Outer Corner Wear Research Articles

Prediction of Drill Bit Breakage Using an Infrared Sensor.

In this paper, a novel drill bit breakage prediction method featuring a low-cost commercial infrared sensor to monitor drill bit corner wear is proposed. In the proposed method, the drill bit outer corner wear state can be monitored by measuring reflected infrared light because the reflection phenomenon is influenced by wear, edge shape, and surface roughness of the drill bit. In the experiments, a titanium workpiece was drilled without using cutting fluid to accelerate drill bit fracture. After drilling a hole in the workpiece, reflected infrared light was measured for the drill bit rotating at 100 rpm. Collected data on intensity of infrared light reflected from the circumferential surface of the drill bit versus the rotation angle of the drill bit were considered to predict tool breakage; two significant positions to predict tool breakage were found from the reflected infrared light graphs. By defining gradient vectors from the slopes of the reflected infrared light curves, a reliable criterion for determining drill bit breakage could be established. The proposed method offers possibilities for new measurement and analysis methods that have not been used in conventional tool wear and damage studies. The advantage of the proposed method is that the measurement device is easy to install and the measured signal is resistant to electromagnetic noise and ambient temperature because optical fiber is used as the signal transmission medium. It also eliminates the need for complex analysis of the measured signal, eliminating the need for a high-performance analyzer and reducing analysis time.

One-shot learning for acoustic diagnosis of industrial machines

Automatic acoustic monitoring of machine health comprises a relevant field as, unfortunately, such equipment often suffers from faults, malfunctions, aging effects, etc. However, it is still an unexplored domain of research where the majority of existing works relies on traditional machine learning based approaches. After providing a critical survey of the available methods, this work highlights the most relevant limitations and designs a solution specifically addressing them. We introduce the one-shot learning paradigm into the specific domain and suitably extent it to (a) classify machine states, (b) detect novel ones, and (c) incorporate them in the class dictionary online. The backbone of the present system is a Siamese Neural Network (SNN) composed of convolutional layers. Conveniently, every processing stage depends on a standardized feature set free of domain knowledge, i.e. spectrograms. Interestingly, we enhance SNN’s classification ability by an appropriately designed data selection scheme. The proposed solution is applied on a publicly available dataset of vibration signals representing four states of a drill bit, i.e. healthy state, chisel wear, flank wear, and outer corner wear. After extensive experiments thoroughly examining every aspect of the proposed solution, it is shown to achieve state of the art results while using limited amount of training data. Importantly, at the same time it is able to operate under evolving environments. Last but not least, we show that the obtained predictions are interpretable, a property which is rapidly becoming a requirement in modern machine learning based technologies.

Optimization of varying-parameter drilling for multi-hole parts using metaheuristic algorithm coupled with self-adaptive penalty method

Multi-hole parts made of difficult-to-cut materials like discs, blisks and casings are common and require high surface quality in aero engines. In workshops, a large number of holes in these parts are drilled successively in one process to ensure their positional accuracy. Due to the fast time-varying drill wear, the surface roughness of the holes is unstable and difficult to be satisfied. To this end, this paper presents a varying-parameter drilling (VPD) method to improve machining efficiency and hole surface roughness for multi-hole parts made of Ni-based superalloy. This method uses varying cutting parameters for each hole to adapt to the varying drill wear. The main issue of this method lies in an optimization problem in which the optimal sequence of cutting parameters need to be found, with the objective of the processing time and the constraint of the hole surface roughness. As the cutting parameter sequence has a high dimension and the surface roughness of all the holes must be guaranteed, the challenge of this optimization problem is the strict constraint with a complicated non-linear boundary of the feasible zone. To address the convergence difficulty of the searching algorithm, a soft computing method based on particle swarm optimization (PSO) algorithm with a self-adaptive penalty method (SAPM) is applied. The hole surface roughness is predicted with a radial basis function (RBF) neural network. Different types of drill wear comprising flank wear, crater wear, chisel wear and outer corner wear are considered, and the grey relational analysis (GRA) is employed to select the input drill wear parameters to the network. The PSO algorithm coupled with the SAPM is used to search the global optimal solution of the optimization problem. It is found that the satisfied solutions can be searched in all the three trials with the proposed algorithm, even though the proportion of feasible solutions is severely fluctuant during the searching process. The drilling experiment confirm that, when compared with the fixed-parameter drilling, the proposed VPD and the soft computing method for solving the optimization problem can effectively improve machining efficiency and surface quality for drilling Ni-based superalloy multi-hole parts.

