Mechanical and thermal signatures as indirect tool wear monitoring indices case study: drilling

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1 Mechanical and thermal signatures as indirect tool wear monitoring indices case study: drilling P.N. Botsaris, J.A. Tsanakas Demokritos University of Thrace, School of Engineering Department of Production Engineering and Management Faculty of Materials, Processes and Engineering Xanthi 67100, Kimmeria campus, Thrace, Greece ABSTRACT A major trend in the field of unmanned machining operations is the tool condition monitoring (TCM). The potentiality of a precise prognosis of the tool wear or breakage, utilizing TCM, has been proven a great challenge through the past years. This paper deals with the use of vibration signals, a well-known indirect monitoring method for tool wear, obtained during a drilling process. Statistical parameters, such as root mean square (RMS) and peak values, are used to extract the meaningful information from the acquired raw data. The results, then, are preliminarily correlated with spindle motor current and thermographic signatures from the same process, in order to investigate any proclivity of these signals toward tool wear mechanism. Both advantages and drawbacks of this effort are being discussed. Certain classification tools, such as fuzzy models and neural networks, is under investigation from the current research team with the prospect of an implemented monitoring system, able to predict either extended tool wear or breakage within a drilling process, using the aforementioned signatures. Keywords: condition monitoring; tool wear; vibration signals; drilling; tool temperature; spindle motor current. 1. INTRODUCTION The manufacturing community is always striving to reduce operating costs while trying to improve product quality and meeting or exceeding customer satisfaction [1]. Focusing to the former intent, production cost reduction is achieved nowadays by using higher cutting speeds and by reducing human resources. The necessity of the latter has lead to the development of unmanned machining systems. Condition monitoring and diagnosis systems, which are capable of identifying machining system defects and their location, are essential for unmanned production. Thus, much research effort has been made in implementing intelligent systems to monitor directly or indirectly the machining conditions utilizing signals from thermal, force, acoustic, acceleration and vision sensors, during a process [2]. A widely used machining process is drilling; it represents approximately 40% of all cutting operations performed in industry [3]. Typically, twist drills are used in a diameter range from 1 to 20 mm. In general, the failure of a twist drill occurs by one of two modes; fracture or chipping and excessive wear. Experiments performed by Thangaraj and Wright [4] indicated that under normal cutting conditions, sudden failure due to fracture was observed with small size drills ( 3 mm diameter), while excessive, but slowly evolving, wear was the dominant failure mode with large size drills (>3 mm diameter). The reason for acquiring the drill wear state information is to enhance the predictive capability to allow the machine operator to schedule tool change or regrind just in time to avoid underuse or overuse of tools, prevent shutdown of machines due to damage and minimize scrap or rework [1]. Due to the high complexity of drill wear and breakage mechanisms, both mathematical models and 1

