composite laminates using compound core-special drills. Int J Mach Tools Manuf2011; 51(9): 740–744.
30.
Dong WangGLiNKirwa MellyS, et al. Monitoring the drilling process of GFRP laminates with carbon nanotube buckypaper sensor. Compos Struct2019; 208: 114–126.
31.
SundarADasCDeshmukhMK. Novel applications of force sensing resistors in healthcare technologies. In: Healthy world conference, 2015.
32.
HouLHayesSA. A resistance-based damage location sensor for carbon-fibre composites. Smart Mater Struct2002; 11: 966–969.
33.
DebKPratapAAgarwalS, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput2002; 6(2): 182–197.
34.
YusoffYNgadimanMSZainAM. Overview of NSGA-II for optimizing machining process parameters. Procedia Eng2011; 15: 3978–3983.
35.
WangQJiaX. Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy C-means. Compos Struct2020; 235: 111803.