Hole exit damage and tool wear during the drilling of CFRP with a double-point angle drill

The control of hole exit damage and hole wall quality poses a considerable challenge during the drilling of carbon fiber-reinforced plastic (CFRP). This study aims to study the effect of the cutting edge type on the aforementioned issues. First, a comparative study between hole exit damage caused by a twist drill and a double-point angle drill was conducted to investigate the influences of the chisel, principal, and secondary cutting edges on hole exit damage. The maximum damage ring caused by the double-point angle drill is smaller than that caused by the twist drill because the drop speed of the cutting force in the secondary cutting edge drill during the exit stage of the double-point angle drill is less than that of the twist drill. Then, the double-point angle drill was used to determine the effects of tool wear on thrust force and hole wall damage. Outer corner wear and flank wear were investigated, and hole wall damage mechanisms were discussed. The damage of missing fibers does not worsen with an increase in tool wear, but the damage of fiber pullout becomes more serious due to increased cutting force. Consequently, a double-point angle drill bit is preferable for drilling holes on CFRP.

Mechanistic modelling of worn drill cutting forces with drill wear effect coefficients

When drilling difficult-to-cut materials with twist drills, drill wear frequently occurs with various types including flank wear, crater wear, chisel wear and outer corner wear. The previous research mainly focuses on flank wear. This study presents a mechanistic drilling force model of worn drill with the drill wear effect coefficients (DWECs) to describe the effect of different wear types on drilling forces. The DWECs under different drill wear types are calibrated and analyzed through a group of carefully designed experiments. It believes the proposed model together with the results are useful in drill wear detection.

Cryogenic cooling-induced process performance and surface integrity in drilling CFRP composite material

There has been a substantial growth in using carbon fiber-reinforced plastic (CFRP) composite materials in aerospace and automotive industries due to their superior properties. This experimental study presents results from a comprehensive and systematic study investigating the effects of cryogenic cooling on drilling performance and surface integrity characteristics of CFRP composite material. Experimental data on cutting edge radius of drill bit, outer corner wear of drill bit, trust force, torque, delamination factor, and surface integrity characteristics, including borehole subsurface damage and diameter error of drilled hole, are presented and analyzed comparing dry drilling with cryogenic cooling of CFRP composite material. The findings demonstrate that cryogenic cooling has a profound effect on reducing the cutting edge rounding of drill bit and outer corner wear; it also helps enhancing the surface integrity characteristics of produced hole. However, cryogenic cooling generates larger thrust force, torque, and thus larger delamination factor.

Effects of Fe intermetallics on the machinability of heat-treated Al–(7–11)% Si alloys