2 numerical methods generally fail to provide a precise description of the relevant dynamics of drilling. Hence, the safe way to implement a system able to predict and diagnose drill wear lies upon on-line tool condition monitoring (TCM). In principle, there are two possible TCM approaches, i.e. direct and indirect methods. Direct tool wear estimation systems are able to measure directly the tool wear via tool images, computer vision, etc. which means that these methods actually measure tool wear as such. Moreover, their application is simple and the reliability is high. However, the automated application of a direct tool wear estimation system is not feasible because the detection system should be able to detect the wear zone and measure it, requiring that either the tool be removed from the machine after a certain period time or a measuring device be installed on the machine. Consequently, any of these practices would cause downtime and production loss, rendering direct methods either economically or technically inadequate. On the other hand, instead of wear, indirect monitoring methods measure something else, i.e. a parameter, which must be a function of wear [5]. Commonly used parameters in indirect methods are cutting forces, vibration, acoustic emission, current, power and temperature. The main advantage of indirect methods is that they are applied online. Unfortunately, these methods present limited reliability and design complexity due to the unpredictable impact of the wear process to the measured signal. Moreover, the sensor cost is generally high [2]. Vibration is a widely used measuring method in indirect TCM. It is logical to expect vibration measurements to react to tool wear; if in a dynamic system such as the machine tool the cutting forces increase, the dynamic response will also increase [6]. In particular, drift forces which can be used for monitoring drill wear, are also the cause of increasing vibration as a function of wear. Dimla, in [7], suggests vibration signatures as reliable, robust and applicable for TCM, in addition to the fact that vibration signatures require fewer peripheral instruments than AE for instance. Furthermore, vibration signals have the quick response time needed to indicate changes for on-line monitoring. Moreover, accelerometers, that are mainly used to obtain vibration signatures, are simple to operate and are very suitable for wear monitoring because they offer the following advantages [1]: Ease of implementation; no modifications to the machine tool or the workpiece are required No effect on stiffness and damping properties of the drilling system Can be easily mounted close to the cutting action, independent of tool or workpiece When properly shielded, they have good resistance to coolants, chips, electromagnetic or thermal influences Easily replaceable and are very cost-effective Unfortunately, vibration monitoring relates to several limitations. Besides the influence of tool wear, the vibration signal is influenced by the work piece material, cutting conditions and machine tool structure. Furthermore, vibration has a high sensitivity to the machining noise occurring under real industrial environments. In the reported literature, spindle motor current [8-20] and tool temperature [21-22] are also widespread parameters for TCM and appear to be potential indicators of drill wear. In the same manner as vibration, spindle motor current can be related to the dynamics of drilling process, reflecting how much power is used in the machining process. Although vibration and cutting force sensors are located close to monitored tool, offering hence more representative measurements, it is much easier to acquire the current of the spindle motor to monitor the tool condition in a simple, but quite valid, way. In contrast, temperature-based TCM, involving infrared or fiber-optic pyrometers and infrared thermography imagers, is a major challenge due to numerous practical difficulties involved in cutting processes. This experimental work deals with the use of vibration signals obtained during a drilling process and processed statistically to extract the meaningful information from the raw data. This information was then preliminarily contrasted with spindle motor current and thermographic signatures from the same process in order to correlate their trend with respect to drilling tool wear. 2

3 2. EXPERIMENTAL SETUP Figure 1 presents schematically the experimental setup of this work. As mentioned, vibration, spindle motor current and tool temperature signals were obtained during drilling operations conducted on Yang SMV-1000, a three-axis computer numerical controlled (CNC) vertical-type machining center. The vibration signals were measured from Kistler 8702/B25M1, a single-axis K-shear accelerometer, mounted on the workpiece longitudinally to the drilling direction, i.e. the Z-axis. The accelerometer has a measuring range of ± 25 g, with a sensitivity of 200 mv/g (± 5%), while its frequency response band ranges from 1 to 8000 Hz. Referring to the spindle motor drive, the relative current signals were obtained with LEM type AK 50 C10, a self powered AC current transducer with galvanic isolation between the primary (high power) and secondary circuits (electronic circuit). The transducer has a primary nominal input current up to 50 A and gives an analogue output signal in the range of 0-10 V DC, with an accuracy of ± 1% and response time < 100 ms. Moreover, non-contact tool temperature measurements were performed with the use of Eurotron s IRtec series Rayomatic 10, a compact digital infrared (IR) temperature transmitter (IR pyrometer) mounted onto the tool bracket, at a distance of d=10 cm from the drill point, with an angle of θ=45 o from the drill axis. The measuring temperature range of the specific transmitter is from 0 to 600 o C, while it offers accuracy of ± 1% rdg and repeatability of ± 0.5% rdg, with a Øtarget-to-distance ratio of 25:1. The transmitter was calibrated measuring the known temperature of heat source. Finally, the data from the above three sensors was recorded, with a sample rate of 8 khz, to a PC using a National Instruments Cdaq-9172 data acquisition unit and the National Instruments LabVIEW software. θ=45 ο d=10 cm Figure 1. The experimental setup 3