The need for bridging the divide between the casting process and the machining process provides a strong motivation for examining the various aspects affecting the machinability of Al–Si casting alloys, given that these alloys constitute about 85% of all aluminum castings produced. Strontium-modified, grain-refined, T6 heat-treated 396 alloys (containing ∼11% Si), and B319.2 and A356.2 alloys (containing ∼7% Si) were selected, with a view to studying their machinability characteristics. Three 396 alloy compositions were selected (M1, M3, M4) such that different iron intermetallic phases were obtained in each case. Drilling and tapping operations were both carried out using a Makino A88E machine under fixed machining conditions. The machinability criteria relates to forces and moment analysis as well as to tool life, chip configuration, and built-up edge (BUE) evolution. The effects of Fe-intermetallics (α-Fe, β-Fe, and sludge) on the machining characteristics of these alloys were investigated and a comparison was made between the three 396 alloys in terms of mean total drilling forces, mean total tapping forces, tool life, and chip configuration. The results demonstrate that the presence of sludge has a significant effect on cutting forces and tool life. The tool life of the 396-M3 alloy (containing sludge) decreased by 50% compared to the base alloy 396-M1 (containing α-Fe intermetallics). Increasing the Fe-content from 0.5% to 1% in the M1 alloy (i.e., M4 alloy) produces a distinct improvement in the alloy machinability in terms of cutting forces and tool life. The addition of Fe and Mn appears to have no discernible effect on the built-up-edge (BUE) width and chip configuration as compared to the base alloy. The dominant type of wear which leads to drill failure and breakage is outer corner wear; there is, however, no evidence of crater-wear. Fan-shaped chips were obtained during machining of the 396, B319.2 and A356.2 alloys, where the latter alloy yielded the largest chips. As far as the alloy Si content was concerned, it was found that the 396 alloys produce drilling results similar to those of the B319.2 and A356.2 alloys, in terms of the number of holes drilled (cf. 2160 with 2100 and 2285 holes drilled in the B319.2, and A356.2 alloys, respectively). During tapping tests, however, the B319.2 alloy yielded the longest tool life, i.e., more than twice that of 396 alloy and four-and-half-times that of the A356.2 alloy.

Mechanism of Cutting Temperature Rise in Micro Drilling on Printed Wiring Boards

This paper describes the mechanism of cutting temperature rise in micro drilling on Printed Wiring Boards (PWBs), which affects the micro-drilled hole wall quality. The drill temperature monitored by a thermography was evaluated in drilling. Moreover, we investigated the influence of drill workload, which was calculated with cutting force measured by a dynamometer, on the drill temperature. The following conclusions were reached: (1) The drill temperature tends to increase with the drill workload caused by frictional torque between hole wall and the land or margin of drill. (2) The frictional torque increases with the spring back of drilled hole wall in drilling. The spring back of drilled hole wall increases with the outer corner wear of drill cutting edge in drilling. (3) The reduction of the workload by the friction seems to be effective in order to obtain the high quality micro-drilled hole wall.

Cutting Behaviour Using Variable Feed and Variable Speed when Drilling Stainless Steel with TiN-Coated Carbide Drills

(x) = fc[1+a sin(2π(x/L) + 3/2π)], ω(x) = ωc[1+b sin(π(x/L) + 3/2π)], respectively. The results indicate that variable feed machining is superior to constant feed machining with respect to tool life, burr height, and surface roughness. The amplitude of variation of feed, a = 0.8 and the amplitude of variation of speed, b = 0.07, is optimum for maximum tool life. The range of surface roughness from the variable feed and variable speed can be limited from 0.7 μm to 3 μm. The cutting-edge wear and outer corner wear were the dominant mechanisms of the drill wear.

Cutting behavior of a TiN-coated carbide drill with curved cutting edges during the high-speed machining of stainless steel

Stainless steel plates were drilled in high-speed machining to investigate the effect of tool life, surface roughness, tool wear and burr formation. Drilling was done using a TiN-coated carbide tool with curved cutting edges. The cutting speed V=75 m/min is optimum for minimum surface roughness and burr height. The surface roughness produced was less than 1 μm. Four distinct kinds of burr were observed, namely, burr without any attachment, burr with a drill cap, flange burr and burr with a sharp remnant. The chipping of the helical flutes and the outer corner wear were the main causes of drill failure.