4 3. EXPERIMENTS: RESULTS AND DISCUSSION The experiments included drilling operations performed by a HSS-Co5% twist drill of 10 mm diameter, under dry conditions, to a number of reinforced C-70 steel workpieces (length=170mm, width=170mm, height= 20mm). Totally 109 bottom holes, of depth=15mm, drilled per each workpiece (Figure 2a). The drilling path started from the 1A hole, continued to 1B, 1C and so on, finishing the first line (No. 1) of the workpiece. The process was continuing to the next lines (2 to 11), until the last hole 11K according to Figure 2b. The whole process was provided with the use of Missler TopSolid, a CAD/CAM software. The selected optimum drilling conditions were S=600 RPM regarding the spindle speed and F=100 mm/min for the feed rate. The duration for a drill was 9 seconds, and for the whole workpiece was estimated around 1800 seconds. The latter includes the dead or rapid movement time between the holes. One of the aims of this study was to intentionally hasten the drill wear by performing certain drilling operations with false conditions (S=800 RPM, F=110 mm/min) between the normal sets of drillings, in order to investigate the impact of the drill aging, and consequently drill wear, to the monitored parameters. Tables 1 and 2 present the RMS values of the averaged vibration (vertical acceleration) signals generated on each of the 109 drills of the first and last, respectively, operated workpiece. The acquired raw data were cleaned before analyzed of irrelevant signals due e.g. to the rapid movement of the drill between the holes. piece holder accelerometer on Z-axis piece holder Figure 2. (a) 3-D and (b) ground view of the workpiece model Table 1. RMS values of the averaged vibration (vertical acceleration) for the first operated workpiece (brand new tool) Vibration (g*) A B C D E F G H I J K *where g=9,81 m/s 2 4

5 Table 2. RMS values of the averaged vibration (vertical acceleration) for the last operated workpiece (worn tool) Vibration (g*) A B C D E F G H I J K *where g=9,81 m/s 2 Tables 3 and 4 present the RMS values of the averaged drilling region temperature for the first and last, respectively, operated workpiece. The tool point, the drilling hole and the generating chip are defined as the drilling region. Figure 3 presents the trace (best fit) of the RMS values of the averaged vibration (vertical acceleration) signals of each workpiece versus the number of the operated workpieces. As other researchers have already mentioned [6], the curve of Figure 3 shows the expected; vibration measurements react to drill wear. The machine tool is a dynamic system and as the wear of the drill increases the cutting forces increase, and consequently the system s response will also increase. As Figure 3 shows, there is a threshold (for the current cutting conditions, workpiece material and tool structure this threshold is estimated around the 3 rd workpiece) where the slope of the curve increases sensibly. According to Tables 1 and 2, the RMS value of the vibration level of the 11K hole (the last drilled hole for each piece) differs notably between the first and the last workpiece. Finally, Figure 4 presents the trend of the RMS values of the cutting region temperature versus the number of the workpieces operated under dry conditions. As the wear of the drill increases during the time and the process, the heat balance among the tool, the piece and the chip is unsettled and consequently the cutting edge temperature increases too. Table 3. RMS values of the averaged drilling region temperature for the first operated workpiece (brand new tool) Temperature ( o C) A B C D E F G H I J K

6 Table 4. RMS values of the averaged drilling region temperature for the last operated workpiece (worn tool) Temperature ( o C) A B C D E F G H I J K ,25 2 1,75 1,5 Vibration (g) 1,25 1 0,75 V = 0,1e 0,9W 0,5 0, Workpieces Figure 3. RMS values of the averaged vibration signals of each workpiece versus the number of the operated workpieces Tool temperature ( o C) T = 16,9Ln(W) + 145, Workpieces Figure 4. RMS values of the cutting region temperature versus the number of the operated workpieces 6