Dry machining — commercial viability through filtered arc vapour deposited coatings

Critical to the economic viability of dry machining is justifying increased tool costs in terms of productivity and ecological savings achievable through the elimination of metal cutting fluids. The automotive industry is a large-scale commercial manufacturing sector in which emphasis is placed on reducing impact on the environment as well as seeking to reduce costs in manufacturing. This paper presents results for dry drilling fully pearlitic grey cast iron with uncoated and partially filtered arc deposited TiN and TiAlN coated Co-HSS split point twist drills using two methods to distinguish drill failure, namely audible screech and measurement of outer corner wear lands. The latter method proved to be of most relevance in manufacturing because it is more closely related to the industrial practice of on-condition assessment prior to drill resharpening. Using the outer corner wear method, TiN coatings (3.0 μm thickness) achieved a modest increase in drill life of 1.4 times, compared with significant improvements of 2.6 times, achieved from a TiAlN top coating (0.8 μm thickness) on a base coating of TiN (1.2 μm thickness). This is a novel result in that a thin top coating of the expensive TiAlN coating over the much cheaper TiN coating can provide a very cost-effective tool coating combination.

Improvement of Tool Life and Exit Burr using Variable Feeds when Drilling Stainless Steel with Coated Drills

Variable feed machining is a significant method for improving the cutting tool life and exit burr height for hard and difficult to machine materials. Four coated drills were tested in the present study, namely, TiN, TiCN, CrN, and TiALN. The results indicate that the amplitude of variation of feed, a = 0.6, is optimum for maximum tool life or minimum burr height. TiN-coated or TiCN-coated drills were superior to CrN-coated or TiALN-coated drills when drilling stainless steel. The chisel wear and the outer corner wear were the dominant mechanisms of the drill wear.

Cutting Tool Wear Detection and Tool Management Using Visual Sensors

It is vital to detect tool wear automatically and control tool life in unattended machining. We studied a vision system that detected wear of cutting tools and predicted tool life. The system targeted measuring cutting drills set in a usual machining center. The system involves 2 CCD cameras that monitor the top and side of a drill, and an image processor. Two image processing algorithms are presented – one to detect drill breakage by analyzing the drill side and one to detect wear including flank, chisel edge, and outer corner wear by matching the top views of new and worn drills.

Fatigue crack growth in unidirectional metal matrix composite : Ghosn, L.J., Telesman, J. and Kantzos, P.NASA Lewis Research Center Report No NASA TM-103102 1990 8 pp

Multi-hole parts made of difficult-to-cut materials like discs, blisks and casings are common and require high surface quality in aero engines. In workshops, a large number of holes in these parts are drilled successively in one process to ensure their positional accuracy. Due to the fast time-varying drill wear, the surface roughness of the holes is unstable and difficult to be satisfied. To this end, this paper presents a varying-parameter drilling (VPD) method to improve machining efficiency and hole surface roughness for multi-hole parts made of Ni-based superalloy. This method uses varying cutting parameters for each hole to adapt to the varying drill wear. The main issue of this method lies in an optimization problem in which the optimal sequence of cutting parameters need to be found, with the objective of the processing time and the constraint of the hole surface roughness. As the cutting parameter sequence has a high dimension and the surface roughness of all the holes must be guaranteed, the challenge of this optimization problem is the strict constraint with a complicated non-linear boundary of the feasible zone. To address the convergence difficulty of the searching algorithm, a soft computing method based on particle swarm optimization (PSO) algorithm with a self-adaptive penalty method (SAPM) is applied. The hole surface roughness is predicted with a radial basis function (RBF) neural network. Different types of drill wear comprising flank wear, crater wear, chisel wear and outer corner wear are considered, and the grey relational analysis (GRA) is employed to select the input drill wear parameters to the network. The PSO algorithm coupled with the SAPM is used to search the global optimal solution of the optimization problem. It is found that the satisfied solutions can be searched in all the three trials with the proposed algorithm, even though the proportion of feasible solutions is severely fluctuant during the searching process. The drilling experiment confirm that, when compared with the fixed-parameter drilling, the proposed VPD and the soft computing method for solving the optimization problem can effectively improve machining efficiency and surface quality for drilling Ni-based superalloy multi-hole parts.

The Automatic Measurement of the Drill Wear by Means of the Industrial Television : Measuring Accuracy of Outer Corner Wear

The Automatic Measurement of the Drill Wear by Means of the Industrial Television : Measuring Accuracy of Outer Corner Wear