7 4. CONCLUDING REMARKS An experimental study in which the efficiency of the use of vibration, spindle motor current and tool temperature signatures in tool condition monitoring was presented. These three parameters have been reported in the literature appearing to be more effective for indirect monitoring of slowly evolving faults, such as tool wear, during a machining operation. The machining operation that was studied here was the drilling. A brand new drilling tool was used to perform several drills under selected normal and false conditions in order to achieve a significant level of wear. During these operations, an accelerometer, an IR temperature transmitter and a current transducer were used to obtain the generated signals of vibration, tool temperature and spindle current respectively. Averaging and statistical parameters, such as RMS and peak values, were used to extract the meaningful information from the raw data. The results of this work indicated an interesting correlation between both of the aforementioned parameters and the dynamics of the drilling process, proving their supremacy as reliable and robust tool wear prognosis indices. However, additional research is required to develop practical vibration monitoring techniques which are sensitive to tool conditions but relatively insensitive to cutting conditions and sensor location. Further ambition of the current research team is to enrich the presented monitoring system with a fuzzy logic and neural network based classification tool. REFERENCES 1. I. Abu-Mahfouz, "Drilling wear detection and classification using vibration signals and artificial neural network", International Journal of Machine Tools and Manufacture, Vol 43, pp , P. N. Botsaris and J. A. Tsanakas, "State-of-the-art in methods applied to tool condition monitoring (TCM) in unmanned machining operations: a review", COMADEM 08, Proceedings of 21 st International Congress on Condition Monitoring and Diagnostic Engineering Management, pp , T. I. El-Wardany, D. Gao and M.A. Elbestawi, "Tool condition monitoring in drilling using vibration signature analysis", International Journal of Machine Tools and Manufacture, Vol 6, No 6, pp , A. Thangaraj and P. K. Wright, "Machining", Robotics Computer-Integrated Mfg. 4(3/4), pp , E. Jantunen, "A summary of methods applied to tool condition monitoring in drilling", International Journal of Machine Tools & Manufacture, vol. 42, pp , E. Jantunen, "Indirect multisignal monitoring and diagnosis of drill wear", VTT publications, Finland, D. E. Dimla, "The correlation of vibration signal features to cutting tool wear in a metal turning operation", The International Journal of Advanced Manufacturing Technology, vol. 19, pp , N. Constantinides and S. Bennett, "An investigation of methods for the on-line estimation of tool wear," International Journal of Machine Tools & Manufacture, vol. 27, pp , L. A. Franco-Gasca, G. Herrera-Ruiz, R. Peniche-Vera, R. d. J. Romero-Troncoso, and W. Leal-Tafolla, "Sensorless tool failure monitoring system for drilling machines," International Journal of Machine Tools & Manufacture, vol. 46, pp , Z. G. Wang, W. Lawrenz, R. Rao, and A. Hope, "Feature-filtered fuzzy clustering for condition monitoring of tool wear," Journal of Intelligent Manufacturing, vol. 7, pp , P. Fu and A. Hope, "Intelligent classification of cutting tool wear states," Lecture Notes in Computer Science (LNCS), vol. 3973, pp , A. G. Vallejo, J. A. Nolazco-Flores, R. Morales-Menendez, L. E. Sucar, and C. A. Rodriguez, "Tool-wear monitoring based on continuous hidden Markov models," Lecture Notes in Computer Science (LNCS), vol. 3773, pp , F. Ferraz and R. T. Coelho, "Data acquisition and monitoring in machine tools with CNC of open architecture using internet," The International Journal of Advanced Manufacturing Technology, vol. 26, pp , S. Y. Hong, "Knowledge-based diagnosis of drill conditions," Journal of Intelligent Manufacturing, vol. 4, pp , R. X. Du, M. A. Elbestawi, and S. Li, "Tool condition monitoring in turning using fuzzy set theory," International Journal of Machine Tools & Manufacture, vol. 32, pp , J. E. Kaye, D. H. Yan, N. Popplewell, and S. Balakrishnan, "Predicting tool flank wear using spindle speed change," International Journal of Machine Tools & Manufacture, vol. 35, pp , P. Fu, A. Hope, and G. A. King, "A neurofuzzy pattern recognition algorithm and its application in tool condition monitoring process," Proceedings of ICSP '98, pp , W. Amer, Q. Ahsan, R. I. Grosvenor, and P. W. Prickett, "Machine tool condition monitoring system using tooth rotation energy estimation (TREE) technique," in Emerging Technologies and Factory Automation, ETFA vol. 1, pp ,

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